From 184683644f2dd2f3ac5232ac08bfc5b0f82d8289 Mon Sep 17 00:00:00 2001
From: dprohe <dprohe@users.noreply.github.com>
Date: Mon, 4 Nov 2024 21:36:47 +0000
Subject: [PATCH] deploy: f83f5b04afc5e27c17ed981e879ed2e22f3b474b

---
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 create mode 100644 _sources/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb.txt
 create mode 100644 modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html
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diff --git a/.buildinfo b/.buildinfo
index 7e4e582..bbafc0f 100644
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.doctree
index 4c8116aa613ba08784f3a0ecda02467caf3b114a..900e0e903d56ec0be6e26f2d1be6efd321803426 100644
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_shape.doctree
index 6148d4f663e2d35fa08e1c7829add39f68aa86c6..8597fe32d79d5eaa73546f7c80ec3e19a9fc5a8a 100644
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diff --git a/.doctrees/_autosummary/sdynpy.core.sdynpy_system.System.doctree b/.doctrees/_autosummary/sdynpy.core.sdynpy_system.System.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readuff.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readuff.doctree
index 199b32eb523d1dc436994e9d18bab31eb4456255..a61245b10daf63d79588d5c52d3759d51c7a69f0 100644
GIT binary patch
delta 90
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delta 75
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diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readunv.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff.readunv.doctree
index b1b2184e17541528cf4ffce3790afc91923a318f..7a5f245ea274486fed490a048220b9c66357a81b 100644
GIT binary patch
delta 84
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delta 74
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diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.doctree
index 155f67c323c3d60da61092d01a6665b2964fbcc2..e955b90e86debe6546b9dbbd1e65bbeae49f26cc 100644
GIT binary patch
delta 4003
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diff --git a/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.doctree b/.doctrees/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.doctree
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GIT binary patch
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delta 256
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diff --git a/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.doctree b/.doctrees/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.doctree
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index 66446ac699fadbae15b9651d5425110c435229d4..23bcaa88b09a27b07a03fc2290c136fc7382d1fd 100644
GIT binary patch
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diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.doctree
index a66f8f1c812eaac6479f89b302eb910dbe71f848..3d3d6d976e1ac70a1b46e98e403b076437e66c21 100644
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diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.doctree
index 28d5bf39cb14626d6cb7cdd94b7c14198ca0be49..249dfdc742f71675692d96c1c415c233b64e9c9b 100644
GIT binary patch
delta 3917
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diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.doctree
index 949990f87a550dbe7820ddee04d802ad61e0ff38..169f170fce3cc1486f439b80fdd839609e01e4d4 100644
GIT binary patch
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diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_frf.doctree
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diff --git a/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.doctree b/.doctrees/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.doctree
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diff --git a/.doctrees/example_problems/airplane_modal_test.doctree b/.doctrees/example_problems/airplane_modal_test.doctree
index 7b6ba5e9feed8850cbcb8e271babea8555effde7..08a03ba20af74f318550ea6b06b105db9d2ef04f 100644
GIT binary patch
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diff --git a/.doctrees/example_problems/rattlesnake_demonstration.doctree b/.doctrees/example_problems/rattlesnake_demonstration.doctree
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GIT binary patch
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diff --git a/.doctrees/modal_tutorials.doctree b/.doctrees/modal_tutorials.doctree
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GIT binary patch
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diff --git a/.doctrees/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.doctree b/.doctrees/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.doctree
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diff --git a/.doctrees/nbsphinx/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb b/.doctrees/nbsphinx/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb
new file mode 100644
index 0000000..5af974c
--- /dev/null
+++ b/.doctrees/nbsphinx/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb
@@ -0,0 +1,3842 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cc4417ad-c73f-4426-8e76-c7a69ae33dd5",
+   "metadata": {},
+   "source": [
+    "# Modal Tutorial 06: Complex Modes\n",
+    "Complex modes can be unintuitive and difficult to understand compared to real normal modes.  George Fox Lang wrote an article in Sound and Vibration magazine called \"Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints.\"  He states:\n",
+    "\n",
+    "> Complex Modes is one of those topics that every vibration practitioner understands -- just not fully.\n",
+    "\n",
+    "The author of this document agrees with Lang; in fact, his own lack of knowledge of complex modes is what caused him to write this document, to teach himself what is going on!\n",
+    "\n",
+    "To better understand complex modes, we will start from the differential equations of motion and develop the theory of complex modes, discussing:\n",
+    "\n",
+    "  - Real Modes and their Limitations\n",
+    "  - The state space transformation to derive the complex mode eigenvalue problem\n",
+    "  - Computing natural frequency, damping, and complex mode shapes\n",
+    "  - Computing FRFs from the modal parameters\n",
+    "\n",
+    "This document will largely follow the equations from the following sources:\n",
+    "\n",
+    "  - [An engineering interpretation of the complex modal mass in\n",
+    "structural dynamics by Lopez and Brinckner](https://doi.org/10.1016/j.ymssp.2023.110621)\n",
+    "  - [Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints by Lang](http://www.sandv.com/downloads/1211lang.pdf)\n",
+    "  - [The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation? by Peeters, et al.](https://content.iospress.com/articles/shock-and-vibration/sav00272)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fee8375-b72c-474f-bdac-a2ddd842070c",
+   "metadata": {},
+   "source": [
+    "## The Second-Order, Linear Differential System of Equations\n",
+    "Most vibration engineers are familiar with the standard system of second-order, linear differential equations.\n",
+    "\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "where $\\mathbf{M}$ is the system mass matrix, $\\mathbf{C}$ is the system damping matrix, $\\mathbf{K}$ is the system stiffness matrix, $\\mathbf{F}$ is the forcing applied to the system, and $\\mathbf{x}$ is the system displacement response.  $\\dot{\\mathbf{x}}$ and $\\ddot{\\mathbf{x}}$ represent the first and second derivatives of the response, velocity and acceleration, respectively.\n",
+    "\n",
+    "For a standard linear, time-invariant (LTI) system, the mass, stiffness matrices are not functions of time (i.e. the mass of the system doesn't change).  However, the forces and responses are generally a function of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24f927e0-5131-4f23-91a1-7c42a9ec8a37",
+   "metadata": {},
+   "source": [
+    "## Real Modes -- Damping is Hard\n",
+    "\n",
+    "Many times, when we are just starting out, we will simply ignore the damping in our part because it is \"hard\" or it doesn't fit with our elegant mathematical tools.\n",
+    "\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "To try to understand the free vibration response of the this system, we can make the following assumptions:\n",
+    "\n",
+    "  - $\\mathbf{F} = \\mathbf{0}$ -- There is no force on the system.\n",
+    "  - $\\mathbf{x} = \\mathbf{\\phi}\\cos(\\omega t)$ -- The response of the system looks like some shape $\\mathbf{\\phi}$ that will vibrate over time (represented mathematically by multiplying by a cosine function), and the vibration will have a frequency of $\\omega$ radians per second.\n",
+    "\n",
+    "Using the chain rule for differentiation, it follows that $\\ddot{\\mathbf{x}} = -\\omega^2\\mathbf{\\phi}\\cos(\\omega t)$\n",
+    "\n",
+    "We can substitute these equations into the previous equation to arrive at an equation in the form of the well-known [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem).\n",
+    "\n",
+    "First we make the substitutions.\n",
+    "\n",
+    "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}\\cos(\\omega t)) + \\mathbf{K}\\mathbf{\\phi}\\cos(\\omega t) = \\mathbf{0}$$\n",
+    "\n",
+    "We can then recognize that the term $\\cos(\\omega t))$ is in general nonzero so can be cancelled.\n",
+    "\n",
+    "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}) + \\mathbf{K}\\mathbf{\\phi} = \\mathbf{0}$$\n",
+    "\n",
+    "We can then add $\\omega^2\\mathbf{M}\\mathbf{\\phi}$ to both sides of the equation.\n",
+    "\n",
+    "$$\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "\n",
+    "This is now the same form of the [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem), which is $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae951b07-3074-4634-8c80-ab60435a9630",
+   "metadata": {},
+   "source": [
+    "Here it can help to further rearrange the equation by premultiplying by the inverse of the mass matrix $\\mathbf{M}^{-1}$.  Note that this is generally acceptable to do, because the mass matrix should generally be positive definite and therefore invertable (said another way, every degree of freedom in the system has mass associated with it).\n",
+    "\n",
+    "$$\\mathbf{M}^{-1}\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{\\phi}$$\n",
+    "\n",
+    "### Geometric Interpretation of the Eigenvalues and Eigenvectors\n",
+    "\n",
+    "We can gain intuition here by thinking about what the eigenvalue problem means *geometrically*.  We have a vector $\\mathbf{\\phi}$ that represents some shape.  Looking at the right side of the equation, if we multiply that vector by a scalar $\\omega^2$, it will simply scale the length of the vector, but not change its direction in which it's pointing.  In the context of a vibration shape, this means that it will scale the shape larger or smaller, but it won't change the shape of vibration.  You get a bigger or smaller vibration with the same shape.\n",
+    "\n",
+    "Now because of the equality, the left-hand side of the equation must be equivalent to the right-hand side of the equation, which means that the left-hand side must also be equivalent to simply scaling the original vector $\\mathbf{\\phi}$. However, we know from linear algebra that multiplying a vector by a matrix will in general change the length of the vector *and* where the vector is pointing.  In the context of our vibration shape $\\mathbf{\\phi}$, this means that multiplying a vibration shape by a matrix will in general change not only the size of the vibration shape, but the shape itself.\n",
+    "\n",
+    "Let's look at a brief example to illustrate this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e58c8729-2539-49d2-a7c7-5913080653e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scalar:\n",
+      "0.5507979025745755\n",
+      "Matrix:\n",
+      "[[0.70814782 0.29090474]\n",
+      " [0.51082761 0.89294695]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set random seed so we can get repeatable results for the documentation.\n",
+    "# Remove this line to allow the matrices and vectors to be random.\n",
+    "np.random.seed(3)\n",
+    "\n",
+    "# Create a vector to test\n",
+    "vector = np.array([[1],\n",
+    "                   [2]])\n",
+    "\n",
+    "# Now create a random scalar\n",
+    "scalar = np.random.random()\n",
+    "print('Scalar:')\n",
+    "print(scalar)\n",
+    "\n",
+    "# Now create a random matrix\n",
+    "matrix = np.random.random((2,2))\n",
+    "print('Matrix:')\n",
+    "print(matrix)\n",
+    "\n",
+    "# Now multiply the vector by the scalar, and multiply the vector by the matrix\n",
+    "vector_scalar = scalar*vector\n",
+    "vector_matrix = matrix@vector # Matrix multiplication is @ in numpy.\n",
+    "\n",
+    "# Now plot them\n",
+    "fig, ax = plt.subplots(1,2,figsize=(10,5), sharex=True,sharey=True)\n",
+    "ax[0].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[0].quiver(*vector_scalar, color='red', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[0].set_title('Multiply Vector by Scalar')\n",
+    "ax[1].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[1].quiver(*vector_matrix, color='red', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[1].legend(['Original Vector','Modified Vector'])\n",
+    "ax[1].set_title('Multiply Vector by Matrix')\n",
+    "ax[0].set_ylim([-3,3])\n",
+    "ax[0].set_xlim([-3,3]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c7ea4b-d78c-45ca-b5f9-29ecf27aa4ed",
+   "metadata": {},
+   "source": [
+    "Clearly, multiplying by a scalar has resulted in a vector with the same direction and a different length, whereas multiplying by a matrix has changed not only the length, but also the direction of the vector.\n",
+    "\n",
+    "The resulting direction of the vector will not only be dependent on the matrix that it is multiplied by, but also the original vector itself.  Multiplying different vectors by the same matrix will generally result in a different final direction of the resulting vectors.  We can investigate this by multiplying many different vectors by the matrix from the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "45bc6338-9cbb-46cd-a241-093ef02801bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we make a 20x20 grid of 2x1 vectors that we can multiply by our matrix\n",
+    "vectors = np.mgrid[-3:3:20j,-3:3:20j][np.newaxis].transpose()\n",
+    "\n",
+    "scaled_vectors = matrix @ vectors\n",
+    "\n",
+    "# Reshape for easier plotting\n",
+    "vectors_for_plotting = vectors.reshape(-1,2).transpose()\n",
+    "scaled_vectors_for_plotting = scaled_vectors.reshape(-1,2).transpose()\n",
+    "fig, ax = plt.subplots(figsize=(10,10))\n",
+    "ax.quiver(*vectors_for_plotting, *vectors_for_plotting, color='blue', angles='xy', scale_units='xy', scale=10)\n",
+    "ax.quiver(*vectors_for_plotting, *scaled_vectors_for_plotting, color='red', angles='xy', scale_units='xy', scale=10)\n",
+    "ax.legend(['Original Vector','Multiplied Vector']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4844ae-ad85-46cd-b741-2321b59604c9",
+   "metadata": {},
+   "source": [
+    "Clearly, we can see that in general, vectors in different directions will be modified by the matrix differently.  However, looking closely at the above plot, we can see that there are vectors in particular directions that maintain their original direction after multiplication by the matrix (a change in length is acceptable).  And if you recall, we are looking for exactly such a vector that when multiplied by a matrix retains its direction.  Therefore, in order to  solve the eigenvalue equation listed previously, we need to find these special directions.  We can't just use any shape $\\mathbf{\\phi}$.  We have to find particular shapes $\\mathbf{\\phi}$ that when multiplied by the system matrices $\\mathbf{M}^{-1}\\mathbf{K}$ do not change shape but perhaps get bigger or smaller.  Mathematically, we call these vectors the *eigenvectors* where the prefix *eigen* is German for *particular*.  In structural dynamics, we call these particular shapes the *mode shapes*, and they have many special properties.\n",
+    "\n",
+    "Similarly, we call the scalar quantity $\\omega^2$ that gets multiplied by the eigenvector the *eigenvalue*.  It is the particular value that goes along with the particular vector.  We will generally have as many eigenvector/eigenvalue pairs as we have degrees of freedom in the system.\n",
+    "\n",
+    "We note that there are linear algebra tools available to solve for the eigenvalues and eigenvectors of a given matrix.  For example, in Numpy, we can use `np.linalg.eig`.  We can then plot the eigenvector directions on the above figure to show that they indeed align with the directions where the vectors are scaled but the direction does not change.  Note also that there are two degrees of freedom in this problem, so we find two eigenvectors, or two directions where the direction of the vectors do not change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "92c994f7-31b6-4014-ade5-8ce28df278a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zZ0XzG8f2uE9jGbIdsZ1Ps2aJYnFFmhhMmiRmewJATyxBY2wHAISP+kJ0xQGAMmUe+nOsVisGDx4MkvD19YXfA3RXu3gRaNQIqL72S1TGSQDA65iFZASpN9+TRGK/AfBPiEWcbw4MwLcAjAXkM8+IjuXSuXoVGDJEPG/YEG3Xj8LRo0CJEs5vK1IE6NPH+/LsTJ0KrF0rnk+eDFSrZtc1aBDcdsD74gsPN0fMvOnmPTJjzW3aZKSB5MkjnmfPTm7ZkvnPtVqtHDBgAPPnz8/jx48/gPL7c/euq2e/aFHy8mWPfNxDo2kiMumot149tZsEPPus6/6dM4f85x8yKUm2OldGjnTvqalZU83Upg8/dK9XkXpvFyZMcNVaoYKIvKrImDGuem11qu756iuROysJvZzE8eG2dsoxDCsJTRMpman1OqWU6rlO+mPJEllyeemSc9REd5xqN24akSjHx4QJ0rSSrrsOEI17mJBAFiokNgQEiH8//VSq1qgosnJlZ61+SOEpX1uu0BNPiAMmMVHUI0nGcd8GI46XIL7/bT6NmJSYtWkt3377LQEwZ86cD9y84NgxcvnUc0zyE00BFqCPXb8ipVwGP/5o37k7XvjG5RjesEG2QIpjUc/LDQmx15i5Wz9MmiRR58GDxkWrUyen8E1CAtmunaGzVCnxb5cuGfvT//PpbTodO5J9+4p97Wj4bN6cuc+Njo5mrVq1CICFCxfOknxWd7z5pqjlWbzYuP5XrSoyP1REv9g2b24crIMGyVaVNj/8YISkdb2pmoYohUO0moAwim/cUC89U2fOHGe9OXOK869nT/LOHdnqXNELflM/3n47nV86eNBb8lzo3dtVa/bsaaQ83rsnLnoffuhtmXZKlHDV+8ILqd4UF+fcyUXSwX3ggPtjYdky2xv27DEuyvrjn3+kaCXJAQNctQYhgcl1G7j/j6RZoOR5zp4VtfSpJeXJQ+fOQxs3ioPGvtPlMHUq2aOHaHijS3sJc40fNm2Sqs8dFotILR+HD0iAVvjw+cpZa5DduXOHefLkIQBOnTr1wf+QptHatj0J8B7CWCLopt2ZpxQ3btiLEmPqPMngICsB4dzNnl0szJVI33Y8h775hqRIFdM39eghutaGhQmDXgpxcYYnoVAhp5tWfLxzZ7k33xTXY19fkRKbEUyjx8aRI8ai9tAh4VnSjeHMXrfCw8NZrVo1AmCJEiV48eLFTKq/P1euiIUN6dwhrWlTNSMot2+LRiFRUeKCq+tduFC2MvfcuiVy9tetM6JUxYpJvBDch5MnhcZhw4z1Vrp1EZJZuVJodIwAnjolW1XafPSRYUwCIp94927y++/TON8SE0UHH0k0bix06nUGTZsKp7NbI00PC0nyQly65KwVELWr2bOnahjx1lvOK+H0WvR4kJEjhVa9QYuue+RIiqT90qVdV+07dkjRevGiCOb0729I8fUl8+E2j/+wWxzEqcNAEl3S//4rrg1Tpzo7cPLnSjZqejp3Fm/es0eZbmidOglplUrG84pPMfFDu3ayZbnlwgWyTMBlxkFYlz8HvcQBA7L2M1577TUCYI0aNZjyMOfpb7/ZD4QBmMWVK0UTHOVqFG1NAbRs2diyxGn7NezKFXHZkuhPMjh92qj+b9uW1DSnteOTT4ooyvbtojGWNAYONEStWWPfHB/v3Dxm1Cjh99I0Me4go5hGjwOOjsPDh42mESEh4mKcGW7cuMEKFSoQAMuWLcurHujT7FgUN2mSs7WuhFchFbt3i38TE4UHRN+3qrbc/uor8e/evcbCZuBAuZrSwmolu3UT/w4dakSplEsBsHHwoIiuxsQYXbtGjrzPL126JK3zxYcfiq7DegTQ11d0/EyTn34Sb5Kkt2ZNUZ9ev77QO3iw2O5yObxyxQhndu/uZZWCX34RjhDH5gvnzglnzv79tjc5noT6Q0KeqaaJqPXFi0bzo2efFWmZ3Z/RxD50Fz2R1P4xJkZcExznj/35p/CWLltGcZEDRIe5mTOFp0cB74NupNWqJb760QW+N/4D+o1EEc6fN5pa7Oj+paHzwAHZ0tzSty+5EL3EIj1HKNf8eINz5mTd39+7dy99fHwIgFsepEZAJzqayQWKkAB3oT779xWLmh07pPkQ3OPQDev7qpMIGA5TUgSBLl2SK5EWixi4AwhvzdWr/Pdfw99RtarRoVzTJDZc+PNP4/xxWBDExZGtWhkvjR7tvF7PTNDfNHrS4b//DMMnODjzDrArV66wdOnSBMBKlSrxlgf7CGuaWJTpB8Ubb3jso7KEixeNNMKKFdVvWTxqlLFvM2sAewu9feetW8JLDpCvvipXU1rExBhefL1FeK5c97ERpk4VK2MJnD8vzrHbt40FzoIF6fyCbtVLWqDpPpZXXhEymjRJ440vveTs6pOAbrscP25IcWpZnZTkWhQISO9Xq9+E9XMsectOsVrXvQ6OD8neh2XLDClRUcIQunbFaqQL6gbv5MnSW2parUaH0rFjSVosTC5lmy3RqpVUbe54+21bJkBoJLXc4qYW362XbFluOXKEbIRtxsHw+ecks/ZU+ueff1i4cGG+4JKfmjmsw0aQAC3wZcs8B5SsTeWdO/aD9XrxevRDCgGRDq0UjoPmFi7kf/+JFDZAoXrwa9eMFKuaNe0pFHFxZMuWhvwxYx4us9k0eu7DkSPGaIDgYNHGOjNcuHCBxYoVo5+fH5d7eMFmtTo7GvVIhaqsXm0sIHv0ULf+hBShVX2mU9my0tcF92XcOMOBe/JkOm9UoMf5mTPGMet2UrxOq1ail7xkdIfZc8+l8YZdu4z/UFa6UB8APU0oVy4359fRo87Rk0qVpGjUuXbNkLJtm8MLaRVUST4Jdbv2rbdSvfDtt0Y4UO/QINmrozcOKVXKYaPj0L/MFq96kN27DVm7dlGEAvUNitXIJCQYjtHV9caKJ/7+yqTdpaZzJyv3QgxJs5Yp67Fc+KioKN55mOLMAwdo9RHXpq8wgr/9lnXaspQXXhD70j+ANXyPECA7dFAs0+bQISPn/dlnefmSxqJFxY85cwrnvnSsVsOyCQ62F+jExYn2+vrp/+67D79ONI2eDHD0qOF5ypYt8xPZT58+7XGDRychQXh19YNEYtOgDKEvzgGxQFOZzZsNrS4LHcWIijJuxmlmLWmaSEZXgLZtjXQWtxe1qChx4S5cWPodRZ/ZkyuXGHDsgmMrsiFDvK7PkX//NaS4ePNSD0vKk0eGRDuxsYYUe9OQ48dFqzF91onjQ7IhUaEC3Xt19Qtwu3biWH3xRenHrJ6Kp5fDkDROuho1lPI4vf++kJU/P2m1aEIfILoFKKSTFNFegCyAm7QG2+olXn9dtiy3bNtG9sP3xvlzvymOsrBYmFBD5OVeQVH26axoGohemArwixwfEBBlZ0pFpBITjSh5wYKMPBtu7zwZECD6gSiBY32GrYtobKxz46v33sua0980ejLIsWOiEF83fB5mUOW1a9cY48GR7nfvklWqCK2Bgco5x5ywWIxuHP7+iuXqukEftOrrK+poXVCoiMaxPalbrbdvu3Gry+Gvv1J5d1PjOEV4716v63Pk8OF0HM83bjh37pLYzIAU2RcuhgQp+vG7i564teK8g6aJyCRALlpk23jlirigrVljaKxrG/ooedKffj+YNs1h44ULTmkkJKUbPKRhoL33nm2D3vlEgWhkamrVErL69yf599+GzlWrZEtzQY/6Lis6xPBSX78uW5YLmka2fiKa1yFafltbtMxyA/K9997jjz/+SO0h/6515iz7d943+2+8cSOLBGYlUVGisxHAs9mrMwBJDAyUfmtyxWF2QfKy5Wza1Did7NdY2ezfb9wzu3QhNY2xsXTS+v77WXe4mkZPJjh+3LjRBQU5NZbIMBcuXGDp0qXZvHlzxntwyMelS2QRUQPIsLBUOfKKER5uv36wWLE02uoqQlSUMdqiWjU3tdTTpilzc05IMFoBt2zp5g36wrdrV69rS43FYmjt29fNG/r1M66A48Z5W54TmmYcry4Rv9QDiMLCpHun9evAxIm2DZomWrm5M3okL9j0Oj+XkUF6wWJZW0rO008blbeS0Ps//Pijw0Y9FS97duGqVIC4OCON2B751+uOcueWniboyNWrxqH461LN6MRRu7b08yg1euvy0jhHq59t0Sa17VXarFhBfgqxALb6+GZ5TpNj84K9D7Pyv3mTicFhJMCVaM8fvlfrO7dj6zBm9fFlXexxf82Szfbt9vRl7aWX2bOncW5JncHjSGys4ZEpXJgMD2dMjPAV6lrHj8/ajzSNnkxy4oQxHy0oKPPjF/bs2cPQ0FACYLt27Zjowf7Shw+LnE3dmFAoCOHCzp2Gsd+qlVgEkxRWhWKDW1avTueEnDZN7GzJXmgdvdsY4CYtU689UKRj06efGueVU2c0i8XI1QOEK1gyelfNypUdNiYluU/Dkty6R89i0lvc88ABcsQIEYnQNeoXtUOHpGotUyaVgaZTrpx4Ydgw8bPFIq1lNSnsLn3X/fWXbaOmGTdwt5a7HPbuNbQeP06RFhgaKjaMGiVbnhPffWdE/WP/XGcI//132dJcePVVm3EW/LxhQCo4KM9qJdtXOMtEiFZd2utZ25rearWyfv36BMA+ffo81N+K7SZSg+ORjf2fPKeanStwyBn+HKMIiNk2SmmNiTGalJQqxXcGR9lPpeHDFdKqn0S2BUpMjHN5xgcfZP1HmkbPA3DypLG2CQrKvGN/y5YtDA4OJgB27dqVyR5MKdmwwTAmqldXZi3ulmnTnMOZJMXZ2a6dcgNy+vYVOgMCUjnN9P+EAgX3pFgb6jm8deqkyrRxHMOsQC/uW7eMY9Wp7/6OHa6GhGQL3rEG/OxZ28ZFi9xHT/7+W6pWvfNgtWqpXtiwwfk/MWjQw+XtZgF6atOYMQ4bT582dEoc8unIrVuGJHuKo2MFfmYLPz3IvHnGvSolheT06WKDr69Ix1OIzp0dItPNmokfqlRRIkXQkXv3xLiF6jhMDbYwmq0TmmosWED+jm4ixSlHrvv02s88c+bMIQCGhoby+kNEirX1xvXog8BP6IHxhg+Pw4DkM77lmQ3xrFpVmaCugT4M0ceHSwdvsl+WundX6FRymMHEUaMYHW3MlwM8N9vINHoekFOnjLSRwMBU+fIZYN26dQwKCiIAPvfcc7TYQxtZj6NDt3lzMvGiikmywr557jlDq92YbNpUxDsVSsOIiDCaW9Sr5+B0drTcMtvqz0M41sssXerwQrt2xgvZsomVnGR697aljJR2uDi/+66rISE5lyA+3pgcb2/A0bChSBvT8x/1SaYTJkjV+tNPhvfcKR1Tb2NasKDh+vNgym1G0Ne5TrXgenFaSIj0NtU6jnbYwYO2jUNsdR1FijiEquUzYoRDgNRqFTMCAJE/rxDx8cbsxF+GOrRV/vln2dJc+PprIW0FOhrfuUL3J52kJLJXQcOY0L7O2m5BERERzJs3LwHwq4dpF5uYyKhCIkp6DJX5zdfen8GVId54w74vG2MLQ0Pv0x1VBv/8Y9d4suMb9tTWJk2UuXwKp6U+oK92bUbfSWKjRsYp/9FHnvto0+h5CE6fdjZ8VqzI3O+vWLGCAQEBBMD+/fvT6kET/IsvjANqXp0Z1MaNl5oekhYxMaJzLiDy+y9epLGYaNdOykDCtHCsrbfnyDoaPaVKpRopLwdNE+txQLTdtgcWS5VyNiSyOnn2AdjmsNaxG73uZrR06CBVJ2lMYW/dmuK41FMEq1UTL7z7rvBmDR0qVeehQ8ZuO3zY4YWnnxYbFengR4p1OOCQikcaY7ifekqWLBccU8bOn6c4qfQUTMVSxvSWr/360bkhhCJOGZ1VqwxpsU3biydlyih3n9KzGBvDoRnIt9/KluWWGV+n8DDE9TO+VOUsb1QycOBAAmDVqlUfKmMlZvRH9n05tPq/6kQjHNm1y14jMx2DCYg1gFLcvWtflMaWqsLQgAR7sFRyCaSBxWJ4t0JCGLPvpH194g0foWn0PCSnT5PVCoUzAEkMCMh8Jstvv/1GPz8/Vq5cmREe7HWoaUbtaj7cpsXXX5j+0kcFu3LsmDFcs25dMnnGt8YZ0b27UjdBfd2YLZs4FpyMHkB6y2KdLanvz47VzfojXz7p3krNoUNtp04UVi9gFKcBojlAUJD0nIJZtiZDAQGpuifnyiVe+OYb8bPEjmikqD/Ru6I5Oc31iJSn8ggeAD1ttGNH24aYGGNsuEKVwuvXG4djRATF0Fx9gxKDLwSaZthiX35Jw1KvUkWhxH6B3hmza4n9xr787jvZslwQ373GrWjkxpOkDjEx5FuhRie0rE4NvXjxIn19fQmAmx6mReyZM0zyDSIBLvDtq0J5qSuJifaWuBdRgjkQzREjZItygy1VQvP3Z9Mc++xBSKWWeQ6DUuOmzXHqqfPpp57/eNPoyQLOHozm3sAGHIJpDPWPNwpbM8jff//N215oWWaxGIv0ZegqnuTOLcZ1K4ZjecQX3VLVdPTvr0xi6o0bRpS2aVPSOjWV0QMoM/ivQwcjEyN+x0FXnY4LdYnMni2k+PiQN+cuFwsfR6vt1CkxeVdyf/PLlw1J9lMoJsbY6KXZXBlBr+saPdq24cYNNyE1+eiOmcaNbRv+/NPQqdCd+/ffDVkpKRTTlQFhsSuE49e89cdzhqNDgfPcEU0TM04A8lB521ChYsU8NjzzYXj6abIjHIxcRYfhffHOXYZDTLiPbtrx/r/wAGzbto3jHyZDQNN48zExsyICuTltrPwUa7fow6MAtsYaNmyooJ27dKld46ScHxIQ/Uok96ZxZs8ekWcNMPmpp/l4fc1+Grk0r/EQptGTRdyYKqZG30QBvus7kSsWPfjnHvLgURofLxYUneGwmABEEr3kfP7UDB4spOVAtOvifNgwZTyVjh3StvZwY/SUKyc9gkKKi5++5vmr12LDstAjKbVqCa2S6xFiYozAjr2gfdcuY38q1IawZk0h6aWXbBuOHzd0KnS30Wvl2re3bXCcf5LFhc0Pg762sDddeO21VBvUQG8OkCMHRXeYIOGpVqcXrGDtWuNrjh3whhEpVSDt1pEjR4S0yjhGTb8mKTit+soVMsDXwv9gS2GtXVsZB5wj4eHkjIARJMAUH38lunO6I2beEvsB+nGJ71RK4jA4dIiabaE+H/2ZP79ora4UN26QeYWB+19wPfojmQEBimWwxsTYu3BaixRlq9oR9muTN3uAmEZPVqFpjG/Qwn4C30Uunnj2/UwvKObMmUMfHx9O8GBiY0QEWb1SMm8hv/PivGpVpQb6JCYaYxou+JRyNSbGjpUtkaSwvfS2wG8FuTF6AFEAqQB9+tjCyNk+YMKrQ0TOi270aJq4OSrQdtWeipnPVnypqNHz3ntCUsGCtrWP4ypTodHcn3xiOM9JGtZF6dJSdaVGPxyLF6fzQKS335YtzYmvvhKyihYlOXeu+MHXl7x2TbY0JyZPFtJK5I2lpqddKpiXo2e8LA6wtX/On18JR1Fqxo0j+2CBcY4/yLA+LzCx3wkmQyzUo1/N2nvP3bt3ef78+Yf/Q5GRvBcs2uDuQAMeOqCe8ciUFGp16pAAr6MQ8/jcVcuQIMV10pa2muibjRVxgoAxG1kZXnyRBKj5+HBgxY32U8jbfiLT6MlKjh+3ewT0R0q27GLBm8Gb4aRJkwiAD90N5T5cvEh+m32E6+I8WzaRO69IFOXSJeHA+Bud3BsTTr2N5XHxovD6DoEwejTd81uunHC3TJumhHf13DlRg5IPt0UUxbGwWSGPv2PAZMECKmv0OHbT3rOHRgggOFiZc4h0Ljm5e5eGld6jh2xpTsyZY9jgPHzYEK1IiqjO+PFCVpUqNEaHt24tWZUr+kzfryo65IyeOSNblguNGokhnxYfW/HZZ5/JluRCcjJZvGASz8PmgGveXKlzXOfSJXK1j2gEEROcP8udWIMGDWK2bNk4Z86ch/o7F7sIz1YK/DjjVXWi4k589pn9GtQVy2Q34nSP7nQBOAxfq7QsMlhiRPS+LzTGfln/8kvvSzGNnqxm9Gj3i/PAQDELJQN97D/88EO74fONB3OvTy455F6rv7/oGajIBf2ff8hP8Y6zxlKlRGes335Tpg/jjBnC6Pkd3bj1dYc+4Qot0kmjGV5wMHlz+1lD565dsqU50by5kNWgAZ2NnsuXZUuz4zg3dfx4imlqgGjvpBB6PwiA3LxJE60RZbjZ7oOelu7jQ1o/+dRIx1IsgV5vA92ttsOO/ekn2bJcqF2bBDRez2sr6lKg62Fq7twRQbLZsKUy5sql3Fw2UhybgzFd2eulzletVto1xkzO2q5y+/fvp4+PDwHw33//feC/E7dlHy0QndDm53lTxdIt8uRJWgOF83IJnmXHjgpmMp4/bx+NsAHN6QMrhw5VZukmuHRJXMMBHg0RqXeAiJbLwDR6spqYGJHzkMqQuFqvq0h9ycBZo2kax4wZYzd8vv/+e4/JjS77mHNk6rE6yhgRjvz6tOhssAlPGnq3b5ctywmrlexd+wT9kMISuaOp6ZM2Feo6RZI3bxrd8V5/Jdlo7bVokWxpTji2BD/5k8PgR4WMHtLoOFanDslXXhE/tGwpW5YTmmbUSf04/pyyERTHwGNKg8ZKRqNIe6YGvy9vyxsMCVEikutISoooNWqGjcZOXb1atiwXfv6ZLIorTILtejlunGxJbmnXOIY3YRvO1q2bbDluOXYwiScg5jDdKFQzS+szrVYrn3jiCQJgr169HvwPWSy8WKAuCfAyinH3BrXOG5Kk1cqkeqI73x3kYd3iN1XKVhZYrWJ+IcAohLIELvLpp6WX5DpjsYguwQDjfLOzLM4QEKPXZGEaPZ7AIZSnPzahKZd9H5nhP6FpGocPH04A9PX15eLFiz2j1TZl7QYKGobPBC+10cgElkNHuCtfBwYgiWdRRtl0kpMnjZrmQwVbK+td1Us6/PzIpOK2/fnxx7JlOZGcTBYWKd/8pIu6Ro9D0xwmNLOljfXrJ1uWC/rwt+9aiqYr9PVVbqGuB/RyI4KabSYGf/hBtiwXRBdMjVdDbUPF+vSRLcmFEyeEtN/RTTwpX15BV7VosjEFw4XGHDlE6Ecxjh4l38PHJECrj6+Yq6Ag86p9Zb8Yxa3elKV/e/78+QTAHDly8NpD1K6dHTnDrnHeU39kncAsxDLFqM3t57eAe/fKVuQGvWAPYD98z8aNletFJdYUNo39MZ+A/P4kptHjCTRNeHoBJpcsa//SD6Im/5x1//Q2489ofO211wiAb3uqkPf2bTIggN8P3c8/0dl+UbduzNoL5kOTnMw7J8NZvDjZD98bq8xt22Qrc0FPA9bre1SYKZOayEh7sxfDOOvfX7YsF/TaiSZB6ho9kZH2Lpy8W1jMclClyYYjr78upC0sbOviVbWqbEkunDwppPXEYuP7vqVeG9sWLcja2GdozOIZKFnBkiVkCVy0pxFJX224ITmZLJfzFuMQLDQqNthVZ/RL4YyECJVa+r4oW45b9qy8zXsIIwGeqdU9S//23bt3mS9fPgLglw9RiJFw/jqjfcV+XB/SibExKuVh2bhwgYkBIhViBTpw9iwFNR47Zk+9+xOdWbmSpl4kaudOarYskl/Qg4DGadNkizKNHs9x4oSoGN+2jffem2S/OZ5Daf49+XSG/4zVauXvv/9OzZNJmvPmUdPIt16+ay/SjMpeSORBKcauXWQ2/xSegTAmtZatZEtyISVFpDqVwnljUZTZ4U1eQHcUfYOBNsuiiWxJLly9KqJR9eBg9Cg0r0VHrz+K9bflkCk4of2bb2x+At8myhq5+lyZH/GCeFKvnmxJbqlTxyE6UaiQUgOTdcaOJSfibSOComCdzKZN5Ce2ek1rYJA4ABQjOpqcGvCmyILwC1Ty+qNp5J+FB5AAE32CmHTqQpb+/cGDBxMAq1SpwuSHqK87VFn0zo9DMLcvvJB1ArMKTeOtmq3sKWPDn76sVn0MSSYnM75KbRLgbeRj9QI3efGibFGpiIqipZTIILmE4syFu5wxw4ufn87cS9Po8SQTJ9pvhhFTfmQKhNV7C/m5asL+B/qTCQkJ3L//wX73flgs5FvN9jIRYgL65QotFEsQFUyfTvbFD8YieOtW2ZJcOHRIeP+PwFZA/OqrsiW5kJAgWgO/hS+ExqJFZUtyyzPPkHWxR2mjZ/JkMhRRhkaFBn7qbNtG+iGFsQixWbtqDagkRXqGLyy8DcfuEOpRsWyKUd+hSDv61HTvGM87sDWsGDxYthy3jBt6l1EIFQ6sQWpq/OmTy0yA8KpHvTxSthy3bJ1xyB7RO9LlvSz/+x999BGzZcvGjRs3PvDfOD3TaOf/Wz31uvOR5K2J8+waPyj6rYpd0xk1YpxdY69sy3jwoGxFriT0EE4rC3zZBJs5c6aXPljTRDe7dDItTKPHk6RyEYT/sILxPiKMH40cXDcmcw3f4+Li2Lp1a4aEhHCbh9K64uPJL8vMtJ9Ux7u/75HPeRg0jezdI4WnIQZdhddUq2hcZ+xY8lOMIQEm5CmsZD79/PlkNziMl1cuKZjcuFF9o+fUKbIKjhoa//tPtiQXIiPJavjP0KhgorqmkQ39HDr17dkjW5JbnstpdMhSctVBcnQ+YwHHEydky3HL1HwficWRr7+S57Wmkb/nfllEJ/xD0/Ugy8Jq0bgnezPhUPUvTGuUZ+r0bj1EmmlydAIvBYr79Qn/qowMV6sbI0nGn73GaN8wEuAmv+Y8dVK1EA8Zs3GP3Xm+wOcFrlvnpQ9OSsrw2iBq9iL7decjjPVeH6foaLJ3b1vO/qG09ZlGj3e5/dcO3vPNLcLQCOSmIUsz/LsJCQls06YNATA0NJR7PLQguBOucUWoCENb4cMjX6qXrx4TQ75d+Ef7yXV9yRbZklxITCR7l9puFJZuVm+RabGQ3cocsmvUjqpXoKtpZPeSahs9JPlikX8MjQoMeHXHW3nnGWk6SUmy5bhlUrDoshEXWkBJRwFJ/uIrro/3ilZVrD+sIDpK4wE8RgK8WVO9hi8kee5wjD0Sdbn1S7LluGXfzyfsEZTTz38oW45btowwnFaH3lSvbTpJbmkx3q5x20T17tXUNB4o0cWeevfPzLOyFbmQFBnPiyGiccplFOMvs+9554OvXBHdCjOQ9RO+94K9ZmsnHud3M71k3O7fL2YiAmT16um+9fDXfzywbeALk0yTv3MDJG/Yhhv+xeAHK/xCgjL8u9myZcMff/yBZs2aISYmBm3btsXhw4ezXGPefD6otuM7nPWriHs+eeDr55Pln/Gw5MgB9FvTG2d8yuN8YCVYE1NkS3IhKAgY+vPjuImCOJqrERLuJcqW5IKfH/DihLK4hiI4VaAJEqOTZUtywccH6No7BId8a+FK/lqgf4BsSW6p+4Q/DgQ+gYi8FYCwMNly3FKkbDCOBdfB7ZL1gMBA2XLcElI8Dy4HlcPNx9oDvurdZlJSAEvh4rjnlxd32r8gDlDFiLyRgPDCNZCEQFhfHypbjlvir97F2TyPwwpf5PtyjGw5btGiY3E6uCYifPOj7IyRsuW4xTfQHzd9i+B4aH3U+LxPlv3dyMhItGvXDjt37nzov+WTJzfiEIJNpV9EozFNskBd1qIlW5CQrzgAYF3TT9B2UFnPf2hCQqbersXGIyJnGQDA9pe/R88BuTwgKhUbNgC1awNFiojFwn3wtyTiZmAJxCAHLkxYiFcHefheTQLTpwMNGgBnz4ptffum+ytB2R7iev2gRpk3UDXSo3Nz72XuHZ3xKI8j0dHRbNCgAQEwf/78POah9pkn/jjBc5vU6pSVmr2/XmBslHp1R47s3RKvojPYjqaJ9CyViYsT0WuVSUhQ0unvhF2f6kJJJeeFOZGUpFxXRhdu3aKWovb1MeHEBdkS0kWzWBm+84xsGekSHx7Lq1vPZ+nfHDJkiL15gTULIq6XtlzknZPhWaDMc+ydtZfJCR4+XzRNDKpZuzbTv2pJ0bhxwnbPX76tVvKTT8RYg0zWTUdcjeeGT70wuPfuXbJrVyO7ArYxDPdpp/4wtoEPST64yeRZoqOjERYWhqioKOTMmVO2nCwnMjISLVu2xIEDB1C4cGFs2bIF5cqVky3LxMTExMTE5BHm0KFDqFOnDjRNw/r169GyZUvZku5PSgoQoGYWgJ3ERGDgQGDZMiA8XKSDqMa9eyJasmKF+LlYMeDSJbWi7jt3As89B1y+7Ly9TRtgzZp0f/VhbAOF9sD/Hrly5cLatWtRrVo1xMfHIyIiQrYkExMTExMTk0cYTdMwePBgaJqGHj16PBoGz4ULwOefy1aRPteuAU2bAj/+CHTsqKbBc+AAUKeOYfAAQM+eahk8ixYBTz7pavAA901te1gU2gv/m+TNmxfr16/Hpk2b8Pjjj8uWY2JiYmJiYvIIs2DBAuzYsQPZs2fH5EmTZMu5Pzt3Ao8/DpQsKVtJ2uzcCdStC+zZI37u1k2uHnfMmwc0bCgMSEd69pSjJy169wZu3wZ69HDenj070LWrRz/aNHoUoGDBgnjsscfsPx86dAi3bt2SJ8jExMTExMTkkSMyMhKjRo0CAIxr2BDF8uWTrOg+LFkCNG8O3LkDtG0rW4175s0DmjUDbt4UPwcFAe3bS5XkREIC8NJLwCuvAElJzq+VKSOMNdX47z/gt9/E81y5xL/duwvDx4OYRo9i7N69G82aNUPr1q1x9+5d2XJMTExMTExMdNQtgwYAzJs3D+Hh4agUFoYR8fFASIhsSe4hgU8/FXUdSUlAvXpAgQKyVTmTkgIMHSqMiWSHrqitWgGhofJ0pUbThMaZMwF/f+fXnntOve6Ut2+LaI+mAaVLi+hZSIjHU9sA0+hRjjx58iA4OBhHjhxBmzZtEBUVJVuSiYmJiYmJydGj9y2yls3IgQMxv1YtzIqKQqCqtTzJySIy8d57xrYOHeTpcUd4uCiqnzHD9bWnn/a+nvTInl2ktZ05A1gsYptew6NaapumAS+8AFy/LppWLFkClC8v6rmaNfP4x5tGj2KUL18eGzZsQL58+bB//3506NABsbGxsmWZmJiYmJj873L4sEjDKlVKtpK0iYyEb/v2ePHgQTQDhF7VuHtXpLH98IPzdpXSxQBgxw6gZk3AofQAgDAmnnpKiqR0+ecf4OuvxfOBA0Xkp3JloHp1qbJc+PxzYO1a8XzSJBHhA4AhQ7zSbME0ehSkSpUqWLduHXLlyoUdO3bgqaeeQkImh2CZmJiYmJgojaYBP/0kW8X92b9fGBApKUCFCrLVuGXfP/8gtnFjYOtWsSFbNuCJJ+SKSs3Zs2II5aZNztvz51ev7qRLF+Czz8R37kiTJkKvSty+DfTvL55XrgxMngyMGwe8+KJaqW1btwJjx4rnXbsCw4Z5XcIja/Qonlbrlsxofuyxx7BmzRqEhoZi06ZN6NatG5JSF6h5gUdtP+sTrh4lHjW9gKnZW5iavcOjpvn/xXWOFPUSBw9K0ZMRSIh6g5YtxeyTxx9Xq/WvjagDB9CpY0dUOnYMx/WNDRsKw0cV7t4Fhg8HYmNdFuJs107J/Ypx44Bjx8TzKVMAPz971zZlzj9SGDe3bgGBgcDixaI+pmhR4I03XN4qjfBwoFcv4egoVQqYP/++BpknrnMKHmUZY8sW4O23Rdv0R4UFC0RE12rN2Pvr16+PVatWISQkBJqmwZrRX8xCvvwSOHTI6x/7UHzwARATI1tFxomNBaZPl60icxw4IKL/jxJ//CHWLY8SixbJVpB5li6VrSBzhIcDGzfKVpE51qwRa0i3bNkCREZ6U06G+P77VBvGjgW++QaoVk2KnvuRnAz89fYOUbSu19aqOFZi/36Mb9wYtzQNOQDYx5urltqWJw+wciWwfr3LANJTZRRLbQOA7dvFAggABgwARowAXnsNie26Ytw4UZKiBDNnAqtWieeffy5S8nT8/AAAJ08K+YmJ3pcHQBg6ffuKBbtex5M7d5pvt1jEfWTMGA9oocJERUURAKOioty+3rUrGRBA9utHHjniXW0PQmwsWagQWaUK+eefpKZl7Pf27dvHhIQEz4pLg127SD8/ctQoof9R4IMPyAoVyEOHZCvJOPXqkaNHZ/yYkM2tW2SePOTx47KVZJzZs8mePR+dfUySrVqRGzfKVpFxwsPJMmVIq1W2kowzdSo5dqxsFRnHaiUfe4w8etTNi6dPk8WKKXeQb9tGVq3qsOHzz3UnLrl7tzRdaWG1kh+12szEwByGToBcuVK2NGfWrePh4GD6AQTAtY5at22Trc4Vq5Vs2JAEqOXLx+Ml2tICXybfuCNbmTMxMWTZsmI/li5NRkeTJDcuj2XZsuI+ogRHjpBBQUJn27YuF94rV8hXXiF9fcm5cyVpJMnPPjOOy6++SvNtsbHk9OlilwcGkidOuH/f/WyD9HikjZ7Ll8kcDtek9u3FAkGx670Ts2cbehs2JLdsydzva5rGZcuW0erFVUXjxkJvqVLk6tVe+9gH5swZoTcoiPz2W7WPB5233xaa+/Ujk5Nlq7k/mkaGhZElSpDXrslWkzF+/FHs459+kq0k41SrRtav/2gcwyT5999iH2/aJFtJxqlVi2zePJ03JCWR27d7Tc/9WLRI7OP161O9EBEhvD0VK0rRlRZRUeLe8dhjtg2ON0FALDAVY3aPDYxFiLNOgLyj1uJcu3ePjevWJQB2d9SZPbs4blVjxgy7xvdKL2QJXOSJYi1lq3Jl0CCh08eH3LyZt2+TL7xg7N69e2ULJBkfL24QAJk/P3njhv2liAjhqM6WTbxcpQqZkiJJ57ZtwnMOkJ07u72Z3bolHE958hj7ePz4tP/k/6zRQwovXerrUp065OLFEr/kdEhJEfckR70dO5KHD2fs98eMGUMAHDhwIDUvrYT+/NNZ73PPkTdveuWjH5gnnjD09upld9Qoy5o1ht4OHR6NqFq9ekJvjRpkZKRsNfdnyRKhNzSUPH9etpqMkS+f0Pz777KVZIwxY4Te/v1lK8kYhw4JvSEh6dwv3nyTnD/fq7rSIilJRNIA8uefU73QrJl4oU0bafrc0bevkPXEExQWm4+PcbErXVq2PBeWDVzDeGRzXViUKydbmgsLFiwgAAYDvAzwQo5qwq3ftq1saa5cumT3Uv/j34GARoDct/y6bGXOrF1r/861kW/w++/JvHmNw6BpU9kCbQwdaohasYIkGRdHTpxI5srlfOj+/bckjXfuiMgzIDykERFOL586Rb72mhGs0h8VKpCJiWn/2f9po8diEUZO6uuTHpmYOlW9BeSyZa5afXzI55+//2Js4cKF9PHxIQCOGDHCK4aP1UqWL++sN1cucs4cddNYpk931lu+vNrpbrGxIlVT1/vEE8o5FV3o08fQ26xZ+hcpFfjrL0Nvo0bi2qEyycnG+rBSJTWdOKlp0sRwNKfrwN+/32ua0mPkSOOY2LfPzRv0g2bZMq9rc8fMmYbeyZNtGzWNfOkl44VXX5Wq0RHd0QCQ71T72/D46o+nnpIt0YklPydzKKZxPMbzHEo7a33+ednynIiMjGTOnIUIgBMBJsOf8988Sr7xhkgnUglNo6VdBxJgNHKwOC4REBlkSkWx792zL9ITy1ZmmycTXNZqNvtCLitWGIKGDGFysshqKVLEdW3ZpImkfWy1Co8+QPr7kzt32l/avl2Upzj6Pxwf//6b/p/+nzZ6SHH/9PV13XHNm5NLl6oX5dU0skED9192YOD902/mz59PQOTwvvvuu17RnDojwfGEUrGu4/Zt1/ur6ulu+oJRf1SqJJxjqvLhh856e/RQ1wgmnaNpADlhgmxF6XPtmrNeqTnZGSApyUinAMgffkjjjdeukS3lp7QkJ4usEF3vtGmp3nDxIpk7t3hxwwYpGh2JjSULFjT0jhple8GxPkahA/vKFWP3NcNGJvkGud5AvHT/yggbN4r7L0CWw2kmw9/w8AHCk6YImkaOHHmbudCWFQEmAfwCbwlfQmysyPFWiJtTFtm/88GYbv/6x42TrSwVtrCk1dePDQL2uhyulSsrcI+7ccO4cFWrxuvn4tN0/ANOtoZ3+eILQ8SXXzq9dPw4OXy4SJFPrffFF+//p//njR7S2WOnPwYNUtebu3Wrq97atclz5zL2+zNnzrQbPh9//LFnxVKkj+qpNo6P4sVFePIBjj2PozsZUj9UTXcbP95Va9Gi6jbpWLzYVe/w4eoalZs3O2v19yf37JGtKm3273c9FuLj03hzRIT0nNNdu5z1NmuWxhuffpqsWdOb0tziGPkDUhUnJyWRjz9uvKhAZGrCBGe9L7xAkfeY+iRcsEC2VFqtZIsWQk4pnOd+1OK17OVcXbuLFsmWSlJkAeTMacj6Fc+IJ3nykGfPig5EShRyiIjvK6+QPrByKxoxCuBVFGHxXNFKrneWfx/OcB+xeNiGhvSB1b6fT52Src6BP/6wHwDzio+jv7/rqSXd8WS1ku3aGV7c//4jKe4LjjaG/njmGUk6t283vM6dOrldFGzb5lzDA4g1ZkYyXEyjhyKVonhxseMcL15PPaVeeptO586uB+mnn2b897/88ku74fNlKkvaE3zwgeui8X5hSJm4W5R37y5qKU+elK3OldSLcv2RK5cwklUj9aJcf3zxhWxl7tmzx1Vr+fJK1lGTFI2iUuudNCmNN48ZI71b01dfuep1SdfVF+nVqknR6Ei3bq4OHDtvvun84tmz0nSSYiHgeF8DyEH195LBwa47PbPdcTzApEmussa13uG60bZok8nFi2Thwoakhthm/PD11+JNa9YokTISH0926SKk9cUPdp09sZhdu8pW50pSEnmhiegAkIhAVsYx+66tX1+2Ogdu3yYLFBDCatViYnSSvYGT/ihYkJTURNdgyhRDkENo+vRp4RRz1OvnJ8movHPHWIwXL+7Wivn1V6OOJ3t2I1Mro02GTKPHht45aMMG504b9epJd4K65dgx8WW3amXv4kggc9kJH3/8MX18fDjXCy6I8HCRvuLjIxxf+oJcxfQ2UhT1OXb3A0STAOnh6TRITHRdwwwZQh486NSYRRmio13XMGFh4rFkiWx1rvz3n3sjLc0SiAsXvCnPhXnzXLXmzi3Szp24cUNU4i9dKkOmnWeecdX7wQcOb7h3z1hdVqokSyZJscZx58m9fJmuISBAeoFdahusGC7zln9h9we05JzYgwed6xPt9k3RtuJJ6dKiMYS/v3RD4t49UROcO7d+PGjcAdEFJyJPWen6UhMfT06a9DObNerGQxBu8g1oTkBzTc9UgdWr7QfA+/jQ6XhQRq+miegzQAYG0nr4CHv1MnTqxoT0rNFDh4z8yw4d7NGTU6eMWp7AQKOZzOuvS9CoaSLSoFtdqbpeapqoRdQDvoUKCedp3boiMpzRLBHT6HFAb/mraaIFnn7gli6tpnf/lVdENkJ0tCiu1vV+9FHG/8bBgwc9pi81r78u0sYOHRJdsACyZEk1F+WkOB5y5XJOHfNCUOyBad3aiEAAYo2oYuqgTurCyc8/l60obU6fdl2IDRwortEu2UuRkbb8IXl88on7Na1LGcSwYeKFKVNkyCQprre6I8TxUbq0g5PhtdeMF8qXl6aVdN/1EyD/nn7RKERxfEjsI3/5smt3o+o4zGfD1rjW8/j5Se14YbWSAwaIvPzq1Q1Zj2On8YPuoEtnXoe3iYgQHufuWGrXuX3kr7JluRAVFcVChUTzgk8hmhe0LymiJ27nNskkJkZ07AJ4zK8aA5DESpXENdfPT7QpVoKff3a6gb3zjvHj0KGiNXxIiGS/R1yc6DsNiJCTbeedPGn4kYKCyFWrRElH2bKSHP2TJ6e5GLBYjFsVIOqjdL/i+PHi/pxRTKPHgdSOme++M1IL8+SRngHiwrVrRrvf6Gg6hVQ//DDzf+/27dtcvnx51op04PRpY1bPmjXGvq1TR800wrVrhYdG05wbiSg4D4+kaDc5cKAwKvWQ78iRslWlTbNmIlKid3IrVCidFIDrcluTXr5sRHb0cyzNVtDffitcfBIZNkykNNWoIbQ2by7KCqZOdVjXXrpkeP/eekua1gsXhAS9jkNfMJQta5vZs2mT8+K8TBlpWknRSKZvXzEwExDZdkXyJfFCwceddQJiNSyRtWvFceq4NnvySeEttT5ly5GuUEG0QypZUqpWHU2zr3f51FPkhkBbHUKpUkoOIvvoIzIASTwL0Q98p08DHjygXnHiyJEjCYDlASYCXF19FI8cUXIerSjwBGiFD+tjF4OCxGiOW7fIZ5+VLc7G1atGo4qGDfndLIv9HOvSRSzUNU2kxEvl9deNk9+2ADtxwtngcZyhKCX7ZudOI3yeKqUmLs45nbhpU/LuXeNXMxtQNY2e+7Bqlbhv6QfHr+o5cOzExDh38UpvQFNqIiIiWLlyZfr5+XGZl1qsOqbgdOqkXltdi8Wo2QgPN0LVpUurOVvm5Ekjfcl2z6CfnxKp726ZO1ek5Tmmjn37bRpvHjcuc+6cLCY83OgWpHe7SXOydv364g1XrnhNX2omTBBNmPRamdy53SxsXnnF2PG9e0vRSQpjbOtWsYbQ5WzbJu57ty4luPa8L1FCmtaUFJHeRhqzpt58k4z7+jteqdZWWJeOWosUkabVEb1bYvHi4ro2+fUz1PQ8kZkzxYVu+HDZMkk6N7U4ONvhB+mV4K7Exoo5LMMxxa5z5Xvblav1O3LkCP38/AiAqwFe8ynKOxdER55duySLS83OnfYcpikYToCcNct4WYl7r6YZTQFCQrhp7hm7E7d+fbFQVwLHdFvb+X38uBFZDwoi//lHrkRGRAiHCyAWWeHh9pdu33aem/jccw8/3sI0ejLA/v3GQeLjI6JwynlGbMTECE+efpCMG5cxrVarlX379iUABgQEcOXKlZ4XS+c0wkGD1N2vpGgWoEdQnn1Wba2RkcYxm2avfX31pgB6JK1s2TS6JrZoQb7/vtd16WiasQ/1rKCQEDc3tyNHjANaoodE1+rY4MKpu+Pp08592RWYmhcXZ8ixB5zffdc1eiI5iqZTrpyQ45SvP3iw2Fi4sDDWqlSRps8RvfGNvWBdzxXJlcvw7CjSvuuNN4xdqLVrTwLUFI3yTJtG5sJd3rHVyLB7d9mSXNA0jU8++SQBsJvtHFr3yi+yZbknKckeQr2AksyOGHbvruC99ttv7dejy2Nm2h3jpUsrlHp37ZoxGbVGDTIhgceOGa3rs2UTGTdS0TTj4uTn59R16fRpsR7QL/tvv501NdWm0ZNBLl4UeYT6FzBsmDL3CBdiY40B24AwLDJy0UhJSWGPHj0IgEFBQVy/fr3HtWqac+OINDtMKYLjfJnZs2WrSR/HlBaXziZWq8jRUQTHNuwujQySk4WFUaqUEp0kzp9Px65x7H//5ptS9DkSHW0Ufjrt1969nQ0JyXUypLgW6Nl2P/1EkafprmNAwYKypZI01hMzZ9o2JCUZG99+W6QPphkO9C56/dyECRQeEb1Li31gjxpYrUbzpsk9HKI8c+bIluZCcrIIOn6Bt4TGgADlZtyQYig5AAYDvAhwT84WtFpUsyJsOLR5bYN/WKqUm+Yrsjl3zp7+E9+kNQsX0giIEghlar+tVtHlSrdujh3jsWNGk7ls2ch162SLpHNHuYkT7Zt37DDGnPj6kt98k3UfaRo9meDuXeEQ1b+jbt0UCmOmIjbWOdPivfcyZvgkJyezS5cuBMCQkBBu9UK/46QkZ62SG0mli8ViaM2WLVXqWESENF3u0DTjeC1QINXN49498YJCBUp6Tdpjj6U6Vh37RW/eLE2fI3pqU48eDhuTkpwHUjVqJE2fI5UqCTmjR9s2HDniOvMkJEQJd6oenZw6leLOt2eP4VX19RUGT758smVS04xA2eLFto1//mnsT70yXIEbxPXrhqzVq2nkPPr5Se/WlpqdDj0LIp4QUR6WKqVcJzSS/PFHsiQuMBGBhidUMTRNY/369QmAH0M0LziyVNGWqceOUbO17vsRL6hZP2u12msItJxhbFnhMgHhrFGg27vBl18aJ9LMmTxyxJhJGhwsGixIZ/duo1Vju3Z2h+ayZcag6pAQh6h/FmEaPZkkMZFOLQmfeEKpLCEn4uKci4PfeSdj65rExES2bduWABgaGso9XpjCeO+e0WAkKEi9phGOXLtmXEAqVXJowvDFF2KhphBHjxrO8qFDHV7QwxUNGiix2CXJFSuMY9Upz9hxiMvLL0vT54g+SyQkxOH7//VXZ0MiWzYlFmvPPy/ktGhh29C1q2v0BFDCpapH053aVesX3AYNxMybunWl6dOJinJzrOqta+vUkaotNcuXG1pv37AIIwJQqCLcQA+Udsy/W+koj9Uq7lcLYTs2w8KcahFU4sjP2zgBPkwAuOax0ff/BRlYLOL8BngL+ZkHd9TM+nDoMDax8o/2Q/QXlbIF9+83jImnnuKR/zQng2fDBtkCKSII+nWoSBH7InrqVMMfV6CAZwaAm0bPA2C1Gv3MAZF3KLHGOl3i4siWLQ2tb7+dsTVuXFwcmzVrxjJlyvCCl2aOXLxoeHrz5CFP749Wr7uBDYcRAnzxRdvGn34S1pBT8YR83nrLcJQfOGDb6Dgd1O6qloumiU5YgEjPtOPYuiU0VAyckIzeccwpMtm+vashocAk9q+/NtZl2r795OOPi7CPrlHvaXzsmGyp9pljdqe5xWKM3v74Y7FNgYiq4/e/ezeFJn2hMXWqbHlO6C33S5akcKPqwlPNwZCN1So6iQHksVIdlI7y/PUXWRcOEWhF++1rFiuPhglj4ppvUd69rFiHBZ1p0+z7sicWs317JTKZnTl2zH6t3Fe8CwFNva8+NpasWFHsy0KFeGzTbXvyQXAwuXGjbIEUN3rd8ebrS27eTKvVOTO8YkU3w6mzCNPoeQhmzzYK2/PlU87Jbyc+3kjv1NNcMmL4xMTE8Nq1a54X6MD+/Ua3vCql4pjY9ik1p8NSpMPr+/Tnnyni23r4x7GnomSio42c/gYNbDeT9esN8SVKKGFIkM51SDt3UhyouptKMSNNb9LWvTtFpzb9YuD4mD5dtkyneqkzp2wrid0OnvRt20Sb0LVr5Qql0dDCPubIMd9p3z6p2hw5cMCQdfo0RdI5IMKqylQyCzp1EtKefppGl5t69ZSJ8Ops326TBodj87vvZMtyQdPIJx7XuAliX2olSqTTa18Omqbx77//5s4Bc+37cuNABac+k+SlS9RsN/3l6MgihTX1smeSk0WEGWBsSD4WwE0CYkSEUqeRwzyzs7PW2ksMQ0LIf/+VLc6G46CzTz5hfLzzcOrGjT3r1zKNnodkxQpxQOnZLF7q9pxp4uPF8Mow3CMgvP+ZPVmXL1/O014Iaa1caawf/8nbm1qhwoolzAqSk4XTHBB1wec3XzbO3BYtlPJQLlliSJs3j+RvvzkvzqWPjBakpBhR7y5dKEZGpzYk2reXLZOkkTYdHEwmjpsgDlq9UlSP0ffpI1smY2KM88mehjF7tnE3tFiEJaxAeo6eitepk23DuHFGroNCrt8NG4zDMTycRl/Vp56SLc0FPXo+b7BDdHfhQtmyXNDb7K/P1tEITSl0DdXZtIl8Cg6tgH/+WbYkFxYvXkwAbIIAagD3hLVUs3mBplGzRcijEMoSPpfVWZw74tDB6Gn8RkA4aJRKRHGI4t7q+6aTwbNpk2xxNvbuNSLibdrwzm2rPboPiBpZT/sPTKMnC9i712gD6OOjXHaDnfh4cm75zzgTr7MkLvCNNzJu+Cxfvpx+fn4sXry4V9Ld9DVZR4iEdM3PTxRSKOVWEWkuYWFCa53HLPZCTAIi700RvZpmRPvy5SNjv57jbEhkzy6KlRRg5kxD1rWP5znr1EPikoeVkiIdU5e0e+B8URQ+YIDY0Lw5+ccfDoU0ctHr5ewzSHWdTzwhVVdq9E7K9h4QNu+qSp0GSWefQcoxB8NcsUFu1645nEut+oonRYooZ0xYrUKWU8qYglEekuzYJpknIFKItDp1lDLGSTI6OppFihSh3rwgCQE8vuyEbFnucQjtv46ZmZot6DX277cXxi706UOArF2bas1iunLFngYcV/ExFsqdaL+tK9L7R9SMli4tvu/ChXlh9y1WqGCc7qNGeedUMo2eh+XuXdJq5fnzRpckQOQnKnYtJEkm3I3n9eAyTIEff0BfTux3IkPr8ps3b7JixYoEwDJlyvDq1ase1/r222LSdQRyGzu2a1clCq4dcVwA3Q5zaCwPkJ9+KluenZMnDSfL70984WpM9O8vWyJJYZzrGW2by73orDE4WPz75ZeyZZI0HPxPP23boMfp9SLxuDglDN++fQ1bjKQRonz9dam6UqPXn1SpQpEmpn/viqQ06syx+Qxy5qQxbCxXLuXSnPTZhAVxw3DIfPKJbFku6CmYy6F2lOfgQXIgvjGOSwXDEm+99RYBsCzABIDr6rwtW5J7bt9mSi4RjtiKRmz2pFW9MSAJCfa5Qdd8ijAX7rJECSV8bgYOLWWt2YJZP+cJe/aJF5rvZgxNM+6Nvr48PmuT/R7v60vOmOE9KabR87BcvizM/p49GTvlO/aoe85+PXzmGWVKJZxIWma0ybLChwfLdae2/8B9f+/q1assU6YMAbBixYq86eFaG6tVTOD9Dq84L3zLlhV3H4V4/XUhbS1aOWsFlGrt8s47NlsM77jq9PFRpm7ik0+EpE1oyvApPxm1CIMGiRHin30mWyJJo7Fctmyidso+IEsxY0JPo86Zk7QmWwzj8dtvZUtzQm+6ULgwRWMQ/a6oQPMCR76w+QxKlbAa08QHDJAtywU9O/DrsHHGgapAGmNqhg4l62CvcS1S7LjUefGZKN6CWK1ZOqqXynjs2DH6+/sTAFcCvOpbjJFXVQpJGKT07EMCTEQgH891gl7wo2Yeh4YvbbGaYWFGN3pl+Owzu8YRId/aDR6lOuBOn27XeKL3R/bbT3Cw6PTvTUyjJys4elR4+Wxf6q3spTgHL/M5LGLHujdVvMfQ0qmzy6JXa9/+vmfKhQsXWLx4cQJg9erVeefOHY/qTEggh1ff4KKVQUHk3Lke/ezMkJAghh67GGi6VkW6XMTGir4FM/G6s8bHHhNW+jPPKBGZuHePDM2hMTtiRCevLl2Ezt69JStz5rJDGdeiRTTaz40dK1uaE3qROEBeXHXM+MEL7egzw48/GqeMU6tqxdC7d75cbpOxLxXrhkaK/hRBSGBkkM2t+uqrsiW5YLEII/dv2DoulCihZJTnzBlyAt4TzkJfP/K4WvNuNE1j8+bNCYBdbMfk5iGKDr1btcp+3ryHj7lihWxBbti2jZqtNnM2XmNAgCLdzxzZu9eeevd3QDcCGkNDFbsU7dtnnzp9uVIr+vtYCIhsjl27vC/HNHqyii1bjNavqR4nA6vxXv/hYmCCKjlv589T0ydApX40bZpuJ6fTp0+zcOHCBMA6deowxsPJrRG3LbzlV8i91hdfVGIAIEmeOEGOD5jgrC9PHlHx+OyztlCAfJYtIxfhOX6HV3iyQX+hs1498aKmKWH0kIaTLTiYTOjZT/zQoYNsWS7Yxkuwa1eK1RsgQhYKERdnNDPYPniheOLnp1wo+u+/bcEdWKjprao/+ki2LBf0kqiVhV8yos+KnDc6miZqTftjvnE9Us5NLWoOHoUoz9t9rjAe4p6Z9PJA2XJc0JsXZAN4AeDeXK2oWdU6JkmS0dGMzVucBPgfqnHUcPUMXMbGUisrUtXPoTRzIJoLFsgWlYqYGLJ8eXvqXR7cYWioMv5VQWQkWaYMCTAqeyF717vy5cXINRk8jG3gCxODJk2ARYsAHx+XlyomH8XtBWtw4lpOwFeR3Va6NHzeecdlc2xQHrD+40CJEmn+avny5bF+/Xrky5cP9erVQ0hIiCeVIk9+PwQ938NpmxaUDfj5Z+DNNwF/f49+fkapVAlo8VJpAMBNFBQbY2OBuXOBpUuB0FCJ6gy6dgX2PDEcr+E7fH24udh48CAQHy+OXzfHsAxGjAACA4GEBODQhTCxMSpKqiZ39LAdmqtXEbxzR/yQN688QW4ICQGqVBHPk3cfFE8qVQKCg+WJckOePOLfetgLn7t3xQ8dOsgTlAb37gHBiEez8F/Fhr59lTlvdK5dA27dIkbga7GhdWugalWpmtzx66/AOHwEAGCJEkD//nIFueHGDaDy4vcRjEQkBeZA4CcfyJbkQtmyZVEhOD/eAVAEAcj543T4+Kp1TAJA5OD3kD3iCqzwxeTK8zDhi0DZklzgqNHwOXcOGnzQHz/g7Y9D8fzzslWlYvhw4MwZaPDB81wAS868WLsWaNBAtjAbJPDqq8D589B8fNElbhFuoyAaNgR27ADKlpUt8AHwgBGWZXg90qPzzTduIxKDMIMh2az86y/vykmXhAS7Fa4/YpCdUzutzVBA6vLly9S85d20zetIhr9da/yQUd757MywfTvP56nNYrhs9wry/fdlq3Lh7FkRmCyDs8b3r2BRrj524PNstpqEqlVlS3LhyhUhLQeijX25erVsWS70twX19uay1Z09/7xsSS4cs2XefQA1W1XrtG5NPodFxvet2EBiUjQPbIaNhsaVK2VLcsFiIdvk3WdonD1btiS3fP3iIVohUp1ix3wsW45bTi3ax2SASQA31FOzeUHSph32/Tg9cKSKp43IcrEdj1/iDb78snJBXDER26bxM4xmzpxyUsXSxaEN6zh8QECNOnczvc0T6N18Uj124AnW8jno1U4V92WFaGqgBQQwKkCkkyQhgHNaLs7UWiM5OZmff/45ExMTPaNT08jSpXn66bc5Fy/b92nSCvkDFZ24c4exJy6zQgVyFkQOjDVPPvlnuhtEkbPGm7DNllFkVo8jZ86ItKwRsHUMKFpUtiS3NGpElsJ543zfu1e2JBdELanGOz62AQ6KdMBz5Pp1IW036oknirWq1qlbl1yFdkJjkyay5bhl7FjyT9hqNytUUNJ43LSJ/AtPkQATCxZXspbn3l2NG/yEo+Be9iLKpFPraJpGa4qVR3OIjozXfIsx+rqCzQsSE3kjT2US4HmU4m8/xspW5Mq9e4zNU4wEeAyV2alVApOTZYtKxaVLTAkVNeR7UYf5ciZx927ZolJx4AA1Wx3PerSgLyzKdDQ2jR5PoGnkS7Zc70aNyDZt7IshC3w5BcM5dliUEgcASVEk3rw5kw4d5+3g4natixtOzbDG3r17EwC7dOnCZE9dJT77jIyI4KwvY3kSosH73WyFaL2p2vhm0VyuesAJ+760zlZv5kR8vGib/zu6CcO3nRpDP1PTsyf5IsS8Hi1HDtly3DJ1aqq6hPPnZUtyYedOshgcOi9s2CBbkgsJCWQ+3LZ7g1VrVa3zeInrtMBWJDVnjmw5bnmxyRljP86cKVuOWyY+awxM1WapGeX5pf9qu8bwL+bLluOEpmns0KED+9fsxDibxm0j1JoVpXOq13j7fpzSYY1sOW653rYfCTAFfuxVfi9lLB/TxWJh1GOik2ksQlg7xynVetGQUVFMKF6OBHgDBVkIN5QqcTWNHk+RkiLGig8eLIygpUtpLVTEftJfQ2F+3WAxE+IViJteuEBOm0aSTD5/hZdDq9h1rnjsvQxNcl6/fj2DgoIIgD169KDFEw33HSywyX32Mwli7sTxMh0VjD+L3vMr0EHcLPNXVlLj8uXkG/iSBJgQnEsNV0wqDhwgu+F3Y7Gu1BhswdWrZDsYiyNVmlY4Eh9PdvH929CoWBtonf4BC4SjwEe9VtU6Y4PFOZPiHySKdRVD08hvg4cZ57VSkxQFFgu5OlBEou6FqhnliY+x8Lif6Mh4OVd1qjZIZsmSJQTAIIDnAO7N01rJ5gU31h+136//zNVPxcQHXpr+p/3a+FXoOCVbaF98zWiUNCR4rnoJBZrGa016ius3fNguYD1//122KGeibt0yjR6PERfnnEcdHc3kYW/Q4uNnP3B/a6eIl9BhsZt8M4In8zSwa/x30JIM/YmVK1cyICCAANi3b19aPbiAtlrJ76tPtms88cXfHvusB0XTyHGN1ts13l6inmedJN9qvNOuMXG/et2dSPLtusZ+1MI92yb9QfmwgpgunuwToKSBS5IzC35IAowILSFbSposyyZaVV8rpV6ralJcew7gMRLgpQY9Zctxy+WTcYxCKAnwYg8Fax9J7vzhpP2cvjhmlmw5btk42Jg8feFbtaITMTExLFq0KAHwA1ta+rlVJ2XLcsv+YsK4vYkCPLldveu3lpzCK0Givvmgby3+t089Azzh/HUmQqSM/RnQnfv3qXePubXIuE9/ETxOTie5+6SfHuj/kWn0eBvrwcM8X6QhrwaWYtQNtfKDdZIj47ivcEfuKNOH1pSMGy/Lli2jn58fAXDAgAEebXQQH2vl5rzduKnPd8ouMu9GaPy94Os8+PkaZTVeOJXEnypO4KUfNio3UV5n51+3uKjDAt75YbmyGlfPvcpVA/5k5MyfZUtJk79nX+W2N37nnanqalwx5zr3DZ7PG98o5iK0YbGQG364zAM9JvLm0s2y5bglIYHc/cNxHmwwkFFHLsmW45aUFHLPlK08UKU3tQQP1YI+JJYUjTuG/8JdNdSbbzR69GgCYKlSpfhP64/5bwv1WrvrRF28yw1lXubGAeoM6k7N1c1nuTNHS27/9ohsKWlyaPoW7glsxEP/3pUtxT2axvW953FjSAeePuHlqKimicLVn35K922R4Xce2DbwIUnv9ovLONHR0QgLC0NUVBRy5swpW44rmob4M9cQUrG4bCVpYklIgQ8Iv+DMtZRcvHgx+vTpA5IYPXo0Pv/8cw8pBKwWws9fvbacjpDKdbM1MTExMXlEOXnyJKpXrw6LxYLly5ejU6dOsiXdF321qPK90GoF/Pxkq0ifpEQiKJsXd2JKihgLkokvLi6WyJ7Dixpv3gRefBHYskX0l09nzf8wtoEiA2ceUXx9lTZ4AMA/OCDTBg8A9OrVC/PmzUOOHDnQtm1bDygzUN3gAdS+yJuYmJiYPDqQxNChQ2GxWNCpU6dHwuABlBoBlyaqGzwAvGvwnDsHvP12pr84rxo8f/0FVK8O/PMP8Nxz6Ro8D4tp9JikyYsvvohz586hRYsWsqWYmJiYmJj8v+DUqVPYtm0bgoKCMHXqVNlyTB4ETZOt4P4sWQLUqgVUrixbiXtiY4HXXhPT1vWh4K+84tGPNI0ek3QpUKCA/fnx48cxe/ZsiWpMTExMTEwebSpVqoQTJ07gp59+QpkyZWTLMckMmgZMnAhcuSJbSdrExwOvviqiJsnJQI8eshW5smePMMjmzDG2Va0KPPGERz/W36N/3eT/Dbdu3ULTpk1x584daJqGQYMGyZZkYmJiYmLySFKqVCmUKlVKtgx1eBQKZ+/dA/r2BW7fBt55R7Ya9xw9CvTsCRw/Ln7u2hUIC5MqyQmLRRiNH34oCrAceeUVjx8DZqTHJEMULFgQr732GgBg8ODBmD9/vmRFJiYmJiYmjw6nT5/Gtm3bZMtQjxMnRCqWyhw8CNSpA6xYISIoqkEC330H1KtnGDwA0K+fPE2pSU4WesaNczV4AgOBF17wuATT6DHJMBMmTMCIESMAAK+88goWL14sV5CJiYmJickjAEkMHjwYTZo0wbRp02TLUYcVK4DHHxepTaoybx7QoAFw4YL4+dln5epJTWSkiO4MGAAkJhrbCxUCWreWJsuFwEBg4ULg/HkgTx7n155+Gsib1+MSTKPHJMP4+Pjgq6++wsCBA0ESL7zwApYtWyZblomJiYmJidL8/vvvWL9+PYKCgtCxY0fPfpjVCnzzjWc/42EhRZpT585A/vxAtWqyFbmSkAC8/LJIu0pKEtuaNAGKFZOryxG9NubXX11f69NHtKpWCRIYMwa4e1f8HBoq/n31Va98vGn0mGQKHx8fzJw5E/369YPVasVzzz2HTZs2yZZlYmJiYmKiJHFxcRg5ciQAYPTo0ShbtqznPiw+HujeXaSMqUp8PNC7N/Duu2IR3LWrevU8588DDRsCqVP5VUttq1QJ+Pln92lsfft6X8/9mDIFWLpUPH/vPfEoUwZo1swrH6+YCWjyKODr64t58+YhMTERV69eRa1atWRLMjExMTEx8Rx79oiUnHLlMv2rEyZMwNWrV1GqVCmMGTPGA+Js3L4tIie7d4u0MRW5ckUYOQcOGNu6dpWlxj3Ll4v6kqgo5+2+vsKgVImcOYHy5YF168TPPj7CkHzsMaBGDanSXNi0CRg9Wjxv21Y0M4iLE9EeX+/EYMxIj8kD4efnhwULFmDNmjUIU6kziImJiYmJSVayaRPQpQtQsmSmf/XUqVOYPHkyAODrr79GSEhIFouzf5CoO9m9W9ROeMlznim2bwfq1nU2ePLlExEVVUhIEIbZ88+7dj1r2RJwGOOhBJomDLTr14GAAGDlSiB7dvWiPFevirojqxUoVUrU9vj5CaPt9de9JsM0ekwemICAAGTPnt3+89SpU7Fnzx6JikxMTExMHglSUoANG2SruD8rVgDt2olC+4CATP0qSQwbNgwpKSlo3749Onfu7BmNW7cKw+H8efFz48Zi4asSc+cCzZuLaJQjnTuLxa8qBAcDgwYJXakjPT17ytGUHp9+CqxdK55/+SXQvj0wYoRIH1SF5GTR/OH2bSBbNuD3352bFngxtdE0ekyyhAULFmDEiBFo27YtDh06JFuOiYmJiYmqWCyiyFrlAY8AsHgx0K2bKGJv0uSB/sTzzz+PkiVLYtq0afDxxOJuyRKgVSujMBwQqUMqsXGjGEKpF607olpqGwCEhxs1MhUrAi+9JAzebt3k6krNpk3A+PHi+dNPA0OHiufjxwMFC0qT5cLIkcCuXeL5rFlA7drSpDyyRs/Jk8CdO7JVZI6jR4XB+yhx8mTG3tetWzc0bNgQkZGRaN26NY479on3IqTRVfJRIT4eiIiQrSJzhIeLdcujRGoH46PAo3ZcAM5rr0cB8tHTHBUlAiUu6AsLBbGff1Yr0L+/6Dalcj3od9+BffoYF7oHMHp8fHzwwgsv4OzZsyj3ALVA6UICn38uCutTLyxUM3patBBpd7ZZf3ZCQpD8ZCu1bF9SGDk3bwpDZ/Fi4JNPhFFha7McHq7A+vPWLaBXL5HeVrq0aKutG9VuIpInTjh3s/YaP/1kdBIcOFCc+xlA08TlTNOyVs4ja/QULCjSV59+Gvj77zRuAIphtQqnweefi8G+jwLbtgFt2gD796f/vhw5cmDVqlWoU6cO7ty5g5YtW+LMmTPeEemAj4/Yv5988mgcE4CIpvfsCTxKAbLEROGofZQMn1WrgEetw/pHH4kRDI8KpGjG8yixc6e6Nd9pMWGCm3vIvn3K7vzYWJFxA00TrWkXLhR1J1WqyJbmnkmTgAED4EOKn/39xSyZTGB1GL7on9Vtgy0WkYLlpilCYu5C6hWwA2IF+8UX4vlTTwGBgbj7eDs0ahWc2axBzzJrlnFBmDhRGOaFCgHffovYWHFN7tYNyJ1bokarVdQc3bwpzqNffwVy5XJ5Gyn6G7RvL5qmZcvmZZ0HD4rZQYA4f77++r6/cvYs8P77wo47dswD/Q2oMFFRUQTAqKgot69v3Ur6+pIAmT8/OXIkefiwl0VmknfeEXpDQsghQ8gzZ2QrSp+UFLJyZaG5Rw/y9On033/nzh1Wr16dAFi8eHFeuHDBKzodOXhQ6K1Rg9y71+sf/0CMGkUGB5M//yxbScbQNLJgQfLZZ8nkZNlqMsby5WRoKHnypGwlGad9e/KNN2SryDinT5MBAeTt27KVZJyXXiJffz2NF2NiyPPnvarnfpw5I/bxqVMOGy9cECdkr16yZKWJppHPPUe2aK6RAweKizNA1q4tW5ormka++66hUX/Ur5+pP3P69GmWKlWKCxcupKZpntEZGUkeP06WL++k1fJ8v6z/vIclPp6sWFFoLFGCcTei+G/dt9jX5yf26ydbnANHj5LZsgmdrVuTVitJMimJnD6dLFBAvLRypWSdH35ofOfTp7u8nJhIfv89Wb26eEuhQuTdu17WGBFBli5tLNCvXEnzrVFR5Jw5ZKNGxn+rRw9xmLt/f/q2QXo80kYPSX70kev1qVYt8uuv1bzxxseT5coZWn18yK5dhQHniWtjVvDXX4Zef39x37p+Pe3337p1i5UqVSIAli5dmjExMd4Ta6NZM6HX11cYFHFxXpeQKXbvNvbx8OGPhiHRqZPQ2737o6F3506ht2pVMjZWtpqM0aCBOOecFrgK89NPYh9/9ZVsJRkjOprMnl3cM1zQNLJPH3L/fq/rSo8uXcQ+3r3btuHuXcMzNXasTGlumTqVBDSuLD/M+Ub98suypTljtZKDB7suKADyzTcz/Gc0TWPbtm0JgO3bt/eM0aPzww92jcdRSTxftMhzn/egjBxp17l/0gaWKUPmRCRz4S4PHpQtzkZCgmEl5MtHXr9Oq1U4IvW1OyDWFlLXahs2iIWjfvN1EBMeTn78sfB/OB6+v/7qZY0WC9munbEI27jR7VvWrROX2OBgZ72lSgmbPi3+p40ei4Vs2tT9dcrfXxgUqnn7N250r7dePXLxYvUWkJpGNm7srDUkRDjE7t1z/zvXrl1juXLl+PXXX3tVq46joQaQZcu6Pe+UQdPI4sUNvU2bkjdvylaVPo7OpmeeUe+4Tc2ZM4beXr3UdTI4oq9ln3pKtpKMMWiQYVimuX/PnfOqpvSYO1fo9fNz4xj57jvxokKRnvXrjWN4zRoKl67u4QHI+fNlS3Ri2zbS30/j5xjlesObMUO2PGfOnOHJlz7nVL8Rrlr//DPDf2bZsmUEwMDAQJ7xYCqHdus2E0LykAA3ohnz4TYTc+YTK1+V2LLFvkhfX3mw025t1ky2OAeGD7cL0/78i6tWkTVruh4Ke/ZI1HjjhmHRlC1rtwxOnCAHDDCCVI6PLl0k3OvGjTMETJrk9FJCAvn++87rHceHn59wUKbH/7TRQ4qoWZ48rjuvbFnheUxJ8ZLgTPDyy+6/cH0BqVpkYvt291rz5BHHdEKC6+/ESfxPWCzi+0+t99VX0zbUZOPgDCNAFi3q4M1VkNWrnfU+/bTahs+9e856p02Trej+FC5s6F27Vraa+1OnjqF31y43bzh5UoSKFaFBA0Pvli0OLxw8SAYFiRcUuWCkpBiOaID8ZbFGPv+880G9aZNsmXZu3iSLFCE/wlj3N49t22RLdGLVKjIwkBwLh/QR3QV9506G/kZcXBxLlChBAHzvvfc8pjU+ntxW5gUSYAKCWB6nGBJCJmzY7rHPfCBiYsgyZUiA5/3KMgSxD2pLepZVq+yibjw9KE1H+rPPStRosZDNmwshgYH2CPQff4hotTu9oaHk1ate1rl8ufMOc2NxnTghIjzuNH/66f0/4n/e6CFdPfuAWDBs3uwFoQ/A3buuIUiA/O032crSpls3V73BweK43ro1/d+9d+8eX331Vd7z4gJi2jT3J1WRIgpdbB1wZ1gGBgpvtIqEh7vq7dZN5D+riKYJL5Ku1d9f7HOVcQz7V62ahgMnvVxTLxIXJ/apo4PBCU0TIcw+fWTIc+H4cedj94svbC9ERho5yD4+9rx+2cye7ax3X4f3XU/AdPLmvUlKivDiP4+feAnFeQepvJI+PmJBrAgrV4prbTFcZhzESbe/Rn9RL1GlSob/ztixYwmAJUqU8JjT7+JFcmC5dfZ9ORYfyV+Qp0FsfxH6tcKHjbDVxSltschWSGGd24p1LJWq8IO3451KEBzvF1JrsMePN8R8843TSxER7qNSqd7mec6cIcPCxIdXrizyh90QHS3q/FLrbdkyY5db0+ixMWSI2HG+vuICpj+fOFGZ+5YTS5e6fumFCpH79slW5p6TJ50XjfpBGh9//99t164dAfCJJ55gdBonQlYTHU3mzOmst0ABEVpdv16RC64DVqswyNwZagMGiEwW1XDMddYfXbqoa/ikdjQUKaJuGmFiouu+nTnTzRtfeUVUgkpm61ZXL6NT7dS8eeKFnj2laXTkzTed9T7zDIVh1r27sTFXLtkySYpgU758hqwXMc+9h0SRG92oVNlsk5BqZ1eoIFuineXLjfXCQvQiAUYjB1fPv057UUcGOH36NAMDAwmAy5Yt84jW9evJonnieQYijeEoqjAASQTIJUs88pEPjkMu5m8l33A5XKdOlS2Q4nxv3944f2ydsJYsMcpm9MfgwRJ1rltnCOrZ0yl6kpjoPmrSqJGXLwexsUYoOp2OQf/9Z/S0AAzHXr585LVrGfso0+ixkZAgOnZVrSrqeEqVMnZsx44ZjlB7DU0jO3cW+l55xcimCAlRMxJBisU3QObIYezbVq3un4536NAh5s6dmwDYtGlTr6W+pV7Y+Poq0HklHYYOddabPTv5yy/iQqGi0dOjh3sjTVXDp2pVV63NmrmJoChwsbh501Vr3rypuvBcuiRaeSnQtnLSJFe9339ve/HWLTJ3brHx6adlyiQpjs38+Z21Fi1K1/Bw6dKypZJ0vo61wlomw991Z1esKFsmSfL3351lFcY1xsNWbDBkCPnEE8oYvsuXi9MHIBvBsNpH4fMML8B0vvrqKwJg27ZtPda84OpV8kKvd+w69ehJtmxKBc6EE6ZECRKgtUIlNqwV73RM5MyZZhDAu4guG+Jhqz9evNhw7urrnBw5xCVMCtevG23jypd3cnBFRJBPPmn8Fzp1IkuWFPbbsWNe1KhpZO/ehpA0jP7vvzeMHB8f4YD+9lvxc2bWZabR48Dx4+Rrr4nnd+8aRgUgzkG3eeYSuXJFRAPv3iV37DC8eT4+4hxUrdj6+nVhlE2fTr79trFvW7S4v+GzZ88e5syZkwDYpk0bJnphFX/hgjB0GjY0anxy5FBijeiWzZtpv4np+/bDD2WrSpsvv3Re4OTPLxw816+rafSkztXOlUscz6NHO7zp2jXygw9kSbRz8qR7g3LECIc36Z0DFPCSOAZI9EfjxrYXHW+ICnRlSL0wB8h62E1NXwHrDwXaKuttwIUkjU/hL3bD7/yvRAdnrR06yJbKkyfFOVWokCFrJl4nAVqCs4uV4/nz5KxZsqWSFL6NS5fIN0dauQ+1SYBnUJalizzYvWnTpk0ebV7A//6jZsshnYUB9vtEt26e+8gHwla0rPn6cnTTXfZjoW9f8a8SbfgPHzY8ze3akZrGRYuMMShlyohzL0cOifdgPU8UEFodWt2dOSMCpvq+HTpUZK+89JKE25ej8fj22y4vx8UJXY7Ou9WrxWv79qW6p2UA0+hJhaMHQdOEB1K33AMCxPejkjHx77/G87NnnUN/Q4ao14hh/HixLtQ0Y+4QIGrs7tcKeNu2bcyePTsBsHPnzkz2QuV79+6i5uvkScPZXLy4aISiGhaLSMH66itjnejnJ7ljTDroRprjgtdtUyYF0q9IkcKkexoBIw/eyUs6Y4bwRktm1y7nNa2vr5gnU7u2bV7W1atGXs6UKbLlslgxZ736Qv3ynFQdL9q1ky2VHWz2gl6DlBsRvICSrpZQy5aypfLFF0Xk9IknDFnZEcOYwNyGezdnTnGzkExcnLiG7dghpJXCeXtUKmrIu8YbFboBR0aSw7LPte/cXtn/YteuslW5wWKh9vjjJMDrKMQiIfe4d68wMhculC3OgZUr7ftywxPv2I/Zd94R6VYVKwpnpFTi40Wtlu6pu3mTCxcaBk/ZskZ53MiREqNoYx2agMyebd+8bZswHHQHuWOq4I4dXs4K2brVuJC2bOmyYD11yrkBS8OG5OXLxutJSZnXaxo9GWDbNpG+oO/47t3T7wMuk4gIZ490x45qha4dj+nUs9yaNbu/4bNhwwZmy5aNADjQC52cjh836nf+/dc4P+vVU69LHimM9MREEf3TF5IVKqg5WyYmhmzSRBwTelS1QAE3qQtjxyph+AwYIELqeoQqIMBNh9emTcXdT3LXrn/+EfX08xzKN/SUBauVTi1WOWyYTKm8ckVkgv32myFpyRJy0ZxYxuYvpZQhERUlnJGnT+vRX407qr7MC3lqMSJnKq3PPCNVq6YZeflt2hhe/d+bTTNWPGfOiIWmEkUSAr076dLs/UiA93xyMe6qt6cjZoyP3ozkTYj0odjGbbh4kcZPPsn47y9ZsoSXLl3ynECdGTPsx2V3LOWcOWLztGlKXFoFd+/aC1MjilZnIBLtziX9OD5yRK5Eks7zmFau5IIFhsFTrpxzPxBpTuc1a4w6HocZC7/84lwK8ddfkvSRIqVDD+sWL+4yHHPJEudSiDffzJoOr6bRk0Fu3xZDdvUvoFw5qjMYKxVJSeQLLxhaa9WS0Howg2ias0OiadP7L9BXr17NMmXK8GQaxW6eZP585zWNIrW/dhydoBs2GFoHDXJ4U0KCMiFAvfzl2DHjxjF+fKo3tWnj0B5LHgcPiv1765Zh/DoFSW7eNG40v/8uSaXgxAnhAElMNG5y331ne/HGDeccSMkpYxERRkMTvX3q/Pkk33rLNXry5JNStTqiNxrS92tinQaGq9fXVxRbKoDFImqDAXLqVxajg4hjfZS7uQESiIkRC51KOE6rj7ggXBuSCSvCi1y7Rn7lL45Ri4+f3auQ0ZK+M2fOMDAwkMHBwTzlyQnCV6/Skl0cAMvRkZ2f0uz3CYWCZvYW6lY/f9b1O0CAfPzxjDU78hqOLZWHDuVPPxmX/PLlFVlnXbtmFB1WqEBGR1PTyE8+MaRLb3qVnGwMcAwMdEpHSUw0GosB4jr7xx9Z99Gm0ZMJLBaRn6kf5EFB5Jw5il04bGiayM3UD5yiRclDh2Srco+mOc+jevLJ+0enkiQWfTim5blJQVUKx/k9q1bZNt69K/4TivHKK7b0m+yp0gdLlxZXaUUWZqRYLwJktWoO5/+sWcbOVmieTJMmQlLfvrYNqY2JatWk6nOkZEkh6YfhB1zbPQJiOI4CJCcbkpYto8jDcAxTff99qmIveRw8aEg7+9mvxg87dsiW5oIemVyK7kboV6VUBQfee/YUkyDyMGNfzly0VNM0tm/fngDYunVrjzUvIElLV3GxikF21spzUV5RfXr88Yf9uPw06AMC4lqgVHfM69eNwulq1fjTdwn2tWCFChnvHuZRUlKM7gTZspGHDzM52bkmpnp1UYsmlWHDDEF62JEidbFePeOl2rWzfh61afQ8AOvXGw0xABFVUTF9iCQXLDDy43PkcFj4KohjK/kmTTJ+r1u7di1Hjx7t0RuHI1arcx3K3LlUM9eNwk7Qu44VKmRLx7JalWxFd/Wq0Z3l9ddtGxMTjRCQPVQhH4d5dMYQ2BYtjI1lykjV58iYMUYAgrdvi7wGR0MiRw5lPDf6gNLFHReI6dC6yy8gQBwHdevKlkhSGOX67tuyhUaebq5chnGuyOpSz2zKHqJRq1df/NCwoWxZbmnYkHwMB4ydq0C9mTuOHydXoKMweIJTt0W8P3/++ScBMCAgwLMZC3/+ad+XI/AV//7bcx/1wISH2xdURwJr0x/JDA1VJJVNx2o1ckSDgvjHx0fsBk/FisqMO3OuF5gzh/fuiYxgfVObNgqkMy5caAhyiIb//be4fOovvf66Z/ycptHzgFy7ZnhQAVHXdvy4Rz7qodm0ySjC9/NTpvmNWxyjU40b37815bVr1+w1PmPGjPGa4RMfT9a3rR/8/cmzL3+q1ERzRw4eNAzfp5+2rW/Dwsg8eZyrAhVAj6L5+QnnudMUyHLllBmQZLEYNVOvvkphTOjGmf7IahfVA7JihSEpZug7zhr1h0txkhzathVyXnrJtqFdO7GhUyfhDVagSQQpFmT6rjt+xOFgUCjCp6MP8htex2EYkuT0S3fop/oK2DpFFCumVHTXkY8aGF6PhCmZu6HGx8ezVKlS9nuWx4iKYmJ+UYy8F3U44GU1UppdePZZEmCSTyCr4gj9/ERNolJ89ZX9+97ZZ7rd4KlUSSGDZ7VD05c+fXjhvGbvtwCIzsRe6P2UPocPG57NunXJhAQmJ4uguK4ze3bPNtcwjZ6HICXFufWyp7+sh+HkSeF81rW++aZ69Sg6H31EZkO83SF5v6/wm2++IQAC4EcffeQdkRTeXts4AXbIsVm0A3XokqISn31mfPc//EBjENUTTyjVHzoy0ugs0707nTyVIu9lqWyJdvRatNBQMnH6d66GhCLHQkSEkJMbEUzKkVt0DEk9PU+RFn/6oLzOnSk8C3rt0fTp4g1Sx5ob/Puvsevu/WpMuOfOnbKluVC8uM04q9jFCPkp4jxw5M03yYbYZuxLhSK7jmzflMwTEG1Sw4vWyPS+HDduHAGwWLFijPVgikjia2JwmwW+fKrofjWzBH/5xf59v42JBMhvvpEtKhUHD9o7XV6q0ZE+0AiQlSsr1MX1yhUj9a5iRe79N8ZpmPYXXygQzL93z5j9kTcveekSr141SnsAkZVy4oRnZZhGTxawfLkRSQFElycVHVS3bwsjQtfZrZuyWVmcMuoa/0RntsQ6NnhCu6/how93A8BJkyZ5RyTF4M/QUNIPKYzwta3WBw1SwKXijMViRCZDQ8nEqrWMA0GJwQcGU6YY0i4OTTW1sk4dBa7egnPnDFlXq7Z21qmH1RShShWyAG7y3YERzrlZc+eKi4IiI9n1VO+GDSk6EOk6FTF2dH51KI2x9n7eyHNR5NjUuXRJSCuPU9R0Q3fmTNmyXEhKIvPn0/gvmhqGmWLXUFJ8vVNLGV7/lHX/Zur3z549y6CgIALgr7/+6hmRJLl7N60Q3/dkvKFi+Za4DuXJI6IneJx+SMn0zBWPExcnwjkA43IWZH7cImyZPcrUG6WkGJZDcDDXT/nPHkzJlk1cq6RjtYpoPSAyItat49q1zkOe+/b1TpmIafRkEakLsGrVEnNzlMFiIRMTmZAgBlrrOuvXV+jkTcW69pNJgLtQn6Mq/MnIu+mHpj755BO74TNd9wx7gdWrxXk8H/2NHdusmTIpQzoXLhhdnPaHNXdeoKcxBVkGiYlGIOqvQq+5GhPr1smWaKdFCzIP7jAFboruc+VSpkvea7bdWK8endv6Xb8uNJ4/L1siSRHl1e0HvvGG+EGh+iid2bOFtBK5oox0jU8/lS3LhUWLhLRZGGh4WBX0dP3+O9kKa43j8uefZUtyy6ofbvEewkiAlx/vnunfj42N5bvvvstOnTp5LhU7OZn3StYgAV5ASX40WsEQj6bZ5xTEIxsr4CQ7dVIwADlggP2YbIN/7NEIRcr1BHrRJsB/esyzB/Hz51co8Kxf2AFaP5nI8eONZINs2UQDE2/5i0yjJwtJShKTbfXrdliYQmtJvTd0kybUxrzDH3usYC7cJSC6pOgzPJQiOZm3C1a179AzwdUYN2dhugvJ9957z274rF271mtSZ84ku+AP50Vv6dIiFKQQ338vpP2Gp521hoUpU4NCijUPQG5EM1djQoGhjzoLF5LdsZRf4C1eneowaGbZMmERKXLX+fFHIcvPj0ycNM0wyhSLTHzzjbE2tyek27taqMOECULamAK2dmM+Ps4DOhRh0CAyH24zwceWJvj++7IluaVDe427Uc/IcVFu9StuO7/kEt6DRJ8gaucvPPDf8mTtadR7n9uvQ8PKrVQxYGZckCAaLDz2mIJN+hw6yk3GSAKi0aVSBo/DMNedFfoSttS7SpWU8WOJrj82CyehQze2aqnZb5Ply3u/q7Bp9DwMsbFuL85Llxoeddiyh5S48Giak+eCAI+gGr/BQL4avIDbfr6g3CKImza5LHotpcuKNoduRvFqmsY33niDffr0YYqXvexvDYpjHIKd9WbPnrVN5h8STRNpjXPxkst+ZZ06Xh7HnDZWq4iWXkFRZ4163rIiNSjx8WSuMHERn/3cv4ZOfdCaIm0dnVLxnrJ5/Rs1ki3LhaVLhbTiuGwIljpBzz0jRghpB0NtOaOtW8uW5JaaNclx+EBoDApSMqx/9SrZ1edP4/tW6HrpyO9jD9hTxi68MDZTv5uUlESrF4potbPnmOAr7kFLfXuq2VzpyhVaQsNIgJvRhEUKWdXzF1y9ak+9O4iaDEQiq1d3mZ8pl8uX7QWwl7JXZghiCZDNm2e6maDnOHfOXvsRV6IiKxSKsp/mzz4rp5OcafQ8DPv2CVM1OFjEEkuXFk3QGzRgbKPWXJ+zG3/EC5yJ1/l90fd45YQCrgyr1agWdvOIzV2U7NGD3LtXtlKD3r3d6y1alPz6a5d0DU3TaJHgKbRYyB0Fu7jX+vHHyhiU4eHkNyFvutc5eLBseXbW/xXLGGTnYEy3DyrkokUieqZQpzx9QHePXA41KNL7gjqjacbw64slbQv1V1+VLcuFjRuFtJcxRzzx979/C0cJPP88WQZnlU7HiowkgxHP27A5ChQZlpqaTz6y8D9UIwGm1K6nzHXSkbhYjTsDxXkTHlQ0086MDz74gPXq1eM+T06E1DReqizaKt9FLs77RJUqewc0jcktRIvGWISwarazcodkusNqtfd5jkMwK+MYa9RQLFs9OdleoB3vE8IqOEqA7N9fob5EcXHkY4+RAJMCs7Oa7zECopPstGnyTnPT6HlYoqPJfv3SNCIIMAFB7IAVzJtX1H9IJyWF7NrVrdYohHJZhzm0WhS68Vy75hw6sz1SatYW+TCRkWn+qsVi4YABA7h8+XKvSI3/Zr7rfm3XTkwJVSiH8OQLIj/HApsxUaMGefq0aEulSLSH4eHs3/AUAfKCn63ry4cfylblgj78sRP+Nr5zVfahA2K2lMaoAOHBVHEGyuHDQpp9OGXTprIluaVdO/ID2CYqh4YqWSfzzz/kq/jWOCYVdPtbreTwAg5zO9askS3JLcueW2LXeGli5lq0njt3zt68YKkHu09em2TsxylVvlOyO2vKN8bxOAgz1QzqffGFXeMAzGLNmuSdO7JFpWLUKLvGvvhBNb+qENK3r11jdywlILrd7tolV5pp9GQVP/8shvy5MSQiitdgz8BlDIKY4Dt2rAIpy4mJIiUjldYbKMj+mM/neljV6kDn0Cdff/yZ9yXevZ1+CpvezjooKIjrvFEAf/s2tVTzWu6+/q7nPzezzJjBC/nrsh++V3pRdMA2p3Al2osnffrIluSW2rXJZ+DQ0kuZu4/BlClkftwyNHqx5i2jXLsmOiHeRS6hceJE2ZLcUq+OledRyhaWelm2HLe8/56VJ1HBZpF3ki3HLf+uTeZplCMB3qv5pJLnTfilOF7yEbMJTudvmGmNTz31FAGwZcuWHqvlSb4ZwQh/0Qprh39jXr2snsWjnb/AhACxRlqLVvzyC/U0ct8+WvzEULs/0IWP1dTUM3iWL7dfw+ejPwMDFRyVMnOmXeMXeIsA2bGjGJ8gG9PoyUrOnBEDl9KI+ET5hnEuXmJzbGDLZhb5Pd5jY52bpDs89qIOB1bbqk5INzlZVBECTMwWZtf5b1hnRlyNT+fXktmtWzcCYHBwMDdv3ux5rU2aMK5cdf7k198eTYneoEYNip1duxh7MZxVyiYyAiLnNmH427JVuaV3b/JrDLOlv9SXLcctM2eSvSG6L2iBgbLluGXPHrIZNhrn+dWrsiW5kJhINsB2Q+OBA7IluaVnwX8NjVu3ypbjlveq/2VoVCgd1JE5j8+xa9S2qLkfVz3+AQnQCh9eX5G5XKzly5cTAAMCAnjCgwNI9j8majSTEMDVX6nnvKLVyktlmpEAI5GTb/e+pJ59GxvLu/mFk+AaCrN59XAlFulOXLrEhBARqT+CqiyaO45btsgW5Yy2fYfdcNyIZgz0TeFnn6kzF9I0erKapCTyrbeMm03p0qJAPJVRcQ2FOTtkJHcuvexdfamJjDT0ffghtRYtnXSuyN6DZ7fJts5sbN5s91AffdwoxD+QvTHvnku7ci8xMZHt27cnAObIkYO7PB1fnTyZXLeOy3+O5CWIyYAXQyozOVql0Jlg925yps8gEZEMLqJACNKV8+fJYX4zSIBxQbmU9Abfu0e+5i86eSUGhcqW45bkZHJEgNiPicFhSu5HkpwYKNLG4kMLqHOnTMUCf+HQiMpfVsn9mJxMbvUVNSi3StRVUuO9Gwm8gmIkwLMV2suW45ZLWy/Zm9PsrvZSpn43ISGBZcqUIQCOHj3aQwrJYzP/td8Ll1Uf57HPeRgOvjzNrvHLKvPUaOyUiv+eeMWu8fXy65QzeCwJybxQ6AkSoh6qXYljPHVKtipnYs/dFOsIgFdQlNUK3KI3/MyZ4ezSjQ9sG/jCxJXAQGDSJOCff4ACBcRj3z7g5Elg/HiwXDkAQBHcwID4KUiKTpKrNyxMaK1SBWjYED7r1wF//YXI/EJno7i1SEGAXI06Tz4JDBgANG2KqjvnYl+rMQCAnCl3kJSgpflrQUFB+P3339G8eXPExsaiXbt2OHjwoOd0DhgAtGqFTn3CsOe1eQCAmIA8SLxxz3Of+YDUrw8EvNIfGnxwI1dlpFwPly3JhdKlgcqdy+MO8iI8byUgPl62JBdy5QKqPZEDZ30rIDJvWdly3BIQABQv7Y8L/uVxt0h1wMdHtiS35MwXiHC/QrhRoy3gq95txmIBAnJlR5xPdtxo00/J/Rh+JRGW3AWgwQcJg99SUuPtY+EIDy0NAMg5bYJkNe6JOhuOmwElEI1QlF/6SaZ+94svvsD58+dRtGhRvP/++x5SCCTeikKET16c9a+IFuve8djnPAwp8SlIRgA25+iAl7e9iABFlhR2rFYkJ4tzZEHBt/DJ7lbIk0eyplT4xMUi1hoMAPiq/Gws2F8FFSpIFpUK/9hIxGjZkYwAfF73N6z/rwCefFK2Kmf8YqMf/Jc9YIRlGdIiPY7cuEG+8ILzNk0j9+7lzd4jebZaFymy3HLtmnNj98REHn9pEo+N/E6eJnekchHtfPFb3txzKUO/GhMTw0aNGhEACxcuzDgvFB9rGrlu9FomJ6gXQdFJTtK4aaF6qU6ORNzRVBoj5JabN6lWHZwb4vVMUEUjKHY0TbkOeC7ExKTbREUFEo+dpTVJjQG5btE0Xl+pZgqjTnJsEk/+nLluplarlU2bNiUALlmyxEPKDG4eDeexX9SaCZeaPXMP89LOa7JlpImmkSuHrua9W6q0P3MlPsbCX577Q+n7zPmDkVzYd7WKSSMkH8428CHJrLG9sp7o6GiEhYUhKioKOXPmlCeEVNLL9r9KVFQUOnXqhDFjxqBjx46y5ZiYmJiY/D9E0zQsX74cnTt3ho+5BjAxUYKHsQ08mncwceJE1KtXD6GhoShQoAC6du2KU6dOefIjPYN5sVOKsLAwbNmyxTR4TExMTEw8hq+vL7p06WIaPCYm3kJLu8whK/Co0bN582YMHjwYu3btwrp165CSkoI2bdogLi7Okx9r8j+A403o3LlzaNGiBa5evSpRkYmJiYnJo05iYiI+//xzc51iYuJNrFZg8mRg716PfoxX09vCw8NRoEABbN68GU9moDJKmfQ2E6Vp2rQptmzZggoVKmDLli0oWLCgbEkmJiYmJo8gH3/8McaNG4cGDRpg+/btZpTH5NHmUSjPOHUKePFFICYG+O+/++pVNr0tNVFRUQCAPKq11DB5pPnpp59QokQJnD59Gq1atcKdO3dkSzIxMTExecS4cOECPv30UwDAsGHDTIPH5NHmxAlg1izZKtJGj+489hiwcycwZIjHDTSvGT2apmHEiBFo1KgRqlWr5vY9SUlJiI6OdnqYmNyPkiVLYuPGjShcuDCOHj2Ktm3bIjIyUrYsExMTE5NHiJEjRyIxMRHNmzdHz549ZcsxMXkwNA2YNg2oXRuoW1e2GvecPAk0bgy89RaQmChGrzz/vMc/1mtGz+DBg3H06FH88ssvab5n4sSJCAsLsz+KFy/uLXkmjzhly5bFhg0bkD9/fhw4cADt27dHTEyMbFkmJiYmJo8Aq1atwl9//QV/f39Mnz7djPKYuGKxAFOmAEmSZzOmx9WrQNu2wPDhQMWKQL16shU5Y7UCX34poju7dhnbX3oJyJ7d4x/vFaNnyJAhWLFiBf79918UK1Yszfe98847iIqKsj+uXLniDXkm/0+oXLky1q9fj9y5c2PXrl146623ZEsyMTExMVGcxMREDBs2DAAwfPhwVK1aVbIiE+W4cgVo0QI4fhwICpKtxj2//AJUrw6sXy9+fu01tep59OjOqFHOhqOPDzBokFckeNToIYkhQ4bgjz/+wMaNG1G6dOl03x8UFIScOXM6PUxMMkONGjWwdu1atGjRwp6bbWJiYmJikhZfffUVzp07h8KFC2P8+PGy5fxvcfAgsG+fbBXp88cfQM2awNatQP/+stW4cu8e0Ls30KsXoKf2h4QAffpIleXEiRNA9+7O0R2d9u2BcuW8IsPfk3988ODBWLRoEf766y+Ehobi5s2bAMScleDgYE9+tMn/MHXr1sX69eud0hNImukKJiYmJiYuPP/88zhw4ACefvpphIaGypbzv8P8+cDo0cClS7KVuCchQdScfPON+LlcOaBhQ7maUrN+vTDErl1z3v7cc6JORhUqVwaOHhWNFVJHdYYM8ZoMj0Z6Zs2ahaioKDRr1gyFCxe2P5YsWeLJjzUxcTJwvvnmGzz33HOwWCwSFZmYmJiYqEiJEiXw22+/oXfv3rKlPDwpKcDy5bJVpE9CAvDyy+LRurVXajkyzfHjwOOPGwYPAPTrp066mNUqDLLWrV0NHgAYMMD7mu7HoUNCM2AYZOXKiRokL+HRSI8XRwCZmLjl8uXLGDlyJJKTkxEYGIgff/wRvr5e7dRuYmJiYqIgMTEx/78iO3fuiBQiL3TBemDOnweeeUYsgAERkVAJEpg7VzQCSEgwtvv4AH37ytOVGj8/4OOPgaefFmltly8br9WsqV4Dg5s3gc6dgfh4IG9eYPt2USM1eDDgxTWZufoz+X9NiRIlsHTpUvj5+eHnn3/GwIEDTWPcxMTE5H+cpKQk1KlTB3369Ln/bLdt24DkZO8Ie1COHQPq1we2bAGeekq2GvcsXw7UqWMYPGFhQLt2UiU5ERkJ9OwpGgA4GjwA0Lw5UKKEFFlpEhwM7NhhGDy68aBaA4PERKBbN9EMwt8fWLZMdJYbM8brNVKm0WPy/54uXbpg4cKF8PX1xZw5czBixAjT8DExMTH5H2by5Mk4c+YMNm7ciMDAwLTf+OuvwLvvAum9RzbLlwNPPAFcuCBSsgoWlK3IGYtF7MPOnY1Ce0AshFXqhHb3LtCmjTB8UqNiA4P164G33xbP27UDPvhAvQYGpEi10xsYzJoFPPmkeD5kCJArl1fleDS9zcREFXr27InExET0798f06ZNQ3BwMCZOnGg2NzAxMTHxNgkJYoFepYqUj7906RImTJgAQBg/aXaKnT5dpDm9844X1WUCEvjiC6FPd+R16SJXU2pu3xbpVxs3ur6m2gDYMmWAkiWB1HXnOXKINDKVuHhRpAZqmtC9aJHYHhGhVgODL78EfvpJPB8+HHjlFeM1CesvM9Jj8j9Dv379MGvWLADA559/jn///VeyIhMTE5P/MeLjRfqVxAGPb7zxBhISEtC0aVP06tXL9Q2aJlJvhg0TxkTLlt4XeT8SE0WNyZgxhsEDiGiKKmgaMHOm0Jq6WUHevGru1wkTjDk348cL3c8+q1azhfh4ESWLiBCRnT//BHLnFo8vvpCtzmDFCiMS1aaNMIAkY0Z6TP6nGDhwIBITExEbG4sWLVrIlmNiYmKSNZw+DVSoIFtF+sTFAZ06Abt3iyGKElizZg2WLVsGPz8/zJgxwzXan5wsuor9/LP4OVs29doU37ghFr27dztvL1dOtAZWBV9f4MMPgaFDgRo1xPev0707EBAgT5s71q8XegFh6IwfL6KSHTvK1eUIKWp29Lqo+fOdzyVV0jCPHhURPlJcl5YsEfU8knlkIz0HDggnzKpVwuh9FFixQhi6V6/KVpJx5s0TujVNtpKMQQJffy3q5dJixIgRGDt2rP1nTfJ/LjERmD1bdKB8VLhwQcxpe5TYulWkbD9KrFsnW0Hm2bRJtoLMERUl7iePElu3iuuGnbVrRSqWwqz8JUYMIdy0SRSzS1gAJSUlYejQoQCAYcOGoVq1as5viIkRUSjd4AGAJk2E4aMKp06JhgWpDR4A4Q06Y/0GxVK2SWFE3rghjBzdgOjZE1Yr8MMPoju0dK5fFwM+SWE8zp0r0q/GjAEaN7a/7dIlYRdFREjSOW0asHCheD5q1H1TBJOTxRru/fe9GFy9c0dEHGNjRc3O8uUZrt0hxRzTyZOBiROzfu35yBo9tWsDxYqJ8ydvXnEtnT4dOHtWtrK06dBBOBJKlACaNQO++079RVjnzqJ+r2JFca5FR8tWlD4+PuJ6Vb68uB7c78IUGxuLVq1aYe7cud4R6IZs2cSi68knhbP2UaBkSVErqacRPwqkpABdu6ZaLCrO4sXqj9xwJClJ1KY+Sn1CFi4ENm9288KdO0oeLElJwtFrbya2b5+oNyhdWqqu9Fg6NxqlB7U3PCX160vRcfbsWcTGxqJQoUL44IMPnF+8dUvcmNeudd6uWgpWxYrAmTPi4pCKvr93Qa1aEjSlx+zZwN9/i+effgp8/z1YoQL+uvckatQAFixQIDhlsYj6mPBw0Vjht98Avc4rd27QxxdbtojgVJkyQGioWHd6nc2bgTffFM9bthT70w2aJpr4DRwIFC4sOoR7rWdEcrL4wAsXRFvtX3+9bwQ6Lk7c5wYNEpexKlWEzTlwoAe6WVNhoqKiCIBRUVFuX9c08tVXSXGLNR7ly5PDh5P//EMmJHhX8/0IDyeLFTO0BgSQTz1FLl5MxsbKVueeRYsMvaGh5LBh5JkzslWljaaRTz4p9IaFkZ98kva+nTFjBgHQx8eHCxYs8KpORw4dEnqDg8mvvyatVmlSMswLLwjNH30k9rnqXLwo9Pbo8WjsX5Ls04csUYKMiZGtJGPs3Sv28YYNspVkDE0ja9Qge/dO9YLFQrZrR6akSNGVHh9/LPZxeDjFhTh/frFhxQrZ0tyyZXkkd/o84XyTXrJEmp6oqCju3bvXeePp02Tp0q6LCYDct0+O0PRIShIHLsDk/IUZ55udd5CHL/dT7Hg9dozMlk3sx1atSKuVW7aQPWqdVmv3jhljfN9z5tg3JySQ339PPvaY8fLjj4vLg9e5fNk410uWtF0ADDSNPHiQHDXKeY0JkLNmeUmjppGvvGJ88IwZab7t5ElyyhSydWsyMNBZb/785LlzaX/M/WyD9HikjR6STE4WO83dtUpfRE6erNaibPt20t/fVWv27GKRs3Kl+H+pgqaRnTs7a/XxITt2JNeuVWvf6uza5ay3UCFx4qfer5qmcdCgQQRAX19f/vrrr3IEk2zc2NDbpInahiXpbAz37SvuwypjsRgX11GjZKvJGM8+K/S+8YZsJRlj9mzDsHRLqhu1bHbuFHorVkz1wgcfCI+JYpw+TQYFCc03Dt0ky5QxTsLTp2XLc+H4jnvc51fP9WZ34YJsac6sXk1OnsyIms2cdebJI2mFex/ef9+usbP/Sr6Hj/kD+qphQOgkJJA1awqdefPy2Lpr7NjRefc+95xskRTOAl3QCy+QmsZr18ixYw0bQ38EBgo7zuskJJB16woR2bKRBw7YXzp7VjhCKld2vwbu3duLa7SpU40PHjjQ7QefOUNWr572mj0oiNyxI/2P+Z82ekgyMpKsUsX9zlu8WM1F+eTJaX/pbduSR47IVujMtWtiDeBOb+XKwqBQbdHbvbur1nLlyF9+cfb0W61WvvjiiwRAf39/Ll++XIreX35x1hoSQk6bpm5U4s4d0tfX0NusGRkRIVtV+lSseF8nlFJ06SK0+vo63eeURXfyBQSQt26lenH3bnFTVIj+/Y3jwX6b+ecf4dUpU0aqttRoGtmihdCaA9FMrFbbOWVAsajU9aMRPBxYx/UiXKCA12/KGzdu5I8//kgtnc+dPz2Wl1CcBBgPW3TimWe8qDKD7N9Pzc+PBDgf/cU6GPF8pcp22cqcGTHC/p1PbvoXfXycD4OAgPS9+V7h4kUyd24hqEoVHtwWy1693DulAZE14nU0jXzxRUOEQ0ZKUpLItMiZ073eSpW8mCWwZo2xIGjWLF3P/aVLRjZO6kdGgsD/80YPKRxHBQq47sAnnyRPnfK81syiaWTXrq56W7RQLyVPZ/58V72+vmSvXiKyohqnT6d98apdW4SCdSwWC3v16kUADAwM5Nq1a72uNylJRKRSa23aVIGbQxo8kSprpWJF4XlSFUdPo68v+eefshWlT/v2ht66dd04nR/gou9JHNNAPvvM4YWUFPGi00a53LsnMgF0vf/+S5FCkjevscMV4qefbItFJHENUqU3VKokW54T0XeS+E+unryMYkxAkLPWp57yqpbExERWqFCBADhlyhSX161W8u23yc8wmgRohQ8bYwsTipbxYl5QxrDEJ/FWIeEmv4KiDMM9d2th+axebf++Z/m+7vYePGyYZI1JSWT9+oaH8dgxRkSQX3zhmh4GkLVqScrA+eYbQ8Tw4W7fsnSpq97gYC86z0+eNLziZcoIj2gaJCcL4zEoyFVzRo1K0+ixsWuXkT7qaPkGBZGffqpWyhgpbrru0oirVyePH5etzhVNI9u0cdX7/vvqRiMGDXLV++yzIo84tdMvOTmZ3bp1IwAWK1aMCRKsz/Hj3Rtp2bOTP//sdTn35cMPXbXmyydSOFVk2DDXG4OKBrtOy5bOel0CJWncBGUQH+/sZChTxuG68NVXYuMHH0jV6Mi0ac77dvLEJJGwr29o21a2RDt37ojzygdWLkAf15Ouc2fZEu0kJ4tdB5DBiOMVFHXW+vHHXtUzceJEAmDBggUZGRnp9Fp8vMgIqIojTIY4eKdjMPPmJS0r/1EqZfDGDXJ+0bH2/dgOq+y7NH9+MjFRtkIbt26RBQuSAFMqVOa86XEMCXE+BEJDydu3JescPtwQ5GAxHjpEFi/urNff39lJ6jW2bTMuqk2buixik5NFOVLqKBpA/vCDlzTevSsK6fUvNp38v717jYzH1Ov0F1/MeADYNHoc+PVXY3Hw448iJVffqTVrip2uEvv2iTzR3LnJIUOcF2Nz5qiXmnfxIpkjh3F863p79CDj4mSrc+XmTUOv/ihcWDgm3JGYmMi+ffvy0KFD3hVq49o11+hU69bk0aNqGpZ79rg30vTUUtVIvdDVjTR7dErTxEpIEZo0cdaaIwd55YrtxTNnRLhKkS4HqevoAFHzx8uXhdUOkO+8I1smSfE1V63qrHVV+aHOG3r1ki3TzksvCUmT8Kb7E06RIjVNI19+2ZA1Bp+SENETu+HrxSj6pUuXGBISQgD86aefnF67dUu3cTVuhjjRbqAgw3BPpa/eYN8+e1rbPLzo9PW/+65scTY0jezQQYgKDKT1wCG3zaYmTJCs87ffDDGvvWbf/NdfxqXK39+IRowdK0HjtWtG6kexYi75wufPO/toypcXNVKAOAe9QnKyaFABiHvRypVu3xYXR775pnM6fK9ewpD39yebN89ceYRp9KTis8/I338Xz2/dMg4E/Xt56y21FuizZokidlKk2zgaaj16iIiQSsycKbQdOGDUHABkvXrk9euy1bnywQdCX6dOxkWsUCHyxImM/X6yl0OEPXo43yD8/Mj1670qIcNYra7Fnn36iJRSFbsRrlzpfs1Yvrytxv7ECXLhQtky7Tje1PRHt262FwcPFhsU8eTMmOGq9ZlnKATrGxTpyLBtm7POHvjFVfyQIbJlkiQ3bRJyciOC/TGfz+Mn3kOYs1aHjlMy+egjB2cCbjMKwjO2tvAL4g1vvOHVG1r37t0JgI0bN3ap57FYxLry0Ijv7aJ742cCIpVQKRITyWrVSIC3Ap3T2nx9RY2EEkyfbt+X1q+mODXy6txZ1PEULiz53nDmjBFieOwxMiGBmkZOmmRETPLkEeddhw6iXtzrUbTERLJBA8ODuGeP08u//OIcJenXT/i+fv9dNPXzmt/O0VM/ebLbt6xf79xvpVgx0rFsum1bESzKDKbRkwpNc3V+Ll/unKdZpow6C0lNc3Z+XbkiIpm61pIl79/NwptYrWTPnkK31UqOHu18QKtWcB0TI6Ltp06JGmVHw+d+aYRbt25l6dKlecSLnSU2bxb65s0jS5WyLXhyq9vN7fnnhUY9VTM4OFUNkjJ5FyLCl3ptu2oVefiwzWCfNUsU/ihCrVrOWvX03ZULImjPGfnxR9kySTo3BdAfXXz/dt4weLBsmSSNdusAWREnGI0cruLHjZMtk4mJok4uIMDwknaCwz7V8423bJEtlVaruPYfPy4ilFMhImcJCOI7fWyrci82W1i7di0B0M/Pj4cPH3b/pjt3GBWUjwS40acFCxfSCLhpwiGbd9+1f+d6Wtu334rGPF27yhZn48gR+81Va9uOL79otR+mzz4rggLt25PffSdRY3y8UXgYGkqeOcOkJCOSCojzTb/XTpokKf15wABD0Pz59s2xsc5aQ0Od094vXPBiDfusWYYQN7lpERHO/Rd8fMTlP/VyPjo68x9tGj0Z/nuGc9Txu8qslekNLBbhNdNvdH5+oshLle6Zqdex8+eLGzMg1mJ//CFFVpps3Gg8X7PGWDwWLJh2CqqmaWzevDn1fPBTXrqaaJrwkFutYjGuh9srVxadClVj0SKyUSPy6lWjlrF1a4dr4IQJyhg+iYninBo50oioDhzo8IaePUW8XZHWylWrkkWLkrlyCa0TJginyJVBnxoXsbffli2TpHBE589vnFudmsfymn8J5wuu1/Iu0iYiQvQq6N+fzIEYHkRNXvMrxjgEO2tVoNPc9esiBfrWLXEPADQeDBYe4KhytcUNoU0bkcerCCdOkGVxxl4j83XQaH75pXc1WCwWVqpUiQA4LJ2K+UvtXiMBJiGAs0ec4K+/iowFpdi7V//yORcvETCaAMyYoYjzNj7eHonS8ufnsJ437adRz56Grbt5s+Qmg6+9Zpzfv/7KO3ecHcytWjmvB6XctubMMQQNGmTffPCgc/fRunUlNg3auNHIw2/UyGlHaZporGAr6yIg+qxs25Z1H28aPZlk2zbxJehfSMGCohZItfoZkty61bmorkULEZJXkc2bjYWkjw/5+edq7lOSXLfOWJwVKCBqZtwRERHBmjVrUm9ucP78ea/ocwxP//GH8f136EBaEtTqyHHnjlHk+d13hlZ7AKJnTyW85jqDB4u14tixRvTk1i2Kg1W/UivStalrV1ESo6c8PvkkRfJz4cLGjvZyNyx3xMeTr78uFgz6tXVbw1Gu0ZPnn5ctlbdvCwdYVBTphxT6wMq1f8QyOZst2pNPeP5Vaoe1bJnt+8cm+77cN8Y2U+zuXaUutK+8Qi6FmBeg5cnDdb/e499/e1/H9u3b2aZNG95LI50udv1O+778Nv97TE4Wu1GpwbqJifbis8soxpyIZOvWhuEQH6/IVz/UqIf7quUK++n+3HMKdVJfsMC4Dg0bJozzssam119XoNnVrl3GMLlGjcikJGqaqEV1HOA5apTEESFnzxoLvRIlnMKiV686lzz4+4tGV1ltPJpGzwOQkCC+DMei8S5dxJemGnfvkk8/bejMl0/Z4ds8e9bZoOzfX735PTrr1xsta/PnT7u94+3bt1m5cmUCYKlSpXjFXknuPSZMMPbpJwMuiQ5IStztnLFaDc9Znjy262H37iIMmJZl6WX0hhA3bxqpjuPGUbio9Z3cpIlUjTp6A8F584ybSPysH5wNiXLl5IpMRdOmZHUcpsXHz9XoefZZ2fLsnDplyLr4ocM+PXFCuHxXrZIt0c7AgULa1hy2tmgVKjAhVpGwvwM3bpCN/Q1jgrYW0co1YUlJ4ZV8NUmA51Caezar07zEiXfese/LNviH5csrmJniMNxzTaWh9q++d2+FDJ5jx4x04Pr1uX5Vkj0rwddXGBXSb6c3boiwPiCcWtevMzxc+LT0fVqggMhUkUZUlDEFNXt20eqO4vyePdu5zujxx8n//vOUDNPoeWD++89o1Q6IL+3bb9W7SGuaOKj06AQgOi4qkjXkxL17Ir3Jcf2oSLaQCxs2CMPHDynMnz/tk/T69essV64cAbBChQq8ceOGV3VqmnNDjqiC5cRKSJV8RwdOnTKMiV69aAykatBAuRNLz3bIm5dMmuqQowyIVoWKcPmyLktjZKkazjp9fZXqONezJ/kh3ufv5d92HjQEKNVaecsWQ1ZSg6biSfPm4sXoaKU8YGXKkI/hgCF43jzZktzy7jsat6AxCdBSsrSUG9StDBTkXBg+xb4vv+uiqAdxzx5qtvz2OXiZYWEZb77jNW7csHeyuZyrGoOQQEA0s1HG4ImNNabX587Nnz6+qGcLMjRUjBSSTnKy0aozIIDcsYMbN5JFihinvPQsVovF6MwH2GsYTp1yHjQaEkJ+/bVnlyam0fOQWCzCIeXYS75pUzWHmh454txqtVYtNXWmpDjPyClTRs3ZQ6QYSvhBwMccgmksljeeadW8Xrp0iSVLliQADh061KsaSdFxsI5twPl3vrZCx65dlVrw6nzqUHJys14n44cZM2RLc+LUKaNjz+naPZ0X6AoN0iSFg60l1rlGTwC7x00F9FlIDRrQuJH36CFqj9q0kS3Pjj7QryzOGvtRubZdoqgaIH+BLcexaFElw+cxMWTvkD+MffnLL17XsG7dOmbLlo2fpDPlMOn8Vcb6ilTG1SHdlOwyyYQEaraF+mUUYy6fSJUCjwKrlWzXjgSY5BvEqjhCQDQJUcYXp2lOXUtmd1puPzxLl1Ym+cApPdAyczbHjjXuS/7+oqGCdH/hmw7t8j/9lMnJ4j7vOGS0TRvRTMHTmEZPFnHhgvPwTVWHmsbHG+kOepTx++8VCM86YrvqzZhhr8FkWJjk0Gw6bPsznHeQhzdRgB+GTOR/29wfc2fPnuWAAQOYKCnEdvWq6DrXHQ4jmBs1EtXZCpGcLFpnAuTGbO0MraGhDoNm1EAEojTe8ivkbEjUqCFbmhMjRpCr0I4p8HMdZ71okWx5dnSDt3qpaCN/eO5c8eLWrXLFOaB3150cMtY4NlWaZWBj5kyyHE7TAltXm6++ki3JLVO/TOZJVCABJtSo5/VVWlJSkr15QXpOqaPV/o+98w6Pomqj+NmEkJAAofcqHZQuIAJSpUhHP5GmUixIkSpdwIIUBSmiIkWlCAoKCALSBOm9995DCeltd873x0u2JJvQkr2D3N/zzEN2Z8OczM7cuW+9YjyGIYAbf77oQYUPwaBBLmltnm4E8UBMnGjX+AGmECA7dTKRwUO6NAVYWPgjl0em8sVR45kzx64xtG1XVn/BsOssUiRRt2o1zJrleNa0a8ddOw2XRUazZBF/kafmoNroSUEMQ748sy9qSsr6WvFdnXAvlciDpyp5jhyRcFn79jzW7Us28d/AQATT29t0zn47J/t9az+Zdy2BvNZ12H3z8gzDYKSHIy3bt5N50t6UBf/iv/xSpUy0WIOwc6dkXq1B/cQpTiay0LduJYvDTS9rIOlCLwWs/i2U7+BbZsVNxuYrJPq6dpVoyvDhquXZia8/auO73HEeTXZtktLIwgtWXvW51ymmWzfVktzSogX5Pbo6ZhcmWYzWmdhYcnBmp/TQjRs9rmHcuHEEwBw5ciTZvODCjNV2jfMrjveswAdlxw7aLGLgfo+ufOstUw2Xwv79NO5V1i/HKwQMvvmmyQyeffvszqFd/jXpjTi7YWaasoDdu+0abxWryhyB0fZbqH17k8zn/v3X3prXWrkKP+oVmWiRUU+3eNdGTypw44Z8mfFfrCkWNTUMGVViYiTcEx7Oi4dD2KhaMLPgFrMhiFUKXOPeP6+Yw41x5Ihr30KAp1CEC/Ea/6g2htaVq82hMx6rlaElK7votfr5y4J6blrmGYbBXr16sWbNmozw8IXx00/kHiRYxCVvXlNN0kk5detRO7Ex8euvqqW5MK7It4k1AiZa6lxS09Omlfozm5e3I8Jjs5nKKxNf1zwJ9/LcihdXLcktXbuSDeCYBHPbNtWSEhEbSxYPuMwY3FsPYORI1ZLcsvCHUF5HDhLgnZqe7yZ46dIlBgQEEADnzJnj9jO2iChe9CtKAjzi/SxvXjVZCgdJRkUxoqAUil9AftZ7PsQ8E/R4IiLsqXfXkJPZcYNvv20ygyckRBq8AAyyZGceXI7PyjKPARkUJN3PAN5Nl9OuMSBAOp+aQuf58/aarahseVm1wFX7cJk/v7qGWtroSWliYmTyuHAhT7zxMZene5WfYTABhYuaRkRITqWziZ3EFo20/KXr3+pzQEmpvHRur+tua9PG0aZKNTt30rBYEmtMm1YWDHNadfPs2bPMmDEjAbB+/fqM8vDfsPH5/ol1BgYq8bImRXg4udO3RmKdOXOaqg3RpZptuQCvczleEX01akjnkCZNTPL0EerVIwvhrKkn6rt3i7QjuNflxyQLkiakWTNyPu51BylZ0lTfczybNpET0FccMOkCTJfGSt5rspNzhGiEV9ILn6Uir7/+OgGwevXqtCXx4Nv1ysf2+2bNCPOkWTpz972P7BrfyLaGHu6X80BY333fJfWuc2cT1Js4YxjSJRKgDRbWxxqmS0cuXqxamBNxcdI0BWAs0rAGNhEwWY12WJg9Rz3aOx0rYjcBxyKjj7KoaEqhjZ5HJTpa2nUtWCDpIW3ayMPP27XVqi1zFg566xqd58KdOyuas+3cST73XLJGxLuYTkA6qJli0Dx5ksyXz63WBZne5eljJvO4OS9gdm8zvNNI3/AERUlbtmyxexhfeeUVxniwwNi6YpX7ayBdOlmIyCQEl6rmqq9ePZlxjhqlWpod2/GTLFGC/BJ9ROMLL9zbYTPVE33sWLIu1jrOpemWjpeSrby45ND4xx+qJbmlboU7jMK92qhx41TLcctnfW8xDLI6sdGnr2o5bvnnl6sMh3QButDQ8ymC69atIwB6eXlxX/yCYQm4tukkoyHpWKvzvm1G+5YR67fb67Z+8H6He/aoVpSYuMVL7ff1l+jDrl1NNTySJG2Tp9g1foyPmScPTXcujT597Rrj66H69jVR2p3NRiO+6yrA17CQgGTRp+Qio4+KNnoeBcMgV6xwXY43qS1fPvLNN3nh7Y/5Ue45rIl/mA8XmTuHVc2ipjExsk6L82pVzkYaLNyGqhyOUXw5806uWmmCUenMGXso13kLRXqOTzec/664q1qhg1u3XIu6AF625OOhRe7bz23YsIF+fn4EwDZt2jDOU706w8Np3Mu1jd+u9h1vsjwDkpUrc1uh17kbFUmAwc07qlbklu+/l1bLBBhV7DnVctyybx/ZBVKca/XzN2V0IiaGfBOzZSzy8ibvmujedmJo5mkOjabwDiXmu7yjSIBxXj6maqHtzIq83UiAERZ/2i5f9fjxv/vuO/r6+rJHjx5u9xs2g3uzyxoKt5CFF/eab/0EW0QUL6WXBe7OowAXzzZDMYcrseevMCRtVhLgPpTj+52jTWfwRG7cwViLPBPXoD6fr2g13W0T9v18+/N6Nt5k9mwGV6xQrcqV0J5DXAxHHx9Zy84sRlnInTva6Hksdu+WqjHnlUofYIuBD0+hCH8ar6h5+tGj0obkPjqvIDc3rTFB+ti5c2ShQg7jLI1jwn4B+XnhlInasH73nXhXvbzs+fSXvPIz/Lb7u37VqlVMmzYtAbB9+/a0esrwqFWLtoD03Okt0ZSbaXLSFmGC79qZ77/nrSAbx/tLCkxw2uzmcw9SMixHpx9LAgxKX0i1HLfYbOTEAHkgXc/xrGo5SbIobTsxwp+prlqKWwyD3GWR+r2LFTxfg/Ig3L4azZuQSeap2l1Uy3HLiVVn7dGJvc1HKNNx5syZJJsX7P38L/tzZm3bGZ4V9oDsem2sYyLc3jxRemcOPCv3dCT8OPJ/R8w3hNtsvBhYxj7nefuVG6ZrxmjcvGVvl74bFdm4diSvet5PkCyR6x2LCy/Cq6xWxWa2UmGe+miCNnpShEuXZC0J55ZouJcn9vLLZLFiiaIrcfBmyG2Fq3DZbNIOLb3cSOzXT4qbO3RgXCZ5YB7O/KJ5BqgLF6QPI0CeOMGbbd5lHLz5b42PVCtzxWoln3+erF6dR79ew2BLJu78OPmqvaVLlzJNmjT08vLiv56KAY8eTX79NbfNOc6d6Wry9CKTxfGdWPnFAS4qMYw3//jXlEYPSc7/aD+XNfueIXOWqJaSJPOGHObGt2bx1uS5qqUkya+fHueO9l/z6lcLVEtxi81Grvr6OHc3GMQrs1apluOWmBhy27Q93Ff8f7y11SyJ/q7YbOT20au5P0cDRt9UmOSfDLY4G//p+D23ZW9Ka6w5x524sCj+XXEgVxbubtahkWFng7g5czPOqzndtBovrjrCQ95lOaPjP6bVuOuTv3gCxTlt4HlzajQM/tVwInd7VebUseGmSxwhyZCbNx/ZNrCQJExKaGgoAgMDERISgowZM3ruwOHhwJw5wKRJwJkzQP/+wPjxss8wgGvXgHPnYJw9jzunbiPbJ709py0pLl4E3n8fSJ8eWLhQ3rPZELV5N2LDYhDYrJZafc5cuQLUqQMcPgykTYvgXaeRqUhWWLJkVq3MlV27gL//BoYMQcSVuwjIm+m+v7Jo0SKQxOuvv576+gD53vPmBby9YbUCadJ45rCPAglYLKpVaDSa/wIff/wxGjVqhBdeeOGBPv8kjD/WOCKNj3lFRkYQfn6Al7d5Nd4KMpAth5dqGcly6bwN+Qt5q5aRJLGxwLWLcShY1Ee1FLc8jm2gjZ7ksNmA5cuB334DfvoJ8DL3jQQS2LIFqFFDtZL7c+0akCuX+Z9CUVFAunSP/OuhoaHIkCEDLGb/OzUajeYJYcOGDahbty68vb1x7tw55M+fX7UkjUbjIR7HNjD5LF4x3t5Ay5bA3Lnmn5wDovFJMHgAIHfuJ+OcPobBc/nyZTz//PMYNWpUCgrSaDSap5e4uDj06NEDAPDuu+9qg0ej0Tww2uh5UJ6ECbrGVKxZswYnT57EqFGjMHbsWNVyNBqN5oln8uTJOHr0KLJly4ZPP/1UtRyNRvMEoY0ejSaV6Ny5M8aMGQMAGDRoECZPnqxYkUaj0Ty5XL16FSNHjgQAjB07Fpkzm6wOVKPRmBpt9Gg0qcigQYMwfPhwAEDv3r0xY8YMxYo0Go3myaR///4IDw9H1apV8dZbb6mWo9FonjC00aPRpDKjRo1Cv379AEgO+ty5cxUr0mg0mieLnTt3YsGCBbBYLPjmm2/gZfbGQhqN5sGIiwO+/BK4eTPVD6VHDY0mlbFYLBg/fjy6d+8Okvjss88QGxurWpZGo9E8MVSuXBmzZ8/G0KFDUbFiRdVyNBpNSrB1K1CxIrB3L5A9e6ofzsSremg0/x0sFgumTJmCHDly4J133kHatGlVS9JoNJonBi8vL53SptE8KAcOADduAC+/rFqJe4KDgUGDgO+/l9c//+yRw+pIj0bjIby8vPDxxx8jd+7c9vdu3bqlUJFGo9GYm9u3byMsLEy1DI3mySAmBhg+HKhVC6hUSbWaxJDA/PlAyZIOg+fll4Hy5T1yeG30aDSK+Pnnn1G4cGH8888/qqVoNBqNKenduzdKliyJv//+W7UUzdNMbCywYIFqFcmzdStQoQLw6afAG28AWbOqVuTK6dNi4LRvDwQFOd4fMMBjErTRo9EogCQWLVqE8PBwNG3aFNu3b1ctSaPRaEzFP//8g3nz5uHatWvIkiWLajmap5WDB4EqVYDbt1UrcU94ONC7tyxOf+yYvNerl1pNzsTEAJ98Ajz7LLB2reu+ChWAevU8JkUbPRqNAiwWC3799VfUq1cP4eHhaNSoEfbu3atalkaj0ZiCuLg49OjRA4B0vaxkxlQdzeOxY4dHOnY9MlYrMGYMULkycOoU0LGjakWJWbNGjInJkyV1DAAaNgRKl1ary5k5c4CFC8X4ScjAgYDF4jEp2ujRaBTh5+eHpUuXokaNGggJCcHLL7+Mw4cPq5al0Wg0ypk2bRoOHz6MrFmz4rPPPlMtR5OS2GzA558DPXt6pGPXI3HihEROhgyRlsodOgCBgapVObhzB3j7bTFwLlxw3ffhh0okJcm77wI7dyau2ylUCHj1VY9K0UaPRqOQgIAArFixAlWqVMHt27dRv359nDx5UrUsjUajUca1a9cwYsQIAMAXX3yhU9selKgoYM8e1SqS58oVoEEDYOhQMSTMhmFI1KRCBYlExfP+++o0JeTcOaBaNYmgJKRkSfN1bLPZpMZo/355HV9r1K8fkMazTaS10aPRKCZjxoxYtWoVypcvjxs3bmD+/PmqJWk0Go0yBg4ciLCwMFSpUgWdO3dWLefJ4OxZ4MUXHSlOZuSPP4CyZYENGwAfH6BdO9WKXDl/XupLevcWAzKeatU81l3sgShcGDh6FJg+PfG+3r0BMy3cS0pEb9kyeT10KDBunBg+b7/tcTl6nR6NxgRkzpwZa9aswdy5c/Gh2ULTGo1G4yGsVitsNhssFgumTZsGL9UTuEOHgOeeU6vhfvz5p9SbZM1qzjbFkZHi1f/2W8d7zZsD2bKp0+QMCcyaBfTpA7hrj26mKE88164Bo0bJz97eEk3JnNl8dUfjxjmMs44dpaFBZCQQGgoEBHhcjonMQY3m6SZ79uzo06cPLPeK+mJjY3HTzEWeGo1Gk8KkSZMG8+fPx/Hjx1G5cmW1YqZOde9NNws2m3jOmzUD7t4FXn/do0XhD8ShQ8Dzz7saPABgtghevXqybkyJEq7vZ8kC/O9/ajQlRVgY0LQpcP064OsrzQyyZgXeeUeJIZEk8+bJAqSAnN8ffpDrMyBAIlIK0EaPRmNCoqOj0apVK9SpU0cvYKrRaP47hIY+UOvf4sWLe0BMElitwAcfSFpO48bqdCRHUJAUsX/+ueO9tm3V6UkIKUbj889LKpYzuXObq+7EYpGi+vTpgYQ1tZ07A35+SmS5Jb4+5uBBeT17NlC3rkSjPvhArTZn1q93pK+VLQssXgykTevYr8g410aPRmNCgoKCsH//fhw5cgQNGjRAcHCwakkajUbzeNy8CTRp4tYbff36dbz11lu4fPmyAmFO3L0rGr/5Ria7HlxD5IHZtg2oWBFYt87xXunS0rrYLJw9K13FXngh8b5OnTxewH5fjh2TGiNSoj3xkYh331WrKyH9+wMrVsjPo0aJAQQAw4cD+fOr0+XMoUNAq1bS9S5fPmDlStN0vtNGj0ZjQgoUKIB169YhR44c2L9/Pxo1aoTQ0FDVsjQajRm5cUO1gvtz6RJQq5YUWbvxnA8cOBA//vgjOqqsSTh9Wibpf/8tr+vVA/z91elJCAlMmSLn8coV131mS20rUgQYP16aKyREQQF7sty9C7RoIWljgYHA0qXA4MHyXtGiqtU5mD4dmDRJfm7fXgydeJyjKCq5fFmio6Ghci7/+gvIm1e1KjtPrNFz4QJw9aq5G5Uk5MwZ14YgTwLnzz9Z55iUNNcniZgY97WTJUuWxNq1a5ElSxbs3LkTTZs2RUREhOcFuiEsTDp7PkmEh6tW8PBERqpW8PA8aWMc8ORpjopyuv/mzZNJmpk5eRLGizWA48dlwp6AzZs34+eff4bFYsG4ceMUCATwzz9A1aqi8R5R9Zqq0ZIUEyYAH30k6XcJef11AA+UOeg5VqwA4tdYat5cDMgXXnCpm4mLA5RmcNtsEuE5dUqMxvnzRV/OnNLcwA137gAhIR7WuXq1pFsCQPXqjvqYB4CUPy82NhX1AXJSmjQRgzxtWunW94jRx6go6XCd0vOMJ9boyZxZasty5RKjcuhQSRk8d868k3SLBShVCnjpJeDjj6VrY3S0alXJc/IkUKyYNF7ZssX8E12LBZg5E2jZUsYIs+sFZGx45x1ZGiDhgsXPPfcc/v77bwQGBmLz5s1o2bIlok1w0Vgsco4vXVKt5MHZsUMyVp4kvv76yXDiO/PFF6oVPBx79jgc+3bu3lUh5YEZMuTefGfZMuDNN821+npC9u+H7cWa8Lp0UV4nMHqsVis+uFeL0LVrVzz//POeViiT2wYNZDbrxK+Rr3heS3IMGCAep9q1Xd+vUAF7wkugYUMTjcnnzzs6iZUqJcb5e+/ZGxhcvgyMGAHUrCndq5UxdKhEIwBgzBiZtMfjtD7UtWsSaGnQAOjWDciQwYMajxyRCa/NJu2q//gj2TojUoKWM2ZIQChfPuDXX1M5GBQbC7RuLaltgKwhlPA6TYKwMGDzZnnevfmmlABlzy62U4o3b6SJCQkJIQCGhIS43R8WRtaqRcpX7NgyZSLr1iX79yfnzyePHydtNg+LT4IDB8jAQIdWX1/ypZfIjz8mN24ko6IUC3TDuHEOvblyke+/T/79Nxkbq1qZe2JjyYoVRW+RIuSECeStW6pVJc+uXaK3UCHy558TX69bt25lQEAA06dPz3379inRmJCuXeVe++UX1UoejJAQ0suL/Oor1UoenMGDySZNSMNQreTBCAmRMe3aNdVKHpw33yRHj3Z6IyqKfPttVXLuy6pVZLp0JNetk5MNkHfuqJblns2baUuf0fEA8fYmQ0NdPvL1118TALNkycKbN296Vp/VKhOFhJMIgPst5ehpOQ/E9OkOnSVLkgDnlh1LgOzQQbW4e0RHk5Uri8aAAPLoUZKk7fJVrvs9hK1ayaUAyFxCGfPnO85l27aJBtpz58gvvyRffJG0WORjJUrIOOcxbtyQiQEgk8d759IZwyBPnyZ/+IFs357Mm9f1cv7ww1R+hhiGHDj+gGPHJvnR27fJtWtlXtm2LVm8uOPcxm9+fjLOJcX9bIPkeKKNHpIMDxcDx82YZd8GDiRjYjwo/D5s2iRfqjutvr5k7drkrFmqVTowDLk4E2rNkoV86y1y2TLzGWuHD5Np07qe1zffJLdvN+8E8vXXHXrLliX//NNV68aNG7l9+3Z1AhOwfbtDb8eOHn4QPCLPPit6P/9ctZIHY9Qo0TttmmolD8a//4reUaPc7IyI8Lie+xEUJGNDmzZOb77zjgzCJuTmTXE81c+wXSaTgMxwzMiKFbT5pXN9aFSp4vKR69evM2PGjATAb7/91vMaQ0LIlSvlBkvwUF5cepjn9dyPI0fE4gUYXaMe+3a6yTvIxII4x7RpZZJuCrp3d5zLefN4+7YYD8WKuV4OH3ygUOOePfZzyQoV7OPT0aPkp586HKfOW8aM4kT3GFFR5AsvOBwGCSzEs2fl2ZsvX9Lz3w4dPOD0HzzY9UtNZpK1eTNZoEDSetOlE6MoOZ5qo4eUa7VBA/cnsF8/c05yly1zeDqcN4uF7NEjkTNMORERZPnySV+o+fKRBw+qVumKc4TKeatYkZwxQwxmM3H6NOnj46q1Zk1yyxb3nz979iytVqtnRTphGA4jIj5K9e+/yuQ8EN26OfSOGGHOscGZsWMdni83Dj7T8c03ojd37gSOphUrJDJhMj77TPQWK3bvjZkz5Y3XXlOqyx2GQbZsST6Lg7xjyey4kBs1Ui0tMQsW0EiTJvHg27+/y8d69+5NAKxUqZLSsSz6swl2jZEQ42fHZPM4mEhK9KRcORJghF8WFvG7TIBsgNUEyL59VQu8x7x59nN5/dXufOst907eIkUUPoNv3CDz5xch2bOTFy4wLk6yF5Ka41gs5PLlHtRoGOQbbzgEfPed248tX5600dOkiQcycuIHfYBs0UKip/fhyBEyQ4bEegMCyA0b7n/Ip97oIcUgbtzY/RdfvbqkjpmNOXMSa/X2lsHLjF7zc+fIrFkTa37pJXOmj1mt8t27uyZ8fSUCGBenWqUrPXu619u8uUSv4tm1axezZMnCLl260KYwd/Prr111enmRw4aZN/Uxfk4bvw0caG7DZ/Jkh9YKFRIYEmYKX9/j3XcdeufPv/dmVJTMcDw6Y7g/sbGONBCLhYzctMuRLta9u2p5iZgxgyyCU7yKXMkaEsrZs4fhz1bhea/CiQeyZctcPhoWFsZBgwYpjWDvW3SSURaZlf+G1nwf03jTKzttcSbJib9HXM8+9vPYAr+7nNbAQJM8g48cIf39SYChpZ5nmaLRSRoQyhxkMTHiTQTINGnIf/6x7zKMxM+I+M0lBdYTfPyx4+DJWLQrV5JFi7qf96Z6cH3pUnnoA2S1avc94K1b5IAB7o3g9OklCvQgaKPnHtHRZLNmjpPo7IUGyIYNyd27U1n0QzJ+vPsbLHducu5c803I1q51H6Hq2dOU2Ss8dco+Bts3Hx9y2zbVytwTFOTeA+LlRb78Mnnhgnzu119/pZeXFwGwR48eNBRdKLdvO+aJzluVKuTJk0okJcvRo4m19uplvvssnu+/T2yk2RkzRpmupKhWzaH1hRfuvTl6tLxhsuKvRYscWrPiJqNzOeVcfPyxankunDxJFvW7xHMomPgCnjNHtTwXdu0iM2cmu2BG4pmuiWqP4uLIUR/buAkyAb6NzMyJa/RGHFc3mahangvGX6vs53E63k10CXzxhWqFlCLrezVGzJyZPH+ecXHuyw8GDFCo8/33HUK++cZl15o1iethALJVKw/Xhc+d6+rxdBM9OXtWdrmbP5YpI8/mVGX7dkd6YLFiTK4ALjRUHgMZncr7AgIcpUoZM5Jbtz74obXR40RMjFyggIROf/1VCs+cL4hXXyWPHUtF4Q/JwIGi6803yd69XY2KmjWl+YGZmDjRoa94ccfPJUqQO3aoVpeYqVMTDwpZstw/b1QVn3ySWO+kSYk/99NPP9FisRAABwwYoMzwcY7AO2/FipkvJctmk+YLCbW++y5pizFZ2I/kTz8lnjdu2EDyxAm5iE1krdlsjjKT+O3g0rMOt97MmaoluhDv7PWClauRID966lTV8uzExpINygfxKEq6v9FM5MnbulUmMIEIZhCykQBXowGvlqwjhYr3OHr0qNIItdUqpQc9vRwPhw74ye5gunzJPPcVb9wgc+YkAcYVK8nCOSNcvv58+cjISMUaExb+rljBkBBXJ3T8Vrq0whrg775zCOnWzT5+hofL9RC/K00asnBhh16Plhv8+6+jILl8eTEmnYiMJEeOdI2WlC0rDZAAsmBB8vLlVNZ46pSkBQLy7+nTbj8WFSXzxfiPAvKn9e4tl/U778jzeOfOhzu8NnoSEBsrKdnxqUtxcfK8jU/hjB/Y3n6bPH8+FYQ/JIYhWuKLfw8ccO1K5+0t3ujgYKUy7RiGFMflzCk3YO/erlpHjDBXepPNRtarJ/r69HFMzLy9xZgw0byRpAzAuXI5Jrnx1+vcuYk/+9133xEAAXDEiBGeF0sp1Uj4YPv+eyVSHohGjdzPHT9pspXWLebK4//118Q68+UjIz8cJC9OnFAt0c6pU4m17snv5IqcMkW1RDv79ztkfYohiYUvXKhaop1hw8jK2MkO+IkT0DexFax8xiv884+kqADkRPQmAcYiDUviKE+vPm0PU16/fp2BgYGsWrUqr1+/rk7wuXM07j0M/kQTAgYBsmlTdZISYRjkK6+QAI20aflq0X2JLlVTND1y9iwOG8aTJ8lSpRxvtWkjQSBvb4U2+ubNjqLZF1+0pwdv2+baYKFUKYlWfvGFpA16NGPhzBkymzgLmCcPeemSfZdhSDZZYaes0cBAGVbj4sQwy57dA4+EoCBHPp2/v1uLJS5OOsklnHN37uzIViFlrrhnz8NL0EaPG+LiEk9mo6NlkpvQ6uzVS6xOlcTFuUZ0DEPqAXPndmjNkYOcPdsc7bcjI+W8xbNunesFXrGipPeahQsXJOIeFiYNF5wHjrfflmvDTHz7rWj7/XfHgOzlJd6chMS3fAXAMQpSnmw28plnHE4fQLw3p0/THBdrAuI7osVvuXJJhPLKhPm0NmqiWp4Ly5cnno97I463/e4NDD/+qFqind9+c9X5ChKIN0UOjtCli0hqjj/cW8Dr16uWSFLmafEp8wC5HDIBvu6bXyZF9g4M6rl8WcbWMR0OMw6SrjABfZk5871h4N4g+9ZbbxEAK1asqK55gWHQqF+fBHgXGZkXl+xzzT/+UCPJLVOm2L/8cbm+tF8H330nNRtlyjxQ3Xjqsn27w5ioW5erV1pdoumffCLzmXffVZg1evGiTKDivUbXrzMmhhw61PX+6tPH4UNYskTqZTxGcLAjPdDf38U6PHUqcc16586u81bDIPfuTWWNERFk1aqOCUmCOk2bTdKGE2ZXvfaa++yqR62p1kbPQxIWJjdiwvzCoUPJu3dT9FCPTUiI1Kk6N8J54YVHs45TmoSD7d27ZKdODp2+vrImilnmvc6Fk7duueYaV6tGXr2qTltC4uKkBs0wyCtXHGmEFov7ee4XX3xBAKxduzbjFHRn+OwzMXSPHnV4e8uVI2M+G58oPK+a1atFX3x6k91b+vnn8uJhY+2pyN9/uz48fH3JzR85GRPvv69aop0RIxyy/BDJM0hQyD58uGqJJOXe9/Mji+EE7yIjw5AgJw8gDx1SLZOkeEtnz5aU7ErYZdc3+dnvJLTy6quqJboQHmZwU1oJq99Kk4M5fO+6NJfbsmWL3UGzTWVh5Q8/2M9lV3zP/v3FoZQrl4myFA4dshdM/hvwMi2wEXBE0SdONEFvkFu37N5OI08efjPyht2ISJ/e1YDcsUPRuY2MJCtVujcw+ZG7dvHQIddutAUKJPZzeNSYjI0l7xnhtFjE20nJ+hg61HX5jYoVFdUkW62uRUROLeYNg/zrL2m24zyMplYdvTZ6HhF3nSQyZ5Y2sWYryj9yxHWSbrHIfCfVi9UegcWLHRFaQLq7mWb9ACfi4lxT8/LkMVdNknMe8dWr8d4TgxaL+9rln3/+mRGKLtwrV+zjtEtK1uKyI2nUqmWq/uB370qedmSko9V97txkbOd7rceaNVMt0c7mzVK6E5+vDZDXX2zleFGhgmqJdlq0kNTBmjXJERiZ2JDo10+1RJLSPXvSJPLPucH0Rzir41+7RpvvvYeByrSrBBiGpNwsQ1MSYJB/Ab7e6l7nPqf0FzPwy+tL7Ofy4qhZnDTJkbZttVpZvnx5AmCXLl3Uibx8mXEB4vH8G/VY+yWDcXHyPEiiK7DniYy0d2K67Z2NuXCVFovYavGEhipOzbbZ7LnChrc3Rzf8136rFyni2m1UGQkWzbT9+DPHj3c1It56S7GzOz4MFi9o3DgahkTOnbNnsmQRO0NJZM8wXBtADBli3/Xvv67lGEDqd0zWRs9jcuUK+d57rtGU3LmlsYeZusIahoQOnXuyZ80qnh+zRFPiuX7d1SmQIYN4081WP0OKrvhB0NfXVBlDLly7Rr78zCl+jZ4shHOcPTv5zx9UuHDSgAFyPu0pTnXqmMqTEG+EHzjgqJs6VaSh44I1QyiVkk++b5/cNyVLkjlwnXEWp4HK29s0BuW6daJz9FtnGAU3Lf3ee0+1RBd++UVkzcZbJMDYfIV46bft4qI2US/7Q4dcozzWad9y8WLVqhJz5XQkz1kKkQDPZnuetNloszly+KdOnUoAzJQpE4OCgtSINAxG15cUwXD4s1rOs2aybx04rV3wCpbTYjFdHxBHV0aAE/M7Uu8aNDCRM3aCY/2lu137uUzOs2c3SSrjl186RHXpwmNHDZd1Jy0WsYmUtiT/4guHoI4dScPg/v32cjP75m5R9dRAGz0pxKlT4hSInwQBUqvw888myJt1IjxcFsB1Xsjy+efNFaUg5cKfNcu1BXPz5qZyotrZts3RPACQ3F4TzXvsXLtGzs7Sh3Hw5hx04m+fJk6UNQyDQ4cOpZeXFxctWqRApZy7OnXI3LjiOKn165um6NqZ+PqO4xanROSWLVXLSsSIEWRfTHB9ygAu60yYgSMVO3A1GvCwTzlXnR07qpbmwoQJZEbcZaTlXtvVTz6RHQ+6WISHGD6cXAppg2Xkz28uT5wTSyo6JsE3/3RtCHLjxg1mypSJADht2jRFCknrHEfItLfX5Idqk+sx/vzTrnEKPqDFYpJmBc78/bd9ovSnb2vGN4Ho189Ez81Vq+wFO5fKvMzAgDiX4V11HTdJ6Uxw7zzG1arDQX1jXJzvVatKUwWlOC02y3r1ePJwjEujPkD6Gsyf7znnuzZ6UpgDBxL3P3/2WfEKmClSceKE5Ew6ewS6dpXmGmbi3DlJcXP2sCxZolrVPaxWqa586SWGv9GVk/OPZUssYRkcYpM6kebxWDlx41QIg9KIhWaDhWcrtXGJTBiGwS5duhAA06RJw6VLl6rReYPMm8fgNeR0fPmNGinsV+qeq1fJAH/DvhK7fdu/X7U0Fw4fMngEpVw1ApKPaxYMgwsXSO3BBrwk+po2le+9TRvV6lzo3Zt8F9NFo5eXB/q8PjyGQbYuuNvxXTvl0ZuJI39dYATEeNxX/q1E+48fP85KlSqxQoUK6poXXLvGCL/MJMDNeJHTppgsPYIkr12jNat0gzmEMkyHSLMtwyQplffy10+hKDPiLn19pb2+aTh1yr42wRX/IsyEOwSkjnvOHJPM4/butbeSDclTgqVy3bHf5tmySWRPeQbP+vV273pMqbLs9eZdlyVV8uSRlFBP12ppoyeV2LqVrF2bdi8GIAXvJmnsQ1Ju3t9/J6vmvcR2mMvJ6MH8mUI5bZri6FRkpOQ17NpFrlxJ26w5/KfpOE7wHsDZeJN/ognPZK3M2Lovq097MgxpfJ9wMgnwqnc+hlWpIw3lx48nt2xRq/Ued7+Zl1hvo0Z2L7XVamW7du0IgGnTpuWqVauU6Ny2jVxhaeKq85VXTNcub0L/a4nPZ+vWqmW5snWr22uUrVqpVubCli3SyCAa93JGv/9e3L+Koo5J0bo1uQuVHIaZCTlwwBHlicxuziiPYZAbcvyPBBhqycCIM9fcfs5qtfKqwm4xl6q2kfMIPw5oecIcE19nbDZG1ZEamSj4siwOmC/VOjaWtheq289jWexnnjym6v0i3Z/u9csOQ3qWxmECknlghiVKSIqD5d4qqHfTZGERnLL7Xnr0MMkavgcP2rt9BafPx8JpL9sfOVmzSqRcVeKGNnpSg5s3ya+/ptG4Mdf8GWNv/uGcqaP0Rr90SRZu6drV0TMd4DjvQXaN5csrmqNv2eLaGiWJLRTpWSfXUfMsEvrDD64rwybcqlQxT+GwYTCm+kvudb7xBhkVxbi4OLZp04YA6Ofnxw0bNiiRurvxsMQamzc31QQucv029+dSYV1UIoYN4z81h/IrfCipTtmzy/2fJ49JXJfCxYtkXax1nMNTp1RLckuHMnsdGk2R3J+YqZ332DVap05XLcctWz/fYNe483/jVctxy6WJju4qX+Uep9zP5o7gkZPsGntZvna7PIFqIt/rY9f4FmbxhRfM1fWUNhtjGzvSdJrjD/r5SeMS5VGTeMLDaS1XUaIn8GFN/ENAlg7at0+1uHtcukRbXikeD0Ygy+AQASl3HDFC7EqVaKMnpYiNJZctExdgfMHMsGEkae+mUbIkmQaxzI4bLIFjHFB7p2cWr0rCyEm4Bb/dhzNLj+d7+IYd8BNbYgk/afyv51PebDYphipYMEmt4fDnKrzM0RjGqa3XmaPcY8UK6ZGfQGss0nDR/341z8BJkocO0UhgpO1uNNQlqTomJoavvPIKATAgIIBbFSSxG4uXuGgMKvGipBRuN9FCoAsWuGiMypxLCvpee021MgeGwSNHyM8wWDyEz5SX97dvVx8tdSIujvwUQ0mAEVnymcogc2a2f3fxBmfMbaJCBAeGQa4NkAnc7YD8pouOkmRcVByP+z5HAjznW5y2KFdHxowZMzh8+HBlXSVJMvTsTd70ljVadns/zzMnzPddX1+znzEWiYyuRGPO/dl898z5CQ7DcQa6sHNn812SZzo6+uYPxyhWrizLKJgGm40XK7W0a+yIH5kzp6QGmmWYjLt1l0G5n7MbZbWxnr6+UudsltKJKxt3a6PnsTh0SCrwcuZMNNllzZqS41a2LJk/v30F5/gtDt7c8m8qX603bojxFb8C5ENuB73KqvPGREVJHDRz5mQ1rk7f2hxGDykhPOcVbO9tSwv1NFVDC5JkX9cV2ldXGpzoI1FRUaxfvz4B8Pv4RR48yYULJMAzvqVohRe31U2sUTljxtBo2YqzcgxkKNLzVNX28hQ6dsxELkJhWvaPecMrJy+VbaxaSpJMy/gRQywZebpGJ9VS3GKzkb9m6soY+PBoSxNejyTvXI/hlkySGnq0lzmjPFf3XOVh/+dJgPvG/OWyLygoyN68YMaMGYoUkheW7uOlNAUZAx/+840Z+ign5vTEZQxBBl5HDv72jRkq7BNz9J2vaIUX96Ecv/ky0jSTdDuGwcNV3yYBLkZrjhxhM8+aS/GEhPBkzhokwM8sQ/nhh+ZbG9I4eYpX0hYkAba3zGPXrhK9NxPXFy3TRs9Dc/u2rHacMG/tUTbnBVVSE8MQr26vXu4NNG9v6bhQqBCZLRuNewsQXS1c3TP6kuPOHelj7OvUyvaddxj36uu8mbEwL/Uwz2rtJMnTp+0RNVuWrDyTuzpDDpxTrSoxISFk7tyMfPNdrmkxhYbV/QQ9PDycy5Yt87C4exgGWbcuzx0I4ebvzeR2c+LUKXlo7onm1Stme5q7cuGCubpJusNmo0RPzNgJxAnj+g3arpvEfZkE15fvZFy4yVzqTtjibNw7JnHNYOfOnQmA5cuXV9e84B7Bl8K44aO/7v9BhRz4/QzXjTJX58CE/N5zHbf+ZM50VZK0WQ3OqT2be/4x14LYzty8HM1vy07joQPmcqY5s2nhVU6tMofHj6tW4p7HsQ0sJAmTEhoaisDAQISEhCBjxowp9x9brcCiRcCGDcCWLcCxY0l/9vnngcqVgcyZgSxZXP+N/zlPHsDLK+X0PQhWK7BxIzB/PrB4MRAaKu/v2weUL+/6uZgYICDAs/qS4uJFYMQI4KefgPHjgX795H3D8Pw5vB83bwJNmwK3bwOnT6tWkzQLFsh12LDhA//K7du3cefOHRQrViwVhTkRGgqk5D2s0WhMy/bt2/HCCy8AALZs2YLq1asrVqTRaP4rPI5t8HQaPQm5cwfYvh3YulW2HTuAyEjZV6mSvPb2Tr3jPy7R0cDKlWIAZc0KfPedakX358ABMTo//FC1kuSJiAD69wemT1etJGlIMW59fB7o40FBQahfvz7u3LmDTZs24ZlnnkllgRqN5mnBZrOhSpUq2Lt3L9566y3Mnj1btSSNRvMfQhs9KY3VChw8KFGgrVuBjh2BJk08d/zHITIS8PdXreK/BQlYLKpVpBg3b95E7dq1cfToURQsWBCbN29G/vz5VcvSaDT/AaZPn47u3bsjMDAQJ0+eRI4cOVRL0mg0/yG00aPRaB6K69evo1atWjh16hSKFi2KTZs2IXfu3KplaTSaJ5i4uDgULlwYV65cweTJk9GzZ0/VkjQazX+Mx7ENTFZEodFoPEGuXLmwbt06FCpUCKdPn0b9+vVx8+ZN1bI0Gs0TjI+PD/7991/07dsX77//vmo5Go1G44KO9Gg0TzHnzp1DzZo1ceXKFZQrVw4bNmxA5syZVcvSaDQajUajSYSO9Gg0mkeicOHCWL9+PXLmzIng4GAEBwerlqTRaJ4wbDYbdu3apVqGRqPRJIs2ejSap5zixYtj3bp1upObRqN5JH744QdUqVJF1/BoNBpTo40ejUaDMmXKoGDBgvbXW7ZsQVRUlEJFGo3mSeDWrVsYMmQIAHGgaDQajVnRRo9Go3Hhjz/+QJ06dfDqq68iNjZWtRyNRmNihgwZgjt37qBs2bK6eYFGo3k4oqOBmTNlqRgPoI0ejUbjQpYsWZAmTRqsXLkSb7zxBqweGow0Gs2Txa5du/DDDz8AAKZNm4Y0adIoVqTRaJ4ISGDJEqB0aeDWLcBDY4c2ejQajQu1atXC0qVLkTZtWixZsgSdOnWCzWZTLUuj0ZgIm82G7t27gyQ6deqEGjVqqJak0WgAYM8e4ORJ1SqSZv9+oG5doE0bICwM+OADjx1aGz0ajSYRDRo0wG+//YY0adJgwYIF6NatGwzDUC1Lo9GYhJkzZ2L37t3ImDEjxo0bp1qORqO5fRt47z2gUyegSBHVahITFAS88w5QsSKwcaO8N3AgkD69xyRoo0ej0bilWbNmWLBgAby8vDB79mz07NkTJl7WS6PReJAsWbIgZ86cGD16NHLmzKlajkaTekRFAZs2qVaRNDYb8P33QPHiwHffAR99BHh7q1blIDYWmDABKFYMmDFDUtsAIEcOoHt3j0rRRo9Go0mSV199FT/++CMsFgsiIyN1tEej0QCQseHEiRP4wIOpKRqNx1m7FnjuOcBiUa3EPTt2ANWqAe++C9y5AxQqBLzxhmpVAgksXQqUKQMMGACEhrruHzgQCAjwqCRt9Gg0mmTp0KEDNm3ahJkzZ8LbTN4jjUajlMDAQN28QPNo7NkjEQqzEhQEdOwINGgAZMsGmK1m7eZNoGtXMXh273a8P2gQ4OOjTpczwcHAkSNA1qyJ9+XMCSjo9qiNHo1Gc19q1KgBLy8ZLqxWK/7880/FijQajacxDAMtW7bEvHnzdKqr5tGIjAT69AGmTjVXClY8hiEtlEuWBObOlfcGDDBPpMdmA6ZNk1S2mTNd9+XJA7z1lhJZbsmSBRgyBOjQIfG+QYMAf3+PS9JGj0ajeWAMw0C7du3QrFkzTJo0SbUcjUbjQWbOnImlS5fi/fffx61bt1TL0TgTEQFcuqRaRfJs2gSULQtMmuTRjl0PzLFjQO3aEkEJDpb3ihYFWrZUqcqVH34Axo0D7t5NvG/AAMDX1+OSkmXOHKBnT/k5fgH03LklHU8B2ujRaDQPjJeXF0qXLg0A6NOnD7777jvFijQajSe4c+cOBg8eDAAYNWoUsmfPrliRxs6uXZJ+lTGjaiXuCQ+Xie9LLwFnzkhKVuXKqlU5iI4GRowAypUDNm923de3r7kiUu++C5w4AVSo4Pp+9uxAt25qNCXFb78BXbrIz889JymNBQpIlCddOiWStNGj0Wgeio8//hgDBw4EALz33nv48ccfFSvSaDSpzdChQ3H79m08++yz6NGjh2o5nuHyZdUKksdmAz7/HKheXSIogYGqFSVmwwbRNnWq471evdTpScjFi2KAffIJEBfnui9bNnOliwHynXfoAOzbJ6/jjYe+fT3eFCBZVq4E2rWTdMFixYA1a6S256OPpG21IrTRo9FoHgqLxYIvvvgCPe+FrDt37oyFCxcqVqXRaFKL3bt326O606ZNg49ZCqVTC5sNGDYMWL5ctZKkuXABqFMHGDoUsFolJctMhIVJO+K6dYFz5xzv58oli1KahQIFpAPa5MmJ9/XooSwi4RZSzunixfL6ww+Bjz8GMmXyeOvnZNm4Ub7juDg5v2vXyvcOSPMCPz9l0nTbFY1G89BYLBZMmjQJUVFR+OGHH9C+fXv4+fmhRYsWqqVpNJoUxDAM9OjRAyTRrl071KpV69H/s+BgiUZ4mdjfevOmtPzdsAG4dk21GvfMny+T3JAQeV28uLm6i61dK0bYhQuJ9733HpA2rec1JUd4ODBxout76dKZr+5oxAhZjweQaM+XXwI3bsg6OGZJbdyxA2jWTFIGc+aUa6FAAcd+xQ0hTDzyaDQaM+Pl5YVvv/0W7du3h4+PD/wUem80Gk3q8M8//2DHjh3IkCEDxo8f/+j/0enTMuE1s8GzfbusFr9uHVCvniyeaCZCQoD27WWLN3gAqZswS3cxEsicWaIQxYu77vPxUVbAniQREUDTphKN8vYGFiwQg+fttyW9zSxMngx8+qn83LgxMGuW3Eu5cwP3au2Uc/CgaAsPl85ta9dKapuJMPHoo9FozI63tzfmzJmD7du3o2HDhqrlaDRPH+Hhqfrf16lTB//88w+mT5+OPHnyPNp/snUr8MILQOHCKSsupSCBKVOAWrUcdTxt26rVlJDNm6XQfv581/fTpAE6dVKjyR0WC1CpktQZXbzouu+11xxpTmbAapXvOX6dm2+/ldcdOkiNjFmYPx/o3Vt+fuEF4NdfXdfiMcNaWSdOyJpGwcFAhgzA6tXAs8+qVpUIbfRoNJrHIk2aNChXrpz99cmTJ7F9+3aFijSap4QvvwQOH071w9SqVQvt27d/tF9etEjqOm7dEi+w2QgPl4LrXr0chew+PkCrVmp1OXPliqzN4i6a07SpuQwJQAzHli0lxSlHDuD11+V9MzUwIEVP/Jpzw4Y56qImTACKFFGnzZlVq4A335Sfy5QRvWZqWABIGmP9+rKga7p0wIoV5urO54QJzMNH4/hxYPx4iaDlzp14y5DBPNHeeP79Vwz0okUl4lesmLQtN4ORnhSLF0tWQuXK4rzJlEm1ouQhpY19tmzynDVjM5uExMXJGmOvvALkz69azYMRHCxLLjRv7nqfnTp1CrVq1UJUVBTWr1+PSpUqqROZgKNHJe05Xz7VSh6c3bvlvjPbWJYchw5Jd9InhYgISYt/5pl7b8TEmG+tiwTs3g1U2vglLIMHAaGhqXKM48ePI126dCgYv7bGw0ICY8faU2+s/hmQpnr1FFSYAhw/DrRuLeuzONOwIS6FZ8aJPTKXU07evMAvv8gE4n//c913b6IeGyvz4UKFJENPGRERQIsWUg+VNi3wxx9ilF24AFStav+YYQD790sGVLt2CsblceOA6dPl506dgNGjHfvc1MdYrXK57N4tmXD9+nmgjGbbNmkIYLXKZHH1apn0PgC3bwOnTsn87dQp+SoGDEiFcqpr1yQV9PJlcRb8/jtQs+YD/3pcnIy/164l3p57TkrXUjQjliYmJCSEABgSEuJ2/7ZtZO7cpIyurpu/P/nMM+SLL5Lt2pE3bnhYfBJ89x3p5eXQ6eNDFi9ONmlC9u5NTp1Krl5Nnj1LWq2q1ZJxcWTbtg69RYvK6wkTyI0bySS+GqVcvUoWKEB6e5M1a5Kffkru3k3abKqVJc3y5XJdNGxILlxIRkerVnR/2rcnq1WT6yCe8PBw1qxZkwCYJUsWHjhwQJ3ABISEyHWxdq1qJQ/OxInkmDGqVTwcNWuaY+x6UCZPJhcsuPciOJj85BOVcu7L+fPkpPwTZEAuXz5VjmGz2VitWjWmS5eOS5cuffj/IDaW7NrV5aF8okyrlBf6OPz2G5k+vdsJxNwmc5kpE3nhgmqRThw9SgYEiMZnnyV9fcm8eXl4fxz79iWzZycbNFD8nLPZyFdfdZzLn3927DtzhjdukHPnkh06kDlyyEemTVOgc/58h8Z69ciYGJfdNht57JjI791b5pH+/vLxLFnIffs8oPHwYTJzZjlotmzkiROJPhIcTO7YIef044/lmVyliuPX4reaNcnbt1NB482bZJkychBvb3Lx4vv+ypQp5Msvk889J3+Wu/k7QPbrJ/NPd9zPNkiOJ9roIckrV8iqVZM+cYGB5Pr1ntP8ICxbRqZLl7RmgOzbN+kv3NPExZFvvOFep8VClighN9v8+aqVOjh8WL57Z63Zs4vOn34ir19XrTAx3bs7tGbJQvbqRe7fr1pV0pw7R6ZNK3obN3ZoDQ0NZdWqVQmAOXLk4LFjx5TqdKZ1azEuP/3U3EZwPL/9Jud3yRLVSh6MkBDRu2iR05s2m2lPdmwsWbAgOWoUxVJr0oTs31+1rCQJDSXH5frSMVB07Zoqx5k5cyYBMH369Lxy5crD/fLduzL7TvCwuPn596mi9ZGJiRFDwtmrBzASfkyPUA4YoFqgE6GhZMmSojFTJobsP8PDL3XnzDxD7dKzZpX5kFJGjHCcy8GDGRtLbtpEDhlCVqqUeP6g5FbbuNHx4HruObleKY7mvn3JWrWStIWZNauHnskXLpB588pB06cnd+1y+7EtW8hcuZKfS776KhkVlQoa7951/VJ/+umBfi0sTIbZpPT6+YkRlxxPtdFDyhf69tvuT2D+/ORXX6WSlfsYbN+etJX73nukYahW6EpcnETMkrpQmzY1TzQtnnXrJJKWlOZKlUgTBSIYGUmWLp1YZ8WK4g27c0e1wsT06eNqALdvT545Q965c4fly5cnAObJk4enT59WLZWkTMbj9b7yivnGhYTs3Cla/f3JvXtVq7k/u3c77i37GPbjj+IRNCFz54re9u1JDh4sL778UrUst1it5IxSX7oODt99l+LHuXPnDrNly0YAnDBhwsP98oULEoVwN+BevJjiWh+bY8do3PNAhllkprsIrzJTJhONt4bhEj2ZUHcF06UjC+IcC+OM/fQ+SkAuRVmwwK7R1rwle/WwMWPG5CfjHveFHDlCZsokAvLmJS9dsu8yDPKHHxzBtIRbtmwemi8EBYknGRDj7D6pCfv2Je1E7907lc5xeDhZo4bjQN9880C/Zhjy57Rs6V5vvnzyDLkfT73RQ8rJnDJFImxJWY9vvinGhlkMipMnySJFkp6QL1xorjQRq1UmBwm1enmRI0eKM8ps/Pij+/NbpoyEhc3GgQOSteBOs68vOXCgua6JmzeZ6MHm40P26EEeOXKTZcqUIQAWKFCAF00w6YmIcH2oFSqUpBPNFFy/7tCaN6+TJ9ekkZN58xx6162juPXy5CFPnVItLRGGQZYtK1oHPbPQIXzePNXS3PLHS18mHhT27Enx43zwwQcEwNKlSzM2NvbBf9FmI0ePprVpc95Nl9NF563cZVJc5+Nii4zmnULlRR+ysBSO8BaysA1+5bhxqtU5MWGC/Tx+jI/dPhvef1+xxh07ZJIFkOXKkWFhDAuTFCV3el94QZx8HuXKFclvBuSh5caCWbfOveMxWzby4EEPaAwNJStXdngRf/01yY+eOkW++27S84UJE1JprhsdLflp8Qd6gJvl9m3xJRUvnrQRXKPGg2fgaKPHiQ0bHBGUmjXJ114j06RxPbkVKpDffy/PY9XcuEE+/7xDW3yea/xWpAg5fbqCASIJrFayY0f3F2327JIfnyA9VjkjRybW6u9Pjh9vnhRCZyZNSqzXx4f89ltz1vp8/rn76yEggOzb9xqLFCnGKlWq8I5JXKcJI5Zp08q5NYszxBmbzfWhVrmyGG6cPVu1NLd8/LFD68svkxw2TF6Y0LL86y+RVhb7GQ5/h3Cz5UOT3PaaG4MnbdoUH2z37t1LLy8vAuCGDRse+vfPniXrlL3FG8juojX8vX4pqvNx+ftv8qecjhl5SywhQL6L6SyWNyJ10oEehY0b7Z7ckBcb84WqtkSXQalS98YEVVy65CiuzpnTXgi1d69j/u68FS2qIPAbGioTP0AmhH//7bJ7zx7XeXzCec2hQx7QGB1N1q/vOPC337r92K5dEiWzWBwf9fNzpOSlTetUo5jSxMWRrVo5DjxsWJIfNQwJMrz5psMejt9KlCA//NDVaH+YoUwbPQk4d07qO9u0kddXr0ptav78ric+Y0bygw88dEEnQ3i4I8fxn38kTF29uqvWHDnIzz4zR8jdaiU7dXI4dbp0cW3OULiwpI2YxRltGHLjORsQzgaw2eZjhiE1MgkH3wYNzJklFBHhSD923jp3JtesIc+fv/xIg1NqsXy5u4ebwU6dFE8ekiBhNPidpldoFCxoSivNufavAM7T5nvvaZdgkmEGatcms+Imz6KQ6wk2UQ0aSZ56343BA0jFcgpis9n4wgsvEADbtm370L//55+SOTQbb5IAo5GWA/GFaDVZ95BrP662n8fv0M1lHJgzR7W6e1y+bPeCGoUK8adJtxN59dOmVVz3GR4u+dfxYrZuZXg4OWCAa9ZN/DM3a1bJcPEosbFko0YOMU5f8KlT5OuvJzYie/TwsMFjtYqHPl5EgmYqhiENrurWddWaObPYHTduiIEZGCiO/1TBZpMOFPEH793b7TMoLEyybuNtzPgtTRr5E9evl1/bvFmui0fJ0NVGjxsiIhJ3BbFapYlA48auVjIgobV589R50uPipCbV+dmweTPZrJmrzvTppdjOKRVVCVarGBING8rro0ddHQDxBtHKleaYm8XEOAaMPXtcG8x4eUltihkif/Fcv+6I+j3zjENr/vziPTEbP/yQeE721lvuU/Hmzp3L2wqLaWJiHGnd8Vs1bGWNwIPs3t180b86dRKcV8ySHx4k+dnDONe1LoDTbCKZNA0V7NhBeiOOa1E38YV7r7DZDJw8EstW6dewKZbxGEq46uzePUWPFRMTw2HDhjFr1qy8fPnyQ/3uxo3ivX3ZssaubwRGEjB4uvLr5gpRBwXZq79PWErQH+H2U/rccyZJH46JsXs+DV9fftxsj11jzpyOTmITJyrU6KZT26pVkjIc/1aePNKE5fXXJWK9ZYuHNRqGeGXjBY0eTZK8dk1uH+csoHz5yJkzZfxftUqev4cPe0jj++87hPTsaZ80xcVJg6jy5V1v/fz55bt3nrM0bpyKKXiGIcXm8QK6dEk0sTt0SIIIGTK4ai1QQBoHXbvm+l9u3Ur++++jydFGzyNw9iw5aJBY8s5fULZsUjdx5kyKH/K+GIb7upjDh8XAcL5BfXxkUnnkiMdl2rFaE3RporQRf+kl13P60kvyvmqCg8UQi3+oLV0qA53zzfnnnyoVurJihWNuO2mS4/v38RGD3gzGZDxxcY5c6HjHHyDXrfMkYsqUKQTAKlWqKI3+OD8HAbJ+rkO0FSxM3rqlTFNSOEcpnY2JQ82HqJbmgmE4UixexGZX0TNmqJbnQps25Ff4MLHB4+dnmhvrzh1HDvwL2GLXuBv3brBUSnEMe1TvT0QE72QWD80RlGJaRDNNGvL2ZZPkZpPy3d7zJMbAh+Wxl1mySAc/QJx0pqBnT4fxmH+Wi3P26lVJ3W/YUHE2hVOntvBeg13Shi0WmQDH+w/69k08V/AIn3ziMlG/G2xw6FCH0RgfLRk/3rWE4OBBD86tnHOC33iDtNkYESE16s4GJCC1yD/+KMGrhKRaTbVhSJu9eBFt29of6tHREixw7mkQ//03aSJZFanhRNBGz2MQHS2WdM2aib+0Ro1kYmwKzw+l+U3fvonbKTZr9ugWc2pgGPLwKFfOVWerVhIRUklQkOvr0FCJ0jpH/v73v8ReCVX06+dwkv77r3jO4nW2by/ZBWZh2TJJX4iKkrG7H8azPtawYwfDfg8dOnSIWbNmJQDWqFGD4Yr+gL//lnNYvrx891lwS96oV890oZ7hw52ikrAy2EsWYbiZvaR5ag8okzGAtMDGXUjQn3b8eNXy7Jw4QXZCEh1OChdWLc/OypWyRlOXzgY3QWYVp1CE2dNH0qhWLUXzbowUMPSudhhgP4+dS/xLQDokmopp0+wa+2KCvQXxyJESUTWFvRvfUhDgbB9H6l2/fo7J7mefKX5GOXVqO1+hJbNkctQaPfusePGdSfjc9QhOXYysDRpy4rhYZs3quNXTpZOGjcHBCrTFM3WqQ1DDhrx1NYajRiXu7FuzpjhklVyfo0e7TjZjY3nmjAQHEurMnl2CCWfPpq4kbfSkEIcPSy5nwm5U+fPL9371qkdk3Jc7d2TQS9j0oHp1MdLMUktjs8n4Xbiw06TNS7zsqtPzErJjh6uRlimTNLtwOZcKTmzCQ16/7pruVKYMefy4x2W5xTDkOUOK3dCn+WlGwZc78DwnvvQ7rbHyx+zZs4eBgYEEwLp16zJSQZeOuDipvT16lBw6lAQMRuPe2g19+nhcT3LMmCGyGjcmq2Kb602v2ovgxMaNIqmbz+xExkR4n6Gq5dl5912yRYULbFDpNsdBPJhWeDkGUZMx5kVHEVrfPAtYpgxlVpFCxvm+fftYrVo17niMdpbhm/cyDlLEMSfgfd6+LZ5e57UplXP4MK1ppcZsNRowe1abvYHX4cPSHl45Bw/aW2jvRGX6IooZMsh6Xc4ofcY7dWo7FVCOAQgjIOlrn31mkkZGf/9tT424VaACS+ULtQ9H3t4yBihf02jBAru3Nbp8VfZ/P9wlAmWxSGvnhAakR5k40S7IVqculy2KYqNGictDatWSP8dTWaza6ElhwsJkouGcpgPIPfTqq1J3YwaPUGSkdHZzFDobBCTNaPZskww+FB2TJ8enEhqsjJ2c6t2LY7ueNNU6KbGx5NixZDo/g/lwkU2xjDMKfsKQhq9KnonHE5ITcPQoWb8+jRdr8Eix5pyFtzgBfTkq7afc2+0b8pdfpHOASSIVViu55DlHmOJiYBlaf5xLxsVx27ZtTJ8+PQGwSZMmjFFwscZHR2NjZYHj8yjguNnjrTcTsGqVZECEhJCfpR3pOih9+qlqeXZmzCAH9whlVObEq+VFd0nZ+pPHIb4t6pttoxkEcVVebNWTK/N2cXS/MQkXz1l5CLLi+fV8FXnqhI0dOqTc/2+z2Vi9enUC4Ouvv/5o/0lcHM9lkYflJeTlpuWS03TggFyzpiAqimGFn5MIKbKyTOYrnmlB/DDcvcu4wkXtGgvgPJ99ViKTpuHSJRr3OrVdQ07mxwUCUi/r8QYFSXHgAI17hSVXfAowF67ah6LXXjPJ+Vy92t7d4XJgKWb3umXX6OMjjmHl/VTivW0AL+Z/gcXzhLkM6xkzShamR+qeEqCNnlTCMMSp8dZbiVvuFS9unkVPrTt28+IL/2Ot8iEuGvPmlV7tplg/58wZRg8dzZtZJUl9O6oQkG4jY8Yo7JoVHS29NWfPlh6KtWvTGpg50aRtx/MfmKMW99q1xJXt8Z4YWGidOl21QhesoREMCijkotMoWZI8cYL//PMP06VLRwBs1aoVbQrdl2fOkDu9qjo0+vqaZiGn8HCHk+Vsrmqu33vFimrFOREeTnLIENrS+fMqEhg+7dqplpeIYc84LSp05AjDg2PJP/5QLcuFRU1m2zWG/SEd8FKy4crs2bMJgAEBAbz0iOH3ve3G2zX+/Kq5zl88F1r3tmvskHGp8o6tibDZeKtGC/s4Xh9r2KGDudKXGR7OsBJi3EYjLathK7NkkWZoZnACkyQvXWJ0dmklGoxAlsIRApK1bJYurca27bT6yWJxF5Cf+XCRgDQAGDDABBEoksa8+TTuhXP2oTwDEezyyJkxQ+21qY2elMZqlT56TtVid+5IpE8WyjVYAsfYDd/xM+/hqdci8H5s3erodd2ggX212+Z1w1gZO9kRP3IMPuJQvwlq0slu3ZJQ1IsvJpqgn8hXh5O9enEm3uavaMOPss3wvFERESHJ3Am7WbjZTqEIPyltkkUL4+LIIUMSaQxCNn7eeJNqdYmw/r7URefsN1bb961Zs4a+vr784osvFCoULlRqadd4oFo3U0V7SJK3btEWkN5eyH6j4PNy7Z47p1qZg/37aY2K5S+WtpJaUrCC9C5t0kS1skRsTSN1MpeL1FItxS2RIbG86CXRx2P566f4/x8cHMzs2bMTAMeOHfto/8exa4yApGOty9zGbYG1aq7/vtV+X89K974Sz/T9ODfM4VUf7vUpv/nGRIbEPc406WHX2B4/s0MHRbU6SWEYvFS4ht0oewkbWLGiJD+YhrAwhvjlsEfzSuAYc+Uiv/jCPE0jjYOH7KmqR1GS2RDEdOnIt982SQooyetTf9BGT4qxcaMs012hguM9q1WiAZMm0WjThjGZHJPkOHgz/LqHex1v3CiuC+dJb6VK0nmhQIFEk+ETAeU9py0qStrTtmjhuiDOfbYtz73rOY3uNP/0k6x7kYzG7d3nqNPojj//lNYzzsbZCjPE7hNgGDSavMLb/vm4x/9F3jrpGh49f/68ImEJ6NWL31ebyb+eG8DwYxdVq0nMlSvktWv89NW9XN98IkPW7hTD3SxdN5yY9d4Obmv+Oa9NmCsuwS+/VC3JBZuNXPn5Pu6q/C7PfrVEtRy3GAa5c/JWHspai+cXp3x78p49exIAS5Ys+VjppbuGLOEJn9I8v80kRa8Jsdm4vumXPJCmAo/uNuFCXCSNyCiuLdiZa9I1445tJinKTUDE2evc4/sCpwUO5urV9/+8Ci4s289LyMsPc87nwoXmqW92Zku/xbyJrGyTfwe//56makZDkjQMrn7hY55BYdZ85jInTTLH+pDOhAQFPbJtYCFJmJTQ0FAEBgYiJCQEGTNmTN2DXbwIDBgALFokrxs2BOrUATZtAv79FwgNTfQr9PVFcLGqyLJ0NvDMM6mrjwT+/hv49FNg8+YH+x0fH8QWLoHw4hWRZfmPqasPEI1//QUsWybn7dixpD+bIwdQrBiQMSPuGhnh3bA+MvTpmvoa78fOncDUqcDChUBsrLxnseD6mx8hZ78OsDxbRq2+hJw/D7z2GrB3Ly6tOIj8jUymL54zZ8Aff0JIn5HIlNmS5MdCQ0Mxe/Zs9OrVCxZL0p9LFe7ehTV9JqRJ49nDajSeZv/+/ahUqRIMw8Dff/+N+vXrP9b/Z401kCatVwqpSx3uBFmRJYd5b+6QECAuPAbZ8vqqlpIkJw7GIP8zPvBPb97vesuaCFSpEwAfH9VK3GOzAat/C0PDVzPA21u1GvdcvAicPxCCmk0D4enH8IPwOLaBNnqiooDx44EvvpCfkyN9euDFF4GaNYFatYDnnwf8/FJHVzwksGKFGDs7diT9ubRpZfJbujRQqpT8W6QIlM7ggoLEYNy0Sbb9++XvAYDixYHDh2HakSkoCPjhB2D6dODyZeD334GWLVWrck9MDNC3LzB0KJAnj2o1SRMbK9dpEthsNtSoUQPbt29Hv379MH78eM8bPhrNU0D37t0xffp0vPbaa1gU7+jTaDSaJwBt9DwKJLBkCdCvH3DhgvvPZMniMHBq1QLKl/e8EXH8OPDnn8DZs8C5c44tPgrhzC+/AK+/7ll9D8Pdu8DWrQ4jqEMHoHt31aqSx2qVyNWFC0CfPqrVJI9hAF7m9cA9CD/88AO6desGABg+fDhGjx6tWJFG89/DMAzMmjULDRs2RP78+VXL0Wg0mgdGGz0Py+HDQO/ewPr1yX9uwgQxisyGYQDXronx42wM3bkD/PgjkDmzaoUPhtWqNhKlMSVTpkxBr169AACff/45Bg8erFiRRqPRaDQaM6CNnochLAxYsEAMhNBQeR0a6n6LiADmzweaNk2ZY2s0mgdi3Lhx+OijjwAAEydOxIcffqhWkEbzH+DPP/9EnTp1EBAQoFqKRqPRPBLa6ElN/gMpQxrNk8ioUaMwcuRIAJL21qVLF7WCNJonmIMHD6JChQrImzcv9u3bh6xZs6qWpNFoNA/N49gGOrfofmiDR6NRwogRIxAVFYU5c+agWrVqquVoNE8sJPHBBx/AMAxUrVpVGzwajeapREd6NBqNaSGJoKAg5MyZU7UUjeaJZe7cuejYsSP8/f1x/Phx3bxAo9E8sTyObaDDGBqNxrRYLBYXg2fjxo1YtmyZQkUazZNFSEgI+vfvD0A6ImqDR6PRPK2kqtGzadMmNGvWDHny5IHFYsEff/yRmofTaDT/Yfbv34/GjRvjtddew6pVq1TL0WieCEaOHIkbN26gePHi6Nu3r2o5Go1Go4xUNXoiIiJQrlw5TJs2LTUPo9FongKeffZZNG3aFLGxsWjVqhU2bNigWpJGY2oOHTqEKVOmAJBW8GmTWRxYo9Fo/uukaiODxo0bo3Hjxql5CI1G85SQJk0azJs3DzExMVi+fDmaNWuGNWvWoHr16qqlaTSmJHPmzGjVqhVI4uWXX1YtR6PRaJRiqpqemJgYhIaGumwajUYTT9q0abFo0SI0aNAAERERaNy4MXbv3q1alkZjSvLly4dff/0V8+fPVy1Fo9FolGMqo2fMmDEIDAy0b7rgUqPRJMTPzw9//PEHatWqhdDQULz88ss4deqUalkajWkwDMPltU5r02g0piMqCli50qOHNJXRM3jwYISEhNi3S5cuqZak0WhMiL+/P/78809Uq1YN9evXR8GCBVVL0mhMQ//+/dGuXTtcvXpVtRSNRqNxJSYGmDYNKFLE44c21eKkvr6+8PX1VS1Do9E8AWTIkAGrV6+Gv78/0qQx1VCm0Sjj0KFDmDx5Mmw2Gzp16oQ8efKolqTRaDzF7t1AiRJAhgyqlSTGagV++gkYPRq4cAGoXx/wcN2/qSI9Go1G8zBkzJjRbvAYhoGPP/4YFy9eVKxKo1EDSfTo0QM2mw2tWrVCo0aNVEvSaDSe4NQp4H//A8aONZ/BYxjAggVA6dJAly5i8FgswIQJ8q8HSVWjJzw8HPv378f+/fsBAOfOncP+/fv1pESj0aQ4I0eOxOjRo1G3bl2d1qN5Kpk/fz42bdqEdOnSYeLEiarlaDRPPhERwMmTqlUkzbVrwPvvA6VKAUuWAJ9+qlqRAxL44w+gXDmgXTsxzOJ5+21538OkqtGze/duVKhQARUqVAAA9O3bFxUqVMCIESNS87AajeYp5J133kHhwoVx5swZ1KtXD0FBQaolaTQeIzQ0FP379wcADB06VNe5aTSPg2EAP/4IVKhgvsgJAISEAMOGAUWLAt9+C9hsQLduktpmBjZuBKpWBVq1Ag4fdt3n7w988okSWalq9NSuXRskE21z5sxJzcNqNJqnkHz58mH9+vXIly8fjh8/jgYNGuDOnTuqZWk0HmHUqFG4fv06ihYtajd+NBpTcuaMagXJs2EDULky8NZbQMuWQO7cqhU5iIkBJk6UJgCffQZERsr7/v6AmQIKFSsCPXu6j+Z89BGgqNZQ1/RoNJr/DIUKFcL69euRK1cuHDx4EA0bNkRISIhqWRpNqhIVFYXffvsNADB58mTdEEhjTq5dAzp2BJYtU63EPSdPipFTty6wb59EeAYOVK1KsNmkCUDx4kDfvsDt2677+/Y1l3GWMaNEnS5ccH0/Tx6gXz81mqCNHo1G8x+jWLFiWLt2LbJly4bdu3ejRYsWIKlalkaTaqRLlw6HDx/GrFmz0NjD3ZA0JiAsTGpPzEpsrBStFy8OrFoFvPOOakWu3L4N9O4NlCkDLF3qeL9PHyBbNnW6nAkJAYKDgXz5Eu/LmhUYMMDzmpJjyxbpznb3rhhAJUvK+599BgQEKJOljR6NRvOfo0yZMlizZg1y5MiBXr16weLhDjEajafJkCED3n77bdUyNJ5m2TLg9dclvcmMrFkDlC0rk/LwcPHyK5z0uhAbK6liRYsCkydLS+V4MmcWo8csZMkihlmDBon3DR8uhoVZ2LABaNhQjPHMmYG1a4EPPgDKlwc6dVIqTS9uodFo/pNUqFABZ86cQfr06VVL0WhShbCwMCxbtgxvvPEGvLy0DzNFCQkBAgNVq0iaq1elZmLJEmDWLI+3/r0v589LytXvvzvey5xZJr9mYdYsYOpUiUYkZMAAIFMmTytKGhIYOVLWuAEkTezqVaBwYeC995RKc2H1akkRjI6WKNnff4uxkzu3tKxWPE7pUVKj0fxncTZ4Lly4gN69eyMuLk6hIo0m5Rg9ejQ6dOiAt956S7WU/w5hYdIF6+hR1UrcYxjA9OmOFsW5ckk7YLMQFQWMGiX6nA0eAPjwQ3N1QnvvPWD/fkfqVTzZs4tBaRYSGjy1awN79gC+vtKi2iw1fMuWAc2bi8GTM6d0cCtfXvblyye1UorRkR6NRvOfJy4uDi+//DJOnjyJoKAgzJ07F97e3qplaTSPzNGjRzFp0iQAwBtvvKFWzIMQFQWkS6daRfJs3gy8+SaQPj3w/feq1STmyBGph9m61fFez57mmfSePw80bgwcP554X8aMQK9eHpeULBERwCuvOPSmTSspb4MHyzVgBkjg448dLZ7r1AGWL5cUwaFDgbZt1eqL57ffgDfekBTBvHmB9eulhstk6EiPRqP5z+Pj44OvvvoKPj4++OWXX9C1a1cYhqFalkbzSJBEjx49YLVa0aJFC/M3L9i2DRgzRrWKpImJkS5dL70EnDsnEQAzpYtFR0vdRoUKrgaPv7+5UpsKFZIIxBdfJN7Xs6e50sWioyUNa/Nmef3550DnzpI2ZpZzSkobancGDyDr9JghrXXePKkrs1qBggWBTZtMafAA2ujRaDRPCa+88goWLFgAb29vzJkzBz169NBd3TRPJIsWLcKGDRvg5+dnj/aYEhKYMgWoVQt48UXVatyzf7+syTJ+vOgNCAA6dFCtysGGDdII4NNPgYSpuZ07S4G7mbhxA/j6a9f3AgIktc0sxMYCr74qBfaAGJSDB0ua4LBh5ohIxhs8n34qr+vUAf7807UJhBkM81mzpA25YcjaQZs2Ac88o1pVkmijR6PRPDW0adMGP/74IywWC6ZPn45+/fppw0fzRBEWFoa+ffsCAIb064dChQqpFZQU4eEyiezVSzz8Jsjnd8FqlehTlSquK8a3a2eeTlikRHPeflsK1p3x8jKXIQGIwfPyy7Iej6+vI0Xw/ffN0/o5Lk5SwlaskNf9+0sNEiCGeZcu6rTFQ4ohFm/w1K0rBo/ZOvRNny7ni5S6qE2bgAIFVKtKlie2puf6dSAoSIxLw5BzHv+zu61UKan3U8mVKxJR9fOT8cDXV35Ok8YcBrs7Ll8Wx0JgoDmiqPeDlOsiRw7zntOEWK3ieDLbeJYcMTGAj8+TcU3EExcnmtu3b4/o6Gh07doVEydORMGCBdG7d2/V8txitcr48CRhswFPWrmUXbNhmP6i/mTkSFy9ehVFfH0xYNgw1XLcc/w40Lo1cOyYvG7TBnHwgY9aVQ5On5bWudu2Jd737rv2H+Of18qwWICqVeXn+CL2eFq1Es+6E6TYmkp6Bdy9K22KT5+We+iXXyR97Lffkl2MMjJSxjmPyyfu8wABAABJREFU2Jk2m3zv8Q0WevQAxo1zTBa8vKSuJxmiomRZnyxZUumZTUq06fPP5XXdupLS9hAHs9nkGe28RUdLAMYnpW7CiROlOx8APPecRM1y5Ej2V0hZvsdqlVOd3GaxyLOvdOmUncs9YY9TB/7+wLffiqF5P+rUAVauTH1ND8J77zkiqvFYLK5GkK8vUKyYjBWqU2CDgiQKfOGC3ORZssg6WAm3LFmk4Unz5il4Uz0CFgvw77/y3MqVS25y561IEUk7NkP0Oh5vb1mva+VKoFo1ecZVrSqLGZt1/mWxiLMsb15J5a1a1fxG5rVr8iwZNgzo0qULoqKiMGPGDLQ1SyGoGxYvBvLnB6pXV63kwfn2W3N1pb0fu3aJrVM172XJ7zdzU4CICDTfvAV/A/i0RQv4KZ2RJ8GiReL9DQ+3v7UkbVtcmGqSZU+CgmS9kwMHEu+rXBlB+Sthybdy7w0fLpl5Sjl7FmjWTGat2bPLBHP9eqB/f9y5A+zcCezYIdutW9LQzeNGT1SUPPzjz+kPP4jBA8D48WdcN3LgzGb5UxJu1aoBc+d6QKNhAF27ijEGyM9ff21/cB04IJmOt2+7bnfuuL6Ojpass+HDU0FjQoOnXj3piJaEwTNtGvDllw6jJt7AcV5uCBCn9fTpMqdIEcaMAYYMkZ8rVpS1mLJmve+vWSzi/H/jDflTk6NECbmMUnxeQRMTEhJCAAwJCUnyM//8QxYrRsopTLxlyEAePOhB0ffBZiMnTiR9fZPWnC8fefq0aqUO7twhmzVLWi9ApklDzpunWqmDrVvJbNmS1psnD1mjBjllimqlgmGQH37oqjEwkGzQgBw2jPzzT/LmTdUqXbl0icyeXbQWKED270/u3Cl/i1np14/09ibffZe8coWMjo5WLSlZjh0j/fzIZctUK3lwnnmG3Lfv3gszXwwUeXXrkssXhJHly5O//qpaUtLcusW4ylVJgDaA/Ptv1YpciYkhe/dONNhe987NbJmtDA5WLTAB69eTFouL1gmlfqCXl7wcO1a1QJK3b5MlSpAAjXTpeHTODi7us5knsldn8eKup7pgQfL8eQUaY2NdJwjjx/P0aXmrVCkZv5J6DvfqRVqtHtBoGOR77zkO3KFDogNfv05WqJD8PCdLFnLVqlTUOHiw42D16pEREff9lQ8+SF5znTrkxYspqHHECMd/XrUqH/bGjowk27VLWq+3NzlkCBkVlfT/8SC2QVI88UYPKSfxo4/kZCV1IkuXJgcMIDdulHtUNYcOkWXLutcaEED+73/y/L3PNe8xbDZyzBjaHwgJtzZtzDcpP306eYO4Qwe5dsyCYZA9eiQ/gBUvLoaFWVi/PvE1Ubiw3I9795pvznv7Npkpk+hMl44cNMgxZs+aNYvTp09Xqi8hhkHmzi1j26xZqtXcH8OQ89q48b03FixQqud+rFlDesHKc2Wby0VhppvLmYsXGVG0pP0ms3r7mOfhQJKXL5PVq7sdtCahF7/8UrXABFy8SFv2HCTAm755GAdv3kVG+iOcANmqlQnGruhosmbNe0auha/5/H7vlBosh32JHKVnzijQaLORHTs6hAwaZN+1YgWZMaP755jFIs5fj5DQo/jaa2RcnNs/ZfFiceC601ylCnnhQipqHDTIcbD69ZO9vw2DPHBAHKL3bOJEW9q05IQJ8nelmMaBAx0HqFmTDA19oF+9cIH85hvylVfk+ZDU/KZCBSeHWTI89UZPPLt3k+XKuZ5EdxdwYKBc93PmkDdupOqfkCzR0WKIJXA2uWz+/qJ10SIyPFyd1njWrydz5HCv1ceHfPVVcuVKD3lvHoBbt8gXX3Svt2VLcssW1QpdMQyJQrjTW6AAuW2baoWJGTs26eu3aFHyk0/cPmOUkVBv5szkBx9sJQAC4OzZs1VLdKF9e4fWzz83wWQsGUJCHFo3rQwjc+Uyh5fJDTYbWbEiOR79HKJVPhCS4sgR3s6Rh0UAfgwwCmBMtZqqVbly6hQ5bx6DXk7swm2VayvNFFDdtj6SJwIrkwAj4ccK2MN5eINT8IHdsfQIc6mUxWYj33jDfg6jx05i587ux9g8eeT0exzDcI3sde1qH5xu3SI//dSRCeC8+fmRS5Z4UKNz9KR580Tj0blz5McfS6QsqedY9+5MvWvYncHjxhtrGGIQDBnCRFG+hNtzz4lRlKIae/VyHKBevWQnpHFxkoU1cCD57LPuNTrPzX19yS++ePB5gjZ6nIiNJT/7TKxcgNyxg1y6lHznHfGGuPM4VKlCjholRlOKWcUPwYYNZP78jont2LFk5cqJtaZLJxGVX34hw8I8rzOey5ddDYlChRIbl3nzylhz8qQ6nfFERZGvv570AFGtGvnbb+Yx1Gw2skuXxDq9vMhu3cirV1UrdMUwxDPq7t7q0iUFQ+spRGSku7HAYEBALwKgl5cXF5goQjFzpqvW75suVTJOPQgnTjh0Ts//mfyQau7Rx2PhQrIbvnMdYM1mUW7dyriMmTngnkH+DMBIQFJMTMThw2SbZjHcjYouF+s5FOTPP5nonBoGbZ3etOtrj58JkOWxl8/iIP395W9RzpAhdo1Gz178+WeZGyQcY3PlIo8fV6Tx008dQlq3Jq1Wnj4t6Vb+/u6ftdmzk9u3e1Dj6NGOgzdsaLdcIiMlHb9evcQanQ0Kf/9UTts3DEmLSMLgMQxyzx6ZSxUtmlhrlizyjO3WzfFe377Jp4Y9NDabqye2cWO3RllQEPnTTzLXis+mcN7SppV0/UmTxEivX1/er1nz4a9hbfS44ehRibbPn+94Lz4k+PnnMml3l6qVKxf59tsyCfaktyc4WPIc/f0dz92zZ8lx48jnn3fvLWndWv6+B4wwpiixsXJzATI+BwWRX33l3qqvWZOcPVutoWazuY4tEydK7YGzzmeekRofM0TUbDayUydXAyL+54AAcuRIteczISEh7r1PnTqZwGvqhtmz3T2UDQLdCIDe3t78/fffVcskKZ5IZ52n8Qy7tbppKu95PP/8IxoDEcw7uPfkM2F4MjaW7JTnb8bBKSe6RAnVslxZuZJW33Q8CjDNPaNnebzWjRtVq7MTn8Y0FgPs57IT5vAGsnN2jgHmMtAnT7ZrnObbJ9EYYApfx/ff2wUFvdiSlStYkzQgjhxRpHH6dIeQevW4bWM0W7d2fU75+Mhc6pVXHLeXR1Pwxo1ziKldm0Z4BHfulNKewEDXc5k+vRgPW7ZImn683lQ1gBMaPA0akJGRNAxxwH/0EVmkSOLvPWtWMXLWrHEErYYMEUfz2rUprNFqJd9803HwFi3shmO8QTZ6tJT2uMtYyptXtP7xR+L5SpEikvL2KOODNnqSwGZLvrDv1i0xGtq3F4s54Rfm4yNFrl9+KZaoJ5yACxZI44CEnD8v+ZlVq7o3gFq2FI+EpyeYv/4qEe54DIPctYt8//2kB5Z//1XnUP32W6mPCAmR+3nxYvKFF1x1Zs4sg8jVq5SLKEXdJg+O1erIcJg0SZy7zh60XLnk+WgPCRuGUk/14cMOfc6Rv8KFpbGEmbBaJQUAIEvgGHviawb6RHDTJhs7duxIAEybNi3/+usv1VJJyjkESAtstMHCqejO+vXVODySY9Ei0fkJhtovAOui31TLSsQvHx9lMAJdb/yXX1Yty4Wf51iZwyuIhVGYANg0/nym9UvFXJtHI2r53/bz+D26EiB7YyK3Tt2jWpqDDRto3Cv8XYu69Eacyzjl/BxTxqpV9uLkE4HPMx0inOft3LVLmjNlzaqwQdPChfYZ7u2iz7NeldCkn58kO3cWx+ft2x7U6GTcxjxfnZM/D2OZMonnTrVqSZmDs6Pz4EGpqU7VsTVBfYzRoAF3bYrkgAGOsd55y5ZNspX+/tt9ClhS88bHIjaWbNvWIeJ//2Po7VguWSLzuNy5E+v08pJgw2efSSpeUtORuLjHSwDQRk8KYLWKlT9kiDTxcedZeeYZsmdPGZcUzYNJysXy1VeJJ+uA5EY2b07+/LPnDKCkmgFERopRGR/GjJ+0AQaLF5ccThWpWitXJh4gtmxhAk+VwVZplvFc1ko8tinI8yLvERcnNV3xEcsrVyR12hGlNPjqM3t4+rVBNFq2VJOf6cSCBaJr3DgpsvTyku880CuU4/tdY9yxU+JtMAErVjiuy4V4jTeQnV/lGMMrx27ztddeIwD6+fnxpAlyNOPTHQMRLBNfeLEMDrFSJXOVoUyeTGbHDYYhwH5yt7WbrFqWCxEXbvKc9zOJB89u3VRLs/PllyIpHWYRAH0B/o5nGQk/xtaqq1qeKzdvMjSDzICOo7i9GUCjujHmSRe8cIGxmaSd5zkUZFbcZIUKMsFNk0YyP5SXnu3fT1tAehLgGRRmDlwnQJYsKd0b409lqVIPVuydKqxaRcPHhwR4yqcks+Kmi3Nr8uTEHv3ffvOwje4UKTuVqTKzeN9NFH0YMiTp1PvIyFS+bA1DirnvCTpe6GWWKBCZaDjKkUOiUuvWKaiJjYlxyVk/UrkjG9SJ472vPpGR+8Yb5Ny5nnu0a6MnFbh8We6dFi0knSjhF+3vL8bFjGkxKW9hPwQXL0qqlrvGOWnTStvIuT9EKXcMnjtHTu1+hKvStaSkEYlGb2+yWRMr/5gfYYrn46mTBqc1XcndFimq2opqBMhX693hP6vUtHqLjU1QF2MYPD1/B38tPIBn4HALTSg5g/v2qM8l+bCnlUff+YrMlYtWX9dWLef9SvDcHk+6/JLGMMR76u9Pzhp9yT5RD/EKZNCk2WzWrBmHDRtGwwQX5rx5cgoL4pz9XAZXfZmhIYYp7pt4hgwhv0Qfl+98WoaPlDqJErKi9Q+c5tOLe7xcCyeNTz5VLc1OTAx56lQYs3oHEACHwcJSOMKpeT+XziBmwTB4vKR0vouBD98ut4ddu8op3WOSII81LJJX80itUQTSsTz2ceBAOcekjAGq6yQjT15iSMa8JMA7yMQSOMYcOSQzIeGE9+xZNRqD/9rGGB8J5V9AfubDRQKSffLrr+aoiQ368ifaIJ7LfSjHzLhN3Mvaee018q+/FOs0DF5/02HwrMLL9IPD4MmZU5ombNigUGdUFG9We8UlcusF1xTLsmWlzujff9U0Kbpz9IQ2elKT6GjJn+zdW4rJvBHHV7Ccf6A596EcT55QOOtYvdr+4+XL5Ndfy/ozz+AMB+FzHsBzXOjT3j7AKyEqihw+nPTxoVG/AdetI3u2usR30/zAhXiNt5GZ0woqXhTBMCR2nCB8dt0rFy9AukxMa/KnOn02m+SI9e3rvqL13vbz0GPqNN4jNpa8e5fk/v1yMTrpC0I2rlpsgqKpe+zcKQ9Dktz5mrR1u4Zc3LIhhnEmajl39aqcwrYl9vKQ5VmGI4AnB3xvuj7xXd62cWLtPzgyxzTGwIe36v2PYa+9ZSqZBw/KNbq84Ae0wcLzeeSej5gxV7U0F1atXEVfeLEwwFm5PyRADuoXq27W64a4oDs8kUEMimmFxjE0VKLm7dqpVuYg5uBxXk8jBkX3zPO5fr3rfjNcm2F/rGU4/BmNtGyQdiOHDzdf6ur1EdPsY3hJHGPLlmpT1d1xtr5U9B9BKWZDEMuVkzmRGb5jkmRkJM/nEw/1X2hIP0QyVy5p/rBxozkMR168yCA/6fQzBR/QApvdyf/dd7I+n2rurvxLGz0pRnJ38IUL5IgRjM2Z13WyqaJgISyMfOstafMWz9WrUvyRoPAnKk2AujUdNmxwrXAvXpzukmuvlFSYsrFhg309hOS2m30+U6dx2zZJ6i1UKEl9Vos3Y/eYaCVeUu6nn34SFxbALZV6qlaUCPsgHhPD09U7cNuPJxJ9JiIigm3btuWBFO0D+nCUL08e3x7MFb/H8E6QeQwyZ/bska/89m3SiI4RN+Dly6plJUnYvlOMuXSDd7oPZcjKzarlJOL0qdOc0XEMQ6+EskWLVFwY8TGICI7h4gbTGRYiUWabTdJwzcTRDdc5s8q3nq0reUgWDtjFb+osMvPtwqlVf+KYNrtM0ZXVHTFRNn6ddRSHvH2Ve/eqVuOeg/+GcGrAQPZ5P4qbNpnE0EnA3BEn+FXm0ezV01BezuGOx7ENLCQJkxIaGorAwECEhIQgY8aMqX/A8HBg2DBg0iTHe3FxwJ9/AjNmAKtWyfQynnLlYHR9B14d2wOBgamvL57du4F27YBTp4CXXwZefRVYsADYuNFVX0AA0LIljNffgFejlwEfH89pvHMHGDAAmDUr6c+kSQO8+CLQsCHYqDEsFcp7TB4AYMsWYMQIYP365D83cCD4Yg1YqjwP5MrlGW1JQQJnzgBr1gB//y3aQ0Nl39ixwMCBavUlRUgIMHIk0KQJ0KCBajVJExMD+PomevvDDz/E119/jezZs2PTpk0oWbKkx6WdOAGUKOHxwz4dGIZ89+nSqVaSJJcvA5kyAenTq1aieVohAYtFtYrkMbvGeC+ll5dqJUljs4k+s57Hx7ENtNETz6lTQOvW8k0fOACcPQv88AMwezZw/brjc+nTA2+8AXTrBlSu7NmrwjCAL78Ehg4VY8wdadMCr7wCtG0LNG0K+Pt7Th8gd/OCBUCfPkBQkPvPtGgBvP02UKcO4Alj1h3XrwOLFwOXLjm2y5dlS3hu33gDmDfPnCOA1Qrs3CkG0O7dwPz5QIYMqlUljc0GeHurVvHQ3L17F/Xq1cPevXuRJ08ebNq0CUWKFFEtS/MfZO3atfD390f16tVVS9FoNBrToY2ex+XPP4EOHcQbXagQULQosHat62eef14MnbZt1Uwqr10DOnVKrAuQSWT9+qKtVSvPRp2cOXcOeP99YPXq5D9XogTw779Atmye0fUwGIYYa87G0KVLQOfOQOnSqtVpFHL79m3Url0bhw8fRoECBbBp0yYULFhQtSzNf4iIiAiUKlUKly5dwpIlS9CqVSvVkjQajcZUaKPnUTEMYPRoYNQo9/szZhRjqFs3oHz5lD/+g/LnnxIZuXXL/f6aNSX1ztNRHWfi4iRyEhIiP99vq1hRDAmN5gnixo0bqFWrFk6ePIkiRYpg06ZNyJMnj2pZmv8IQ4YMwZgxY1CoUCEcPXoU6UycbqfRaDQq0EbPoxAcLAbNypWJ9+XJA3z2GfDaa1IXo4roaKnRmDLl/p9t0gRYssRtPYJGo0k5Ll++jFq1auHcuXOoVq0atm7dCosZUx81TxQnTpzAc889h7i4OCxduhTNmzdXLUmj0WhMx+PYBmlSSZO5OXRI0sDOnHG//+pVMYpUGjwAsGOHRJh++UW0BARINCf+5/gtXTpzV8VpNP8h8uXLh/Xr16NVq1aYOnWqNng0jw1J9OrVC3FxcWjSpAmaNWumWpJGo9H853j6Ij2//AJ06QJERjre8/YGsmcHcuRw3Xr2BJ55JmWOq9Fo/lMYhgEv7WzQpABLlixBmzZt4OvriyNHjugmGRqNRpMEOtLzoFy8CNy+DcyZ42rcZM6sIyUajeahcDZ4duzYgU8++QS//PIL0uuewpqHIDIyEh9++CEAYODAgdrg0Wg0mlTi6Yv0aDQaTQoSGxuLYsWK4eLFi6hTpw5WrFihC9A1D4xhGPj555/xzTffYMOGDfBX2ZBGo9FoTI5uZKDRaDQK2blzJ+rXr4+wsDA0atQIf/zxB3x1UxHNQ0BS14dpNBrNfXgc20DndGk0Gs1jUqVKFaxYsQL+/v5YtWoV2rZti7ikFhDWaCBGTqRTbak2eDQajSZ10UaPRqPRpAA1a9bE0qVL4evriz/++AOdOnWCzWZTLUtjUn7//XcUK1YMv/76q2opGo1G81SgjR6NRqNJIerXr4/FixfDx8cHv/zyC77++mvVkjQmJDIyEn369MHVq1dx4MAB1XI0Go3mqUAbPRqNRpOCvPLKK/jll1/QokULdO/eXbUcjQn5/PPPcfHiRRQoUABDhgxRLUej0WieCnQjA41Go0kFnAvT44dZXbehOXXqFJ599lnExsZiyZIlaNWqlWpJGo1G88SgGxloNBqNyXA2eAYNGoThw4crVqRRDUn06tULsbGxaNiwIVq2bKlakkaj0Tw1PF2Lk2o0Go2H+ffffzFu3DgAQLp06TB06FDFijSqWLp0KVatWoW0adNiypQpOvKn0WieTiIigFOngPLlPXpYHenRaDSaVKRmzZqYMGECAGDYsGH46quvFCvSqGLLli0AgP79+6NYsWKK1Wg0Go2HuXEDGDECKFECULAQs470aDQaTSrTr18/REZGYsSIEejXrx/SpUuH999/X7UsjYcZP348mjZtisqVK6uWotFo/mscPQqULAl4mTCeceIE8NVXwI8/AjExwOjRQPHiHpdhwjOj0Wg0/z2GDRuGwYMHAwC6d++OOXPmqBWkUcJLL72EgIAA1TI0Gs1/ARJYvx54+WXg11/NZfCQwL//Ai1aiDH2/fdi8JQqBXz0kRJJJjo7Go1G89/FYrHgs88+Q+/evQEA3bp1w7lz5xSr0qQ2JPHFF1/g0qVLqqVoNJqHISICuHVLtQr32GzAkiVA1apAvXrAuXPKDIlE2GzA4sXACy8ANWsCy5a57v/+eyBtWiXSdHqbRqPReAiLxYKJEyciLi4O1apVQ+HChVVL0qQyy5Ytw+DBgzFu3DhcuHABGTJkUC1Jo9EkR2goMG0asGIFsG6dajWuxMQAc+cC48YBJ0863v/mG8DPT52ueKKigE6dgN9+c7+/WzegRg3PanJCGz0ajUbjQSwWC6ZNm+bynmEY8DJTWoImRYiKisKHH34IAHjvvfe0waPR3L4NZM2qWoV77twBJk8Gvv4auHsX+OsvwNdXtSohLEwiJF99BVy96rrv9deBBg3U6EpIunTAokXA778Dr74qKW7x5MwJjB2rTht0eptGo9Eo5dq1a6hSpQrWmc2jqHlsvvjiC5w/fx758+fXrco1Tzd79gBNmwIHDqhWkpigIGDQIKBgQWDUKDF4WrQAGjVSrUyYNw8oUADo3z+xwZMhgxhCZuLIEeDDD10NHgCYNAnInFmFIjva6NFoNBqFjBkzBnv27EHz5s3x77//qpajSSHOnDmDsfe8mhMnTtTNCzSpQ0RE4smlmdi/XwyIypWBNGmAunVVK3Jw5YpMzgsVkghEeLi87+cHTJyoUpkr7dpJXUypUon3ffopkCeP5zUlxfr1kr526RLg4wN07SrvN2woESnFaKNHo9FoFDJ+/Hg0bNgQkZGRaNKkCXbt2qVakiYF6N27N2JiYtCgQQO0bt1atRzNf43YWGDKFGDYMMCMi9weOgS0aQNUqCATdh8fYPx41aqEq1eB998HnnlGUtmiolz3DxoEmK3ects24Ngx1/cqVAC6d1ejxx1z50p0LCQECAwEVq2S6E5gIDB9uimuU230aDQajUJ8fX2xZMkS1K5dG2FhYWjYsCEOmDEFRPPArFy5EitWrICPjw+mTJkCiwke9pqHIDZWtYKkMQxg/nzx+vfrB/TsqVqRK0ePike/bFnpLhZPr16AWRbkzZEDaN5cog8JKVQIGDjQ45KSxDAkGhXfma1sWdFvsQDffivRM9WQEnHq2BGIiwPy55dW1XXrAgEBwNKlpjEitdGj0Wg0ivH398fy5cvxwgsvIDg4GPXr18fRo0dVy9I8Ii+99BIGDRqEQYMGoUSJEqrlaB6Us2dl4hYSolpJYkhg9WqgUiWgfXvRGh+tMAPHj0sa1rPPSiG7M9mySUTKLKRJI+2Ug4MT75s0SYrxzUB0NNC2rTRXAIA6dYBNm2RNnnffBapUUasPECOnWzdg+HB5Xb48sH27XAfxvPSSEmnu0EaPRqPRmID06dPjr7/+QqVKlXDr1i1069YNNHOuviZJAgICMGbMGIwePVq1FHNgGKoVJE94ODBkiERP8uQBsmdXrciVXbtkLZZGjaRGBpACdrMYEiSwcydw44b7/Z98AmTK5FFJyXLtmkzE42soixaVfxs1kgiQGbh7V/T8+qu8fv116SYXGChG7+efK5UHQFp7N2sGzJwprxs1EqPMTDVGCdBGj0aj0ZiEwMBArF69Gq1bt8Yvv/yi06KeMEJCQmCYfYLvSUjgl18cEzezYRjAzz8DxYsDY8ZIBKB/f9WqHJw8Cbz2mnj0N2xw3TdwoHmMM4tF1mYZOzaxcfPcc45idjNw5owU2h88KK8nTABmz5bFMidPNkXdCS5flkU9//lHXvfpIymN8e2zGzVS3gUNV68CtWpJ9BGQ73jZMjHGTYwJkgE1Go1GE0/WrFmxePFil/esVivSmCF3W5MsHTp0wM2bNzFz5kyUKVMm9Q4UHwE0wwQtKS5fliLrTZvkZ7Oxa5fUmWzf7njvgw/MY0gcOCAGz6lTifflyiUTYTOxeTPwyiuynkxAgHSVA6QLmlnGrgMHpI7nxg3A21siFG++CVitUpNihpqjI0fEqIm/ZyZMkNotM3H4MNCkiXRoA4DPPgMGDzb3eHSPpybSo7NENBrNk8iiRYtQoUIF3EgqdURjCpYvX44///wTe/fuhbe3d+odKDxcUlvMOsEwDFlEsUwZYPlyoEsXIH161aocXL8OvP22RE+cDR5/f3NFecqVA06ckCL2hIwcKYaFWVizRoyJsDCJ9KxbJ/qbN5e0PDOwebOktN24IS2plywRgwcwT4Rv82aJQl2+LN3u5s83n8Gzbh3w4ouOltQ//yypoQ84Hqmei5vE/H48SODmTeDiReDCBdd/L16UWrC1a8U5YiasVtF97ZqMw87/RkRIpNWskcK4OKkBDA6W1NPgYEnvbNlSosQajebxiY6OxqBBg3Du3Dk0aNAAGzZsQFazrmb+FBMVFYXevXsDAPr06YOSJUumzoGOH5c2wG3bps7//7icPi1FzRs3ymsvL6BHD6WS7BiGLOI4erRMzhPSvbt0xTITs2dLS2VnihcHOndWo8cdf/wh9SaxsRIl+/tvMXhefdUU67IAEOP7f/+TyWDGjPK6Vi2Xj+zcZcHVq5I15rwFBHjIv7BkiTSCiImRid/vv9/XYJw5U8qScueW+W3Cf1Pc1/DTT+LEsFqltuj336W5wkMweLD8qQULynqrBQo4fi5YEMiXz5HFlxpYaOJK2dDQUAQGBiIkJAQZM2Z02XfmjJy8Awcchk1SrFrlvjOhp9mzR+r5zp8X4yYoKGmr99dfZcxQzZo10l79zh2HkRMc7IhcOzN+vHpnCSmLF69eLWNHTIyMxfE/J9waNZKujyqdpjYb8M03Yqj7+ckNH7+5e50lC1Ctmjq9ABAZKc7c9OmlO2W+fPJvgtvUVAQFifPjpZeAvHlVq3kwjh4FIiJOo0WLWrh27RoqVaqEtWvXIpOZioITcOGCPLyeFMLCAGP/QQRmMKTz0CMwatQojBw5Ennz5sXx48eRPjUiG7/+KpPd8HDsnncCld4obp5gj9UqXa+GD3d9GLdujZj5i7Fjh4y3DRooUyhcuwasXCme6aAgx/vp0iHu1Hlcs+XApUviaL90SebFyhpkzZoldRIkwguUwvn6XfHsrH74p+dvOFW2DWJi5FQ7P8+sVrHdPNbQbf58qeWx2WRQXbcOKFEC27YBb74RC5t3WpfnV9q0rs8zX18gZ075OlItq/DHH2WibrPJwVatcnufX74MPP+8zM2cSZPG1QjKlAmoWFE0p9htPm2atB4nxVr5668HGovCw+X6TLh8Tzzp0zuMoGbNJGjo4/MI+kiZvH78sbwuUEDuo0dI4Y2JAapXB/buTfozuXLJM+S99yQYl3CcS842uC80MSEhIQTAkJAQt/uDg8lRo8jAQFK+lcSbxUI+9xzZrh05Zgz555/khQukYXj0T7Fz7hz55pukl1fSmgMDyYEDyYULydOn1WmNZ+tWsmrVpPUCZPHi5OHDpM2mVisp52vWLDJr1uQ1V6tGRkSoViuEh5Pvvpu8XoD09yc3blStVjh2jCxa1FVfhgxk6dLkyy+TXbqQH39M/vADeeCAarXC+PGis1gxsls3cv588soV1aqS5s4dslAhslevo8yWLTsB8IUXXmBYWJhqaUny9tvk+fOqVTw4H7U+SWu2nGRQ0CP9/pkzZ+jn50cAXLhwYQqrIxkbS374of0mOxZQkZMmpfxhHpmDB8nnn3c7YPWqsIl+fmSZMuTt26qFkmFh5N5OExPpnB7Qz+WZbLGQ48YpfPbOnCkiANpKleZrta4zA0K4AS8RMNw+G4oWJbdv96DG776za+Qzz5Bnz7rsnj37/s+ztm3Ja9dSUeOXXzoOVriwTKiS4PZtcsSI5PWmTy/zyKioFNJnGOTgwY4DlCghk8T7EBJCbthATphA1qp1/7nZr78+xrUcG0t27uz4DytUeKiHZliYXJczZpC9epF16pCZMyevuVGj5K/l+9kGyfFEGz3xBAeTI0cmb/y4MyxefJF87z1y2jRy0yaZYHiKw4fJli0fXGudOmT//uSCBeTJk543Lmw2cu5cMl++5LVmykQ2bkx++qnclCqNips3ybfeSlpr5szkq6+S33//QOOMR1i2jMyRI2nNQ4bIGGQWbt8m69a9v3F5/bpqpQ4GDHD/YHj3Xbm/rl5VrdCVeEMtS5b9TJcuEwGwdu3ajDCLxZ6Adu3ImjVJ69oNqqXclxXfXuR5FKAtW45H/j+aNWtGAKxbty6NlJ4lX74sDyqni3VM5rGMjk7Zwzwyly/T1qgJrX7+iW6q3ahIwGDBgvJnmAFj5iy7vq2oxuMozgikYw5cd5nYLlumUOQPPzjOY+nS5PXrPHxYnldZcdPtGNutm0wuPcZXXzkOXrKk/QuOiCBXrSL79SPLlk36mfDMM/K5VMMwyEGDHAcsW9ZlYDcM8sQJcY526UKWKpX8M8xiIbt2TWEDLTaW7NTJ9UF561aijwUHk+vXy3OgbVtx2D3IvDFfPrmU4uIeQ2NIiHgwna2R0FC3H42LI48cIX/5hRw6lGzRQr7nB52Tx//327Y9iKyn3OiJJzhYPMsZMzpOYr16Yiw0bEjmyXP/k543r5z4AQPIn34i9+1jqj5gtm0ja9d21VC9uowj8U4Ud1vGjPJ7/fqR8+aRx497xhCKiJDomn+CZ1zatO51pklDVq5M9u5NLlqk5uG3caOcz/t990WLkt27k7//Tt6963md8dy4QTZrlryx9tZb5IoVZEwMZfBcskSZ3thYcR640+rnR65c6eRlUh22vCfh7beTvxaaNvXwJCIZoqLIAgXite2kxZKBAPj115NVS3NL586kBTYG5Sj9mE/c1OXq/hs84VWCBBhXq+4j/R8hISGsXLky06RJw6NHj6aswPXrGZc1sQdk6dfnUvY4j8H8+fIsaINfE+nsgJ+YLZtMLk3Bb7/ZUyzOpC/LTLjDQficE9DXLrtQIQlcKcPJ4IkuWpqTBl9nhQpJj1PZs5NLl3pQn2GQo0fbBRjly/PguiCOHStzLV/f5MdVHx+ZEEdGpqLGuDixUOIPWqMGo64Fc/NmcuxYsnlzMlu2pDUmnMvUqSPzwBQlLEwmpfEHad6cjIjgnTvk2rWi83//I4sUSf58ZslCNmgghrrzexMmpMA5vnzZ1XLt1o2Mi6NhkJcuyXN97FiyQweyXLmk54DOc4FKlWTuMmyY676GDSWj6EHRRk8C7tyRMGXGjGSVKq77bt2SSfCUKeQ775AvvCApOcl9Wd7e4gn43//ITz4h//hDoqQpZWQYBrl6Ne2D26hR8n5oqESgJk0iO3YUp09yaXEZMkios08ficocO0ZarSmjMSGXL7s6KfbtEwNuwgSyVavkoxUFC4o3eOpU+b3U0uhMdLR8d/GDcqFC5GefieHo45NYY6BXKKtXFyN6yxbPz90MQyJQzsalu+u0XIYzPJ2tKi/V6ajc+zt1qtwr7r7zKqVCufuV4Yxc9Y9akfeIi5PnjDut3d+KSN2H8iPw448kYLAbvmMuLCQwgPXqGUqN86To3p2sjzUkwMNz96mW4xbb7WCeSl/e/qVbP+j1yP+X1WrltgdxTz4Mu3fzTr5nGYs0Lhfn/oAXTJFC7MyO6XsYDhmoriEnCfAqcjFLQDR371at7h6rVtGWRgb6Uyhqj+zkxSXmxLX4ufGjZjimDDNm2L/n076lmR03XMalgABXR+grr3g4gm4YLmHyk1mrsWjWO27H0MKFJXLepYvjvZo1JRKQqkRFyQTk3kGPPNOUtatEJDkh9/GRAEvfvuTixRLJiQ9sFCkic70U99Ndvy6z/3si9lZ5h/9rHXffqEjWrKJt8GCx38+dE2137si8MCCAHD48hRy2Bw+6pPVsa/4533/PYI0aks2TnE6LRaJRrVvL/Om338Tx4TzPW7BAPvvyyzK/eli00ZMEd+7ISU8iGmfHMOQCWr6c/Pxz8o03yGeflShFcl9uAf+b7FFiTYpFL2w2qeN5++2kPxMeTv77L/n112J0PPts8oZQ5XSH2f35nanmYN+5U7Ivpk93fd8wxDD88UcZ/J59NqnIlcHW6VZyaPtzqSMwAadOiWfE19eRJhYWJlGTDz+U3PMO+Inj0c/xsEEYe/l9x8lfeD6d6ORJRz3Vp5+Shw6JQV+qFNkOcxkCsYR+QgcO9vtKZcCHJLlmjWNQTJ+eLFk0jt3wnX0y1CLgb+7cqVZjPJGR4iTIgltsiSWchF7cj7L83G8kz5xRrc4Vq1WcbtWwlTHw4dyM7zPijli5sbGxjDNRRKVvX3I+2pIAZ1edbspgz9bBy/h3RsfkyPh+hmpJbjle9jWXAfP4B1+rluRKRARv+8vk6CaysghOcR/K8WPv0Vy3TrW4e5w5w2ivdCTAi8jHAjhPX19xIsaf2rffTt2Mjvth/fMvu5hDKGM3eNKnl/nI4sVikAFkunTyvPV00Dx4yFi7xvWozfQIdTgJA2WSO326a9lMv34SeZg50zOZKEF1X7drnINOTIPYRIZDs2ZSl7NpU+JoSFyczPUnTEil6yE2lsG5HWknwzGK7mq0smeXjKOhQyWJ4/z5pL/vpUulVibFDOCLFxnlK+lSMfBhO8xNcn6ZK5fMp/r2lRqu3bsfrKRhxoxHM3biCfn9d230pAYxMWLwzpsn6aFNm0qUIgBhHIbRDEEGhiADoy/fTNHjPuzgEBEhocEpUyR0+NxzZGmvY5yHN2iDhcfSVUjVETLeaEz2A7t3MzhYQqLDhpH1alvZMe0v3Avxtv5T+t1U0+dOzvz5boqtrVbpIAFwf7Nh7NvsJL9N9yGDEUgC3Pz2TI9pdCYuTmrWunS590ZoqIT+3IxCp/84pESjM8ePk8WKGmyXaQUN55kFwDAEMDLMA6G9ByR0z0muzPQGI5DOrnFfptpmyMJLxF/35kWLMnXj4sYzaBhkdHQ0W7VqxU6dOtFmkhDAV1+R8/ru5ua2Ux8sQVsRJ5cf557ynbm7UOuH1nnu3DkOHz481euqTs/dxjNpS3CZ76u0wWK+gjOSuz9axGAEsoHfJgJke8zl8pk3VMtyYBhcX3MEbyA7a+c6xjFjpN5zzBhxxE2YYIKs25gYbs3ejIdQhoX8b7BdO0mzdp6U79sn/SJUpQtar0o66J9owvTekaxRQzLdtm1LOhPi++89Gz07v2QPQ5CBE9CXFthYooSk2/7wg2S+3O97DgtLfb3/vj+XsUjDLphBgMyZk2zSROZFv/9OXrz4cNdjagz768vLvKc21tuN72rVJMPt66+lxuhmyk57H4qQmze10eMRYmLIKVNoy+7I3YpL46u46jEBJ0+SHTvScAr/RAbmTNRZxWPExUke4XvvyevoaOn6kiBZNaR0Vc/kuSVFSIjkCzi7MJz0GRYLw97rr04f7xVR7tiRZKJvlH9mpbU9dvbuZexL9dxqvP78K+ZpmXePa9fIZwuFsUeWuYyo3YQ3PjOn198wpDg07GaU/aG4adMment7EwDffffdlC+kfwRMYns9MLY7dyWE/hC0aNGCANihQ4dUUuXAGmPlsF4hDG7wWqof61FZvyTY3nDju2/VX4MJuXKFXD4ryGVy/vnn0s3VLGxYHcOVP91MsjPYrVvqm9jM++o6ly+OuW/2jCoMg/yy13kuW2oonZQnx/nz5Fc9z3LpUikTMMGQnYh1a6yc0lc0nj1rvjH9cWyDJ3adHo9iGMCCBbIGwblz8p6XF/DWW7Iycv78ntfzxx9A69aO986eBT79VBaPstnkvWzZgEGDgPffl9WmPU1UFPDGG8DSpbICdunSsjjctWuOz9SoIQsuNW6sbrGcs2elif3Ro4n3Zc4sPf67dwcKF/a8tngMAxg3Tq5Bq9X9Z9q0kev0kRrxpxDh4bJgWVAQbEG34XXnFiy3bwG3bwO3bkmT/pUrZYE9E3HmjKyh+NdfkJV3VZ7DZAgPT7w2xIIFC9C+fXuQRO/evTFx4kRYTLOAy3+PlStX4pVXXkGaNGlw4MABlC5dOtWPGR4OpIebL99E/PijrHUydKhqJQ+GiW9zjUaTDE/tOj2pjmFIPla5cq4e61atyJTu1POgxMVJalPjxvL6/HmJOToXIGXJQn7xhdr2U7dvSxu6pJJBX3mF3LxZnb54NmxIekGfOnXMEZW4cuX+faHTppW2bj17mtN19ATgyZb1Kc2sWbMIgAA4ePBgU0R8/otERUWxSJEiBMD+/dVGfs3GnTt66NFoNKmPjvQ8LiTwzTfABx843tu+HfjoI2DTJsd7L70EfPEFUK1a6mlJjpgYoF07YMkSWTq4cmXghx/EZQXIUsH9+8vKviojY5cuAY0aJY6ceHkBr78u0aeyZdVoc+b77+U7TypyAkiE5+uvZVlmFZDA2rVAZCQQECARu4AA183fX50+jWn45ptv8MG9MeyTTz7BsGHDFCv67/Hpp59i+PDhyJMnD44fP44MGTKolqTRaDRPFY9jG2ijhwT69AEWL5bJ+rFjwJAhkj4WT7lywJgxMpFXlTYSFSXpbKtWJd6XMaP8DR9+KIaPSg4flvN05UrifXnzAsuWARUrel6XM1arnK+pU+V1mjSiLW9eIF8+xxb/ulw5MS40GpPz5Zdfon///vD398exY8dQoEAB1ZL+M5w/fx6lS5dGVFQU5s+fjzfeeEO1JI1Go3nqeBzb4Ol2D5NiKEyeLIZD586SmGwYsr9wYamTadtWohSqCAuTmpN//nF9P21aYMAAoG9fIEsWNdqc2bwZaN4cuHvX/f6rV4GOHYFFi4AyZTwqzYVjx4B69YA33xSjJkcOtd+vRpNC9OvXD4ZhoFq1atrgSWEGDRqEqKgo1K5dG23btlUtR6PRaDQPydNr9JBAr14Ob39oKDB7tvycI4cUjL/zjhgWKgkOliL/HTsS74uNFQNDRZOChPz+uzQtiIkRA6xECdmKF3f8XKQI4OenWinw3HOyaTT/QQYMGODyOjw8HOlNXAD/pDB27FjYbDaMHDlSN4rQaDSaJ5Cn0+ghpVXTN98k3jdgADBihDm65AQFAS+/DBw44H5/kSLSCe2vv4BWrTyrzZngYIlGrV8vxk3WrOq0aDQaOwcOHEDDhg3x1VdfoV27dqrlPNEULFgQv/76q2oZGo1Go3lEnj6jxzDE4Jk+3f3+JUukdkZVs4J4rlwB6tcHjh8HAgOl8N95e/ZZcxhmgLR17tRJtQqNRpOAuXPn4saNG+jUqRP8/PzQ2rnNveaBuHbtGnLnzq1ahkaj0Wgek6erkYFhSLeub79NvM/XVwrWK1cGqlYF2rcHvL0f/5iPAgn88osYNeXKyTpAOp1Co9E8JIZhoHPnzvjxxx/h4+ODP/74A02aNFEt64nhwoULKF26NFq3bo3vvvsO/mZIJdZoNJqnGN3I4EEwDFmk8/vvZUWycuWASpXEyKlcWQrrzbJSmcUi9TEajUbzGHh5eWHmzJmIjo7GwoUL0bp1a6xYsQL16tVTLe2JoE+fPoiMjMSlS5eQLl061XI0Go1G8xg8PUbP9u1i5OzeLalhvr6qFWk0Gk2q4+3tjZ9//hnR0dFYunQpmjdvjtWrV6NGjRqqpZma1atX4/fff4e3tzemTp2qmxdoNBrNE87T06e3enXpxlapkjZ4NBrNU4WPjw8WLlyIhg0bIjIyEp999plqSaYmJiYGPXv2BAD06tULzz77rGJFGo1Go3lcnh6jR6PRaJ5ifH19sWTJEgwcOFB3IbsPX375JU6dOoVcuXJh5MiRquVoNBqNJgV4uhoZaDQajcaFO3fuIIsZFjc2CRcvXkTJkiURFRWFn3/+GR06dFAtSaPRaDT3eBzbQEd6NBqN5imEJD755BOUKVMGp06dUi3HNJw7dw6BgYGoWbMm2rdvr1qORqPRaFKIp6eRgUaj0WjsREVF4bfffsP169dRr149bNq0CYUKFVItSzkvvfQSTpw4geDgYN28QKPRaP5D6EiPRqPRPIX4+/vj77//RsmSJXHp0iXUq1cPV65cUS3LFGTMmBEFCxZULUOj0Wg0KYg2ejQajeYpJUeOHFi7di2eeeYZnD17FvXq1cONGzdUy1LCt99+i7lz58LEZa4ajUajeQy00aPRaDRPMXnz5sX69euRP39+nDhxAg0aNMDt27dVy/Ioly5dQr9+/dCxY0esXLlStRyNRqPRpALa6NFoNJqnnIIFC2L9+vXInTs3Dh06hL/++ku1JI/St29fREZGokaNGmjSpIlqORqNRvPfJTISuHxZyaF1IwONRqPRoGjRoli3bh22bt36VLVpXrt2LX777Td4e3tj2rRpunmBRqPRpDQksHMnMGsWsG0bsGmTEhna6NFoNBoNAKBUqVIoVaqU/XVISAh8fHzg7++vUFXqERsbix49egAAPvjgA5QtW1axIo1Go3kErl0DcudWrSIxN24Ac+eKsXP0KODlBfzzD5ApkxI5Or1No9FoNIm4ffs26tati1atWiEmJka1nFRh4sSJOHHiBHLmzIlRo0aplqPRaDQPTlgYMGMGUK0asGOHajUOrFZg+XKgVSsgXz6gf38xeABg2DCgRg1l0nSkR6PRaDSJOHPmDI4fP47IyEj873//w2+//QYfHx/VslKM4OBgfPrppwCAcePGIZMiz6NGozEhUVESlfD1Va3EFRLYuhWYORNYtAiIiAC6dgVatlStDDh5UnT99BNw/Xri/S+8AAwf7nldTuhIj0aj0WgSUaVKFSxfvhx+fn5YtmwZOnToAKvVqlpWipE5c2asXLkSXbt2RceOHVXL0Wg0ZuDgQaBnT+C994C0aVWrcRAUBEyYAJQuLZGS2bPF4ClWDJg4UbU6ISAAiIsDQkIS78uQAZg3D0ijNtZioYkXJQgNDUVgYCBCQkKQMWNG1XI0Go3mqeOvv/5CixYtEBcXh06dOmH27Nnw8tL+Mo1G85BERwN+fqpVJCY8HPjlF0kV27kTyJEDOHRI/lWJzQasXi3Rk2XLJG3MmTRpJOrz/PNq9Lnj1ClJaztyxPX9uXOB9u1T5BCPYxvoJ5dGo9FokqRx48ZYuHAhvL298dNPP6F79+5P9AKesbGxuHjxomoZGs3TQXS0THhfecV9ypMqSGDXLuCdd6QBQLduYvAAUnSv2uABgNBQYPt2YP36xAYPAIwebS6D5/ffgcqVExs8HTqkmMHzuGijR6PRaDTJ0qpVK8ydOxcWiwXLly/HdTNNXh6SSZMmoWTJkpg6dapqKRrN42GzqVaQNCdOAP36AXnzAh07Am3aAIUKqVYlqVfffANUqABUqSLRnfBwx/733hMDzQxkzgyMHAm8/37ifbVqAQMHelySW6xWYMAAoHVrMdQyZgSWLJEmBoULA9OmqVZoRzcy0Gg0Gs19adu2LQDg+eefR24ztkZ9AC5fvozRo0cjKioK6dOnVy1Ho3k0zpwBvv0WKFUK6NxZtRoHsbHi7f/2W2DjRsf7zZsDb7+tTJYdEpgzB/j8c6mRSUjx4lI3Yxbu3gXefFNS25wJDAR+/hnw9lYiy4Vr14C2bR3r7pQtC/z2m9QazZsnhq+JylN0TY9Go9FoHolz586hcOHCqmU8MG3btsXChQtRvXp1bN68WdcmaRJDAmZcoNZmA/76S6IUq1ZJJ6xNm8wx8T1zRiIms2YBN2+67sueXepjcuZUo80dhw/L+XOO8KRJI4tmVq6sTpczBw9K5OTMGXndooWcyx9+ABYsEENDNZs2Aa+/7khbfPNNuT7j13W7ehXIkyfFD6trejQajUbjUf7880+UKlUKE8zkGU2GdevWYeHChfDy8sK0adO0waNxYLMBS5cCffq4r51Qya1bwNixQNGiQLNmYvj4+UnEwgwGz/btwEsvicaEBg8AfP+9uQyeFSuk+5mzwQMAH39sHoNn7lxZe+fMGWmb/fnnki5Wt66kCqo2eEiJiNWtKwZP2rTyPc+e7TB4gFQxeB4XPeprNBqN5qE5fPgwYmJiMGDAAEwzUc62O2JjY9GzZ08AwPvvv4/y5curFaQxB6GhwKRJktbUurUUXJthLSpSFpt8802pixg0CDh/3rH/iy8kfcgMxE/O69VLvO+tt8yxfgwAGAYwahTQtKnU9aRP71gzpnp1OceqiY0FPvhADJuoKCBbNmDNGmDwYDF+atcGVNcihoRIfdaAAeIsKFRIOsh162bOCGlCaGJCQkIIgCEhIaqlaDQajSYBQ4cOJQAC4MyZM1XLSZLx48cTALNnz847d+6olvN08H/2zjs8iqoL428KKbSE3nsHESlSFBCpUkXaJ4J0FKRJUVGkidItNBEEqVJEeu9IRyB0pJfQEmp63d33++Ow2d3sJoSUnQHu73nmSWZ2svtmZnbmvvece+7Dh+To0eSFC1orsefyZbJ/fzJTJlIsBvnll1qrEkJCyKZNLbriL++8QxqNWqu0EBFBfvyxvc6CBcmgIK3VCU+ekM2aWbSVLk3+9x8ZHU1mz05evaq1QvLWLbJaNYvGqlVJf3+tVdly6hRZvLhFY5Mm5KNHTpeREm+gIj0KhUKhSBZjxozBwIEDAQA9evTAkiVLNFbkGIPBAA8PD0yYMAFZsmTRWs7Ljb8/8PnnQMGC8nupUlorEkhg504ZVF+yJDB1KhAaKq+VKCFVsvRApkzAypXApEn2r2XIIONm9JKa6e8vqWKLFsl63brS8+/iAixYIAPutebMGUlb27BB1lu1ktLUpUtLWtaGDUDRotpq3LULqFRJonuAVGvbuxcoUEBbXdYsXChRvStX5Px+/z2wfj2QNavWyp6PNDBhqUZK3JzBQPr5kVOmkH37kjExaSAwlTGZyEuXyHnzyB9+kPUXgYcPyY0byUWLtFaSdAwG8uxZcu9erZU8H7GxZECA1ioUCgsmk4m9evUiALq5uXHlypVaS3LI9evXadRTD3ly8PMjly7VWoVjzp0jO3cm3d2lFzhrVvLBA61VCX/9Rb72WsLRE709CE6dIgsXttc5c6bWyizs3k3myGHRNmiQPKA+/pihnwzizp06CPQsWUKmTy/6XF3J8eMTbFidP0/OmSPtAqfdJkwm0eTqKhq9vMgFC5L853v3Sltxzx4yPDyNNEZGkp98YjnP2bOT27Yl++1WrJCg6vr1ZHKD7inxBi+N6YmMJP/5Ry6A994jM2e2nKPff3eC2GQQFUUeOEBOnEi+/77t/UOn7QbGxJDHjpHTp5MdO9pGOrdu1VqdY4xG8uJF8s8/yYEDyVq1yAwZRPPOnVqrS5gnT+SanjqV7N6drFJF7onr12utLHFCQsijR+V4jxhB/u9/+tdsjckkpliRdIxGI7t06UIA7Ny5s9ZyXj6uXCE//JD09tZdyoth/yF5gMVvoM+Zo7U0CyEh5LRpNObOY6czpFMfXWWLceVKywPKzY3G6m+RAPd61WeL5iYOH07+/bdk6Gmi22SS3mQ3N9Ho7S03ezPbtzM2NJIVK8rLJUuSH31E/vSTNNJDQ52gMSZGHvbm85wt2zMb6rGx5Ouvy+6ZM5P165Pffktu2JBG3j0oiGzZ0qKxaFHyxInneouoKEsbzN2dfPNN8vPPxVjcuZMKGq9fJytXtmisXj3F95+QEDJ3bnk7FxeyfHnys8/En966lbT3SInpeWFLVgcFydipfftkOXpUxoDFx90dGDRI5njy8Ul4yZQp7SPGDx4ABw6I7gMHgGPHHGv29ZVIe5Ysjhdv77TVac2dO1KcxbwcOyYTLMenaFHgp59k3F22bPIzSxbnF5chZbznsWOW5fhxGXsXn5o1pQBOrlwyplErTCbg2jXg1Cnb5eZN+30//FCqVWqN0Qhcvw5cuiRz0Fkv9+7Z7lu7NrBr3k243b4pKxphMACBgVJs5t49xz8DAqT4zL59cv1qjV6r5zrCaDTijz/+QNeu3eDurn1VqdjYWHTp0gUDBgxA1apVE95x3TqwYSO4eHk6T1xSCQwExowBZs2SC/jLL4EJE2B+amt6bTx+LLOsb9li99KNfG9h7eB9yJ3XFXnyALlzy6T3mTJpoNPMggUy2Do2Nm7TTRTEazgLo3cmlCwpmXjmn+bFabNlmExyrs1pdlmzAitWAK6uMDR7H8XCz8AfBW3+JGNGoHx5oEIFy1K+fBo+zyIjZfLOhQtlvVAhmZenYsW4XQwGaets2AB88on9W7i6yvRCVapYlgoVUrFdExgItGtnmTemUiWpfFaokM1uJBAeDjx6JAXyHj0CNm0Cpkxx/LbFikl2l3mpUCEFNS/OnJFiAJcvy3qzZnJME3joGI3ShnnyxH7ZtMl+Gh8zhQsDb78tdRrefht47bXnaJNt2iSFPZ48kfV+/aRim4dHgn9CSvswODjxZfNmabMnpLlWLVlq1pQsxPj3uZSUrH5hTc+jR8Bff8l1cvhwyj/LxUVuyPHNUObMiZslH5+kX0i3b0slwvnzpXGYXDw9EzZEjpacOZOfVn3hglQhXLBA7iXPg4uLfL61EYr/0/x7oUKSAp5SSPkyzZoFLFsGREQk7e/Spxfzk9hSurSUyU9tTCYp0DJ9utxnEvtGurpKAyJ/fqBiNn94lSyI/Pkl9Td/frkRO8PAxcbK8Z04UaY8SIxWNe5h9oXauPT+ELDnp3jzTW0KJIWGAj//LPdtcyq/Nd6IQD9MQ51NX6FxY+frc8StW0CPHsD+/VKl1tNTlrp1TJg9x1UXFWvjs2aNFMX68IMIXLvjj9KlS2ui48cff8SQIUOQM2dO3Lx5E15eXrY7xMbiSc8vEXDgCkr8tx7uepuq+8oVhNdrjgz+FwAAT1yy4I2MVxHlnQW//AK0b6+tPADAkycwfNYP7sv+jNtkgBsqwQ9n8HrctgYNpOquJhWBTSYE9f0WvjPHAQCC0ueFb8RdAEAjbME2NLL7k/Tp5Xs3cKA0wtKcsDCEtOqCzNtXAgACc5TD5FrrcCK4KB75h6PKzZWYE9Mp0bcoWBD47DOge3d5rqY2phv+iGzSChn+Ow4AuFGsLubUX45rIdkRGIi45dGjxJ9h1rz5JvDppzLVS2o8t6L2HIZru9bweCDn93zVLvi77q8ICPaOMzbWP6Ojn+/9XV2B996TY9ysWaLt/wQJm70E3gN6wi0qAnRxwb/NvsPOqt/gSbCrQ1Pz5IncT5OLtzfQpo0UAnz33SR07huNCBk8GpmnjAEAGDwzYG+nOThR6sNnmpngYJs+hWSTPbt08HbqJPcMO9Nz+zZ8ChRI3hyezx0bciJJDWFdukQOH04WKWIfYc+YkaxYUSKH2bJZ0o1TvpjYDXM4B92eO5fSZCIPHSJ79SJ9fOzfO1MmCf95eqZMoxciOA5fcWLOSck/CU+JiSHXrCFbtLBEta0XR9uSsuTGXf6FNhxff3uKNcYnKIicMcMSsk7uUgnHuAt1OHd0EmOvKeDyZYnKO7ourJe8uM3LKEbARMDEdljGbajPg3ui01yjNSaTjOd65x3HOrPhAc+gHAlwN97hTHyadrnHSeTBA0k/t/5+ZUIw/0Et/odS7J/nL20FxsNoJCdPJj08ROcidOCMLv9qLStBoqLIKZmHsr2LGzOmT8/Dhw87XcOdO3eYMWNGAglXlQtcvI0EeKjvYierSzr3piyLu0gHYxLfeYe8eVNrVVaYTLxZ9j2bL/0kDI5brVpVBynEjx/zQabCJMB/UYV5cIdnUZbz0NnufpU9uxSce/jQyRpPn2akqzcJcDXeZ0aEJPn5VLcuuWqVpGelJcYllmtxMgbRDbHJep5mzEh++qkMT0ttAj8eTAKMRjp+iplPn49J15ZQO6ZYMRk6cft2CgUajbxTsg4J8CGysiG2JLtd4uJCZslC5svn+PVatci5c8nnzgB79IiPMuQnAZ5HaZbBuRS1n+KesZnI/PnJcuXIEiXsX/fwINu0Idete/YY/OB//mFSvIEjXthIjyNISRtbuFCiQMHB0jt6+7ZEE8z7REY+262GhCT8mk/QTcyI7YmG2C7vufwvuLRrm6z/MSpK5kRbsADYulV6/UuVAv77T9xtZKRj529egoIcby/9cD9+jemOUriEaBdPeP53KtWq6Ny7J8Va5s6V9CZAeh3nzbPvSYn/M+73B0TTgLkYEzUEvgjGo8xFkO3uGalOk8qQUhRl9myJTkRGyvZhw4APPrD0UIVevY97jz1xM8gHgYFAyN0wdL8xHD2jpsINJtx+syXy/7s61fU5IjxcooLTpgHnzlm2jx4NZHV5glZTaiPvo7PoXu4wOl/5FrWjdwAAgr6eAN+xXzpFY3yOHJGCQ6tWyTHPjGDsQj1UxnHbHffuldi1xvj7y/FcO+8xNrMR3sQxAEBgxmLIFXRRHxP/WXFl8WF4dfsI+WOvw7/15yj4989aS0qQWx0/R5el07DLZIKvry927dqFilYpMGnNRx99hKVLl6J69eo4cOBAghORnv7zDMo3LwyXzFrmXSWMIcaEsXV3oOWBL7DjhyMY8JWX3i5LHN8VjINNxuC16OMojisog/9QsExG/PCDTNGih/TM+V+cAyZPwpjcvyJfifT434XRGP6gH55AKk8VKQIMGSLTyljPrehMfqy+ApFHTuHHzN8hXwFXmwh+/vwSVTc/b9Onl17wvn2BcuWcp3FqpmE4HFYOa7w/ssmCyJ3bPjMiUyZJqzI/bytXlqhO+/Zpl41w61os9hXrjKnoD7901RPNMomfbZI9u2TgVKsm72WOkHTvLo+r1Br+sH5OIEw9P0F/TMVt10Lw9X2+zB3zkjmzaJo5UyJ8gGTMdO4s10axYsnXOK3DYfgumYHP8CvCXTI5zIJ6niVTJtvHaa9eko0DSArbxx8DbdsmPaU8JelteG6b5ERSMlgpMlIGczVvTv74YyoJMhrJX3+Vroqn9jS8Rl3y2rVUefs7d8gJE8gyZcjtyQ18hISQffrYWOjQDp+mSRkVk4nct4/s2lV6HAIDk/iHly6RderE6TN5eTNs9OS076qiFAeYNk0K+bz+ulUhl/Bw6ZY0DyRcu5YsUCBOoyFPfkYsWZ3m+uJjMkmRnFatpMDLsEERZM2att09T3+PqlyDhmMnnK4xPpcukf26hfGAy9t23TmG/IWe40JxAgEBjCxZ3kajqXBh8sgRrZXZEhtL7tjBqO17uXjQcRnYrnNCAwL49ttvEwCzZcvGs2fPOuVzd+/eTQB0cXHh8ePHnfKZacnmzeTZLWkfZU4uJpOMER+IH9kz20r+8YdTbuXPRUiI7QD6+nWNBMhKlcjly/WhNzBQdDri9m25/xcvTv78szzHtMDfXzQmpbLsvHnSVPrkEyl+5AwMBmmOJVVjfLp1k2IAv/2WdpXnQkKkPkBQUMoLUZhMMrVP587STkitwhb370tEOTU0xufxY7JCBYmoJnd6JFW97RlERaWCmCtXbBrqzJSJnDUrTepKm0zJvKlt2SITglnHZHftSm15DgkJcdCWjY21vapjYshx42zziurX12RiMJOJPHjw6UPGYLBUH5ozRxyGWZ+rKzlgQMJPIydy82osr1ZoaR8Xzp6d/OMP/UxYFxlJNmiQcJy7bFmpD6o1/v4SZ8+YkXzrLQa06sVfy//K6N0HkpEToHBEUFAQq1SpQgDMlSsXL168mKafFxMTw7JlyxIAe/funaafpbAwcSI5Y2wQIyP0P8/C/ftk48bkjh0vzrQQe/eSmzbp5xafFOKery8QL0Bfkg0xMS/eMY6OTvn3TpmetMRoJH/5xVLrHSAbNdJXYvWjR2L1rRvqgwenYeH2JGAyyaClqVNl/ehRsfdmjVmykPPna//UMZnsImNxS6VKolsPmExkz56OdVauTJ4+rbVCISZGBn5ZR05cXCSs1rs3uXgxeeOG9uedlKTya9fsWhIvUsPiReDRo0d8/fXXCYD58+fntVSKjDtiwYIFBMDs2bPzkQYzhb+qvEjfmRdJq0KhsCcl3kBv9Wr0xcWLktB54ICs+/gAv/wiSZNaJSpfuQIUL25ZX7VKEjrNpdXKlZMZmxMr0eoMfv4Z+O03KXM4ZIism0zy2ocfynHMlUtTiQCAH38EZsyw3ZYhg8w23LcvdFPSaeRI4PffHb925ozUZZ8zx64sp1MxGiWZeMcOKRPz9ttAzZpwqV5dHzNzxyeBMSZ6mez8ZSFr1qzYvn076tSpg6tXr+LixYsoUqRImnxWx44dERsbi/Tp0yPrizZT+AvMi/SdeZG0KhSK1OWlKmSQahiNMunMiBGWSWmaN5dGfN68ztMRn9WrpYLAqlUyoUjfvsBKKXEJd3cZmf/NN8mro5iarFkDtGol/fzW5M8vo+6aNdNElh1//SW1MuPj4wOMGyeTDOhh1PCvvwJ9+ljWXV2l1mfdurK89ZZ2o2+tuXlTJmhI0QQGipeVu3fv4uLFi3j33Xe1lqJQKBSKF5SUeAOddGNryI0btoX4z50DunUD/v1X1rNmBaZOBT76SNsyNMePy0RwZcpIebrPP7dMGlWlikR3ypfXTp+ZY8fkWFkbHhcXiUaNHevEmd6ewb59UjLEEVFRYiyLF5cJJrTk77+B/v3FSJhNTq1a+oycFCqkbaRJoWvy5s2LvFadRtevX0emTJmQPRUmFbl8+TLy5s2LDGlQ/VGhUCgULwevtum5fRuoX19mxTUYpCbkd98BMTHyeqtWkvqUO7e2Ou/cAVq0kNqPfn6SXgdIPe4xY8QA6SEN6+ZNiYiZa1SaIWWW0KgofZie//4D3n/fcp4BSQts2FCW2rX1ETmJiZGZKAMC0ma2OYVCIy5evIh69eohZ86c2LVrF3x9fZP9XrGxsWjVqhWCgoKwatUqvPnmm6knVKFQKBQvDTpoKWtEeLg0fK9eBXbtAr74AjhxQl7LkUPMTtvkzb2TqoSFiZG4e9d2e+3aMoajRAltdMUnOFjS1gIC7F9zcRGjsXy5pORpGTELCACaNJEUsQ8/BBo1kmhOvnzaaUoIDw859wrFS4bJZEJMTAxOnDiBxo0bY9u2bciUKXlz5UyfPh1nz55FtmzZULRo0VRWqlAoFIqXhVfT9JhMMuDaz0/W69e3vNa+PTBlihgfrTGZgI4dLWbMmqgoS3qb1sTGikE8e9ayLUcOMRRNmkj0xDw7rNacPi0pYxUrqhGtCoVGlClTBjt27MC7776Lw4cPo1mzZti8eTPSP2eE9d69exg5ciQAYNy4cciml/uMQqFQKHTHq2l6RoyQMRvW5M4thQref18bTY4YOhRYu9bxaw8fAkuWACVLAilIDUkxpERvduwAqlcHGjeWpXJlfZqKhg21VqBQKAC8/vrr2Lp1K+rVq4e9e/eiZcuWWLduHby8vJL8Hl9++SVCQ0Px5ptvonv37mmoVqFQKBQvOq+e6fnzT+CHH+y3u7hIipbJpI/G+pw5wKRJlnV3dxnA3rSpLKVKaZsmZubSJaBOHSlSoHpZFQrFc1ClShVs3rwZDRs2xPbt29G2bVusXLkSHkmoQLl3714sXrwYLi4umDFjBlz1cN9WKBQKhW55tUzPoUMy744jwsKA9etlQHvlys7VFZ/du4HevYGcOSVq0qyZjDvRY8WuUqVkUSgUimTw1ltvYcOGDWjcuDFu376NsLCwZ86xYzAY0LdvXwBAz549VfEChUKhUDyTV8f03LwJtGwJREdbthUuLAPFmzcH3nlH+/ltAKl8dvq0TIhapYo+ok4KhUKRhtSpUwfbtm3Da6+9hixZsjxz/7CwMJQuXRp37tzB2LFjnaBQoVAoFC86r8bkpGFhMjv82bNAjRoSOWneHChbVh8pYgqFQqGw4Z9//kHt2rXhksg9OiAgALm1nlJAoVAoFE4jJd7g1QgjrF0LDB4MBAYC+/dLgYBy5ZThUSgUCh0yZcoU1KlTB59//jkS65dThkehUCgUScUppmfGjBkoXLgwvLy8UK1aNfz777/O+FgLHTpIiWo1waNCoVDoHvNkpVOnTsXXX38dZ3z27duHjz76CHfjz1umUCgUCsUzSHPTs3z5cgwaNAgjR46En58fKlSogEaNGuH+/ftp/dEKhUKheAHp3LkzZs6cCQCYMGECvvvuOxgMBvTp0wdLly5V43gUCoVC8dyk+ZieatWq4c0338T06dMByEzcBQoUQL9+/TB06NBE/zbVxvQoFAqF4oXj559/xqBBgwAAzZs3x/r165E1a1ZcvHgR2VXkXqFQKF45dDumJyYmBsePH0f9+vUtH+jqivr16+PQoUN2+0dHRyMkJMRmUSgUCsWrycCBA/HD03nV1q9fDwAYO3asMjwKhUKheG7S1PQ8fPgQRqMRuXLlstmeK1cuBAQE2O0/btw4+Pj4xC0FChRIS3kKhUKh0DnffPMNypcvDwDImzcvevToobEihUKhULyI6Kp629dff43g4OC45datW1pLUigUCoWG7N+/H2fOnIGLiwtWr14NNzc3rSUpFAqF4gUkTU1P9uzZ4ebmhsDAQJvtgYGBDkuNenp6InPmzDaLQqFQKF5dzEULunfvjqpVqwKQyUl3796tpSyFQqFQvGCkqenx8PBA5cqVsXPnzrhtJpMJO3fuRI0aNdLyoxUKhULxErB8+XIMGzYM48aNAyCG57333kOjRo2wceNGjdUpFAqF4kUhzdPbBg0ahN9//x0LFizAf//9h969eyM8PBxdu3ZN649WKBQKxQtOpkyZ8P3338cVL/D29kaBAgUQGxuL1q1bY8eOHRorVCgUCkWSiIoCQkM1+3j3tP6A//3vf3jw4AFGjBiBgIAAvPHGG9iyZYtdcQOFQqFQKMzs27cPb7/9Nlxdbfvm3NzcsHDhQkRHR2P16tVo0aIFtmzZgtq1a2ukVKFQKBQJEhAAbNoErF8PGAzAypWaSUnzeXpSgpqnR6FQKF49Dhw4gJo1a6JmzZrYuXMnPDw87PaJjo7GBx98gM2bNyNjxozYsWMHqlWrpoFahUKh0JDwcCBDBq1VWCCB06fF5KxfD/z7r2wvXhw4fBjIli1Fb6/beXoUCoVCoXgeDAYD+vTpAwAoVaqUQ8MDSOGblStXom7dunHjfE6cOOFMqQqFQuF8DAZg3z5g6FCgRg3g8mWtFUna2ubNwGefAYUKAW+8AQwfbjE8Pj5igFJoeFJKmqe3KRQKhUKRVH777TecOnUKWbJkiStekBDe3t5Yt24dGjVqhIsXL8LFxcVJKhUKxUuLwQC4uQF6up88egRs2QJs3Cg/nzwRfWvWiMHQkuhoMTvz5jl+3c0NWLECKF3aubocoEyPQqFQKHTB/fv38e233wIAfvjhB+TIkeOZf5MhQwZs2rQJ9+7dQ6lSpdJaokKheBm5e1ciFZs3A9WrA0OGaKuHBM6cATZsEKNz+DBgMtnuM2kS0KKFNvqs8fQE/vhDjtunn9q/PmUK0KCB83U5QJkehUKhUOiCoUOHIjg4GJUqVcInn3yS5L+LP6/boUOHkDt3bhQpUiQtZCoUiueF1FfkJDYWOHTIYnROnZLtH34IDB6srTYAmD9foidRUY5f79EDGDTIqZISxGAAJk8GRo2yf+2zz4Cn6cp6QBUyUCgUCoXmHDp0CG+99Vbc79WrV0/W++zfvx/vvfcecubMib179yJ//vypKVOhUCSF6Gjg4EFg2zaJokybBmjdjrtzR1LDNm8Gtm8HQkJsX3/rLWDnTsDLSxt98Tl3TjTF11m3rvwf6dJpo8uaM2eAbt2AY8dkPVs2Gb9z7RpQv75UbUtlnSnxBirSo1AoFArN8fDwwBtvvIFKlSol2/AAQNGiRZE7d25cvXoV9erVwz///IPcuXOnolKFQmEHCVy8KCZn2zZgzx6pKpYnj6RmaW14Tp8GWrYErl93/HqxYjI+Ri+GZ/t24JNP7A1PyZLA339rb3hiY4Fx44Dvv5ffAaBtW2D6dIn47NoF/PWX9jrjoSI9CoVCodAFRqMRkZGRyJgxY4rex9/fH7Vq1YK/vz/KlSuHPXv2xE1uqlC8kJDAlSsSidi3T1KwKlXSVtPjx6LHbHT8/W1fz5hRtGo90N5MRIRESY4csd2eJYukuulhTOCTJzKe6I8/ZN3FBXj9dUm/y5pVtBcvrq3GEyeArl0tKYE5cwK//gq0bi3rv/8O1KkDlCiRJh+fEm+gTI9CoVAoXjquXr2K2rVr4+7du6hYsSJ27tyJLFmyaC1LoUg6t25Jj7l5uX1bti9aBHTsqK22kBCgc2eJjjjCzU0G4Ddq5FRZCXLligyy37XLdnu6dBJVeecdbXRZs2YN0Lu3TOYJAGXKiPkJCADatdNeZ3Q0MGYMMH48YDTKtg4dpFCBdSnqNB6/pebpUSgUCsULSZ8+fTBq1ChERkam6vsWK1YMO3fuRM6cOXHixAk0btwYoaGhqfoZihecwEBg1SqJmsyerbUa4P59YPlyaZyXKAEULAh06QIsXGgxPJMna294AElXW70a+PJLx6/Pnq0Pw2MwABMnAuXLWwxPxYqW1+fO1d7wBAaKqfngAzE47u4yx82JE1IRrWBBOZ5a6jx6FKhcGfjhBzE8efMC69YBixfbz72jp4IV8aGOCQ4OJgAGBwdrLUWhUCgUqczBgwcJgAB46NChNPmMU6dOMWvWrHz//fcZFRWVJp+heAEwmcjz58nffyc7dyaLFyelT5ps1440GLTXN3UqmSGDRVf8ZcgQbTVa8+gR2b27Y50jRmitTjh+nKxY0aIrSxbyjz/IkBB96DSZyIULyaxZLRorVyZPnrTfTysiIsgvvyRdXS0au3YlnzzRTFJKvIEyPQrFC0RkpNYKno+wMPLff7VWkXROnHjxjvGLisFgYMWKFQmAXbt2TdPPunjxImNiYtL0M15ljh0joyON5JUr5KpV5KhR5BdfyA1ASwwGcsoUsnlz24al9dK0KRkdra1Oa9avJ11c7HV27EgajVqrkwb4okVkjhwWbSVKkJkzy++dO2vbSCfJ8HC5/tzcbI3tvXuWfT77TFud/v5k48YWfZ6e5IQJZGysdpric+AAWaqURWOBAuTmzVqrUqYnMS5dIrdvT0VRaYzBINfUvn1aK0k6AQHkpEnSifYiYDSS27bJMyQkRGs1SePqVXLQILJ1a62VJI3798nhw6WdsWiR1mqezeXL5PjaG1m8uNZKnk1QkNYKUocZM2YQAH19fRkYGOi0zzWZTJwzZw6j9dTQTQRdn+/QULJ/f94rXJ1hLlYRijJlyDt3tFYnXLhA1qjh0PA8rlSXpvAIrRUKQUHkgAG2PepPl8DK77HhuzFcvVrjgNSlS2S9ehZt6dLJjT4ykqxUiaxfP85A/vwzOXky+eCBkzXu2EEWLWrRmC8fuXat/X4ODOSECeTevWnshYxG8tdfyUyZLBpr1iQvXnzut4qJIadNkzZYqhIeTn7+ua35/vRTMhUCEIGBcjpScvtNiTd4KUtWP3okabELF0qhi61bn/89DAYZpxccnPAS+sQA7/s3MXJhsRRrPn8eWLBA0iPv3wdu3ky6zqAgKfhhvZi3Rd4LQt7sMeg1ImeKNcb/3C1bJB12wwagQIHE58kyGKTQy8OHcn6sf5qu3UDZBvnQsm3aljYMDATmzZPCIteuyfi7TJnsdT54IPsGBMjPwEDAcOEKmvQv7tQiNCYTsGOHTG+wcaPceRYssOi8d0/SvG/dAm7fIiLOXcfnU4sihYWvUsTVq8BPP8nYy6gooIzbJZQuVQKbN7vg6oVY4P599B2XTzuB8bh7F/juO+DwnLPYb2yHvW/dxp5NHqjTJL3W0hzyz7RTWPFnLKYcqAI3N63VJAxNhItrwnndDx48wLBhwwAAY8aMQc6cqXt/SoxBgwbhl19+wZYFC7CkQ0ek+zTpk6A6nZs3cb/DKFy+G4YKld2Rbux3aVYRKVlkzIjQUpWR+8bUuE3/eVXErWFb0SBPDugis79ECTxmFmSNt/kgaqCh31qUe9cbX34p1Yw1+07dvImICtWRPlgGsEdlzQMXXx94XrsAvPkmsmxdgZOl0+GDD4BChWS+x+7d7YdSpClbtiC2WUukM0YDAB6WqQX/YbOQuVoZ5DYAGd97D/jqK8DDAwBQqxZQpQrwzTdAmzZSfbl27bQd7hE9ahw8R38Tt36pwWe41XscsuTPjGw3gezZgfTpn2pwtR/S7uUlGkuUkKlnOneWitupSXC9VvDZsxYAEOOZEX5tx+N+697IEuiKLDFSSC5LFsDb+9nHKl06qQb++edA48Yy/KtZM8DTMwUCQ0MRVqoSMt67InqzFMb+rnMRUb0ufA7LFDzWS1J0WpMjh1wT3boB7dsDH38MvPnmc14XZ88+3/9kxUtTvS06WhqGixbJT3PZ8Jw5gaVLn21g4i8REYlry4UALMOHKIHLyH33BNzyPP9D+/FjYNkyacj++69l+xtvAF98YW9gHC2Jjct9HaewEq0RnDEfKj/ekSr10i9flgbtggXS6DZTty5Qr569oTH/DApy/H7NsB6L0RHHXu+Oeqd+SrG++JhMMnZx1iwpjGIwWF5r0EBugmZjExgoem2/EcQg/IQJ+AoHPlmId2Z9lOoa4xMSIsd3+nTg0iXb18qXF40BAfK/AUA6xGA6+qID/kTAyoMo1qpCmmuMz/HjMlb0778tutwRi39RFW3wNx4jK1agLQq73ESx+4fhkt2ZT2t7njwBJkwApk4FIiOJvaiNWtiPyyiOh9lKo0bAahlMqhPCw6VToeXsxsiLe3hQ8wPU3zdSa1kJsv+tL+DTrhHKf17f4es9evTA3Llz8cYbb+Do0aNwd+Kx3rp1K1q0aIGYmBh0BDD50GXkqq5xCdgECDz7AB7lS+ICSuPrgkswfnkRpGAKozTh2NC/8caED+EOIw6iBppgE4Lhi7ffliJPNWtqrRDYX+ML1Dw8OW79BN7Au9iNYPjGbSteXCoFd+okDTmnQuJUtndR7sl+TMEAjMZIjMdQ1MNONM64H94FcyAgQNoMZry8gI8+Avr1c1JF6KAgBGQtAw9G4wtMwjx0Ba1qYWXwNiFnblfkyoW4ZdEiwLo+SenSYn46dUobw+a/xg95P6iKyyiBnvgdB2B/8Xl6ynM/Wzbbn9mzi+kdNcqyr5sb0KSJNNCbNk2dKWcO9ZiLGnN7YAsa4VPMgj8KOdzPwwPw9bWYoISWa9dkqhwzWbOKmejSReoOJMdkHnjtU7x9bjamoh++wViEI+GeVHd3eyP0rGXDBmnfmClZUq6Jjh3F1D+LkMeP4ZMtW/IqOyc/wJT2PCuEZTJJymGvXjI+LaGxf6m5eHqSzX33MtAtd9zGqB8mJfl/io0lN2wg27YlPTzSRqOrK9k74wJGuHiTAI0uruTOnck+D2Fh5Pz5ZK1aqagRBo73GB63IdIzM3n3brI1xicwUELVxYolX6M3wrnMtX3chqACr6VpbsF//5F9+pAZMyauKwsexf2eA4Hci5pxLz5q2jHN9MXHZCK3biXr1nWscyjGkgC/xzf8D5a84MixPzlNY3zCw8lx40hfX4vOzphnIzzKPb0M7tEJR45Iyvy72Gk5z1mLyQWjQy4fCGQs3Hi91scO89Pv3btHb29vAuCBAwc0UEiuWbOG7q5uBMCyZbvTpPUYhATw9ydbZNhBd8QQkCEKI0ZIWote2LzRyH7Zl3An3mUGhNrdB5o2tR+X7Wx+GhnEdvn20wBXnkMZZsf9BO+vOXOS339PPn7sXI3ftL7A5oVPM336p9lEmMlCuJ6kZ9Xbb5PLlqXtdWEwkH0qHuBbxQNT3N7y9CQ7dEj9VLKLF8lBr21lhdJRzJXLdkhPSpdcuWSYUEpvu+vXmTik7EaWK2ti3rykt3fqaYy/lC1LTpz4/E2raT8Es9dr+1iuHJk/v20mXlov77xDzpmTeFpvStLbXthIz7VrwIAB4hiTire3xWlmzvz87tQnM+E5/Udg6FAp2efhIV3Fn3ySJDt94oT0yhw48Gytbm7PdvgOl/TRyDRiIFx+mylvlCOHhLrq1Uv6gbJi1y4JnZ45k/h+np5A/vwJ96BYb8vh9hg5BnaE69bN8sdly0rpy5Ilk6XRGhJYsULCp1evJr5v/vwyt5t1z1SuXEDu3EC+2Bso9PkHcDtzUnZu2FCOY9b4SRIpx2gEfvtNKpHeuJH4vv1f34NOvmtxtc/PKBV5EuWGvQ/3O08nhOvVS+rlP00vSEuioqQXd9kyOc7WETQAKIULOIk34IVoy0YXF2DSJAlZaFDS8skTSWXz85NI2b17QLrQR7iA0siBh3H7Gdw84N6uNdC3L/DWW07XaY2/v1QqdXUhes2rirx3jgEAnuQvj8wzxsGteRPdlQe9NeJ35E4fgnRfJXyer1+/jo0bN6Jv375OVmfhr7/+Qvv27WEymdCnTz9MmzYFLjo7lseOSaXY6GjLEhUlPftt2+rj1F+7Brxf0R9XQnIgCpYQibu73E/z5JHqtgMHynyFWsJKlXH6+3V4q22+RLM5cuaU1Kxx42ReSGdCSmbE7VvE7TsuuH1b7rETJ8prCZEhg6Q4TZ6ctN7ylBITI6n41pkS1inht249u63j7S1p5uPGSfsgtSElc+LhQ8cZKPG3BQZKentipE8vbc9hw+SYpwbR0Qln8zwr4yc8/Nnv7+MDjBwpj7TkRquMRsksMmdDPW/2lPlvkuo4cueWbIyPP7a/z6VoDs/ntklOJClu7v59KRYzcCBZpYq9s3d3J/38UqE4S1AQ2aqV5Y0LFSKPHk3WW927R65cKQPTq1eXsYDWmn19yYcPk/HG/v5k1aqWN6pWTbalAnfvkqtXk199JU7c3BtlXvLmTWJRgJMnbQcZtm0rg2HTgIAAiaqNGkU2ayY9NdaaCxWSnn+7cl07dpDZsll2/PJLp40effKE/OcfqVzavbtc015eIiMjQnjTrTANld4k//rLchLc3WVgpEbExsr41vXrZeDqJ90NPJ35LfsunNmzNdOYEDFdetrpjPDyZfjAYRIu1At//SX6ChYkFyzQvrxuYiTr5qUNCxYsICAls4cOHaq1nBeSBQvI8eMlG2DrVvLUKXku66HQmB3377NTJ/kqeXmR5cuTbdqQw4ZJ5eAjRzStxJsgI0faP7tatJAaAn//LYVY9Ha8x4611ezhQb75Jtm7Nzl3Lnn6tL4KlZHSVoj/2CpZkuzUSR6xfn76irKSZP/+9poLFiTbt5ciB35++jnORqPUQmje3F5znjxSYG/aNGkmJvaIU9XbrAgNlWptI0aQ774rocP27VMo5PRpyS8xn5333kvVB3tEhIR5x4+XiyFbNqmm+Fzs2EFmz27R+NlnZBrOSREbKxlAM2dKhcpSpcQbJMrixZZYrqurlHxzYlqJyUTeuiXmbdgwsmFDcuIEkzz1DAbZ4ccfLdVz0qeXnAGNiY2VyniX6vciAZqsK6pky0bu2aO1RFumTnUct/bwILt0cX7eSEIcPGjv3CdP1l9Jv5gY6R356acXup72gwcPNEtnS4zffvuN7u7uXLp0qdZSFGlMeLgYsxs39GcSEuLxYzEKU6dKh5geTVl8/P3lltW9O/nbb1LSXO9TZF29SubOLe2CESPITZv0339z7pyY98qVxfwsXy5tHD1z+LAUhStZUq6P+fPl2D9PU1CZnkSIjpaDnGx3vnChpaHu4kJ+912a3y1NJum5SdJFYDRKl4q5oe7tLZo1wOY0WR+jmBjb7ojs2VM0xihVmT5dNP37ryQZmzUWKSJdlnph2zZ7E1GuHHntmtbKbLl+3fHkehkzkv36SUhID8TGkhUqWLry5szR71P5/n2d1y1OGj169CAAjhkzRmspdly9elVrCQqFQkNCQ18cI2zm9u00S5RJM86dS3mJ7VdyTE+aExUlg1lmzZL1bNmAJUtkbIdeCAqSmorr1sl6sWLAqlXOT0KOz65dksD5wQeS5NuuHbBvn7z25ptS4qtgQW01AsDFi0DFilJeJnNmSTgFpKzb0qVOrgeaCMHBwGuvSX1qa9KnB7p2leu0uA6qT5Hy/dixw7KtSBEZyNatmyQW64Wff5bv89ChGteqfTX4999/Ub16dZDE/v378fbbb2stKUHu3LmDnTt3olOnTlpLUSgUCkU8UuIN9FOTVU/cuCGjRI/JoGFUrSqj47VsqJO2o7nOnAFatQKuSC11NG8uExP5+moiL44nT8SIdeggo1jbtLHUtu7eXeoUenlpqxGQkfedOlnqaZoNzxdfAGPH6qpcMQYNsjc8gIwCzZhRPw32+fMthqdOHRnt2by5fvSZIYG33xazqIeR4C85RqMRffr0AUl06tRJ14bnyZMnqF27Nq5du4bo6Gj07NlTa0kKhUKhSCXsZ2d61dm8WYqbmw1Pnz7A3r3aGp7YWCnJYubPP4Fq1cTwuLoCP/wgk9BobXhIoHdvaaAvXCgN33v3pJrY7NnAnDn6MDyAlIuxnhwJkLImUVFSdkYvbNwoEyNZU62aTORz+7aUUCtSRBtt1ty7JyXzunaVMoW7d+s3guLiIh0ZyvA4hTlz5uDYsWPInDkzJk6cqLWcRPH19cUHH3wAAPj000+xePFijRUpFAqFItVIWWZd2pIaY3qSjMEgpVDMg8TTpyf//DPtPzcpDB8u5Vqio2UiF+uxMdu3a63OwuLF9mM58uWTQVV64tgxqXjmaLB98eIyYFwP1bEePZKSJuZSQ127inY9cuRIyhN1FS8dDx8+ZNasWQmAU6ZM0VpOkjCZTOzTpw8B0NXVlStWrNBakkKhUCiekhJvoKMcHiezZIn0RKdPL0XaP/oI2L5dXitVCli5EihXTlOJAID9+yWSYzJJSo45AqWnsTEAcPOmRMXiU6OGfsbGAJLO1rGj7cQy7u5yLfTqBbz7rkTP9MCAAXJ9Tp4sEZQ0mCMo1ahaVWsFCh3yzTff4PHjx3j99dfx2WefaS0nSbi4uGDq1KmIjIzEH3/8gfbt28PT0xPNmzfXWppCoVAoUoBOWndO5upV4NNPZVzM4cMymN1seNq2ldng9GB4goOlgW4yybrZ8Hz6qRQG0IvhMRplHE9wsP1rf/8tZkIvKWPffANcuCC/Fy4s43du3ZIxW/Xq6cfw3Lsn46IuXQIGD9a34VEoEqBmzZrIlSsXpk+fDnc9jZN7Bq6urpg9ezY++ugjGAwGtGnTBrt379ZalkKhUChSwKtXvS02FqhVCzhyBKhfH/jnH9nm7i6zxQ8YoJ9c/44dZfyONSVKSIW2117TRpMjJk4EvvrKdlvOnECXLkCPHqJZD+zaBTRqBDRrJsaxYUP9mByF4iUlMjIS3t7eWstIFgaDAf/73/9w/vx57Ny5E3nz5tVakkKhULzSpMQbvHqm59tvJV3Mmrx5gb/+kvQxvfDnn2J6HJEhgxQKaNXKuZocceKEDKyPjRWz2LAh0LOnVO3y8NBanQWTSY5ZgwZAvnxaq1EoXmpIwkUvnUcpJCYmBiEhIciePbvWUhQKheKVJyXe4NXq5v7nH0lnssbVFZg5U1+G58YNwFH+e9WqMm/Q3bv6MDzm8TE5coiZvHYN2LIFaN1aX4YHkPPcpYsyPApFGvPo0SNUqFABS5cuhY771JKMh4eHjeFZtmwZ/Pz8NFSkUCgUiuTw6kR6njyRSTsdzXcCSHRixgwpW6wlRqOUet6/X9azZgU+/ljmuClfXlNpdmzZIhGexo31Na+NQqHQjF69emHWrFkoX748/Pz8XqixPM9i3bp1aNmyJbJmzYo9e/bgNT2lGSsUCsUrgJqc9FmQwCef2BseLy+ZPLNbN+Cdd/QxvmP8eODAAUnD6t5dqop5emqtyjHvvae1AoVCoSOOHTuG2bNnA8ALV7wgKdSpUwdVqlTB0aNHUb9+fezduxclS5bUWpZCoVAoksDL9URKiHnzpIqYmapVxeh8+CHg46Odrvjcvi0G7do1qSymUCgULwgmkwl9+vQBSXTo0AG1a9fWWlKqkzlzZmzZsgV169bFqVOnULduXezduxdFixbVWppCoVAonsHLn9526ZKUpM6QQdLEunbVV+UzhUKheAmYM2cOevbsiUyZMuHixYvIkyeP1pLSjAcPHqBOnTo4f/48ChcujL1796JAgQJay1IoFIqXHlXIICGMRmDpUmDxYomi/PijMjwKhUKRyjx+/BhDhw4FAIwePfqlNjwAkCNHDuzYsQPFixfHjRs3UK9ePQQFBWktS6FQKBSJ8HKnt7m5ASNHaq1CoVAoXmrWrFmDR48eoVy5cujbt6/WcpxCnjx5sHPnTtSuXRstWrSAj55SpRUKhUJhx8ttehQKhUKR5nTr1g1FixaFl5cX0mldAdOJFCxYEH5+fsiSJctLMy+RQqFQvKy83OltCoVCoXAKderUQfXq1bWW4XSyZs0aZ3iioqLw9ddfIyQkRGNVCoVCoYiPMj0KhUKhSBa7du3CrVu3tJahG7p27Yrx48ejWbNmCA8P11qOQqFQKKxQpkehUCgUz83jx4/xv//9D6VLl8a///6rtRxdMGTIEPj4+GDfvn1o2bIloqKitJakUCgUiqco06NQKBSK52b48OF4+PAhihQpgooVK2otRxdUrlwZmzdvRoYMGbBjxw60adMGMTExWstSKBQKBZTpUSgUCsVz4ufnh5kzZwIApk+f/koVL3gWNWrUwIYNG+Dl5YWNGzfio48+gsFg0FqWQqFQvPIo06NQKBSKJGMymdCnTx+QRPv27VGnTh2tJemOOnXqYM2aNfDw8MDKlSvx2WefaS1JoVAoXnmU6VEoFApFklmwYAEOHz6MjBkzYvLkyVrL0S2NGjXCihUrkD17dnTt2lVrOQqFQqEtRqMsGqLm6VEoFApFknjy5Am+/PJLAMCoUaOQN29ejRXpmxYtWuDatWvIlCmT1lIUCoXCeZhMwOXLwLFjshw/DlStCkycqKksZXoUCoVCkSQ8PDzQs2dPbNmyBf3799dazguBteHx8/PDhg0bMHz4cDWZqUKhSDkkoPW9xGQCrl61mJtjxwA/PyA01LJPr15ieFy1TTBzIUlNFSRCSEgIfHx8EBwcjMyZM2stR6FQKBQADAYD3N1Vn9nz8PDhQ5QoUQJBQUEYOXIkRo0apbUkhULxokACN28Cp04Bp0/LEhkJzJoF5Munna7Dh4GWLYHAwIT3+ewzYPr0VDNnKfEG6qmlUCgUikQxmUwAANenvXTK8Dw/2bNnx6hRo/D5559j9OjR8Pb2xldffaW1LIVCoTdCQ4GzZ20NzpkzQEiIZZ9y5YDt24E8ebTTCQDVqwPbtgFNmwK3b9u/3qcPMG2a9tGop6gnl0KhUCgSZeHChZg5cyZmzJiBKlWqaC3nhWXAgAGIjIzE119/jaFDh8Lb21ulCSoUWhATA1y7Bly8CFy6JD+vXweGDgUaNNBO14MHQPPmwJEjCe9TuTKwdSuQLZvzdCXEmTPAN984Njz9+gFTpujG8ADK9CgUCoUiEczFCx48eIDdu3cr05NChg4dioiICIwZMwYDBgyAt7c3evbsqbUsheLlxGgE9u2zNTdmg2NdSSxDBmDVKm0NDwDkyAHs3SvGZ9s2+9fffhvYuBHw8XG+Nmtu3ABGjAAWL5bUOwBwdwfMc5L17w/88ouuDA+gTI9CoVAoEmHEiBF48OABSpcujQEDBmgt56Vg9OjRiIyMxOTJk/Hpp58if/78aNy4sdayFIrnhwSCg4E7d6S337yY199+W6Inbm7a6HNzA/z9gc8/B6KiHO+TIwewaROghw6dAweAkSOBnTvtX6tfH1izRgyaVjx4AHz/PTBzJhAbK9vy5BHNjx4Bw4YBAwYAP/+sO8MDKNOjUCgUigQ4efIkfv31VwDA9OnT4eHhobGilwMXFxdMnDgRkZGRuHTpEt555x2tJSkUSWfhQlnMBic83PF+330nqU9aN37bt5c0LEfzihUtKqlixYs7X5c1R46Icdi61bLNx0cMJSCRn7/+Ary8tNEXGgr89JMcw7Awi76hQyWqkz69pLINHAj8+KP25zwBVPU2hUKhUNhhMplQq1YtHDx4EO3atcPy5cu1lvTSYTKZEBsbC09PT62lKLTkwgUp8xseblkiImzXzdvy55eGZ44c2umNiQF69wb++MPx6+nTiylq3dq5uuITGSkaJ02SymfxqVRJIjy5cjlfm5njx8XsbNxo2VasmGzLnh1o0gT43/+ARYuAdOmcry86Gpg9GxgzRqI8gBivfv3E8GTNatn3zh0gb940NzyqeptCoVCkkFWrgFattFahHxYtWoSDBw8iQ4YM+PHHH7WW81Li6uoaZ3hIYsyYMahRowYaaD2uIBkcPgwULChtHl1BAsuXA56e0ojMnl0GgGfNKmMQ9EC+fEDPnsD+/Ynv17UrMHUqkDGjc3Q5ghSDFhHh+PUCBYB164A33nCqLBuCg4Fff5UxJffvW7ZXriwmA5BUsVWrAK0mDj55UozNunWWbYULyziZjz+Wa3PHDjnnv//u/PRAkwlYsgQYPlzG7wAyx063bqI7f377v9GydHZSoY4JDg4mAAYHB2stJUFiYrRW8GxiY7VW8GwMBq0VJI7JJIue0Zu+Q4fsNelNo5kffyRLltRaxTO4cYMHF1522sc1bdqUADhhwgSnfearzMKFCwmA3t7e3LNnj81r//yj42dNSAi5ejVnTY3ia6+Rjx9rLcge08pVpDTXbRZTlixkjRrkkSPaCjQaadq4ifT0dKiTPj7ksmXaaoyIoPH3uWTFio41AnIsAwK00xgQQOMXX5GZM9vqatmSPHyYvHJF1j/6iIyO1kSi4cRpmj5oZauvQAFy9mx7TUFBpNHoXIEmE2PXbqTp9ddtNbZqRf73n3O1JEDww4fJ9gbK9CQRg4G8cIH8+29y5Eg5/x8UOMrvMuqrQfDoEbltGzluHNm2Ldkv5zKOrbZGa1k2xMaSfn7ktGlk+w9NHO8zlr8Pdu6XyWAgw8IS3+fePXLRIrLrR1GckuFrHtmu3XX45Ak5f7799pgYcssW8vOPAjnZdwwjwpx8g0yAhQvJHDks6+fPk9+3O8Vfqi7WTlQCLJ8aQID09SV/b7WJK7/101qSQyJGT+AKz4/4z5h/ePv0ozT/PIPBwEWLFjH6ORsHd8fOY+StBzTF6rgn46n7joyU+7oeiI6OZpMmTQiAGTNm5MGDB2kymnhw7G42b2bi9OlaK3RMsN8VBqfLyrB0Pvwd3dmv3E6Gh+jr3F9tN9SugR7j5sllRb5i0M0greWRsbEMSZ/TsZF46y3y+nWePk3u2KGhxlOnbHRF5i1C46jvLNs6dyajomz+JCTEuRKjf5oep8fg4sZbdTvxyYFzlh3OnycHDXK+kbAioF77OI1PMubjkS6/8sKpKC0l2RIby0fZisdpvJivDld9dZgHDpA6aIqTJINPn1amJyHCwsiIiKTvbzKRd++SW7eSkyfL97hSJdLLy/Y+1AzrGIb0JMDgH39Ptj5Hn79vX9L2ffhQdI4dS7ZuTRYubKvxc/wkNyd4kQcPpprG+Bw6RC5fnvDrwcGic8QIsl49MmNG0ecKA2fiUxLgfe8C5O3baabRGj8/8s03yTt3bLeHh4uBGDyYLF9eNHojnFvQkAR4vVBtaSU5mV27pCNowABZj44mN28mu3Ujs2YlcyCQZ1COBHitUS+n67PGYCC/+kqOXb585Nq1ZP36ZDFc5j3kIgEGjPxVU43WrF9n4j7UpC8esyKOMwzpGYb0DPprq9bS7LiduxIJMAQZec89H6PPXtJakkMe+BbjfbdcPOzTgAcO6DPSfHH/fQbkr8yD7rXYIId+TG5kZCTr1atHAPTx8eHUFhNIgB9gJXPkcH4jMilMan+c4fAmAYYhPbeiAX96fR5jovTSiiN/fv0P3kGeuIfjqdc7cFyvGwTI0qXJy84LoDrEZCIXZOrDGyhoabTDlYcbjeDtG/IFunFDXho4UJPHEB89InfjHW5GIzbFerrCwNI+d2mECw+1mcTr1+zD+N26STvKWfeAf7ZE8BoKcyr6siBuxLWFypQhe/Qg58+J5aVLCWccPHlCjh+ftsf3jy//4y3kY19MpSci4zRmzEjWqkV+/rl0tp47l7QMmHPnpC2Ymkx5axn98AYbYTMBk027smBBsmlTcuhQ8s8/ydOnnz9o9uBByvSlxBu8tKYnLIycNElSVhJKCwgJES8waxbZrx9Zpw6ZLVvCkVvzMjzbDBrgKoYiU3aG7zyUwv9U2LWLrFpVIq/xuX9fGuQ//CBRpkKFEtbnAiN/TT84bkNwgXI03fRPFY3W3LwpWgFy3TrZZjKR166RixeTvXuTr79OurjYa/RAFFe5tYnb8LBaY3EdaUhoqDwwXF3JvHmls8fPj5wwQcxY/MyCTAjmP6gVtyGww0Cn5mdFRYkBMx+/Dh3ILl3ILFksGnMgkGdR1qJx7Byn6YtPSAjZooXlGjRrzIdbvI5C0rvq7sVHq/Y8872cwb59ZD2Pf0iAkzCYt5FXTIVnNoacuKK1PBsC9l607WV18eKeLn8w7J9jqdpraTKZOHPmTIYn97sYFkYjXOIabRfz1Unz73VyOHuWvOZShPeQi+6I0U8vK8mwsDDWrFmTAOjpkZWdMJiHUI3pEcYRI7RWZ8+2uf4c7fkDa+AA0yE67jL9+GNNO9RtWLGCPJClKXfjHVbGUbvnUZYs8vzVithY8s/ZYexaV5zNTRRgTeyN0/f225KC6yrNDr72mgRenElICPnHzCj+73+WdlIB3GQTbIjTWaIE+dln5Jo10tk5caJsr1BBssvSmrt3yVnTY9ixI1mkSMJtpBw5yPffF30HDtgGqIoVk79dsSJtHvcXLpDTf4llly7SuermlrDODBnk3PfvTy5YIPet+Ebo338lm++776SNkxocOWTkLz8Z2b27tEnTp0+8TezuLtdk+/bSCb9uHXn9esLHb8QI6ahP7jWsTI8VYWHSs5DzaaT4ww/F9Jw9Sy5dSn7zDdm8uX1UJKEvRt260sM+Z45cCNEDvrDsULx4qnQRHTtGNmxoeduJE8lNm8gxYyQVtUCBxHXmzi3Oe8QIct3f0Qx/3xI+Za1aqZ5kHRpKDh9uG/0aMYJs04bMkydhnQUKkP/7HzlzYgiDq9azvNChQ5onrK9eTebPb2VoMsn5daSzaFFycJeHfFysipXTHe5Uw3P6tBjGxM7728UDGJhdDI/JxUUuUo24ccMSHXOBkePxJQEyO+7zmmdp0ejuTm7YoJlGa06dkjT5lfjA5qBGwYPNffdy2TJ9jT9aXfk7uwsg1sObxvkLU/VzzONKypUrR0NyBtodPRqn74JneUbcTvs0vORyoXL7uOtUb74sODiYr79elQAI5KYHApkDgcyQQdJu9YK/vzxPE7pHDXRuP1Hi7N5Nv+Mmdu3qeOiMuzv522/aa4x5vw2X//aYLVqQHh4JH1sPD+nY1cJYGo3Sbhk7lnz3XTJdOnt9bm62nbMuLtIR+uSJ83TevStDEgYOJKtVc6zTfCzfeov84gvJArFuPh07lrYaIyIkW2bGDImMVagg12JC5z19etHar5+kvJ88KanZgLR7p061yzJMMUajDIdavVrape3aSfQsMcNmbmfVqEF+8okMZ9izRyKGR45Y9mndWto7z4MyPZSHlrXZMS9ZsyZ8oVtfRG++SXbtSv70E7l9u4OxeJGRcqbNf1SjRopjdBcuyLibZ5kv6yVvXnnIjBpFrl8vX2qrAyYhC+urKRXjtEajfMkSMzbmxdVV0gL79ROz6W8OND14QFaxMhP9+6fpXfvmTUv0IaHF11eiZzNnyheb9+5Jt4V5BycO5DYapUcvoYddmTIypuy/PQFk2bKWp8ncuU7TGJ/9+20NZBv8xVi4sSQu0M+lokXj0qWaabTm2jXpKCiCq3FRCfNyNF11TnhnI+dNCU61XrOUcu4c49IXzYuxeInnf1I8g6CgIObKlYsAOH78+OS9ybx5JMBLKM4z23XUOneA6Zcp7F7zAgF5EOuNx48fs1KlSvz22zls0MBy+ntpm8Fqw759ks5atWrCDaBx47RWaUtQkDTUE3oe9OunYUpmSIiNSwwOlqyJli0Tfia8+67V81UjwsKko3bgQLJcuYSPLSD33qVLtTHDERFSFGTsWLJZM2kfPqst4+IiGRbx0+HTkshIMQa//kp27y61IxIzQvGXQoUkMpTWBaIiI8kTJ2QM7xdfkO+9J2ntz9LnqA3Zpk3SH2mvtOkJD5dGYnyzk1BDvFQpObijR5OrVkmg5pkXxqNHZM2atmbieQYKxePWLbJnz2e75Pz5JQQ7erR0kCfaw3f3rnQRmP+4T59UveL37iUrV05Ya8aMZKNGEmLdsSOBMOvNm3ICzH80Zkya3fliY8UEZ8iQsOZ69aSHxeYBd/OmxOjNO82YkSb6HHHrlkQWHWnNhXusib3s0IE03AkQ96MDwzN/vu3D2AVGnoYYxifwiXvh3IBZmmm0JiBA0hcA8kcMdHyws2dPfJCak+lb54ytvg8+kJZbKjNgwAACYKlSpZ67eEEcgwbxgXd+zvrmRuqKSwseP+a9e3K6ndmgeR5irW5OO3ZIf5Gbm36KL1gTHCwNX3NvuTkVCyB/T71hrykiNFQaZtbaHC0NGzo3IvEsdu8mvb0T1uvjQy5ZorVKC3fuSMThWcf4isZZxEaj1DaYM0c6vYsXT1hvhgzSvtEqKhwVJYH0336T9mOlSs/u0C9XTlINnW0wHz+WNuOMGdJJ8/bb9gX1ElratiXPnEn8/V9J0xMeLlGZXLkSP4BubtKDf/x4Mn3K1au2DfWBA5MdmXj4kBwyJOGqlObF1/c5s+YuXLCNI48bl2pX+bVrYhKfdaHmyfOMhsP585b8MhcXOSlpxOHDtv4vocXTU3p94rh8WUbpAfJUdFQuLY1YutQSora+djNkkN6oeel7c1nG7qxZ/B4f5tTe8BgM0rjJnFlurI0ayUDR5W1X2B/oH37QRGN8goLIxo0l5XXuz8E0ZMhkr7VDh5SPskxFjhwht7w5zHJBTJqUJk+wU6dO0c3NjQC4bdu25L9Rx45c+M1/uhnLkRTWrpXbvN65f/8+W7duzVmz7vLrr7VW82yCg6WzbsgQSS1au1ZrRRZiYyU6cvCg9G/8+KMMIG/TRrTmyyeB9Es6qBVy7ZpE0ooXl0dojhxy33XUjmjfXh8lw6OiZIz0s57BXl7k999rVj3ajoEJ9INZLwUKiMHUQ9pmVJQ8d5+luXp1SS3TEpNJ+pQ3bLCNIThaXFwksersWcfvlRLT40KSzpoT6HlJbNbVu3eB06cBf3+ZaNff3/L77duA0WjZ95NPgFmzkiHg6FGgWTOZ3MrFRSa66t8/2f/PjRvAlSvAvXtAQIDjn8HBsm+bNsBffyVhYttDh0Tj48cymdXcuUCnTsnWGJ/Ll0XbkyfPXkqWFM0eHvHe5MgRmVX48WOZUXjxYqBdu1TTaA0JXLokEzFHR8sSE2P53XrJfPUEwktWRPfugMv5czJZWUCAHMclS4C2bdNEY3wMBrlmPT1lomNPT1ni5iK7dAkoWxbInBnInRv47z+5MObOlYnLNCDuGFp/LU0mmZDuzBnbnTNlAho2BMaPB4oXd6ZMG6Ki5Li6uEAm+BswwPJi/vzAb78BTZtqps8RhljCvWxJICxMJlisXTvVP4Mkateujf3796NNmzZYsWJF8t/s9m2Y8uaHq2vq6XMGJhN0r7lp06bYtGkTypYtix079iB37hxpPfF5qhIZCXh7a60i6cTGytcuSxatlSQMaft8i4oCMmSQeVe1xM8P+Ocfy7PMvHh42G/z9JT5TH18tNW8ahUwZIic76QsZcsC6dNrq/mff4B69aS9my6dNA9y5wby5HH8s1Il2U9LTp4EqlWT69ZM1qwysXGhQvLT/HvhwkCVKvbt4MS8wTNJiXNLa5Lr5gwGSRXav18c+bhx8ca+JIW1ay3xZC8vyYVzAhER0rNz8GASypNaa8yQQeoY641t2yw5ZhkyyIApPRAWJt1m9+7JSEVzORpPT90Mto8j/sAvFxfyjz+0VmXP33877rYpW1Y/552UG4Q5xw2Q0bV6mYAgPseOyWja576BJZ1FixYRANOnT09/rQcHKBLk6tWrzJcvHwHwjTfe4GM9dOkrFArNOHFCUsEePtRPpcRnMX++pOht3izjVZNThv+VTG9LU6ZPtyT9Zs8uAz/0xuzZFo05c0qyp9aYTLY5bsuXW5JOs2XTftZra0aMEF09e1qSTTNk0LZuqSOsy5yYlxw5pLjCtWtaq7NgNNqXm/P1JadM0d9U8mvXir4SJeLlN+qQ8+fT/PjduHGDrVq14ji9jThX2HHhwgXmzJmTAFi1alVdTNytUCgUzuSVTG9LE0wm4KuvgMmTZb1ECWDzZqBYsbT/7KRCAqNHywKItq1b9aFx6VLg7Fnghx+AmTOBPn1Eb/78wLZtQJkyWisU/P2BUqUkF8CMj4+c6xo1tNMVHxKoWxfYs8f+tXz5gO++k/Q2PeS4rFoFtG4tv7u6Sk7pmDFA9uza6nJEw4YS5x858sXKt0ljSMJFD9eSIlHOnDmDOnXq4PHjx6hVqxY2b96MDBkyaC1LoVAonEJKvIF7Gml68YiKkrEw5nz2t94C1q7VvtEWFgZkzCi/GwxA797AnDmyXqUKsHEjkDOndvrMhIZKQmyuXJKkO3KkbC9dWgxPgQLa6rPm669tDQ8gx7VCBW30JMSWLfaGJ0sW0d+3r34a7CaTGDAAeOcdYMoU/R1LMyEhMraoUiWtlWhOdHQ0PD0949aV4XkxKF++PLZv3466deti3759+Oyzz7BgwQKtZSkUCoXu0fnQTSfx6JEMYjcbntatgR07tDc8RqMMpjeZgPBw4IMPLIancWNg9259GB4A+P57qS5x4oTF8Lz5JrBvn74Mz6FDUqQgPuPHi4m8ft35mhxhNErU0YyXl6xfvQp88YV+DA8ArFsnlSz++kuuSb0aHkAqLyjDA5Jo2LAhOnbsiMDAQK3lKJ6TSpUqYcuWLXj99dcx2hz1VygUCkWivJqmx2Cw/H71qkR1DhyQ9cGDpfGmh0blzJnS279jh5To2LBBtnfpIlEocwRIay5cAH7+2XZb8eLA9u3aG0drTCZg4ED77blzSzUvPz+gSBHn63LEkiVSBc3VFejRQ8rojR+vz1JCrq5yDbRtq49UO8UzWbJkCfbu3YtVq1YhOjpaazmKZFC9enWcOHEChQsX1lqKQqFQvBC8eqZnwwaLeThyRMZwXLokjbWpU2U8jx5ql967BwwbJr83bSpaAdn2xx/a1x00Q0oZ79hY2+1XrgDlyolWvbB0qeU4AhIl++kn4No1oF8/iabogagoYPhwieydPQv8/ruMi9IrLVroo5NAkSRCQkIwZMgQAMC3336LggULaqxIkVxcrZ5Va9asQbdu3WC0nq9BoVAoFHG8WmN6wsJkcH2LFmJy2re3TB6wZAnQsqXWCi0MHizjDwBLZGrGDOCzz7TT5IjVqyWiE5/SpcWgffih8zU5Ijzcki6WPbv83ru3TGqgN06eBJYtA6pX11qJ4iVk1KhRCAgIQIkSJTB48GCt5ShSgcDAQHz00UeIjIyEq6srZs+ebWOIFAqFQvGqRXpGjJDKXYsWSS96ZCSQI4eMQ9CT4dmxQ6IS8fn5Z2DCBIsZ0pqICPt0sQoVZGzU2bNAx44y0acemDRJzve4cTJuZ8gQfRoeQMyOMjyKNODs2bOYOnUqAGDq1Kk2hQwULy65cuXCggUL4Orqirlz56J///7QcWFWhUKh0ASdtEidgJ+fVJUCgOBg+anHktTR0RKNckTBgjIIO1Mm52pKiPHjxUQCQNWqwLffAs2a6W9cR2iolKS+fl0GsisUryAk0bdvXxiNRnzwwQd47733tJakSEXatm2LqKgodO7cGTNmzIC3tzcmTpyoqvIpFArFU16NSI/BIPOGmEy220lg/XqpPKUXJk2SMUZmXFyAVq1kLMrOnUCDBvowFVevAhMnArVqSUnqw4eB5s31oS0+mTJJREoZHsUrjL+/Py5evAhvb2/8HL/wiOKl4OOPP8asWbMAAJMnT8aoUaO0FaRQKBQ64tWI9EyfDhw/br/95k3g1CkpGqCHqljXrsnEnoAUKvj4YylPXLq0trocsWmTmJ3atbVWolAokkChQoVw8eJFHDlyBIUKFdJajiKN6NmzJyIjIzFgwAB89913ePPNN9GsWTOtZSkUCoXmuFDHib8pmXU1Dn9/oGxZGchuxscH6NVLKnbly5c6YlMKKVXa9u4FPv1UIhN6rtilUCgUCt0yYcIEXL9+Hb/++qsqaqBQKF4aUuINXu5IDykz15sNT4ECYia6d9dfqtO+fVI+e/FiIGtWrdUoFIqXhPPnz8PPzw8dOnRQ4zteIb766iuQjDvn1r8rFArFq8jL3f2zapWM2XnjDeDPP2Ucil7HdtSqJXOzKMOjUChSCZLo06cPPv74Y4wYMUJrOQonYzY5sbGx6NChAxYtWqSxIoVCodCOlzfSExIi40527ADq1tXnAHtr9K5PoVC8cCxfvhx79uyBl5cXunfvrrUchUYsXLgQS5cuxfLly+Hp6Yl27dppLUmhUCiczssb6cmYEZg7F6hXTxkKhULxyhEaGho3+eiwYcNQuHBhbQUpNKNr167o0aMHTCYTOnTogHXr1mktSaFQKJzOy2t61MBNhULxCvPdd9/h7t27KFasGIYMGaK1HIWGuLq64rfffkOHDh1gMBjQtm1bbN26VWtZCoVC4VSUM1AoFIqXjPPnz+OXX34BAEydOhVeXl7aClJojpubG+bPn4/WrVsjJiYGLVu2xJ49e7SWpVAoFE5DmR6FQqF4yejfvz8MBgPef/99NGnSRGs5Cp3g7u6OJUuWoFmzZoiKikLbtm0RFhamtSyFQqFwCi9vIQOFQqF4RRk5ciTCwsLioj0KhRkPDw+sWLEC7du3R79+/ZAxY0atJSkUCoVTePknJ1UoFIpXEDUvi+J5UNeLQqF4EUiJN1DpbQqFQvGSEBoaGve7asAqksr58+dRqVIlXLhwQWspCoVCkWYo06NQKBQvAf/99x/y58+P77//HiaTSWs5iheIQYMG4eTJk6hXrx6uXr2qtRyFQqFIE5TpUSgUihcckujbty9CQkLw77//wlWV7Fc8B4sXL0bZsmVx9+5d1KtXD/7+/lpLUigUilRHFTJQKBSKF5wVK1Zg165d8PT0VMULFM9N9uzZsXPnTtSuXRuXL19GvXr1sHfvXuTJk0draQqFQq+QQGgocP9+4sujR8CQIUDHjoDGadfK9CgUCsULTFhYGAYNGgQAGDp0KIoWLaqxIsWLSO7cueOMz5UrV1C/fn3s2bMHOXLk0FqaQqHQGyTw+efA1KmJ75c9O7B8OVC3rlNkPQtlehQKheIFZsyYMbhz5w6KFCmCr776Sms5iheYAgUKYNeuXahVqxbOnz+PL7/8EvPmzdNalkLxarBvH+DnB4SHAxER8tN6sd5WuzYwbhyQIYM2Wl1cgF9+AdzdgZ9+crxPlSrAypVAwYJOlZYYqmS1QqFQvKBcuHAB5cuXh8FgwLp169C8eXOtJSleAi5duoSvv/4af/zxB3x8fLSWo1Akj5gYwGgE0qUD3Nw0T616Jg8eAA0aAKdOJbyPhwcwYQLQvz+g1dhNEtizB/j9dzE1MTH2+3TrBsyYAXh5pfrHp8QbqEiPQqFQvKAcO3YMbm5uaNSokTI8ilSjZMmSWLlypc02o9EINzc3jRQpdAEp0QatogvPy8OHwFtvATdvynq6dLaLu7vl9xIlgGnT5KdWeHkBXbtK2pgjSpUCli4FKlZ0qqw47t8H5s8H5swBLl92vE+6dMD06UDPnro0mSrSo1AoFC8wV65cQbp06VCoUCGtpSheUiZNmoStW7di/fr18Pb21lrOy8mxY0CRIkC2bForSZwuXYB164D8+YECBRwv+fIBerlOLlwA3nwTCAtz/Lqrq5iM0aOBjBmdKg0AEBkJbNokZmbjRiAqyvF+PXpIOpmzDafJBOzYIVGdtWuB2FjLayVKiLnJkAHo0wfIm1ciP9Wrp6mklHgDZXoUCoXiBYDUZceZwkmYn9TOvgbu3LmD0qVLIywsDE2aNMHq1avh4eHhXBGpwaFDQI0aWqtImFWrgDZtgMqVJcWpYUPR6+mptTJbwsPFRPz3X+L7FSsmYz1atHCOLmsePAC2bQO2bpXl/n3H+1WqBMyeLcfcmcTGipFYuhRYs0YqoJlxcQHKlgXOnZN1X18xHG3aOFfj3bvAvHnA3LnA9euW7R4eoqVnT+Cdd0Tvr79KsYK//gJy5UpzaSnxBmoyh2RCAqdPEasXBGstJUFI4Pj+SOzf5SDfUieQwIGtYTbfKb1BAjvWRcBg0FpJwphMwNF9CfQQ6YTYGOLmldhn76gh4aEm3Z7niRPlPNNEzJo1C0eOHNFakh3cuUtrCclGr91/Zl0rVkCT+2S+fPmwceNGeHt7Y9OmTfjwww9hiPclCfO7BAQFOV9cEjFFRCFy4NfSWNPpHEQPHwKmjJkk4jNuHPDuu0DWrECTJtLDf+6cthdpSAge1W0D4+sVJXqSEBkzAsOGAf/+63zDs38/gopXAXPlkvLIixY5Njzp04shO3LE6YYnevI0RGXLK+d10SKL4alSBfjxR7k+J0+WbbVqyfgeJxue4OYdYSpQEPj2W8tNp0wZ4OefxQz9+SdQp46lB6Z2bTFxTjA8AOTLklyoY4KDgwmAwcHBWkshSYaFkWvXkp98QubPZ+Iv6M/zLmUYeTNQa2lxmEzkkSPkkCFkqYIR3IKG/CfL+2R0tNbSbLh9mxw7lqxR5B4voCQ31xgt4nVEVBQ5dy75QdGTvIdc3DdktdaSHLJ9OzmgyFpeRyHeWHdKazkOuXyZnFpgAv/1qsWwqwFay4kjKopctEh+Dwkh1+fqxm0letMQEq6tsHjExpiYOTM5Y7qJy7PWobuLGwHwxIkTWkuz4UH2Urw3ZTkvVP2Yuz77iw/uxWotyZ5Tp8gtW3j2y/lc/tEaXr9OfvsteeeO1sLsuTxvH78ps4onv1rCXLnI33/XTsu2bdvo4eFBAGzfvj0NBgNJ8vjkXTzm9iavv9+fJPn4Mfn339rpdMT+BqNIsQw0eXuTP/wgX34dcTJHvTiNDpdcucjvvycjIzXRFxNlZJhLhoT1pU9Pfvkl+eCBJvpI8vbm03F6jHBhUJlqNA0fQc6ebdHZuDF5/bpmGo9+YtFyM1NZnmg9hqF+l2x3mj2bHD2afPodczaHy3cnAUbAi9vydOLy/vt5+ZJ+2mfBQUHJ9gYvhukJCtJMw9Wr5NSpZKNGpKenXKsuMHImPo27cO9/NEAzfaR4hUOHyEGDyIIFRZYXIrgVDeI0RsxaqKlGUp4xK1aQTZqQrq5kNjzgGZQjARrgStOJk1pLJEk+fCjPlly5yKK4wnvIRQIM9chK6sSAk+SZM3L/roJ/GQ5vEuDtvFU0N4+nT5PnzsnvJpMYxxaeW2iAKwnw4ttdNdVnzY8/khUqyGn9tuTyuO/Lmc6TtJZmwWSif98JBMiPsYANAQLg28Wq0KSnjgKTidFuXoyFW1yj47chl/llnzDeuqqfTpe5X12kycWFUS6eDEBOrvduy+o4yJgYrZXZYhw+kvfc8pIAZ7v0JEC2b6+tpnXr1tHd3Z0A2LVrV0bfusftGVrE3cP3TjvJefPIokX1088WHk7281lg30gvUYLcvJnLlmmtkLx7lxzoMZ2ByGGj0ZQnD9m3L7lnj2YNYDOHD5M/ug7hGAzjcIyO02jw8JLGR4D2nVmLF5k4Ff3YDsuYFQ8JkIULk6vfn0dD9pzksmWaPx9/GPyIYzGU5XGKgIkA6eFBNm9OLlxIPnlCzYytmYF1T7IPptEXj22+MiVLkgMHSkerlt/vx4+THxB5MUyPExua0dHkzp3yHS5d2v4+6QoDV/l0sfQmNH9fkx4jo5E8eFAuwAIFbDV6IYJ7PCyGxzjkS02/6KdOkQMGkNmyWTT64jFPurxh0fjHfKfpiY2lwwbOlStknz7SYQWQuXGXV1Ek7sZu+mev0zRac+wYuWOHZf3OHbJ7dzGOhXA9zpRF+eSg6fIVTTSaCQoiixcn//yTfPSIbN1ajONj+JIAQ3IXl65gHfDkCZk1q5zrhqVv8gl8SIB38lXRT4uNJP/7jyYXF9bHNs5BZgJgOriwY4fLHDWKDNRLoDkgwO6GaYArr/q8QYaGaq0ujgPbwmw07sPbzJZVR+aR8v0Z2TuQoa6ZSYDj8WVcZ7/WPnfFihV0dXUlAG7ZuJF3p/zFs+4VSID7Xd5myRLSkJsyRVudcZhMDOn3DffkbMN56Mxp6MNpGb7kpY+/I3/8kVWyXePixVqLJO8fuEQjXHgL+fgzBvBt7GPZ0kauW2d7zidO1O4auHePHDaMHOU9jlHw4BT0Y27cZfXqEt1z5MsiIpyr8c4dctw4aaCbv+a1sYdZ8Jj165NLljhfU3xu3SJ/+YWsWZN0cbG9baZLRzZtSs6f/9QAacSNG+Svv4oWb2/7tnDGjOQHH5Bz5ohpdyYp8QbK9FC+yH/8IQ20TJnsT66Xl0QnZk6NYWjzDy0vtG3ruPWcAmJjE/ZQRiO5f78YiPz57XXmyUMO6h3Bx1UshodffZWmd8jYWHLbNvvtjx+TM2aQlSvb63y3cjADC1e1bPjttzTTF5/QUPkSP3xo2XbwINmqle3NJ4f7Y97yfU1W3NzI9eudptGaEyfILFmkByg0lBwxwmLKfPGYF93KSI+gl5eE+zTEaCTff1+0NWlC5stHZkAoT0OOozFDRvLsWU01WjOl8/G4jox/UIsEGO2Rgbx06dl/7Ex+/ZUE+ARuLPA0ypMdvejiQs6cqbU4Kw4ftvuy30EeXtp1S2tldoS6+8RprI09LFdOa0W23LwpnW5DMJEE+BXGxR1WcxRVSxYtWsSff/45bv3SRRM/zraBB1CDH2MBATJ7dl0Fxmk0kpMnS6+6+Vj27GnJzDp9WmOBe/eSBw/y3BkjmzWz/SrVqSOdX6R01AwapK35jZo1n/PG3GLRorY6ixYlp02ToQBm+vUjd+1yvkaTSdpL3bqRGeJl5fn6kp99Jsc0/nG8fp08f955Ou/ckWyiWrUcG6AmTch58xLvKwxP42zsiAhy40bpFC5c2L5NB5CVKpHDh0szJK2Dksr0JMC+feS6dfbbjUYZ9zJihONGOSBpYr17kxs2PL2goqPF1pp3+PhjafGnIpcvkzVq2HaKGo1yL+zfXxqR8XXmzSuv7dtHGkPDyfr1LS8OHZqmd8a7d8natSX6bta6bZukYJhTAeNMRA5y8GDy3L9h0r1hfuGXX9JMnyO9lSrJx4aGkitXyvG21pklCzlicBijq7xl2bhQm9TA06ct0bGmTaWXN05n+iheK1xHVlxc5J/RmHHj4l+fJq5waWPZoAONZvyvG3gDhVgEVzkMY+I0LqjzB3ft0jyTxAZD67YkwG+eGp4CcOMklz489fEkfUWkli+3uQDC4c3v3j+mtSqHRBaVzoKtaECArFdPa0X2PHxI1qoaxSsoyk8xM+7QTpumtTJb/P3JiROjWLasfOdL43yc1mHDtFZnz4kTZJky9s/S4sUlUq0Xdu2yPK/My0cfWUxbGvdnJgmDQSI81avbP0e/+UaeuZ9+Ko13jR6jJOV5/8cfYizin/fy5cmffybv35d9r14Vk7R0qfN13r1LTp9OvvOOvQFydyffe0/SxR89sv27gQPJWbOccz2YTNLxMnGimHF3d/tjmj27NJGXLXMcrYqNlSFLyU2SUqYnHiEh4kgBSxTiyRN5JnfuTObMaX+S3NykAT9hgnRG21w8kZHS6jTv3KNHqraKTCYZoJohg0RrDAbyn3/ETOTJY681f37y88+lF8NofPom4c41PHv2WBrhn34qBtI8nsj6mLZoQa5Z8zQgFhFB1q1r2WH8+DTTF5+zZ231xe+hKlJEeltCH0XLnUUDU2bNuXNiFOOfe1dXsmcPE8Nbd7Rs/OknTTRas2OHaMuMoDhZX2OsReO332ot0YZxjfeQAPfjrbgxKMvRlrlzmThzZqoHcJOP0cho3xy8CjDdU9OzBmBs+kzadJ8mxsSJNhdrO/eV9PfXWlQCPL1XvokjBMgOHbQW5JiwMHJMpVVsh2Vxh7ZVK61V2bJv32P6+lYjMNbufuXtrc8CEWFh5Btv2N9f33/f6pmqA4xGcvFi+2erHm+rBw5Iv7B1Y93Dw7azdrQO6hVdvEh+/bV92ypdOsn2+ftvy7Z+/bTrV7p3T7Jl6tSRZ2t8A9SokbQbHz603HqbNXN+unNQEPnXX89uW48fL+OQzee/XDnpfDh48Pk/U5keK7Zssb1BfPGFuGY3N/uTkSMH2amTmKEEQ4fh4WQDq3SxPn1S9a54/74lJQiQ9Lrcue21Figgbv7gQQcfHx4uXZVxrc2v0+zOYjLJF8zR8TQvpUvLPvfuWf1hVJStmRg5Mk30OWLHDjJzZsdaq1WT4goGA+XAtm+v+RPlwgXbqI61MTt7lhJDNm/s21fzp4i/v/TsuCOGW9GALjCyMTbSCHn6XSjelLHR+mlJnDlDzkQvm4Pr71KAP3772CYtQxeclmpERoBzAHYCGJ01J+nnp7Uye8w9TQC/wAQOGaK1oETo1IlhDd5nunQiefBgrQUlTEy0if3aBcRdrlmy6KthPm/ePOKpIS9c+Ge7+1bPnlortOXAAfL11xN+fo0bp7VCeyIjpaPTkd7vvtNanS2XL0vqmKNxIADZpYs+AtSxsZKy1bo14+4Djpbq1WUMjpYEBEgqc9269gbIzc22EzdnTs2y8Wk0kv/+K827KlUcH09zFlXFirLu4iJDNp5n2KcyPZRwX6dOCV+45qVyZYlKHD6chAdHSIg4JvMfDx6cqg3MjRsdN27NS6FC8pGHDyfysU40PEFBZMuWjrVmzCgBsIMHHXx8TIyts3NiXH7+fMfhV0DC3XEyTCYxEOYXe/XSxExcviwpi9Y6XWCM+/23qnMtLzRvrnkeVlSUGEeA/AArSYAD8HNcUYAnOUvy/qUnmmqMT4vGMbyP7DYH2eTiIsnK/funetpqipgyxUZnTMGiUnFDjzwdiDAH3eiT2WSXgqErhg0jT59m//5yaCfpqFifI0wmqQZsvhT05nlHjRoVZ3x69/7taaqbLK6uzh0j8SwMBilW9MkntsV1rPVaF47RGpNJBt/7+r5YRi0wUFIGHemtX19fqYT370uKW/nyjvVmzy4Vy/RAYKAMg65fP/HO508/peadeObx8m3aJNzxbF4KF3Y8PtwRr7TpMZmkp95RWA2QIgStWkke5HNVmAgKsh3w8e23qdYIDg+XnpCETn7p0jLm6JkfFx5umy72zTdp1lA/eTLhGxggg+0cVkSJjSXbtbPs2L+/U8yEySS9DYl9yXx8yE2bnv6B9c7/+58mZuLaNftKfHlwh30xlSVLkhPqb6PB9amDq1JF+zsapcfGrHULGtqKz5RJX60dSlpmQ2xxfEF88IG+nsQkgxs3Y5jZ8Lz2Rrzwqc4oX558913mzhrNH37QWswzuHmTpDQgMmbUdrzB8/DTT3KpTp6stRJbTCYTv/zySwKgi4sL581bwD/+sNzP3n9fa4WOiYkht26VaphZstg2cvWSmhkVRa5aJab3nXcshWziL3q6Jh4/tk3scLSUKxf3NdQNK1cm3Enq4kKOGaOvKOv9+9KHnNAxLlFC2pJ6IDpaMrIHDyaLFUtYc7duzy7w+sqanrt3bWsLOFrc3a0atknl0SPb2NyYMSn/Z55y9ChZqlTimoEkDHdxouGZN0/MY2J606eXvHibsRBGo4xmM+/0ySdOMTzR0fZRv3TppITle++J4fzxR3L1akl14tSplh0bNtQk9n7zpkT2AKnO07gxOWoUebXdUBqKFhfXaS4tWKiQLhq/C55OfVGjBjnv2yv2F0XduhJnN48Q1RiTiaxaldxRsKutTjc36erXOtk8PkYjv/LwYn6A68qV150hs+PNN8lHj1i/vi78eJIZOTLpPYx6YPFifZoIk8nEfv36EQBdXV25fPlyRkZKYzxrVhmDqmeio6Wt0LmzdIhVq6a7+UtJSj/iyZPS29+1K1m2rGUcjV7KhD98KA3cOXOkafK//8ntIX5kLU8e8vhxrdUKW7dK53n8AgKOOnj1EsW+csVxJd/4j7fRo/WTwBAWZl/4Iv6SO7cY/YRIielxIUnolJCQEPj4+CA4OBiZM2e2ee3yZeCbb4BHj+QwmUy2i/U2Ly9g8WKgcOEkfOiDB0D9+sDp07I+eTIweHCq/D///gtMnAi4uQGenrJ4eVl+j7/+4YeAr6+DN4qIAJo3B3btkvVhw4AxYwAXl1TRac369cCqVUC2bPZL1qyW37284v0hCfTqBcyeLeudOwN//AG4uqa6xvgfO3cucOMGULSoZcmXT447AGDDBqBOHSBjRuDPP4GOHWV79erAjh1AhgxpqjE+BgMwfTqQPTtQrRpQvPjTUxkaChQsCAQFAZkyybqPD3DwIFC2rFM1xic0FFi0CGjWTCTiq6/k4o5PvnzAvHlAgwZO1xifa9eA+7eiUf39XEBwsGzMnRtYvhyoXVtbcQ64vHYtXmvZEjEAVi1bhg/+9z+tJSVMdDTg7w+UKIFz54By5bQWlHRCQuQrVrCg1kqSzsGDQI0aaXLLTxEmkwmffvop5syZg3z58uHy5cvw9vZGcDCwfz/QtKnWCpNGdDSwbZvcduvU0VrNswkOBo4eBY4cAVq1AsqU0VpRwgQHy73YvNy/L02s3Lm1ViaYTHI/ePQo4SVzZuDbb+X60JLNm4GrV+V6jYqSn+Yl/nqlSsCXXwLp0mmnl5R2+7Zt0hQ0Ly4utuvmbf/7H9Czp/19LjFvkAQR+sXpk5PevWtby3L6dOd87vMQFka++65FYyqm3aUaJhPjkuUBzdLFHGI0Slx9wQIZVGWOZZcrp5/uGzO//GLfBbJ2rdaq7ImKclxqrmNH3UxEGsfatRZ9tWo5f1a1JGIymdioVCkC4HuNGtGkt++4QpEABoOBgwYN4oULF7SWolAoXkJS4g3Sttv9ReLWLent/e8/sZWzZwN9+mitypbwcInw7N4t699+C3z3nT66+44elZ8kMHQoMHWqrLdsKSGBuDCLxqxdC5w7B4wdC7RpI2GWwoWBrVsldKUXDAbgl1/st7dqJZHIe/ecLilBVq+WCKmZrFmBFSvkvGfJop0uRyxfLj+HDAF27gTy5NFWTwKsWbMGWy9ehIeHB6ZOmwYXPXzHFYok4Obmhh9//BGlSpWK2xYeHq6hIoVCoRCU6QGA69fF8Fy5InG1BQskpqYnwsMll8hseIYP14/hOXYM6NJFDM/o0ZY0p8aNgWXLtI2nWkNKGiAAXLwIREYCOXNKrDVfPm21xWfVKsnRi0+DBsCcOfpqrP/2m+X3Jk2As2fFUOqNiAhgzx5g5Upg0iT9XJfxiIiIwOeffw4AGDJkCEqUKKGtIIUiBWzbtg1FihTBwYMHtZaiUChecZTpuXxZDM+NGxKNWLoU+PhjrVVJT35srPxuNjx79sj68OFiLvRgeADRc/480L276AKAunWlcenpqa02azZtAk6csN3m4iIRle3bNZHkEFLGklmTM6dcm5s2JXFwmpO4cAH45x8ZBzV7toyX0pMhs+baNek0aNVKayWJMnbsWPj7+6NAgQL45ptvtJajUKSI6dOn48GDB2jcuDGOHz+utRyFQvEK82qbnvPnxfDcvi29vn//DbRrp7UqYfhwaaDFNzwjRujL8OzfD2zZIr/Pmyc/a9YE1q0DvL210xUf6yiPNYGBMhJQT6OY9++3pAsCYib/+08qW+jlvJuZNUvO9+nTjkcc6onXXgNKltRaRaKQxK1btwAAv/zyCzI4uaiGQpHaLFu2DLVr10ZISAgaNmyI0+YiQQqFQuFkXi3TY67YBACnTklZloAAiUasXSvjT/TA+fNiIObPl3I3ZsMzcqS+DA8p44riU7euHFc9sWOHlLaxpkgRYM0aKYFilX+uOeYoT6lScu7nzNHXeCMzBoOU6dqzR0rkKVKMi4sLFixYgKNHj+KDDz7QWo5CkWLSp0+PDRs2oFq1anj8+DEaNGiACxcuaC1LoVC8grywJaufmzt3pF7fn3/KGJSGDYEnT4D06SUqUa9e6ohODd5/XzRZM3IkMGqUJnISZOdOGVTviJYtJQqQM6dTJSVI7drAvn3yu5cX8PXXwBdf6CsaBchYowoVpAT01187qAWuUCgULx5BQUGoW7cuTpw4gbx582Lv3r0oVqyY1rIUCsULRkq8wasT6Rk2TKpM7dwpBufJE5mnZcsWfRme/fvtDU/NmsDTgc26gZRjGp9KlaTnf/Vq/RievXsthqdVK0kVGzFCf4YHEEPu5ycRPWV4XhkiIiLQv39/3LlzR2spCkWa4Ovri23btqFcuXK4e/cupkyZorUkhULxivFqRHpOnAAqV5aGuouL/PTxEcNTvXrqCU4ppBgcR1VuvL2lBHDr1s7X5YgNG6R8tpl8+YBx44AOHdJ8AtLnpkEDGbc1daouJslUKOIzYsQIjBkzBmXLlsXZs2dViWrFS0tAQACmTJmCMWPGwN3dXWs5CoXiBSMl3uDlNz2kRHLMpZ4BwN1dKk699VbqCE0tEhpX1KKFzCujl2nOTSYxkSdPStWuoUOBQYMkVVBvnD8vY3b69QM8PLRWo1DYcfXqVZQrVw7R0dH4+++/0VovHRsKhRMwmUwIDQ2Fj4+P1lIUCsULgEpvS4wNG2wNDyADsBs3Bvr2lbla9IDBIObBmrfekrSstWv1Y3gAmUPm1CmpKnb5shQz0KPhAYAyZYDBg5XhUegSkujfvz+io6PRsGFDtNJ5OW2FIjUxGo3o2bMnateujcePH2stR6FQvOS83LHl2FgZrB4fDw+gc2cZKK6XcR3z58ucJwBQtqykijVvrp9KbWaMRjFiJ07IgHu9o7fjp1BYsX79emzatAnp0qXDtGnTVFqb4pXi3r172LRpEwICAtCoUSPs2LFDRXwUCkWakWaRnh9++AFvvfUW0qdPD19f37T6mMSZNUuqYZlxcwN69JDoxNSp+plEMSJCqrPlzw/88YfMedKihT4b7ObJPF8Ew6NQ6JjIyEgMGDAAADB48GCU1PkcQgpFapM/f37s2LED2bJlw7Fjx9C0aVOEh4drLUuhULykpJnpiYmJQdu2bdG7d++0+ojECQqylHh2cQE++kiqdv3+u74mogSAxYuBgQOBS5eArl3FnOkVV1d9mjGF4gVj6tSpuHHjBgoUKIBvHc13pVC8ApQrVw7bt2+Hr68vDhw4gBYtWiBSL2nnCoXipSLN0ttGjx4NAJg/f35afUTijB0LPHoEfPAB8N13Mhu7XunaFUiXTmsVCoXCifTp0wePHz9GtWrVkCFDBq3lKBSaUbFiRWzZsgX169fHrl270Lp1a6xevRqenp5aS1MoFC8RL+eYnuvXJYXt6FGgShWt1TwbZXgUileOjBkzYsKECVrLUCh0QbVq1bBx40a899572L59O44ePYqaNWtqLUuhULxE6Mr0REdHIzo6Om49JCQkeW+UK5dMjqlQKBQ649atW8iXLx9c9TaflUKhMbVr18a6desQGRmpDI9CoUh1nuupO3ToULi4uCS6XDBXIEsG48aNg4+PT9xSoECB5L2RXssnKxSKV5rIyEjUrl0bNWvWxM2bN7WWo1Dojvr166O51cTX9+/fh8lk0lCRQqF4WXiuSM/gwYPRpUuXRPcpWrRossV8/fXXGDRoUNx6SEhI8o2PQqFQ6IwJEybgxo0biI2NRbZs2bSWo1DomqtXr6JevXpo0qQJZsyYoUq6KxSKFPFcpidHjhzIkSNHWmmBp6enGrioUCheSq5du4bx48cDAH766SdkzJhRY0UKhb7x8/ODv78/Zs6cCW9vb0yePFkZH4VCkWzSbEyPv78/Hj9+DH9/fxiNRpw8eRIAULx4cfWwVygUrxwDBgxAdHQ06tWrh7Zt22otR6HQPW3btkVISAh69OiBn376CenTp8eYMWO0lqVQKF5Q0sz0jBgxAgsWLIhbr1ixIgBg9+7dqFOnTlp9rEKhUOiODRs2YMOGDXB3d8e0adNUb7VCkUS6d++OyMhI9OvXD99//z28vb3xzTffaC1LoVC8gLiQpNYiEiIkJAQ+Pj4IDg5G5syZtZajUCgUz01kZCTKlSuH69ev44svvsDEiRO1lqRQvHBMmjQJX375JQBJDx04cKDGihQKhRakxBuomqkKhUKRhgQGBsLX1xf58uXD8OHDtZajULyQfPHFF3GTnv/xxx+IiorSWJFCoXjR0NU8PQqFQvGyUbhwYRw9ehTXr19HpkyZtJajULywDB8+HFmzZsWHH34ILy8vreUoFK8mYWHAvHlApkxA5sz2S6ZMQIYMgA7nolOmR6FQKNIYNzc3FC9eXGsZCsULjYuLC/r27Wuz7ebNmyhUqJBGihSKVOLECcDHByhQAEiXTms1iZMxIxAeDvTvn/A+Li5ifkqUAKZNA2rUcJ6+RNCfDVMoFIqXgC1btmDkyJGIjIzUWopC8VIyc+ZMlChRAmvWrNFayqvBo0fAi3I/e/AAWLECCAjQWknSuH0bKFYM8PYGihQB6tYFevQAfvgBWLIEOHwYCAwE9DAMPzQUqFQJyJs34X3c3ICePYGdO3VjeAAV6VEoFIpUJyoqCn379sXVq1eRLl06fPvtt1pLUiheKkji8OHDiI2NRbt27bBu3Tq89957WstKGWfOAOXLa60iYWJjgcKFgS5dgN695Xe9kj078NtvQLt2QMmSQO3alkWPkcEGDYD33wfWrgVu3JBl9277/bJmFSP0ySfOSx97/BjYvx/Yu1cWPz/AaEx4/0aNgF9+AUqXdo6+50BVb1MoFIpUZsyYMRgxYgTy5s2LCxcuqLE8CkUaYDAY8NFHH2HFihXw8vLCxo0bUbduXcc7G41iKt54w6kan4tu3QB3d2DqVECPY5ZMJqB5c2DTJmlwN28O9O0L1Ksn6UxaYzQCT54AISGy7N8P9Otnv1/BgrYmqGRJ5+kPDwcuXADOn7ddrl2T45sQ6dOL0Rw8GMiTJ2013rsn5mbfPvl55ozj/XLlkuiTmWLFgJ9/Bpo1S9PjGRIcDB9f3+R5A+qY4OBgAmBwcLDWUuy4fp2c8NkN/lRtqdZSEuTmTXJq272c99m/WktJkJs3yRnvLOfev+5pLSVBblw3cVaNeXx4N1prKQly7XwkF7dYTpPRpLWUBLm46zZ3fHdAaxmJcnrRSfofvpOi97h+/Tq9vLwIgEuXpv794cbKY4wNi0r1901NjP9dJE06vBYfPhRdRiMZGkqTiTQYtBaVOJcukTExWqtIGKPBxDWLQmhatVqTcx4TE8PmzZsTADNkyMD9+/fb7fPgzD1u67SIpooVdXvCzw2ey4DCVUmArFiRvHJFa0l2PMpajLEe3qLReildmpw2jQwJ0VSf8cQpe23PWjw8yN69yUePnKIx6NuJz6cvUyby66/J+/edoo8kAyo2cqylZEmyRw9y4ULyxg1y3z7ZniEDOW4cGeWc51LwgwfJ9gYvhukJCtJaCkm5V65fTzZtSubBXV5GMRLgnWEztJZmw+nT5Mcfk5XdTjAImRmKDIzYsFNrWTY8fEgOGkS2cl9LA1x526MwTRcvaS3LBoOB/Pln8muPSSTAC7lqkU+eaC3LjnXryHkePUmApyt3kQadzjh72si96erSAFeeaD9BazkOeXQnklfcSzIImXn0y+XJfp+WLVsSAN99912aUrkRGPvgCZ+4ZqG/WyEeGPdPqr53qnH7NmPhxnMeFbj95zNaq7Fl3Tryf/+j8chRxsKNx92qcM3Um1qrsmfcOEYcOskA70I8iOrcM2wb+eOPWquyI+ixkad93uZZlGWsu6fTGo7xiYyMZMOGDQmAmTNn5tGjR+Nee/jAxJPpq8c13EzT9fW8JslbN4287lrEtoHp40OuWaO1tDiOHzUyGume3UDv25f87z9NNK7++XrSzUStWuTs2eTjx07V+Eer9STAaKTjJc9yvPZmWxqGjSCXLSM3bLDo8/UlR47U5Ds1t/gPNMKFJ1CB8zL15ar2f/H+aQcd0xMmkB07kndS1lH4vDx6lPyAyIthejSO9Ny7R/7wA1mokFyL2fCAZ1COBGhwcWPgzJWa6iOlg+2ff8gmTURjUVzhPeQiAUZ4+jB43ymtJZIkw8LI778nM2cmq+IwwyG9Rg+zlWD0nQday4vDz4+sXJlsivU0woUE6F+6ARkbq7W0OAwG8ptvyG6YE3ejvNS4v9ay7DhzhhydYUKcxv8GztJakh0mE/lXqW8tGn/dlaz32bRpEwHQ3d2d586dS2WV5H8th5IAY+DOUysvp/r7J5uoqLge9MivR5EAw5Cem5bqo8Mqjr17LQ1KgAa4cmXbpeTnn5MXL2qtzsIXX5AuLraNtPnztVZly6lTNJUowShXrziNV9ae1UxOeHg433nnHQLgmDFjZKPBwKj5S3kzfak4jRFevjQFOq/XPCncu2viinz9HTfOv/hCF6G+C2djObLkEgYhc8JGwt2dfO016XU9ftzpGg/ujuKnpfewIo6zGC6zCv6Ne37HRaR++EFSdTRiw9IQtir7H90REycrXz5y8mQycuJUMnt2cuxYUqN2r8lELvj5EWuWe2xzatOlI//3P3LPHquA7sOHmmgMCFCmJ9Uxmcjdu8l27eR7bD7xmRHEs96VLRsWLnS6NmuMRnLVKrJaNYuk3LjL625FpVfLy0se9BoTE0P++iuZO7fFlAUiBwnQmD2HbkL5YWHkkCGkmxtZDmcYgozSyCxa0uk9QokRGEjWq0dWwjFGwpMEGF7pbTJaXyl4p0+T9X2PMgbyJbpf8wPdpD1Z+9e/RpyJ03iqSrdkvZ/JZGL58uUJgIMHD04lleSFC5JJwNu34xqYK3L1SbX3TxVOneKdtgMYcDuW0TnzkQB/R3etOnwT5vRph401U5MmXKl935WFQ4fsNJ7ccpcmE7l1q9birJg/30bjJ0W3MzJSXtKifygkJIRz5861jbAePszYpi1sdP77eje93IaEbdtoKl6c4V5ZbBvp1lEJJ/emO8L4y5Q4TY/hy12ow8XZB/Da8D+kp9BJ6U2JYTKRK1eSpUqREzGE95CLP2Igv250nFcu6+Okm0zk9u1kgwa2p7lJxj0cOSSMAQFaKxSNBw+SnTqRnp62OsuUIadM0S7xJSXeQJmeeDx5Qv7yi3QIWJ9kT0+yR/swBleoadn4669O0xWfqCjy998lxdJaZ6WiT/gwfwVZcXOTdA4NMRolalu8uEVjdjzgvcwlZMXbmzxyRFONZrZsIQsXNmu8z5tuT1eyZJHEep1w8KD0DGXFQ16DaDTmzKWLh6J1JuqpU2ShbKG8CDnXUdnzatYz5Ii+faVhdv6MgUdcpNfgkUcuGh8kP53g2rVr7Nq1K0NSMbd9xQrpPL3btAcJMBQZuGqmDp6K1mzdSgLc5GFpXFZxOSZj9maQl/USlPL3t2tQxsCdvd69wBo1tBZnhdEoX/KnGk+gAjt1kmfT229rLS4eAwbE6eyIhfz8c9k8fbpEebUkIiKCN27cIElGHjnFXbk+pAGuJMDpHQ7GGZ/ISF2010mSq1YYWCjTIxbDZb6JI/y9zWYaFv5J/vWXtqnLsbGS771uHQP+vck2rU1xXyNXV3LgQDI0VDt58YmNJdd8c4QF88baBKL69KEuTIWZ48clguLqatve/PRT/dw3Hz6USJR1O87cfOvWjbTKJnUKyvSkAkePysnzjjdGr3hxOdkP70TZ2vKJE9NckyOCgiSNMk8eW51Vq5Krl0TQVLOWbtIhtm0jK1Wy1dm6SQTDK9Sw3CnXrtVUIylRk48+srrhIIqX89S0GMed2o2Hss64MZnIqVPlxu0KA7e4yGBDk5ub5DZqzKNH5Pvvy+8nT5LZspFz0E00urhoehzjExAgl9+vv5Lj8k6NO/mPZiZ/LE+aMGUKf5kQxVL4L66xNjnjSD1ku9gSr8f/MXw5DGM4GYNZo+RD/QwzCwmxMz0/4XMCZJcuWouLR9++cRrH4Sv53ruSBQpoLSwesbFk3bokwC8wgQD5999krlzkt99qJys0NJTvvvsuCxYsGGd8IiLIzm9d4u/ozsOoyi8GGWgykatXkz/9pJ3W+Fy6RJYvb7lMa9eWNHszesnEXLuWzJ/forNgQRmWoiciIqTN5Otr0ZkhAzl8uGYZZA65epX87DPSy5ItShcXsk0b55uKhDAaJULVqpU0jaxvpVWqkHPnkuHhaa9DmZ5kEhZGzpkjYzesT56bm5zU7dufdqzExpItW1p2GD48TfQkxt275JdfylgYa63vvSdpeKaYWLKFVQh/0iSn6IqIsN929KikXlnrfPttct8egxxY88YZzh9QumiR5XeTifzjDwnkmCW9Vs7EwKZddRHNCwuTEH1AgPSgffihRdZPmUZYVnQywHnIELlJb95MZs1KtsYKi8Yvv9Rang3Tp4usAvCPS2G8V6VZslPvzp8/n8oKn1K+PHdWHsKV+IAEGIgczJU+hHnzkvXrMy6VSHPGjbNPyQE4CJO17nuxxWSiyapL9QGy0ReSuz52rNbibNkxbFeczjrYZfN80tHQQuHBAxoLFeaCrJ/HNdbMnYZapZEFBgayVKlSBMBixYrx9u3bJOWZ1agRmR/+zIvbHDRIOr2yZtVXnZrwcEktMp/33Lktmert24ux1AMhIRLssx6C1q6drUlbvVr7KNDjx+RXX9maiuzZJXgVP8oXEaHddXv/vjQxrdslAPnuu5KNope0zNu3yVGjbALSBGS4ZP/+ZFo9EkllehIkoRvYuXNkv35xY1njlnz5yNGj5WTGYTRKdQrzTgMGpNlVd/myfTrAhQtk9+5SVdH6odehg6QPkRQ9Xa0a6k5qYJ49K9ExMxcvkm3b2h7TcuUkw85kogwW1rARvHGj9JSGhorWOnVsw8ljx5KG8ZMsG/toO26iZ0+LpylTxiLrmwrrLStt2+riLnjzpm3eb3748zGedq1VrqyvsUaHDrH22wYCJq5DMxJgqEtG/vWjP//99/kblJs3byYAdu/ePXWrtYWF2eY8AOyLqWLWCtg2KjSnv/0g7EXowPz5TLo69SSldftUY2/MiJOsl0ZkHLGxDPPOxlBkYDpE2xxef3+txdly6xa5Z+op/lOiu5331bKX+tatWyxSpAgBsHTp0gwMDCQpnQXvvWfv04cO1U6rI0wmctYsy/PfzU0yT0qWlEaxnq6DI0fIChUsx9LXV4qjGY3yLGvaVB9m/fZt0WMdqShUiFywwFLNfMcOKRKk5aM1NFQMWYECttfoG2+QS5bYH8vVq7VQKTpWryYbNrT/PtWpI8MbHD0DnjxJ/vFVpscB69ZJb4OZ6Ghy6VLynXfsT0yjRnLS7L6QJhPZq5dlx+7d0+xbcOQImSOHRG1IGcf6wQe2vSfp00vb4mmk3sKXX1p26trVKd/UM2dEb/36MpTkk09sbyIFC0rGS9yUCD//bHnxww+dnpt8/LiEtAE5pdYN9Lp1nw7ZWb/ecsDr19f0Dr1ypcOOc07+7CpN5jh9mTKaz4tgxrpH0hUG7kFty0Wrl1yMp4Q1bcte+JVtsdzGTKRLJ8HH5/n6REVFsXjx4gTAQYMGpa7Q/fvtLoDDqMqdrvV5cbHO5t5q185G5zFUohci9BKEtKWoFHk551qObrDk+2s9/sQRpq7deCR3c7v7gIOpaDTlyhVp8BbBVTutqVjTI1lcv36d+fPnJwC+/vrrfPS0BHB4uIyVs9bq5SUGTm8cPWqpHhu/UamnaYdiYiSVzHqYQK1almya3r110UdHUjqU27SxPZ6vvSbNgI0bZf3rr7XXGxMj9bLiX6uFC8vUSOZ0sjfekH5lLVOJr1yRYoPZstlqzZVLTKR10bwtWyRimZx0OGV6rDAayTFjpO3aoYMc5K+/JnPmtD0J2bLJyUmwaJjJJPk61g31NLq7rF8vbUOA/O47yd+Nr3X06ATGgE+yiky0aOGUhvrp0xIWNvfmWN/gsmWT3GibtJu//7aYidq1nT5i9OZN+zFQgHT4zp//9KZ25gyZUdKcWFLbSm23btmHttOlIzf8FW7pSsuYUbO5EOJz8iSZCwFxWr/GD3HCZ1Sao6eid2RsLCO9ffkIWeJKuh9EdRYuYEhWPY3vv/+eAJg7d+7UT8O17iiwWs5009HgAzO1LGMJA5GDBXCTvr668eS2PB1oOLvttrjD6uLiOFVXczZsYORPM2x60AHp6dUbERES+Y9/yebPr/3UYZcuXWLu3LkJgFWqVOG0aUEOnwmAbfaCnnj4UNLE4+vVW1omKeNT4lcmMy9OyrxPMkeOSOqYtUbrlK2vvtLe+JCiYeNGx+3DUaMkqwaQ5A+t054jI2UowVtv2Wp1cZGI34YNctwBmYfXriP/GSjT85TQUNshI1my2E918Pbb5OLFSbgoRo+2/FHz5mlWJ3/2bLsMlgSdvB3Wg4dr13bKU/vUKYvhsV7Sp5dBq3bzyB44YEmiLVPG6WYiKMi+hwQg33zTaoLj+/ctZdt8fTWNTBgM9jdgWUxc42MVTlmxQjON8WncmFyFliyBi6yKw4yFhPyOFW3DO7d18LSwxkH0ZMLri/no4oPnfrLduHGD3t7eBMDFixenvtYOHey07mquQ8NDxpX1Mbi6syb2EiCHDdNaVALUrUs2b847dyxpQ4UKaS0qAaKiSH9/+vtbyv0D5PjxWgtLmLlzbcdNALqYNYFnz55ltmzZmCVLFvr5neTixWSRIvb3WldXSd3WExcvkp072w8eB6Swzb86C/ySlsI7jto2f/2ltTpbzKXgK1Z0rPfLL/VhfMw4ygSyXmrX1s8MG6dOSYTP3KdsXqzbkdmzy/w/SUWZHkrExlHjFpCD3bu31RiYZ/HTT5Y/rlcvTWyzyUSOGOFY7+uvO87ZtGHdOssdsEIFB24j9TFX5HL0kHA4d8TFi5Y/yJ3b6ROCRUfbF1SwXtq3J+9ciyJrWlVq27HDqRrjM3681cPMavKyz71mxr1gGjxEU43W7NpF+uIxo5GOa9Gcl1GMBBiTO79+7rpWPOo9zPHFUKSI5Ds8B61atSIA1q5dO3XH8piJV49+adWfdPXgteFp7ujZPr8SkPTRp8Mn9MeHH8aVoO/eXQ5vw4Yaa0oCR45YzITGww2fiZ9fXBYhAalKpQf8/Px48uTJuPXoaGmY58hhezto3lxDkQ54/FiKr8SvhmpeihfXV1TVaBTz66iD1Hx/OHBAa5X2REbaznlovXzxhb6MDymPrB49JBMkvt6yZSXLRS+EhJC//Ua7qLW1eZ8+PWnH+JU3Pdu326cDWRuI5zouv/9u+eMaNdKk5EhMjG3dgfhLjRrPmHJl717L069oUaeMaE7I8JgXD494hc4CAy1PvQwZnD47s8kkJWgdac2ZUyaMXrzIxIj2+qjURkpvnWUiXBPnoBvr1SO3jD5Mk/muVqeOPkaDUh5sVaqQXTHX9gC7uDxft40TuZO3sv0FUb36c7fQt2zZQgB0c3PjmbQYDBIUZKNxWrGf9FcQwExoqOjs0YNXr8jcHb16aS0qEawavf/9J5dr374a6nkOli3TZ6PcEU+eWAqK5sihm9uWDWfPnmVUVBRDQiRFyLo3Wg/RKUecOCFje63qcRDQV8l1g0GC6t9+K1kVjiIS2bLpZx4akrx2LeEGuXkZPFhfxsdkkkJHCWUL5c1rc7vTBSaTTGya0DHu3v3ZIyBeWdPzrBNuXlq3TmLm15Illm/nG2+kSf3K0FDHVWPMi5eXRKw++yyBE3/qlKXsXK5ciQxKSj1OnLC/waZLJ4MTP/lEUvT8/KwqdISFycRBgERPNm1Kc43x+e472x6Ed96R3Gc/P6v88smTLTtp3HUaGkqWeDpfa4EC5NxOe2Rl1SrLRAh58+pqVjVzA+xQpnjJ2+nTS+x9xAi5FvRCQEAKbg62rFmzhjlz5uTn5pkYU5udO+M0fp/tJz3N6WrPpUvSUxMVRYNBxvg54baUarRsKb39LwqjRknj7EXAZJKB7a6uMm+bnti3bx8zZcrE999/nzFP09fv35cCrenSSV+Inhq48YmMJJcvl0JM5mbLcp1NM2bmwQNpXnXqJM0W8+23eHF5TS/cuSNzDw0fLmnb8SOAADlokH6ui02bJFHF+pjGXzJl0jyBxYa1a+3nw3TU8X/3bsLv8UqanogISyVpV1cZqF6pkgyS6t5dehhmzJA246FDSYj2rF1rSRcrXdpqwEfqERAgRsHTU0KP778vtRJmzZI2jr//MwZ8Xr1qSezOnFncSBrj5yeRkQoV5LjOnCmVZBJ04gaD7XxBs2enucb4LFwo2Uq9epFr1lid++vXLZOh6qhSGynpl//7nzQMDAbaXtxm56ajfIDoaHkorP090Gbek7glVy7dRXv8f1hoq/GLL1I0wjooKIghaZVTMmECCXDvBz/ppV5Fwly6ZPOEGj1aQy3J4NChBNJzdYrJpHkfzXOze7cUFNIT27dvp6enJwHwf//7Hw1WhYquX5cGulZlgJ+Xmzeloy85g8KdjdEoTZfx4yVxoU4dnRYRoXzX/P2lHfnNN5IGmzWrVEnTi/ExExws7bXly8nvv5cxYG+9Je23dOnIP//UWqFw7pzcb+fPl+nd+veX4p+1aokJNlfZzZOHPHzY8Xu8kqbn+nU5wffupUJRte3bLSNaixSJN1FP6nHpktycktXOCgggi8l4CXp6kv/8k+r6HHHx4nPckEwmCVGZG5UajGQ2meTacHhD+uILGeGno0ptpKQ7Pq2iKjx5Yj8SuH59yX9zcuW7hAgNfVrb49df7Q1PrVqJd9NoREzb9pbo42+/aS0ncdq2JX/6SbeNgcTQunJQcnjRNEdG6q/R9Sy0npzSERs2bGC6dOkIgF26dKEx3sNZT5OVJgWjUcdj6RIgJERf0Z5nYTJJKpyexlA9i6AgGWGg2xTpeISESNvz8GHH7eVX0vSkGvv3W+pF580rV7PeCAqSdDtzz/+aNVorsrB1q+XpO3GipeH78cf6eiqHh1sGfpljwRpXakuQGTMcx3wzZpQ6kHoifv3MIUPSrNJhijAYJIk8UyZy8+Zkv02fPn24ZMmStClcYM2qVWn7/gqFgiT5999/083NjQDYu3fvtP9uKxSKFJESb+BCktApISEh8PHxQXBwMDJnzpz6H+DnB7z7LhASAmTPDuzdC5Qpk/qfkxKiooD33gP++UfW584FunXTVpMZgwEoUQJYuBC4cwdo316216sHbNoEeHhoq8+aOXOAnj1tt23cCDRpoo2exKhUCThxwnZbtWrAn38CxYppo8kRt28DBQuK3cmUCZg3D2jdWmtVjjlyBGjTRs75668n6y22b9+Ohg0bws3NDf/99x9KlCiRyiIVCoUW/Pnnn/j4449BEoMGDcLkyZPh4uKitSyFQuGAlHgD1zTSpH/OnwcaNRLD4+MDbNumD8NDijZATEX79hbDM2GCfgwPAPz9N3DjBjB4MNC5s2x77TVg5Up9GR4SmDbNfnvr1mIo791zvqaE8POzNTyursCIEcC+ffoyPACwYoUc29deA44d06/hAYDAQODw4WQbnujoaPTt2xcA0LdvX2V4FIqXiA4dOuD3338HAJw/fx6xsbEaK1IoFGmBu9YCNOHaNaBBA+DhQyBDBolKVKyotSph61bgt9+A1auBXr2ANWtk++DBwBdfaCrNBhKYNEl+P3pUfubLJ8fSx0c7XY7Yuxc4fdp+e5UqwNSpQJ48zteUEHPnWn4vXBhYvBh4+23N5CTK8uVAx45yvWbIoLWaxGnRIkV//vPPP+PSpUvIlSsXRo8enUqiFAqFXujevTty586N+vXrw0NPnXYKhSLVeDUiPYGBlt9v35b0q7t3AU9PYO1a4K23tNNmDQl8/z2wYQPQu7elAdy5MzBxIqCncPvu3RKViM/48WLc9ET8KI+3NzBlikTQSpbURpMjIiMlhQ0AOnUCTp3Sr+G5exfo0kVSG/VueFLIrVu3MGbMGADApEmT4KM3U69QKFKFpk2bwtPTEwBAEvv379dYkUKhSE1eDdPTvTtw/Tpw/z5Qv76kZLm7S3pOvXpaq7Owdy9w4ABgNAKzZsm2Zs2A33+XNCc9YY7yWHPnjkTR9GQkbt2yRMsA4J13gDNngP799XdMV66Un8uWAQsWAGkxji21yJNHIpF6MuJpxODBgxEREYGaNWuiY8eOWstRKBRpDEl8/vnnqFWrFmbOnKm1HIVCkUq8/OltR47I4OXSpYEdO4CLF6WhtmgR0Ly51ups+f57+21VqwKXLgFly+qngXnmDLBli+223LkletK2rX50AsDMmWIiM2SQaFmvXvozO2auXJE0vIIFtVbybPR0jtOQs2fPYsWKFXBzc8OMGTPU4GaF4hUhffr0AIDPPvsM3t7e6NKli7aCFApFinn5q7e99559utXvvwM9eqRcYGpy5AhQvbrj1z7+GPj5ZyBbNudqSojOnSWtCZDGb69ewNixgK+vprLsiIwEChQAKlSQ6m1FimitKHFMJv0asleYf/75B8ePH8egQYO0lqJQKJwESQwcOBBTpkyBq6sr/vzzT3z44Yday1IoXnlS4g1ebtNz6JD9eJ2GDWXcRPbsqSMytWjRAli/3nbb668DM2YANWtqo8kRt2+LeTAYpGrX7NlAjRpaq3LMypVSrOKTT16ZyIRCoVAoUgeS6NWrF2bPng03Nzf8/fffaNmypdayFIpXGlWyOiFGjrTftm2bpA+tXet8PQlx6pSt4fHxkcH3x4/ry/AAksKWLp0ULPDz06/hAcRIfvqpMjyK5+bevXvw9/fXWoZCodAQFxcXzJw5E506dYLRaES7du2wJX5qt0KheGF4eU3P/v3A9u3221u2lHET77/vdEkJMnas5feuXWUMT9++UmxBTwQHA/7+wNmzwFdfifnRM3rXp9Atn3/+OcqUKYMlS5ZoLUWhUGiIq6sr5s6di3bt2sFoNOL+/ftaS1IoFMnk5U1vq1cP2LXLsl6+PPDLL0DduqmqMcVcuCBFCipWBKZP13fkJDIS8PJSkRPFS82OHTvQoEEDuLq6ws/PDxUqVNBakkKh0JjY2FgcPHgQ77zzjtZSFIpXGpXeFp+9ey2GJ3t2mTzRz09/hgeQMTG//gr8+6++DQ8g89sow6N4iYmJiUG/fv0ASNUmZXgUCgUApEuXzsbwBAQEwM/RXHUKhUK36Cx/KpUYOVJSw/r3B4YP119VMTOkaFWTHSoUumDKlCm4cOECcuTIETchqUKhUFhz9+5dvPvuu3jw4AF2796tOkcUiheEly/Ss3s3kCkTcO4c8OOP+jU8gERNlOFRKHTB7du3MXr0aADAxIkT4avne4dCodCMTJkyIVu2bHjy5AkaNGiA8+fPay1JoVAkgZfP9JQpA6xbB5QsqbUShULxAjFkyBCEh4ejRo0a6NSpk9ZyFAqFTsmUKRM2bdqESpUq4cGDB6hfvz6uXLmitSyFQvEMXj7Tkzu31goUCsULhslkQrFixeDt7Y0ZM2bAVU0Sq1AoEsHX1xfbtm1D+fLlce/ePdSrVw83b97UWpZCoUiEl7d6m0KhUDwnjx49QrZs2bSWoVAoXhACAwPxzjvv4OLFiyhatCj27t2LfPnyaS1LoXhpUdXbFAqFIhVQhkehUDwPuXLlws6dO1G0aFG4urrCZDJpLUmhUCSAMj0KheKV5e7du6hXrx6OHj2qtRSFQvGCki9fPuzatQt79+5FgQIFtJajUCgSQJkehULxyjJkyBDs2rULAwcOhI4zfRUKhc4pVKgQ8uTJE7e+detWBAcHa6hIoVDER5kehULxSrJ7924sXboUrq6umDp1KlzUxLsKhSIVWLZsGZo0aYImTZogLCxMazkKReoRHQ34+QEGg9ZKksX/2Tvv8CiqNoqf9BB67713UJqggihN6Sgo+iEICIqIIgg2EBFFUEEEpYMUAUUBAem99947gRASQnrfnfP9cXd3tqUJ2XvR+T3PPGRL2JPZKffthtFjYGDwnyM1NRXvvPMOAGDgwIF47LHHJCsyMDD4t1CtWjXkyZMHe/fuRYcOHZCYmChb0sPj8mXgUahbiogAUlNlq8gcp04BGzc+GoZEQAAwZoyYMdmyJfDpp8DffwP378tWlikMo8fAwOA/x48//oizZ8+iUKFC+PLLL2XLMTAw+BdRr149bNy4Eblz58b27dvRpUsXJCcnZ+6Xjx3LXnEPypo1wKuvCo+/yty7B9SqBaxYAaieulyuHPDSS0DJksC77wL796ur2WwWhk5SErBtGzBuHPDCC0DBgkDNmkD//sC8ecCFC0r+DUbL6gcgNRW4dM6EGnV8ZUtJk7hoM2LjvVG8hLqpO3fvaChY2Bu+6u5GREZoyF9QbR9BYgKRI0jd75kUm+wROCEhIahatSri4uIwZ84cvPHGG3IFGRgYZDuaBnjfvQPcvQvUq+eRz9y9ezfatGmDhIQEdOrUCb///jv8/PzcvpcmM5ITzAisXh44cAAoVcojGrNCzJ5TyLV3A7w/HA60aCEMinz5ZMvSIRH53Vzkb1lfGDzlywMhIUDTpsCECUCzZrIVAmfPImL2ChQsmwvIZbf99JOImFgpXx545RWgZ09hTHiQxPnLkLpxG/KYI0UEJ9Ly7/37QFbq1Dp3BiZNEkbdQ+SBbAMqTHR0NAEwOjpathQHzGZy8WJyVKFpPOjXlPE3wmVLckHTyGVLNS7O2Y/rSvWllpQsW5Jbli9O4i7f5lz3/BQhWkH+mn2XJ7zr8tBXG2VLSZO/xx3lUf/GvL7urGwpabKx3zLuKN6d9y+ESdXx6aefEgAbN25Ms9ns8NqOVmO5vcVoxoXFS1KXMUefHMzd/eYxMjxVthT3mM0812IAd4xYy+tXTLLVuMV8P4qXenzCv97bwoun1bw2mq9e5+WPZnNOhxW8E6zodx0WxlOfL+fUmj8x9n4KOWqUetdxk4k7+y/gT6W/ounZ1uR333n04zdv3syAgAACYPfu3WkyuZ4Tmlnjrhpv8nSeJ4RvqHt3j2rMDAmxJt4MqGj1XYmtVi0yOFi2NBu3992wadMCA8kcORz1du5MnjsnVeOJEYsdNWVmq1OHHD+evH7dIxoPNByUdY3WrVQp8pNPyIsXs03fg9gGj4bRExkpWwpJcS1fvVocfx2xkiZ4kwCvPfU/2dIcOHOGbNmS/AATbQfi7WHfy5blwP375Ks9Nc5Af5vGmL+2yZblgNlMfvaxiZvwLAkwySuAqcF3ZMtyYcmceJ5FNfE9B1YgU9VbIB1eF8a7KEwCPFqyvVQtJpOJM2bM4JEjRxyev7PnChMRQALc0nCEJHXpk7jniO18+avLXNly3LNli03jymG7ZKtxi7bqL5vGX78LkS3HEYvREDf/d5vGaT8odk6vXUt+9x0jPpskro3w582SjcnKlWUrc+H8woMOizKtZ0+Pa1i7di39/Pz4zjvvuDhaSHL7lBNMga/j4nHzZo/rTI/f399DM7zcL3JPnZItjyQ5pfMWxiEo/UW5jw85YAB5R869fEyd5TyPKryFEoxGHpq9vDNnTPj6ks2akX//na36UlPJj/P/xM1oyd/wIuf49Oe2xiMYP3o8OXMm+fvv4tgsWlTXFhhI9uxJbtpEujHqHzYXLvzbjR4FIj3bt5NNm4rvtzH2MR7CgxBfugoZrkakJzqaHDpUnBudsMJ2gYpp9CyZkiJbno2NG8mSJcmB+Ml20tx/5W3ZshyIjiY7diS/wKc2jfc+/1G2LBcWLCCnY4BNY/ii9bIluXD3Lrkixyu2xVH4TjWjUYdLdxbHIvIx9LQa57QzIY06kQBvohQvnU6SLcctKS+K7/o8qnDtGsW8/lbee48EeA5VOWaMbDFOTJhA/vEH+fPPJMAI5OfTT5M8epSMiJCtTnD/Ppknj8vCLPGpVrKV6SQlkc8/7+Ltv1+0mhQ5J0+epOYuCnbqFLXB7zLJP5eDTnOVamSyOlFIc0Qkr5d50v2CPG9ects22RKZkkJ+/mkq63mf4Cz0Td+IyJlTRCZjYjyqMT6e/PRT0t/fKkVj03rxjKtcz9XIadqU/PhjsWiKi/OYxvv3yY8+IoOCHL/icePI2Fjqjq2mTYUhFBXlMW3kfyHSI9HoOXyYbN1a/+Ir4hLv+xYSHqMiRcgrV6Rps6Jp5MKFZLFiQmN9HGG8xduhVatGKhIpi4sjB1mipk9ju82zZX7qaaWMskuXyBo1yLb42/bFp774snJpG7/8QnbGCpvG2P7vy5bkQmoq+UmtlTaN1wd+JU3L7t27GZfGjePStA02jeteUM+4JSkWvRaN40r9JFuNe+7do9lP3M2HYQJPnJAtyI7ERP3nOnVEBAVvsV8/eZLcsnCh+J4LFiQBRiM396MRU0uXE+FnVfjsM5eF5LbK/WWrciQ6mmzSxNGYgBf3b46VKis5OZnz588XRtD16+SgQdQCAlz2Z9IX30jVaSM8nGzQIH0jwt+fXLJEtlKS5LHVwQz3KeKqMUcOsmxZsmFDYRD37k1++60wkD3MpUvkCy8IWS9hGVPgy4tFmjJuiOeNnLS4c4d85x3Sz0/fhUWKkH8O2szkk+el6TKMnmzg3DnyxRcdz5fGFcIYW9SS0xoURB465HFdzpw4QT71lK6xtPctRuYsod80L1+WLZEkuW+fyHwAyDK4zgifQpYHZcgwuTUe9mzYQObLJzTeQwHdcPSwNygj5s8nS+KWTWNKjbpSLtwZMWrwfd5GcRLg3dKPSTNuQ0JCmDt3bpYpU4bXrl1zeE1LTuH1IJEeeM63FuOjFUslsmBq38kW5ZkwVr3vmiQ5ebI4HuHLIgjl/fuyBdmxdSv5/vsi9Gi5YL6I39imjWxhTly65HZRua+dYiGpiAgyd24HjR9jHI8elS3Miagosci109mp4C6GhsqRo2ka27dvTwD8+OOP9Rfu3CE//NAh4hPvnZNx527KEeqMpolzZ/du3hw7j1PzfsxleIlHUY+JvnZRKg/XTLllwwYm/TSHMzqsZkMcYDlcZW7vOH72mVL+VZLkX3+RrUueZhDibBGVH35QK0v96lWyVy/Syy67sWxZct48j2SzuWAYPQ+R69fJPn1Ib7s0y9KlyXnT4qk1bCSe8PYm16zxmCZ3REaSgwc76mzTLJYJ1errXpdd8vPpk5NFTZtVZw7EM7hwPd3rosgdUtPEtdrbm/RHEg97N9SN29OnZctzYO5c0htmW62RFphDFHIpxvLl5Fz0FpEyL19qx45L0/Lqq68SABs1auSSU3+67/e2k2j9yK2SFGaAXZTnLUxTxZchsN71NE0UNgP8Hd2YM6diwdEDB8Q+rFrVti+r4wzfLrNaGBqqoGm2KI99dKJrgxuylbny8ccOOl/BYj7zjGLfOynydR57zKZzMH5gixbyFpbTpk0jAALg2LFjXbRue2aMzaG1pdBLqvncSJL37pHPPqunaD3/2B2Gr9hJzpmj1Pm0ebNYw1kP08ceU+92mZhIjhkjSmOsOmvXJnfskK3MkdOnyS5dHP0x1auLbFxPnvOG0fMQuHuXfPdd+zxLslAhctIkMjE2lezQQX9hxoxs15MWZrNY9BYurMspXpxcsshErVMn/clFi6RptHLqFFnPLk21TGmNoc/00J9YulS2RJJkQgL5v//pshbksetcsnixbHkOzJkjvC3DMEHX+PPPsmW5cO4c2TlwnU1jykejpGnZsWMHAdDLy4uHnKKzKcGhjPEWtQmb8r2oVPYQKW6GmkbRdQhgMEqySX3FojwTJ4q6hH37bd93G6xj1aqyhTlx6pTbCMpF7yq2KKkyi3Vr3otlW4c2BMhbt8TL9pl6Url3j8yle/mbYjcB4b1WjogI2w1pHl4nQA4bJk/Ot99+azN8vv32W5fXv/4klkPxLW+jOIfU3KSk4ZOaSg4Zoh+qJUoI34JqREWJSIVVZ0AA+f33amWLkiKiYrnU27ZXXyVDFOu1cvAg+dxzjjobNBBZeZ64hhpGzwMQFSWKynLm1L+8PHnIL76wZDRpGvnWW/qLn36abVoy4sgRx/RkX19x0Y6JofjB+sJnn0nTSArH78SJjgZk795kwufj9SdGjpSq0cqtW46ZD1/Vsmsn+dZbsuU5MGuWxVOFw3qnn06dlFipmc1i/UOK4/HxytG8AeFeS6pSS1pBbkpKCmvVqkUAHDBgAEmxu6ZOFa+feeINEmACArl3yXUpGtNj/35y4qvHHKI83yiS5m+jf3+yZEmeLNueBHgvZ2l6w8TnnhMvh4YqcYiKFYUbo6cVNjAsTBTuKrNgGzvWQWNP32UERNrLsWPk2yr1fRk5Ujd2a92yBdNSUsgbN8iVK2ULtCM8nFrt2ryZr5Zt9/7+u8PLHmXs2LE2w2fatGkur3/5JRmARDbHNjZtKkqUVGTuXP1+HxAg6k1V5I8/hDPb+t03b046ZTsrwbp1ejkAIPwK336rXmreli1k48aOl9QWLUQ5Q3ZiGD3/gPh48ptvyPz59S8rMJAcPlxfvJEkv/5af0OvXlLu3hER5MCBjvmUzz5LnrU2wZo5U3+hRw+pK4yrV8mnn9blFC5MrlhB0eLU+ge0ayclEdT5I/fu1Zs/AOSE3meoWa3fBg2k18hs2KD/PGOGkBWEOF71r6KH+BTpHLh5szh3NE3Uwv2EgSRAzdtbuIUkMWnSJAJggQIFeM9yYl+4IBwGB6cdsH35v1b9XJpGt1y5Qm7ezO3byT/QxRbl8UcSr14Vb1HCkCBdXJPb/Z7lUHzL8SWnsE1rje3ldijXsavlsW6/4UXbqQQoVLq3aZOus2BBdmufZDMmChQQvg5lCAsTacB+fty2xWyT/eOPwhs8cKBsgU7cvUtz7TpsUifetqA8e1YYaJ07e17Oxx9/bDN85s51bUP/1Vf6odCkid4o6+BBKlUzt2+f4/30/fcd0wdl1H64IzTUMXEnd25htClzPbWQlCS+e/sOatWrC0NDJTRNODZq1nS8vHbsSJ48mT2faRg9WSAlhfzpJ/0mZ42YDBigpw7YsHbRAchWrbLdW+180plMYrFrn95dqhT522927928WfwB1itiQkK2arTnrF3nYU0jZ892yHRg585incHz50V1HkBWqSKlm5ymie/Yut/mzHH0TP06M1ZcUQBhCUt2/1y9Ki7GUVHk9On6Pv09fz/9waZNUjXa8/LL4rv/9FOyBbbqGj/8UJqmkJAQ5smThwA4c+ZM2/Pz55NeMHM/hIvqGsry4gnPnTeZ4s8/yaJFeWisniL4NqbS35988kmyWjXypiL1zWzWzMWYSII/G+AgAeHvUILYWAeNcQhiKdy0PVWhgmyBdkRF2ZxECwoOYZUqjrtYueYLw4eTlSqRFAaZ9b4KkHXrSlXmntBQXt8XYnN6Vqum1/J6etampml87733mCtXLu5Io4hjvF2SROPG4vB46y2pl1e33LpFNmqka33uOb3L+qRJjmsGmWiaMHTs+3B07EiH5hbXrnl0OZUmN26QL73keP537+7+OJVpuJlMYslcvryu08tLpOc97BpUw+hxw5EjjvmaJpMoc6lQwfEL6dkzjZq7zZv1Pn1162Z7XPnECWE0WDlwwLFDpJ+fSL9w6GJ47pxuTJQtS0+2o9m3TxQEkqLpTPv2utY8ecTCUtMors7WwuE8eaRNQ16wQEg4f140gLBqLVGCPHhAE6t265OSm1SQ4kIBOHqk3i+zXH8gMxndiXv3dAMyCHG8DHGSaVWqSL1r3L59m926dWPDhg1F84LERPL4cQ4YQPbCfNu+7J3rd/bsKTyT8fHS5DryxRd6pMwS5QlAou3rV6QzrMB5RQ6wH2YSEOtgZfLmzWYHjR9ivINsGV7+dLG4Tp8vdcJ594q5PSoRGsrbDTvy9df1eXbWzdtboQiaE3//7ZhBAYjMQk+jaRovZ7Ay/OYbXWOjRmI2ZECAQs4PC4mJjvUzFSqIAvgOHcSaRqWuZNeuiRQ3q9ZChYS/iRTrMUnJPW7ZtEkY51atQUEiEcmakJKaKuY0ytabnCwCC/ZRP19fEfG9fdvxvYcP/zO9htHjxMGDou1wUpLYoatW2ZoK2bYOHZj2DImTJ/XBa6VLuwkBPVyuXhUHyMiRIlOgr9NMrbZtRUqOA+HhugWXO7dHJyKHhIhIWWCgiDrZR6KeeUZ0wCMpLE1rQa6XF7l6tcc02hMcrNuGJUroWp94wlIgOG2a/qR9C1FJ2DXqsm2tqt2kOZ/FLfnYY0oNrbN0KSZAfo/3SIhuUxtH73a5yMkgNtYyk2PnTrJePT5ZI4IhEFfkLXiGgMbcucndu+XqdKB7d4cDIAL5eRT1OB+9+HpPxRK77XOEAc5CX9vDyZNli3PC0h7JVLkqC+RKdjjHRo+WLc6JN94gGzTg/v1iiLy91kaNZItzJenQSef+C7ZNtZQck0l0MP/mG8d7AkCWKyffUD958iTXrVvn8vzEia77tndvCQIzQNNEZMfatTVXLj0LRIZRmR5ms2hqYD8mqVcvvZRbpWtYcrI4BuwzaqpUIdev17N3v/5atkpBfLyIUNrfHnLkENFJa/Rv4EAxByirho9h9Nixb59ur6xeLRa29heI5s3JPXvS+Q+Cg8mSJcWb8+bN9nbFoaFkRcvon5o1hbFm1Vq2rKiHcTkgkpJEjgsg7obr12erRuePdt6ngFhLTJ7sdLOwb2f6lZyhlJomUkGc9b7xhsVDcuCAHtFr2VKJxONWrZw8pTDxfIkWwvMfFCTCVYqgxcaxUfUYYURiD80QbtPJeJcvvywn59zt1HPSVhx+EZVIgKnwYS2cZL58ChWwW6lRw+WgvYhKbFDqjqeHX6dPSoqDxhP+j9siUjlzenxQd8ZYPTSbNvHddx138R9/yBbnxMyZts6MY8Y4aq1TR7K2NEhOJrt2db3ejhsnW5krS5c63m/tN5mZwxcuXGCBAgUYEBDAzZs3256PjBQZFPYedKs/MbtqJx6UjRtdfCL08xPNOFTjzBmHrua2zcdHGMgqcfu2yFKy12ldEgKi6ZEqREaKsSX2tUl58ojbsTXBZsCArDkaDKPHwu7dLrPSbNvjj4vC8HQtyqgoPSTk709u2/ZQ/o70Ps6+pbN1CwggR41KI9VG08jXXtPf7KbjS3by5puueuvVc5Oru2yZ/obu3aXFXK0NAJwvuqNHk+Hn74nhqIAIXcmaVmfHxo2uekdCr2S9N16hqxnJC9+s4NcYwQAk8hxEGuM1r/JcMlveNOkffviBr776KkOc+3w6WZN78AS75d7A84sOyc8JsCcpSS+IsGw3UYrlvK6rMHrLkTt3dJ0FC/KNZ6/bHirVYcxKmTLiekQRPbc/z5SafUQKgZb6x9RUx7SxKlXkSkuP1FTylVcc960yzSycuHlTdJtyvub26CFPU0pKCjt16kQADAoK4q5du2xz5KwZC87b88/L05sWZrNwPLtb49SpI71PkFtSUkTzW2e9BQtKL/N1y/btrllMgIiwWdP0VCE01HUsjP3Wt2/mDR/D6KH48u3bTlu3atXEkMQM1zTJySI3y/qL2Zw0n5jomEtqvyBPNxJl38r03XezVaMz9gX19lvevCJNwDY74tgx3ayvW9epEMlzXLni/pgAyMIFzbxYqZ3uytm5U4pGe8xmsn59MRzVqrMBDjLVy7IA7tpVrcU5yZ1V+jIJ/lyIV207985iebksd+7ccdu8gCkpbg8GzcdHuE9V4sQJB413UZhVcJ6ffCJbmBtOntTdzRs38tNPdemqFC070LChQwVw27ZCa65c8lOaMuLKFd2pV6aMbDXpYzKJtCvrsVCokHKXLhtmMzlhgh7wt/o8ZTbGTEpKYtu2bQmAuXPn5gFLKDoiQvSMsE/Fsm7Z7KPNMnv3OkYfnDcFMsld2LfPbV8Wm3NXmZpPO1JSRJKKs96AAPWOCVKUP/Turac+2m+9emUu2eY/b/Rs2SJyBd0dqC+8IOpk0sVs1ivHAZE0mY2kproOoLLf8uVLYx22dKnjH+bBVKxduxxvCvZbpUrkiBGWuqOwMJGXZ73TSXKPmM2OrbOtW7NmoqFF6mg749HNYDgZLFok5HyEcayB02xQNYaxxUUqFkuW1BNhFSEmysw7Xo65FuY3B0jV1KtXLwJggwYNaLI/Pw4ccDkYzH7+ln7qirFYnxWVGJiX9XCUDRuqN6OBpLj4Arb8pd9+Ew+tM3qUw6m4ZO1aofeJJyTpySLz51ucNoVlK8kYs1mkrVhPuYsXZStKn6NH9QaegKhJkUlCQgKfeeYZAmC+fPl4zC4nLDhYeMbtF44NG6ppWJ48Kepj7OtQAKF9/37Z6nRSU0Xzgnbt0l7rvPKKWvv4+nX3Bo91y51bHNcqoWmie659upv91rNnxs0u/tNGz4YNttpUt1vx4mS/fhlMsLYbrsbBg7P1qNY010YFVkfp44+TH3wgQsIuufD79ununTp1PNoOJzhYdIqx11unDvn556J/gm13paTouQI+PlLdDJMm6Vpz5iQHDtD0xhWbNukte7p0UeIqlpQkCmgB8oJvDV6v35nm13vrB4dqScUkV35y0PVALlxY9FSVMJFw165dBEAvLy+bZ9SGUwWwOWcu9aqrrXz0kdAZFMRZvXczKMhNIxNVWLpU9Ci2hEmsKWOrVsmVlVnMZuG0UW6WTBpommhfmzu3bCWZQ9PIIUPEMaHqwEp74uPJQYOE3po15d8aYmNj2bRpUwJgoUKFeN6pnvPsWXELs17ali2TJDQTxMSIrl61a+t6q1RRM3oSFSV8T127ujrUs9knniXMZvL4cbHe6dTJfY1akSJqORz++kskADllcDts3bun7+T7zxo9a9c6hnnLlhUXgLFjxWvOKf1use/c1aVLtkdPrOsZLy+RyjR0qDgI0h1dc+2aOHIBYX3cuJGtGu1JTNT77jduLNIA3Lb4JkUbDuu+/PFHj2l05tw5YQjXrElOnWrpNm4dY3/rlliYA6KDhCKV1t9/L4YOzh92yvUKMHKkbHlumV1ylHvrffx4j68WUlNTWadOHQJg//79Xd9g1/vblK+Agp0L7OjQQbgaN2zg8OGinl1ZNm50OIdMJpFjrkA/kEwzaZKtX8AjQUSEmIXxqKBpomPTW2/JVpJ51q4Vt9zsniyfGaKiotigQQM+/fTTjEnD2bl/v/A3VqyoVGNPt2iayBzp2VNc5t57T7ai9ImPF/Uxr70mUvm9vcVlT0VMJhHZ+f57MXfIWgNWrpxru2jZJCaShw6JuusBA0Sk0n4937Vr2sfyf9LoOXlS5P99841w3FuGrWeNlSv1+PATT2T7TJFNm8QJvmpVFrpaRUXpo25z5PD4dPtffiGnTMnEwLbZs/WjtW9faS4ya6vMHTvsJFy5InTt2aNXAwcGKtNCJjlZaI6Komhz4pwDMGqUKEz7Rwd59nDnDhlZ0anVTVCQtHSxKVOmEAALFCjAcOdkfLPZ5gIzFS0h2vSoTKVK4vumGBcm29ucVRQoj8sSUVHCW/oosWWL+jVI9miaeoXVGXH3rjqaIyIiGJ9BSETTRCPXR+n8u3tX+MiuXJGtJHMkJ5Pr1ol0fgkz1rOMySRmVn73nVhaqG4Qp6SIktb580XJ+sSJ7q9zD2L0eJEkFCUmJgZ58+ZFdHQ08uTJ83D/8/37gZYtgcREoHJlYO9eoFChh/sZD4rJBLRvD2zYIB7//jvw4otyNVkJDwcuXQKaNgX27QOaNwdSU4EnngC2bQMCAmQr1Bk9GvjiCyBPHiAmRjw3Zw7wxhtydTlDimPxyhXX14YOBcaPB/z8PK/LHbdvA6VK6Y9LlQL++guoX9/jUjRNQ61atXDu3Dn8/PPPGDhwoOMbTpwA6tUDKlYENm8GypXzuMZMEx8P/PEH0KuXbCUGBgaK8tNPP6FTp04oWbKkbCkGBh7nQWwD72zSpDaXLwMdOgiDp0gRYP169QweAHjvPd3g+eordQweAJg7VyzCb98GunYVBk+JEmLBppLBo2nAL7+In60GT9OmQMeO8jSlxZEjrgZP7tzC2P3uO3UMHgBYu1b/uWFD4OBBKQYPAHh7e2P//v2YOHEi+vfv7/qGnTuBOnWA3bvVNngAICjIMHgMDAzSZPLkyRg0aBCeffZZ3L17V7YcA4NHiv+e0RMeDrRtC9y7JxYYa9YAFSrIViU4dkz/+ccfgWnTxM+9ewMjR0qR5BZNA2bMAFavBlq3BkJDhaGzYgVQvLhsdY5s2wbcuOH43N69QOHCwMSJcjSlxbJljo9r1gQOHVLL2LWyZo34t3t3YMcO6d97njx5MGzYMPj4+Li+aDIB27cDxYp5XFeW8fKSrcDAwEBhunTpgtKlS+PChQto1aoVIiIiZEsyMHhk+G8ZPQkJIl3syhXA2xv47TfhpVYBTQN69gTOnhVe9PfeE883by4MDJUWQxs2ANeuiZ/PnhX/zpwJNGokT1NazJ3r+lyhQsJgGz7c83rSQtMcjZ5XXwUOHACqVpWnKS0SE4EtW0Ta4NKlQI4cUmSYTCasWLECGWboDhkC5M/vGVEGBgYG2UjZsmWxdetWFC9eHKdOnULr1q0RFRUlW5aBwSPBv9voIYFbt8TPJhPw8ssiDQcAfv4ZeOEFedqc2bABOH8e+OADoVPTRH3HH38A/v6y1Tny88+uz330kfD637zpeT1pERUF/Pmn43PPPQecPCmMX5XYtw8IDhYpbD/9BCxcCOTMKVuVe/buFTVRn38u1RifPn06unbtis6dO6f/Ru9/92XOwMDgv0WlSpWwZcsWFC5cGEePHkW7du0QGxsrW5aBgfL8u1cDW7YAY8cK4+fdd4V3HwA++QR480252pyZPFn8u349EBcnPNNr1gAFC0qV5cKNG3pqkz3+/sDbbwNlynheU1osWwYkJYmffX2BCROEcalaCh4gtJYuLepO3npLrcieM82bC8NcImFhYfj0008BAG3btpWqxcDAwMDTVK9eHZs2bUL+/Pmxf/9+dOzYEWazWbYsAwOl8ZUtIFuZMEF40AsX1qMTvXoJQ0glzpwBNm50fK5AAeHtf/ZZoEULKbLcMnOmMCLt6d9fFNrnzi1HU1rMmyf+rVwZ+PVXoEEDuXrSwmwWTRaOHlWzoYYzvvIvGyNGjEB0dDQee+wxvKmaA8PAwMDAA9StWxcbNmxAq1at8NJLL7mvaTQwMLDx721ZfewY8Nhjjs8995yol1EtXWzAAGFMOPP44yKNqG5dz2tyR0qKiEaEhYnHxYsLfe3aydXljrNnRSOA3r1FU4hcuWQrSpuUFMDHR2wGGbJv3z40bdoUALB371488cQTkhUZGBgYyOPevXso9Cg4zAwMHgJGy2p3fPut63Nt2ug1PqoQEQEsWOD4XGCgiFLt36+OwQOI7mxWg6dnT+D0aTUNHkBoXbJERHtUNngAYYQbBk+mMJvNePvttwEAb7zxhmHwGBgY/OexN3giIiIwatQoI9XNwMAN8vNUsoPr113b/wKiW9euXcDs2SLlTQVmztTrTgDg6aeFvsqV5WlKi59/FjVG06er2UbZnnffVS/dzuCBmT59Oo4fP458+fJh/PjxsuUYGBgYKIOmaWjbti0OHz6MGzduYN68efA2GrkYGNj4d54NkyaJOgl7SpYU3v9Vq9QxeFJTgalTxc+5cwtjYts2NQ2eM2eAfPnEv6obPIBh8PxLqV+/PurUqYMvv/wShVU5jw0MDAwUwNvbGx9//DF8fHywYMECvP322xm39Dcw+A/x76vpiYgQHcQSEsRjb2/h9f/iC/UWwkuWiDSxF14QBk+pUrIVpU14uCiyV7mrmMF/ApPJBC8vL6No18DAwMANS5YswauvvgqSGDJkCCZNmgQv495t8C/hQWp6/n3pbT/9pBs8DRqIwZ7ODQ1UgAQWLQIWLwZeeUV9Y8LwqhtIRNM0W5qGrwLd4wwMDAxU5ZVXXkFSUhLeeOMN/PDDDwgKCsK4ceMMw8fgP8+/K70tMRGYMkVEdKZMEY0AVDR4AFHHM3++iPQYFyIDgzQxm814+umnMXbsWCTZ178ZGBgYGLilT58+mDZtGgDg66+/xmTrLEADg/8w/y6X6fz5YqbN5MmihkdlcuQQm4GBQbrMnDkTe/bswenTpzFgwAAEBgbKlmRgYGCgPG+//TaSkpLwww8/oEOHDrLlGBhI599V03PuHFC9evYLMzAw8Ajh4eGoWrUqIiMjMXXqVAwaNEi2JAMDA4NHipiYmKzPOjQwUBRjTo8Vw+AxMPhX8dFHHyEyMhL16tXDwIEDZcsxMDAweOSwXxj+/fffmDdvnkQ1Bgby+HeltxkYGPxrOHDgAObMmQMAmDZtmtGtzcDAwOABOHHiBDp37gyTyYSAgAD07NlTtiQDA4/y74r0GBgY/Cswm822VLbevXujadOmkhUZGBgYPNrUqVMHffv2BUn06tULf/75p2xJBgYexTB6DAwMlOPYsWM4ffo08ubNi2+++Ua2HAMDA4NHHi8vL0ybNg2vv/46zGYzXn75Zfz999+yZRmoW1rvypkz+liYRxDD6DEwMFCOBg0a4MyZM1i0aBGKFCkiW46BgYHBvwJvb2/MmTMHPXr0QGpqKrp27YotW7Zk7T8hgb17s0fgw+TXX4HoaNkqMmbDBmDSJMBslq0kY65eBcqUAT77DAgNla0myxhGzwOwZ9U9LO63TbaMdNn03UlsmXlFtow0IYFVQ3fg5plY2VLSRNOANe9tRkqSJltKmqQkmrHr692yZaRLxMUIXN14OdPvr1ixItq3b5+NilwJO3ANiXdjPPqZWSXxzFVxUKrM3buyFaQPKWalPQIkJqr/dV+7JltB+qSkAId3xMuWkS63Tkfh+LIL4sGPP2brZ/n4+GDhwoXo3LkzkpOT0bFjR5w5cybD3zvy2UpcX3tGePtfe00cnApBjTjduC8il24Q5/iuXUCrVkBUlGxpNhKPnEVw8YZIHjkaOHBAGDqVKwNDhwJNmwKnT8uWiLtDv8H9/BVgfuZZoF8/YNw4YUDu3QvUqSM0f/klULYs0KcPcPKkbMmZhwoTHR1NAIy+f1+2FAciI8m3+iZzO55mKnx4+f2psiW5kJBADu0Vzmsoy0ivfLy7aINsSS6kpJCfv3SacQjiNf8qjDt0VrYkF8xmckrbNSTAE4VaMvlWmGxJLmgauazRtyTAvbX60ZyYLFuSW7ZV6sdEBHBH26/SfM+xY8e4b98+D6qyQ9N4uuBTDPMqzM19F8vRkBHJybzrX4pX/Kty/citstW45949JnkF8GhQM66beEq2GreYjp1kklcgdwa24qYFIbLluCVq4V+8G1iaO7ya8/D+VNly3HJ30mJezlmbO/AUL1+WrcY9wROX8HJAdR70bsRr1ygumDExsmU5cH7CKoZ5F+Fpn9oMmfM36e0tFhrZTFJSEtu2bcvevXvTZDKl+959X25iMvx43ys/o9v3JAFy9Ohs15gV1vb9Q+gCGNugOfnkk+LxY4+RERGy5ZEkf2880aaRAFmoENmzp/7Yz48cNYpMSpKiz2Qi/yr8hqPGzGzPPUf+/bdYNGUzN29abIPo6Cz/7qMR6fFWR+bKlUCN6kT9OYPQHDvhCzO81JEHQDgKmjQwod2Cl1EON5CLsUj1CpAty4HISKBLqzi89PtLyIkE5NfuIdU/p2xZDmga8GGvUPRY3wcAkDclDF65c0lW5cqCTy6gw8FPAQA54u/BO8BPsiJXDkzaixaXZyMQyWl6B81mM/r164cnnngCc+fO9bBC4Pb01agZsQuFGQ6z2cvjn58ZEuf8iiIpt1Ah5QKS/dWcu8HFvyKAyaiVcADRAWqmJvps3YQAJqFu0n5ciy0kW45bct+9jCJJwajMC9iwRc1Gq3m8YlAx/hSq4xy+/162GvcE5SAqJp/D49ohfNQnFOzbDzh2TLYsB3yD/FFYC0NN8ykU6NdF3Hy2b8/2zw0ICMCKFSswZ86cDLtjevt4IxkByM9I5Fnzq3hy/HjgijqZJMyXH6dQGwCQ6/AOYLcl++HoUaBlS+DePYnqhHUQW+Vx/IJeCENh8eS9eyKKYiU1FfjiC6B+fWDfPo9rTEkBLjd+FZ/gS8xGX2zGswjPXQH0zeAatHkz8PzzQK1awOzZ2RoFfKAgfTYYYQ8NW6TnH1hzD5s7d8gXXxQG7WD8YLNuU/sPFJ4jBdA08uefycBA8hsM1zV+O1m2NAcuXiSrVNa4EK/aNKasXCtblgNmM9nvDTPXoQ0JMNk7gClH1fNab/jbxL1oQgKM8i3AlJt3ZEtyITYylWf96pAAb/pVYHJUgtv3TZ8+nQCYJ08ehoaGelZkaipD8lUjAR71acD42Oz3VmUZs5kxpYTGDWjF4GDZgtxjqlOfBPgnOnPdOtlq0qBtWxLgKnTg55/LFpMG77xDAtyDJ9i8uWwxabBwIQkwEQHMkYMMD5ctyI6bN8k9e8jr16l5eZEAL6OCuOfs2SNbnSAuTty0x4xhUq4CDp5zbdA7HpeTmprKd999l2fOnHF9MSmJV7qPdPXwP/+8Mmsgktyyycz+QYsYjdyuWmvXJu/elS2R27aRJYub2QAHOQqfM8K/qPvoiZcXOXgwGRvrcY3bt5OVK+tSShdP5YaZ18lvvkk/4hMYSNavTw4dSmZTltaD2AaG0ZMBmkbOmUPmyye+z1bYQBO8xYMWLUSOlgJERJBduwpZ3bFUPwBfe02pC9K2bWT+/GQ/zNQv7iM/ki3LAbOZ7NOHHIJJNo2myT/KluXChQvkZ4ETbBpjZvwqW5JbVj79nU3jue//dvue8PBwFihQgAA4ebLnjfToCdNtGuf+T9G0sT/01I3e5RTVeOyYTWMHrOKxY7IFuSEpicyRgwT4DqbwrbdkC0qDF14gAS7Eq/TzUywj68ABMiyMXLHC9n0HIoFrn59KulswyyAxkaxSRSwcnRZm99cfkK1OZ9480sfHRePdgtU9LuWzzz4jABYrVowXL17UX7hxg6xZM+2F7qpVHteaJpGRjH7h5bS11qghvB2HeeMAAMEvSURBVNiSCQ8nO3Qg2+MvpsL1+3fYypYl16/3uMaEBPKjj/TDMxAJDM4tHG8MCiIff5z83//I8ePJv/4iL18W+XHZjGH0ZBOXL5MtW+rHXZ2A80wMzCseVKhA3rsnRZczu3aRpUsLWbVwkgneQeJB/fpkfLxseTZmzSJ9fcl6OMpEBAiNTz9NpqqTr24ykb17k3VwnEnwJwGan39BKcORFOne7cqdte3H6Ge7KKeRJI/+FcwY5CIBHqvYLc339e/fnwBYu3Ztpnr6eIiNZUxO4Wlb6/0Cw9Qr2xLfbYMGJMB9aMz331PvuyZJvvsuCfAOitIXKQxRsVxm61bbRb0qzrFLF9mC7LA/h6tXJwF+gU8JkKtXy5PlwoYNwqP71FO2fXkJFRmPHIy/L6cWwS3bt7tdRH7S7ohsZY6sXSsWkU46V0y77VEZERERrFOnDgGwdOnSvHbtmv5iYiI5bZpYgDvpTC5RVp21xo4d5AcfMOXJZxjrm9e9EVG1Knnbs/vWHdq9CJ5t1ItLvV/mQrzKX/A/HqzZm6beb5D9+5MDBpBvvy2ivkOGkIcPS9F57Jgoi6qMC3wBq1kn91XOmWWWtuQwjJ6HTGoqOWGCzRlIgOz41H0ml7PE+vLkUcKbZTKRY8aImkeALOQdwfsFLOH7ggVJ+wuWREwmEekEyDyI4jXfiuJBkSJKXHismEzk66+TORDPMxALDq1oUSXC4faYTOTzbUzch8bihpO7IOnpdLBMkJJCbszTjQQY65WLsefc52MdPHiQXl5eBMCdO3d6WCWZ/NFoEc2DN8d0P+3xz88UmzbZLkYdsVLNtLGkJLKASNGZgGH08lLKn6Hz0UfC25+rFAGNTZvKFmTHpUvkp5+KfRkYSAL8qNhcAuSQQQrtzNRUcf12WkxuwrP86SfZ4pzo189FZx0cV+8cOnBAFLXb6ezjt4CHDnlWxt27d1mtWjUCYPny5Xnr1i3HN6SkkPPnM650VcfI1MDPPCs0E6Qka/yoxxV2w+8ch4+4I6gNU/Nb9nHlylQlR/j4cWGHWXdngwbiUqASqankxImO6+KWLSmlgYlh9DxEjh0TETvrl5ovHzlvZgq1554TT3h7iw4VkgkOFkESq87yZUy836StrnHTJtkSSYqUjPbtrTo1bskvFsH08iI3b5Ytz4bJJKK0ADkNb+k7Vrk7ozAgh8Mur3bpUtmS3LKk1982jWf7fef2PSaTiQ0aNCAAvvbaax5WSDIkhMn+OUmAs9CP9hkdSmEJOZ9GDQb6m5Vxqjrw+++277s6zrBwYdmC7LDvKGS5wB9/vA8BsmJFebJcSEkR4XC7FdC2ekO4Gi/wo6JzZKtzZPBg1wgKxrJiRY9kuGSe+/fJoo41EzVwmmXLSimVSJ8LF8jy5W065+F1lixJj0dMb9++zYoVKxIAq1at6r7G0mTiyVG/8bhXPRKiruvCWsVW6hTB0++/17Mc8+fTuHtpsEjJW7DAI93GMkNcHNm3r36Y5spFLlokW5UrzhlQOXIIY8iTDi7D6HkIJCSQI0c6pta+9JIl9dNSUEqA/M794s2TrFxpc6gSILt3JxOHfqw/MXGibIkkyevXRd2gVdb8x/QGEPziC9nybJhM5KuWngodsErX+N57sqW5MG8eWQ16WpvWrZuSaW0XjifwiqVo+Hq+OmleEc1mM+fMmcOKFSvyjoQ8a3P/ASTAOASx3/PqRB0dOHDAdky+hgV87jnZgtKgXTsS4M0SjQmQtWrJFmTH+vXi5Ll3z7b62fbmrwTInDlli3OiRg0XYyIcBZkD8aoE7wX79rnobIZdBIT9qxS//eagsyrOq3qJF4uO+qIZyE2IaGSjRiK7zJNcv36dZcqUIQA+/vjjNKdhHGzdorGz/1ruRlNu8m/HkyfUux+R5Jo1wpAAhF9hxgzZityzdKlIJrIerr16qWecO9e6A8KX5KkaTsPoeUCcu1SUKCEMC5Kis4r1hT59pC4wExMd7a8cOcjZs0ltuV7gzB49lFgE79vnmP0wu/9+an5+4kHr1sp4V1JT9Rb5xXGbMQEFxYM6dTx/l8mAPXvIHH6p3I9GwuApVEi51DtSfLXzy3xq+/Lvr8m4U1JGMyKyhbNnafYWXo4x+Iz793teQqbo0kUYj15l6YsUTpggW5Abbt2y5dn+8uQMAlTLOLOmB7ZuradiLb7LIghlQYSrtaiwtgm12770/owAOXOmbHF2aJqobbW7IT3dOIkA2aiRErchHU0TVeMWrV+8ftmWcKDkeR8TI04ggFUsBpqMnkSXLl1ixYoVuS6DjIdt28igHBqfxnbWzh/MEyc8oy+rnDzpWJL0/vuKRSUtXL1KNmmi66xcmTyiWBka6djVGBBBg48+EkGE7MQwev4hkZHkm2863l8GDCCjoixv2LpVuAQAslkzacOiSPLsWceoSZ064jmeOaO7L2rXFjFSySxeTAZY+hQEBpJ/zo4gy5QRT5QsSVUqxVNTyVdesWTbwcxTxZ7TRStQs2XPzZsiQ+NDjNcPgmXLZMtyy9Ix55kMYeBebN4vzfelSO58qHXsSAK8i8Js/YRKrbHsOHvW9n0Pwo8EqNaCYuxYcSJ99ZVt4du9TRQBET1Vhp07HS/0vr5MLlqKwSjJ3IhWa7DmqFGOWv392bVZKAGxwFCKT3XnBp99ln/+qT/csUO2OCeCg8ncoo1x3JnrtltS7dpksorznJOTyZ49ubzlNNs+/eYbz8vI7HV6+3a9F0PBgqJORUVCQ8knntCP0xdeIBWYiuJCSoowIKxpeX5+Ik1PKWeChRUryOLFHY207duz7/MMo+cfkOGXdPmynkNWpow0j7qmia5n9sVj77xjCUJERekhqnz55FSU2WE2k599pussWpQ8sM9sa71KHx/Rak4BUlNFUMyqdcnjdlOSFavEjYsT2Q7VccbWUU6l1Y+m6dlrt29p3O4jEn6j/ApSC3ff4fDQoUMsU6YMf/vtNw8qFVy4QLEis3zfb2Ea//rL4zIyxZ02r5MAY4MKMwfiWbSoYje9MmXIvn1prmS5Dr32Ghs2FD9+8IFscXbYpQjab12xnICIoprNwsMqnSVLHHW+8Qa//lq/zKemioXb2bOyhdLBKOcXX9Bk0m9J7duLtyQkiI7HSjB1qhB36xb/1ksO+eWX+lsUmUIhMJuZunUH27ShLTK1Zo08ORcvXmTPnj0Zn0ZR4Y4dIl3Uavgo2bKeYv1kzfAARCquUqmjdmzeTBYrpmt9/nklEzzcBhHefNMuiPAQMYyeLJCpcFx0tK1dKHPmlOayiIwU9TpWrQUK2LXDN5v1cL2Xl/SC+/h4R61164roBMfbRSYk5uXY38hSUkS9llXW6A5H9NS7jh2VWFXu3i3+1TSh1QepPADLalKxtLajR8lJk8TP3zdYbNuxYePdF16bzWY2atSIMpoXaBpZvpzGxDoiRfA8qrBW1RRVsi0FK1eKnIsbN5gCEWn+qdQ4AqLZhlJYo8yW7WDrjzks3yw+gy38+mvR80WJ3Pnjx10MnoRnXyCgWdfrbNaMnDtXtlCKUJ691tOnefSo/nDhQtF8QZkeK/XqCWGWzovT9ZFXPHNG1CTMny9ZoxWzWbj5LYX51lpOf3/y3DlR8vX++5I1uiEyUu9tkTu3nohw5oyY0ecJTCYTq1atSgBs3bo1E9NI/965Uzd8ChQQ9wcrKhmUmiYC1dZjtUgRdWbWOhMWJowdq9ZixZTqA+VAuuUiDwnD6MkEmS68Mpkcj64VKx74szOD88Vg717H3NPmzZ26K37+uf7iuHEe0WiPfR50SAht3l2r3RAbS+H2sXaG6NBBah3PgAEiOzElxdHoHfi/OGrWu0nx4kqMFI+KEl6y0FDRkhwgR+BrXbSE6Eh6jBsnCi9nfRvFOxBdkm6Vb5bm9z1r1iwCYO7cuRni4bZE9+6RL2GZbV92xp+cPdujEjKmbVvRVcXSHSsauZkXkQTEIu2998gpU2SLpEi/cRM9CUZJFsZdWv0ISsyXOX/e0eDxysGKPtdc5J88KVsohRvaUh+V0LwtX31V9Kbx93fUumWLbKEWJkwQKcGW9O+EBLJwYaHR2ojs008la7Tn9GlhRVAsJgtayjifekp4pgsXVqbk1IELF/T1S8WK4lrWpw/57bee07B7927mzJmTANihQ4c0U9927dL9Ifnz6/Uon3+uXkTlt99s3eHp7+/YMe3IEXVSHzVNOBet11UvL3GbcHboKuCzdRlqCktyysPqVWQYPU7cuuV488pSi70PPtDf+NVXD/YHZJLQUDHTjxQ217hx+sHi7S28kA7Fdn/9pWvs2tXjR/nNm+JGER8vvDilSulyhg+3aL17V88fLFvWc+4oNxw6pNuv3brpWvv2JbV+/fUnNm6UptGeHyxN7p55RvxbA6eZ4m1Z8bz0kmx5Ljz5pJA2BaLLRip8aDrmfvUYERHBggULEgC///57z4m0XBAO7k7mZUtXuV1oRkBj797CG63EgpfUi8Mtd7evMcJhsZs3rxIDxYUIJ6shBb58AntsT5UurUih8LVrDjp3PD/exeDJkUOhuUJWV+nGjXzrLZfdTECZTGFxQ3j2WZrN5Mcfi+tqtWqOWl95RbbItFm40HXfeno2TmbZsEGfy9esmVikV6jgWSNty5YtDAwMJAC+9NJLaQ6T3r3b0fA5fJisVEk0ZFCNgwcdU8g+/VTs03feEY5HlThyxDGS0rixnpa7Z48iDjEL1qGmVq358ongg/2Sdf36rP+/htFjR2ysqH/45Rf3Q0bTHaY0d67+xp49PWJMmExCU/XqYk6ndaELiHR5a5qTjQsX9H6G1auLLi8eRNP0QNgHH+iFi35+4mC2/VHWuUZ+fuKKIglN0w1e+4HX/fqR5uV2VbfDhknTaI+mOQ4p80EqTwU2EA8KF1amCYSVyEhhoD+GwzRB3I3nFx7GN99037Z24MCBBMBatWp5tpFBu3bkypU8/L/Jtp3bBHsdjHUlSErSVzWW7Tyq8DjqsAeWEBDpQ0pw+rTLanEIJjk8pcyCISREF1WzJsNDUlwiJ0oNKe3YUXSr0TRGRjouyKzbgQOyRdphKYg9c8Yxm8K6NWwoWZ8bQkLEPax7d0ePNKDURAUXJk1y3b9r13pWw7p16+jn50cA/N///pdmO+s9e2y9I2z3Xy8vNWt9goP1TE1rZKJ6dbGEOXVKtjpHYmPFIHWr1jx5RCngzJlCr0odCTMaalqoELNcU2sYPRZMJr3M5bXXXIeMOluYDuzerccNGzXK/p57FqyNenx8HIcxd+0qZqo5EBOj1xrlySNSNjyMO69YgQJOTSBGj9Zf/PFHj2u0Z906V71vvEGabwTrjSrq11cmhm3trGvdRuIr24PDIxUbfhEezt9+I71h4kEIw+wGSjMnYvncc7YMEhuHDx+ml5cXAXCHJ1s7aZoITRYuzPggkcvyO7rZ9nGPHgqls5w549atPxVvE9DYtKlCWu2aQRBg3PMv0VojY72mOQ9zl0ZEhK7V4kmydm60btZouxKMHOlQCLNsmethoeLCkRT3AmeDskAB2arcM3++ftu33554QrYyV+7cIT/5hGzRwlXv8897Xs+KFSvo4+NDAByXTor9vn2ODkdAdI5XkdhYsnNn90a7MlFgOxYt0o1KQHeOlCkjNbnGLe4yrsaNEz/nzOlY+5URhtFj4b333K4XMs4lvH5dT0L24PjjDRv0doTWLTBQeHJdjDNNE5aQ9Y0S2k3duSPC1PZ6fXxEj34bGzfqf9RLL0lNMDWZhLPU+XjIk9PE00We0c+8c+ekaXSmUyc7hzRO2bq17SvTXbmLGFu25Duv3ONbmGYT3Rl/sm9f9wWrY8aMIQD27NnTszovX3Y5CD7DGHbCCvZscEGtcUz2PX8t23J0pTdM9PVVzOO4YoWus0oValHRtvoIQBzLyhAfL0T101uob9/uuKsXLJCoz5lNmxxGJGiabfarbVOsq74Dv/7qet11ceIpwtatrtEpb2/1Fo2aJvar/WBy6+blJad569KlS/nEE0/wvpsvNyVFLGrdzNolIA5xFUlOFqmDznqVnI9G8b03aOCq94UXFHKQWXBXW2/dSpRwqltPB8Pood6J0n4LCCD/+CODX4yN1VfGOXKIxFMPEBzsGNmxbk2apFFb8JXu8efo0R7R6Ix9PYyz4fPWW2Ty1Vu68VipkvTm97/84l7v+Px2HeUUmvZ3/Trp75Ui9ilSbdGTpLzqpbVZ6znWBnRhJPKSAFfjBY7/WkvXzt28eTNv377tMZkkxeAoNwfC6YDHeP+yYiubr7920HiqwNMMQCIBURiqFLNn69dNizVmba0LiM5tymAyiQvuPb2FuqY51p4o0QI6Ha5edUwRuXRJtqL0sW/cCahbJ0MKv5f9nFVApAupyJ07js4x6yarRXx6g6UvXXIcDWG/1a+v3qJ882bHGmXn9aSE5JoMiY/XIyYua53xstW5JyRENOdy1luvXuYqNv7zRs/atS5p8LatceN0LrZms2Ms00PDHlNSRP64O72A8Iw4hPrWrdOjJ+3bS7lS/P67e621aok5QvHRqXpFe0CA9MlkiYn6PFTrVrgwuXTYIWrWgbNduqjR6sTCqPdj+CMGiQUu7K5iy5fLluaKkyGRgED+PfWKbFXuGTLE5cA96teQN44r6Hru08fh5HqtfSQBsSDzUMZt5pkwQei0C5F88ol4qmxZRRoY2LN4sctT338v9ObKpaBeN1h3OaDQ7Js00DQ6NGFQ1YiwEhbmOLSyVy/ZitJG00Rqk33mRf78YgEskylTpvALNwVRR46IlDbn9YObU1I6Bw+K8gh3aY/N0m5KKoXkZHGOOaeT2jukVRsSHBcnHHhpaX7hhYxTCf/TRs/x4y6jImxbzZqiQHn79jTWth9/rL/Zg9GTYcNctZYsKZ4/etRJ6+XLeiywSpXsmfSUAeHhooe9VauXl/A0bd1qp3XECP0Ns2Z5XKMzE22zRjXmyiUKqmNCYvW2JyVLOnh9ZZOYSPbOvZxmePGt3Atp9rVccXv0kC3NPW+84XoQ58snOnE45YXMmzePt2QWdzRp4qBzv3cTHt3u+fMoU1i9IaVLk8HBfPll8XDDBtnC3DBihOgFb4c1481+2KMyuLkJREQIH83TT0vQ8w9ISdETE5To4JcBqal6ne3YsbLVZExioh6ZKFJErQWuO0JC9P0LUGr7/cOHDxMAAXBCGrlgW7Y4jrcoV84hi1Mp7twRy8KiRR1vcyp1R7Ny/7747lu2dC2ZKF7cNpZKCUJCyGnThHFjH7m23wYPTv//+M8aPbdvO4Yic+YUi/Hp0zPhBbP3VL/4oseubitXOnpm3nxTGGVuPz4ujqxdW3dFSkritg5wy5uXHDqUvOLs0F+9Wv+j/vc/6dGT+/fFvvX3J3/qvIERWy1RJ+tC3ctLoSEXgl9+IX/B/xzP/CJFlJgb5IKmuYbRANFkw+nEO3LkCL28vJgrVy7Pp7WRwhUWEGDTuAdNueF3uWmX6VKokDh4LblWvXqJRpJKMmMGnQuigoNJX1+PlUU+FF57TVzXHhX27ROXMNVqTtIiLk4sdHv3lq0kc1hbbwNqp+RZ0TRx/8iXT6QHybz9jhs3zmb4/JhGEyNNE8kLVaqIfWwdbK0qSUmigZO1biYoSG8RrSK3bokItn2dz7PPqhnJTkwULauHDHFsw52RcfmfNHri4kR3tmrVxA3Lqe4zfQ4c0BdC9euL/8wDXL0qirVefln0IUi3YZimOSbDZliclD2sXi1aKE+bZhk46sy1a3qMvUYNj+3L9Bg5UiwWr12juNO+8IKYQGbdlyNGyJboQod2qUzK5VShWquW+GOWLZNuSDpw6ZL7uL/TKsxsNrNJkyYEwFdkDeqwDmkCGF79Kc6b4tkW71kiIkJ0MrEbCz58uFpeOgfceGo0jXz7bQlaHoBdu0SB+KPE228rcanNNKGhosXuo8ScOaLE7lHh1i3Rxc3u8iGFTz75xGb4zE4n9JSaKkpq69aVksCSZTRNDI3v0UPMj1bplpwWFy6IaFXlyqJTsOpcuiSMnXbtRBBjzRr37/tPGj3nz/9Dazs4WO/rV7SoGKzmIU6eTMNwcIeenyXcTpI4dcrN2mbVKvFvcrIeqw4KUqISWNPsCnyTkkR4yhoGBISlrEh7aiuJieT9Fdvdx3mrVhVXLpX4+WdHjV26uC04mTNnDgHIi/KQeoeTFi0Ydk3xVeLBg/q5ZeFR8ebbo2qXrrTQtEdPc1SUmi1000O1XiyZ4VFIIbRH0zy6pElDg8ahQ4cSAL28vLho0aJ035+QoFBr+0wSHPxoXZs1TdRVqZpK6I6EBHLnTvdZUP9Jo+cfER+vD+8JCBB5AiqyaZPemaFNG7XikomJIo5+5IgYbGFd+C5cKFuZK6tWuRoRkyaJ/DzVkrWHDnXV2qaN67AbFbBv4/f2226Pz/v377NQoUIEwIkTJ0oQaaFXLxHbl13hmxketVWsgYGBgRs0TeNbb71FAPTx8eEZlfurGzxyPIht4Iv/CiTQpw9w5Ih4PGsW0KSJXE1WkpOBK1eAGjWA69eBl18GNA2oUAH49VfAx0e2Qp1Vq4CoKKHx0iXx3JtvAq+9JlWWW5YudX3u/feB2bOBFSuAypU9r8kdpNiv9rz/PjBhAuCr2ClqNgPbtomfv/wS+PhjwMvL5W2ffvop7t27hxo1amDIkCEeFmlHvnzA9OlAjhzyNGQW1b5rAwMDg3+Al5cXpk6diqSkJFSpUgU1atSQLcnAAADgRZKyRaRFTEwM8ubNi+joaOTJk+fB/rMvvgBGjxY/jxgBjB//4AIfFkuWACtXAvPmAc2aAcePA0FBwL59QJ06stU50q4dsH69/rhaNeDoUfUWlQkJQJEiQHy84/OvvgrMmAHkzClHlzvOngVq1hQ/+/mJRfobb8jVlBZHjgCNGwvDsXdvt285duwYGjRoAE3TsHXrVjzzzDOe1WhPcjIQECDv8w0MDAz+o5CElxunmIHBg/AgtoF3NmmSz61b+s/Ll+sGT4cOwFdfydGUFjNmCI3dugmDBwDmzFHP4AkJATZudHzu/HmgdGngo49EdEoV1q51NHj8/ICffgIWLlTL4AH0KE/hwsDWreoaPACwfz+wenWaBg8AVK5cGcOHD0evXr3kGjyAYfAYGBgYSMLe4ImNjUW7du2wc+dOiYoM/uv8eyM9LVoA8+cDkZEiepKYCNSqBezdC+TOnR1y/xkXLohoiT3DhgETJ8rRkx4TJogomTPDh4tUJ39/z2tKi27dgD//FD+XLg38/ruIUKjIE0+I43PVKqBsWdlq0icuDsiVK1NvNbx8BgYGBgYA8OGHH2LixInIlSsXNm/ejMaq3o8NlMeI9Dhz/DiwYwcwahTQsaNYUBYqBPz1l1oGDwDMnOn63O7dwNChwMWLnteTFqQwIu0pUkSkuk2YoJbBExMjIj0A0Lq1SL9T9QIbGgqULCm+c9UNHiBdgycxMRGaXbTPMHgMDAwMDABgzJgxaNmyJeLi4tC2bVscO3ZMtiSD/yD/TqNn6lTx78KFIs3Nzw/44w+gfHm5upxJSgJ++cX1+ePHRRODSpU8LilNDh8Gzp3TH7dqBZw4AbRpI09TWqxaJWo5Ro0C/v5bGLyqEhAA/PZbpqMnKvPhhx+iWbNmOHnypGwpBgYGBgYKkSNHDqxatQrNmjVDVFQUWrVqhTNnzsiWZfAfI9uMnuvXr6Nv374oX748cuTIgYoVK2L06NFISUnJro8UREQAixc7Ple+PHD6tFikq5TN9+efQq89jz8OHDsGvPMO4K2QTWqN8vj6isjO+vVAsWJSJaXJ5s3C2BkzRq3Od+7In1+t7/kfcvz4cfz000/Yv38/7t27J1uOgYGBgYFi5MqVC2vXrkWDBg0QERGBZ599FhdVymgx+NeTbaut8+fPQ9M0zJgxA2fOnMGkSZMwffp0fPzxx9n1kYK5c0UExZ6LF4GxY4GbN92215WGfWqbtzfw2WeiY5tzjY9skpNFh7kKFYA9e0QNj6oLdRL4+mvRZc7AI2iahkGDBkHTNPTo0QMtW7aULcnAwMDAQEHy5s2LDRs2oE6dOrh79y569OgBhUvLDf5leLSRwcSJE/Hzzz/j6tWrmXp/louVzGaREnb9uuPz//sfMHkyUKBAljVnG+fPA9Wri58rVRKpeKrMDXJm+XIRlZo+HXjQ1uEG/zp++eUX9O7dGzlz5sT58+dRqlQp2ZIMDAwMDBQmLCwML7/8MqZMmYJatWrJlmPwCPEgjQw8Og0vOjoaBdIxPJKTk5GcnGx7HBMTk7UPWLvW0eApXly0g+7QIYtKPcCsWeLfgQOBb79Vr42yPfXqiW5oKkXJDJQgKioKH374IQBg1KhRhsFjYGBgYJAhRYoUwdatWx2eMzp+GmQ3HstRunz5Mn788UcMGDAgzfd8/fXXyJs3r20rXbp01j7E2sAAAHr1As6cUdPgSUoCNmwA1qwBfv5ZbYMHEJEo40Jk4IZRo0YhLCwM1apVw3vvvSdbjoGBgYHBI8j27dvRtGlToybUIFvJstEzcuRIeHl5pbudP3/e4Xdu376Ntm3b4qWXXkL//v3T/L8/+ugjREdH27bg4ODMCzt/Hti0SUR3Vq8WXdHy58/qn+cZwsOB7duBF16QrcTA4B+TmpqKAwcOAACmTp0Kf5XalhsYGBgYPBKkpqaiX79+2L9/P1q3bo2oqCjZkgz+pWS5pic8PBwRzh3HnKhQoYJtARQSEoIWLVqgSZMmmD9/PryzUACfpby9wYPFfJbJk9U1dgwM/mWYzWZs2rQJbdu2lS3FwMDAwOAR5dy5c2jevDnCw8PRpEkTbNy4EblVm6tooAQPUtOTrY0Mbt++jWeeeQaPP/44Fi1aBJ8stg/O9B9GArt2AU8//YCKDQwMDAwMDAwMPM2JEyfwzDPPIDIyEk8//TTWrVuHoKAg2bIMFONBjJ5sq+m5ffs2WrRogTJlyuDbb79FeHg4QkNDERoa+vA/zMvLMHgMDDxEVFQUvvrqKyQkJMiWYmBgYGDwL6Fu3brYuHEj8uTJg507d6Jz585Ich5BYmDwAGSb0bNp0yZcvnwZW7ZsQalSpVC8eHHb9m8iJlrt/vImE5Dd82AfFJNJtoKMMcYI6IwePRqffPIJunbtKluKgYGBgYGH8MR9sEGDBli3bh1y5syJTZs2YfLkyVn6fVN8csZvkkzSJTf16prmeSFpwJA7iDt0zv2L5849Gou2NMg2o6d3794g6Xb7N5CQAEx9dgWOlO6EhJAo2XLcEhsLLKv+OTY+NhJMVfMgjbxPbC76Krb1WaCsZXHnSgL25HseJ3/aLVtKmlzZch17i3RG8I7MzcD6p5w4cQJTLV0SP/jggyz97vGJm7Cr/ru4fyUyO6Q9FI4OnIk93X9ATJi63sXTL3+JAyP+RHioWbYU95C4+OJH2DdhF4JvqnlOa5HRuNrvK2z65iiCb6iz2LDHdOEKro7+Bb8OPYzoSDU18tx5nBu/Cj+9vFOlNZsDvH4D+z/+CzM6/S1bSpqYI2Ow/sXZWNR5uWwpaZJwNxabKg7Aur7Zr7Fp06ZYvXo1/ve//2Ho0KGZ/r3QoyG4XaAWDg+apz958qRSa4vT03bAt0p5BHd+B7Cvjx8yRCzaFOBQvxnI1agG4qvWByZOBOybim3fDrRs6fjcowQVJjo6mgAYHREhW4oD58+THSqfYzRykwDPV+8sW5ILwcHkwAobaIYXCfDsy2NkS3IhIoL8uvRUUlySeH3qatmSXIiLI5cUeZcEmAof3j9yVbYkF1KSNe7J05YEeNenGFNik7LlczRN45NPPkkAfPHFF7P0u+akFF4JrE4CPJKnRbboe1C0yChG+hQgAf5d6R3Zctxz/TpT4UMCXP7sT7LVuGf/fts5/cc7W2WrcYt5+Z82jSun3ZItxy2RX/1EAoxBLv69VpMtxy03+40R9xdU41Y1v2peavsOCXAvmnDHDtlq3HOuXg8S4DHU5ebNstW452jFF0mAYSjETYvviie3bfPY52uaRrPZnO57dhfpbDuvT/b8mtQ0skcPcskSD6lMn9RUckeONjaNSUH5yMmTyZQUsnJl8plnyMREqRrDw8mD3o1sGm3b00+TM2aQixaJx/nzk3/+KUXj9u0W2yA6Osu/+2gYPf/gD8suli4li+WM4RmIBVxCQF6mnL0kW5YDx46R9Yve5l0UJgHeK1iZWpQ6+5Ak790ju1Y9zQQECoOnWmsygwuapzGbyc+e3Go76c88965sSW75resSm8bD/X7Ots9ZsGABATAoKIg3b97M0u8efv0Hm8Y9X6h5V7/df7TNuN0yQ61z2kr8gPdJgNHIzdWLomTLcYs28C0SYDBKcvECk2w57nnzTbEwQi3++KNsMWnwjlis70cjfvKJbDHu0T7+xLZYf/VV2WrcY5o0RdwHUYCNG4t1sGok/vaX7frYqshxRkaSTE6WLcuByL1nmeQVQAJc5dOFZzfcJPPlIz2wPjObzXz77bfZv39/aul8gTcPhfK0fz3bvrzw/BCxOC9enIyJyXadmeHMvmhOyzOCSfC36dSqVSNz5hSP27cXRpBE9mxNYp8CK7kU3W1rtDS3t94iExI8qu9BbAPD6MkkSUnkoEEkoPE3vKh/4avVik6sXUvmCUrldjwtFnB+geTx47JlOXDvHtmoTiJPoLYwHHMVIkNCZMty4dMhMbyKciTA8AKVyfh42ZJcOLjhPkNRRFzgCzfNNsMxKiqKRYsWJQB+9dVXWfrd5NvhjPLORwLcXrCLkosOhoczwU9Ebhfm6M/UVNmC3BAZydTAXCTAifiAd+/KFuSGxERqecV3PQ4fcf162YLcoGlk2bIkwAkYxtGjZQtKg5YtSYBz0ZvNm8sWkwbDhtkMs8BAisW6aqxbZ7tfF8A9Ll8uW5AbUlKYkl9cx7/D++zfLYIqWpF3h0+07ctLvlXFz8uWZfvn7tu3j97e3gTAd999N33D53Q09wY+47o4/+CDbNeZWUJCyE61r3A5uro3JHr0IE1yHUZhYWTr1mQuxPA1LOD2oLbUfHzc661dmzxzxmPaDKMnm7l6lWzQwHLeQD/p+dlnUnU5M20a6e1NjsUnusaZM2XLciA8nKxbl5yMd5U1HEly1ixyOoQ32AxvmnftkS3JhdhYcmmefiTAZPgxZt/pbPusoUOHEgCrVKnCpKSspc+dbDaQBJiIAB7+7Uo2KXwwUoaIxVsS/Dl+UNaiWB5jwgQSYAp8+XR5RTX+9pvtvK6C8zx8WLYgN5w/b9P4LDZx0CDZgtKgWDES4DBMYI4cyjn+BYMHkwB34ikC4h6kHFeu2L7vxtjHypVFSrByvC+iuKEowr1owriCpWUrckTTyLt3GV+qssOCN/WlVzzy8fPnzycAAuCIESPSNXxu773Ouz7FHBfmPj7kqVMe0ZoZ4uLITp3IkfjKvSHxxhvSs19MJvKLL0gvL4sk31/SjvjkyCHS3zzg1TSMnmxk1SoRwQXIFthKs5e3eNC2rXRL3IrJZLtesjXW2+p42LOnUrH8sDDhEGiLv/UTRcEVx+bNZDvv9TaNKe8Ply3JLRM77LBpvP5a9ua/XLt2jZ07d+aGDRuy9Huxu4/TBHHO/F7l42xS94Dcvi0iogAnYQgvqZjZlpxMliwpIlF4lX36yBaUBs8/T0LUTwDk9euyBbnhB5FqmegTxAAk8uWXZQtyQ0SE7dxuh7UERKmUcljSBI8Veo4A+dhjsgXZYb33mUyknx8J8H/4hc2xjbu7fS9Xmz3LlomoTrduDovIGOTiLVXKzUwmsQh3s9iN88tLc6JnLPKff/7ZZviMGeOmTlnTyG++EQaOu4V58+bqrImSk2l+/wN9veZue/ddJfRu3kz2yruSyfBLP9UNIF98kbx/P1v1GEZPNpCSQg4frn+PVXLcZGIeUSPD8uXFTUkB4uLIzpbavZII5n3fQuJB1aoiFKAIVoOnCEJt6VisWdPjuaAZcfYsWSZPJIMhFpipVWpILyx0x9o/k3gOIr0gLH9lJTVS03ilbAsS4C2U4IUj6hyPDrz9NgkwFjnZ+YlQ2Wrcs2CB7WJUD0c5Z45sQW4ICbEtNgbgZwLi+qQc7dqRAE+Wfp4A+dxzsgW5Ydcu2/ddO/c1AuR338kW5YbXXycB3mnwgu1eeeyYbFEWIiNFfs7339scBsE5q9AML47IPU2d22NyMtm4sdsFZOuWqbKd/TpmMzlmjFuds7pv9JiMSZMm2QyfCRMmuL5B08itW8WJ7W5Rvnixx7RmyM2b5JIlPPH0OzyKejYHocP26aeyVZIkQ85G8sVGN1gdZ9gQB9in7Bbe+HGV2J8zZogL1JgxYuE8fryoCckmDKPnIXPrFvnkk/oxV79GEhPqWLpZBAaSR496VE9a3Lmjp935IJXnCj2pazxxQrY8G3fvkrVqkYDGtRALDgYEKKWRFIZZhQrkPLxOAiJ/9dAh2bJcCAsjv8n5ue0ATV63Jds+K/IBkvTvz1pu0zjnmYUPT9TD5OpVahYv8Jf4mAsWyBbkBk0j69QhAW7CswREhpZyTBSpvya/AObDfQYGKuGkdCQxUaRhAPyr1RQCZL16skW5YcYMce7kzMnn25oJkF26yBZlx9KlYrH+8sskQHOnLixaRGNDHOA7KjU+tKZAOG3PYSPdBQqkcf26KLh30lkQ4fzhB9ninPjjDzIoyEHnjxjE2bM9J2HcuHEEQB8fH168eDHtN+7fz4TWHR20JuQt5pHmC1llzRqyWFA0W2EDv/T+jHdqPKPv52++kS2PpOg+N3KkvjuDgkQzN09jGD0PkY0bycKF9S/19dfJlL4D9Cd++cVjWtLj1CmyTBld1vamdkeiJ68+GXD3rgjoAORg6B28VLuSJyaSzZqRHbBKOQ+LPZpGDnr2nK3zS0TH17Pts6Kjo1m8eHH26tUr68ZPQgLv5SpLAtzv3YQht1RxVzph8VRHIi/L5L6vYq8KcVGyHJNt8TcLFVLQmNA0q2eD5+t2J0CWKiVblBs2bdIN8ZEX1NX5rqXmsUEDjhsnfixSRKHvvUcPkfFQpYoQV7s2bxRvxOXoynz5FArgBwfbUtvstzK4zly5qFYzkNWrXXRWxCUGBnq0RjxzHD9uawZCgDdRir4+Grdkn//Nhc8//5x//PFHpt57d/NJ/pX7FVsk5Uyb97NZ3T/j2DGyRAn9EPhydAq1AwfJSZPICxdky7OxZo2jjT5ggGeTTQyj5yFgMpGjR+sFW4GBFCkkc+fq3+zbb2e7jsywcSOZJ4+Q5OtLbnjPrkbmtdeUuTOGhpI1aljuiThBk6+lRWO7dspoJIWUnj3JArjHOygqNNatq2Tl8JxZZltnvvichURniGzi/fffJwBWrlw5y80LwoaMtR2TP/c5kE0KH5CzZ6l5i5vgx/iSAwfKFpQGbcRch+u5ahDQ2KmTbEFuOHLE9n3P6/637RRSjg8+EDrLleP8eZrtWq8EmiYcLUlJempOr17cvl2/vKfn1PYos2e7jaC8jF+VyyBinz4OGrWAAOYIENEzpaJSJDlihGNEKu9BkW1SX8HbUViYmN1i0foYDjNfPvLcOTlykjPYQSEhZOvyFzkT/RiPHFw59qSHlGWN4GBx7bR3vCv33VMEJxvZjfOpX5+8fNkzn20YPQ/I3buO6Z+VK1u6PB85ItKwALJJEyWOvNmzhaEDkHnzkrt+vUkWLCieqFZNmTqeO3fI6mKUEXMggfdL1NDdlaFq1U18bskU+xUiVYN+fsql3pGiCdHbAfpiwzw/+3KxTp06RR8fHwLg+qz2HQ4OZqKPCMsvCXhdxUwCwUsvkQDvojBzIlbFTEZxHFq+70FBcwmILDLlsHTxYrFiHNgvlQD57LOyRbnBEo3iwIFcs0a/5isT4ateXXQDsF7TR4xg0qwFfMd7GgFy3jzZAi3cuOFq9AQEsFWTGAKi27YynD2rezMBsmZNW4qOry/ValySmuqQW7971Aab7I9V7AOTnCzc/AC/8f+UgEgRz0ZfnFuuX7/OqlWrcnkG/chDQsQyqRRushuWc+5cDwnMIjExtp4wBMgWLbK9N8A/IjlZv/Rb16SemFdqGD0PwM6djuHEl16ypHveu6eHb4sUoew2KmazYy5luXLkmeMpZNOmFssihzLtGK0XFkDca862HKQLX7tWtjwHrMOFu+F3u5jyl7JluWAyke0bhvI+8pEAE558LtuiZZqmsXnz5gTArl27Zvn3w1r1JCG6D80eq978JZKiLs/yfQ/BJNatq1TwUceSfpdSqBj9kUSA3LtXtignkpP1Rfrw4exqGT3Ro4dsYRZWryYPHxbXcOs5vmIF9+/XH964IVukBfuVjt32ZvWdBMh+/WQLtKNqVUednTpx/nz94RWVutNbu/0AZOfOjIwkCxQQD7t3ly3OiVu3yEKWhkTLltkCVd7e5O7dssWlwU8/MapifVoC52zWLFvr2F0YMWIEAdDX15dr1qxJ9732DlkvL6rZFIbC/rX02LH5tJU6p+z47Tcyd25d6/vvZ2+MwDB6/gFms2NnQz8/8scfLQsfk0l0fQHEG7Zvf+ifnxUSEsSF2XpANWpkCZZ8+KH+pCIui5AQ/V7o5UVuHqJPmua778qW58Du3aS/P1kYd3nfx3KTadiQKk6m/OorcjFeIWEZOJuNceTFixcTAHPkyMEbWVwNart2277v8QXGqxAcdY9lcXkTpRiARE6ZIluQG27dstUjHOj8FWFJxfLkYiJT/Pmnfo6fPm3LeFGmG/0vv5C5culpTr6+ZHQ0L1/S6AMRlTpyRLZIC/arHDs3rzUrr1o12QLteOcdR50LFzIuTl/8KFUSuW+frvPDD0mKZlPWpw4elKzPmQ0bxA10+nRGRwsnpzWKEhMjW1wabN3K2RMibPv01Vc950gymUx85ZVXCIABAQHctGlTuu+3T70HxFw+FdE0cZxaA5WFC4tDWUUuXLD12yFAPvGEaE6XHRhGTxaJiCA7dNC/nLJlyQP2ZQef2A33lNwnNCxMHDxWOV27WlIx7IseX39dqkYrt2/rda1eXuRvP4ToHqvatZVqq3z5slWaxr8DO9vSM3j2rGxpLhw9Sj7vrU8V1776Ots+y9q8AAC/zGrEy2xmZMXHSYCXUJHLFqi2OrewZ49tX/bHDAYEKNOB3hFrfn9QEN/qIRYTTz0lW5SF6GhyxQrxc6dOQmeDBiT1xcTo0bLEOfHrr46L87x5yVataKpRm3kRSUCsMZVg4kRHrQC5ZYuDXenp1KE0+cvOoeXvT0ZFkbSN7mGpUsqMshO0aOGwwk1K0pM5WrRQMNL72Wei9S9FRop14du3r2RdGTBkiH5YeLJDXkpKCrt06WJz2O3YsSPd94eG6k2WAOXmuDvw55+2ppMMDBSRFRVJSBDRaOs+LViQ/Pvvh/85htGTBQ4edGg6wvbtnRY8K1fqL/boIfVKeP688OxY5QwbZhnQe+OGHpuvUUOJYRi3bolaKGsYfuEvZj1aFhhInj4tW6KN+/f19Ls3/BfqO1jBYomEBPLxanG8CuHqS61RWwyRyiYOHTrEokWLslKlSlluXmCapTf9eL/iKnXmS9ihmTXb4ue6X0X6IkXN4ZQxMdSsU5EHD7ZdBz76SLYwC1evioXu0qV6keGPP5IU2cB2D+Xzu13qqt2mLfvNJl2Zwvvlyx11Nm1Kahrv3tWfWrVKvFX6Ij0mRv/u27e3PX3ggK7VuuCRrpUk11sGTttlbixc6KpVGUwmB2vcPr195UqJujLAZCJfeEHX6slzKykpie3atSMA5sqVi/szmOirj9MQ24wZHhL6Dzh4kCxaVNf6zTeKnFdumD9fN9IAEUd4mAk0htGTCTRN3ISt3St9fMRB47Awu3BBb4tWs6bUpgDbt+stAX18yJ9/tryQkqKHfoKClOhlGRxMVqqkGzyLFtExd2DqVKn67C8MycmiyBYgS+IWk3NaFpbNminjlgwO1n8eMoScgGFikebl5ZHYdlRUFE9lsj5M08TYBkZHMz6PuCJvQCtu2azm1Xj5QL1lcU8sIiC6GCvDzp3i38mTSYBmL2/e3XfFdiplkK7uOexqoqzH5rYR62j+YwUreQu9S5aI4yMsTLJWe0eWZUtt257UNBYrJp6aMkV0I5I+kcCuCx4BsVC3YHUqffihcDINHSpRpxVr0f38+ban7LqXs1s38XjoUMp3gmiaGMoUotcZms16p6zatcUt4P59erT1cmZJThbyAZHmZO0HFBys3uI3Jkbfr/7+jrVI2a01ISGBLVu2JAA2adKEWgYfaB2cbj3lbGstBbl2zTEtr3//bPWBPhCnTunOZWs09c6dh/N/G0ZPhv+PY01M8eKkS+QzNlaPdebO7dGe6M4H7YIFunGWKxe5bp3di8OG6X+IpDv0mTP6DSw4mKxYUTd4Fi+maDbv7697ACVfkadPFyebponUALH7NF6tZhmUmiOHQr1gybZtRdvPTZvIejjKVFgKz5QpktC5do3MmZO823u4WEzCh282Uyeq54Cm8UrBhiTAG7lr0hsmliunwGLMSmSkSL06c8YWjl6Gl/jUU/opf/OmaFl/9apkrVu2uI2enMnViH5IFlHUN8QiTfrA17VrHQ20nDnZvPwNvv223sSmXj1xGZCczSxW3FatDRtSM2tcuVKk4/buLZ6uVEncwzp2lKyVJL/4QtysnFpLTZoktPr56bcsJVJI16xxuR9ZA0BW261HD+XKT22cPq03lLXeWtu3VyqRwsbNm+I4BUQqubUMdezY7PcvxsXFsX///gzNZKfYsDDHepRp0/TXFOkPZSMy0rHbcOvWtsxSkmoZQbGx5Cuv6FqLFiW3bXvw/9cweuzQNLEosHLihO4hA0QbVZeBZJomrnTWN1lz1T2AponFgaaJbfRoXUapUpbW2Vbsc6j79PGYRme9LVqQu3aJi5q9wfPrrxQFR1bzvlgx6W5es1lo/PxzEdmz2YvN7eZMKFTFHh4uInutW5OlS5h4EA2Ex794iWydIr1y5UouXrw4Q6+YM8uWkZVwkckQVvoUDHY8ZmWjabqFYOfx74w/CYg0jOnTFengY42e5M1r09kI+x3sCh8fseiV3iDCOQ0LYJRPfpbBdYen8+RRoB203XBXAuSkSQ49YOw36d2xNE3PNli9mqSjAWGvtVcvuVJJishzu3a2hydPCsNxyhTXfavQbEUHNE2sC6xORkBMqFAV6/FgdSwA4v6mIocPi4QUQCwLLlwQ/lBriqYnSchgYm54uON8HGuCSsOGCjU6sZCSYu/AFZFVa8+hb79VywjWNBE9s/rBvb3JceN0Z2NoKLO8ZjCMHju+/55s1Ur8PGeOKCcBRBHg6NFpeBi+/14/ejycNL90qfjYo0fFXFGrjPr1RWMAG9ev6/luNWtKW0lYnaYvvaTXG/n4iL+DpK1nPwElqoNXrdKDd1ZZrz55nZr1iWeeUcjV7zjz711M1h9kY/P7mJgYW/OCuVnsAjhsGPkX2pMAw1GQFfLf59tvi5kSCpSaCcu8fn1RHGXJYTiIBgQ026718yMzSP32DE61J5qvL7fjaW7H0w7GhIwFgwuzZrmsan9+YbXLQleJga/btumCGjQgTSaeP++6KPf1FYeJdOrWFcesxQGRnKw3iLHflIhGpKY6XJtMJt2AcN727JGoMw3++EPsR2vKs3ULDFTLY26P2ey6j2vVkq0qbVas0JswWP05LVp4VsPcuXNZtmxZXs0gRH7vnp5CCOgNcp97zkNCs4Cmia6uVq3Figkjs1UrUQGh0LKGpDAcy5fX9bZrJ/b333+Ljr9ZWS8YRo+FI0fEAqZQIT0VABCP01x/79ih961u1cqjdR0REXrRr7UvASDC1Q7lRMnJZOPG4sWcOaV1GDOZHIv+rAbPsmWWN6xYob+gRMK5sGns9ZYsbmby03ZuvWvXZEt0oG1bIa00bjAWOUmAO/J34ltvZV+r0g8++IAAWLFiRSZmpsNeYqIt1DC8tt5VbiB+IiA8OsrUnlit3gYNbDpbYYPDMfHTT7JFWrAPRVo2M7zYCStsTz2XfeOZsoZzl7ERI2wzr+w3JQa+7t6tX6yOHrU9bTcD0naIKEHnzpZCOR37QarWTZnueE4HZFiYyFJw1quEse5EdLQ+6s55sztUlGHJEtFtv2RJV73nzslWlzb2TRis27Fjnvns5ORk1qxZkwBYvnx5BtsXzbrh3j3hc3DWq4AP1y1Ll+opj0FB+s+SS6ndEhnpODKrdGk9ySorXQkNo4diQWifxmbdmjVLZ67o7dt6O4wyZTzeC9Q6NsJ+GzzYjd31/vv6GxYu9KhGe+bNc9Xbrp0lrHrrlm651aunxDCRY8dc9b6FafqCcrpaPSrv37c2Q9K4CqKnegxy8YnSwdl2gzhz5gx9fX0JgGszOzh2yxby889pSkzheS+RyngCtemDVPr7KzZ/dswYhwMgxKs4u2Mpu+AP5kA8//c/RYwI0jFKatmGYLLtobe3SB9Sgo8/1nU+9RSZmsrLlx3l16mjyL49eFAIGjbM4Wnn65kyJXPz5rm4aTVNb4Zp3SZNkqIuU+zf75qOp0QKqRtiYsQ6wfleoWInr6QkET11Z6QpOFOb+/Y5FrPbb56ctBESEsJKlSoRAKtUqcI76VTUJyWJ2i5rdMq61aunXvTEyp49+nQQ65YrV/bNyXkQrLOHrI0f7TdbxlAGGEYPbYPLHbannkon9z05We+CFhAg4oIeZPNm9xeCHj1EAyfbYsG+85DEBv0JCe69dwCZP6+Z9+pboic5cigz68b5mKiAy4yDSDA2t26ryIrMgslkm2beFXq9xNRqU7KtAFjTND7zzDMEwI5ZqYr+/HPS15cRL71p09kCWxkQoGDbV3u3kt32GcawTh0F6k3ssa9OBcghQ2jtWg2Qb70lW6Adb70lRBUubPMqaZrjjfeHHyRrtHLsmJju6JQ/YT9IU7I/yZE0VlanT+tJCYCwjVRm2jTHw/mbb2QrSpuYGNfIn8rzcGbP1msk7BflKrJrlz6w3H7z93943bwyw40bN1i2bFkCYM2aNRnuxskdE2ObW+12U+YaYUdYmEhzszZlsd8U6COVJsuWuerNkydzTXr+80aPu7QK+wuB23z9QYOkuaASEvQGAM5b6dKiEC0+nuLbt656ateWmnDuJvOGgYHk8OFk/OcT9CenT5em0Z47dxxvCl4w80guSxusvHkd+0KrwHffsf8zl5gHUbwN0fLmZvFGNCVnX7rlkiVLCICBgYEZ5jo74JRQvhxdGRDg1GVQFeyHclm2L/Ex8+bReOmSbHFO2A/l6tyZNJlsk9jz5pXeE8SRl18WrlD7rjHU53P4+4s0ESU4fTrNg7N/f32XK3c8uMH+tqXyrBZSLLbs61SHD5etKH1iYujQKbFOHdmK0ufAAVdHpKrHcGKimNXi7N0fNcqzOi5fvswSJUoQAOvXr8/7Tl0HSXHc/vWX4+BS61a2rFIz1kmK7N0OHdJeA9vKDxQhMVFcx5wjwdatceOM6+n+00bP5ct6xxX7LXdu4anZtcuNpbtggf7GN9/M3j/CDdZB6/Zbw4YitGcb4JScLJ4ERB3P+fMe12klIoIOHmdvbzF1NziYIkJmvZJ16qSMW2HUKF1vjhzk1o52zSqkD+NwJbXp01zv1ZY/QeQumL19ROvBbCIpKYklS5YkAI7JytjslBS9HY9lM8OLKTnzqpdfERHhcqJNxAcENPXqC1JTdTd+o0a2EJS1qPb77yXrc6ZtW7dFJWPHCr09enheUpqkU6e5f7/QW7CgMpeudLl3T78W283YVJb4eH0GiqSGo1kiNpZ8+mmh18dHkWYs6XD3Ltm8uX6JGz9etqL0OX6cfPxxXW/hwp43Is6dO8ciRYoQAKek07nVZBJpbmXKON5GpLe1T4Pz50Xqo/1QUEDUjSvRLt6O0FCxH+3nI9lvGfUT+88aPcnJDvXJ9PISGSKLFqWTtnLsmN7SrWFDj9eeHD2qr228vMguXYSl7nLDHTJE/8Mkjwv/4ANdSteudgWTcXF6W6ESJTxeE5UWiYniYvo2prJlS/LGxvP6d96xo3qrm+homn2cXGAjRmT7x27fvp0dO3bMXPMCK/bj1u23Dz5Qb79u3eqgMbjLYAKapxs0Zo4rV4TO8uUdeuq3aCFOMektqp359FO3xsQmy+xXpwCQsmia8Og+/7xsJZnHMrdWrdbw6XDxokhb6dBBtpLMERurGxK7dslWkzEpKeR77wm9yjTjSIfUVHLCBP2WLKPW6+TJk/zyyy8zNaIhMVHUzxUsKPQWKCAK8lUlPFz4H62DlwHR2lxFNE2sid99V9+/1rXx5s1p/95/1uixDj2rVEl8ydY+5WkSEaH3zCtUyONVXqmpwsuRM6doWGAd1uXCH39IjUTZc/26SFV55hmx3nWgX7/MHaEeZs4csnzucJq9faht2653vitQwLNJxJnFzcR4du4sDAllKtctfPutq9axY9UzeEjhSrI7j35bpvHZZz3aoDHzbNokWtI7RXQ7d7aNa1GLNKywqChxiVW14Ncd338vZmw+KqSkiBqJ69dlK8k8K1eKLmmPCnFxwuGgXIQ1HRYvFl5+xRqSpsmlS2If164t//aRmJiYofMvOpr87DOxfvOAT/KBsTZksEZTtmyRrSh9kpNFA+BOnUTyULFibmZqWvhPGj379gl7YM+eTJ4wZrNoNWbNz5JwBCxdKmpj3KSR6ly5ojezr1tX+uCISZPExGpNozgCrQ0f7IcTKpSsbR3wev/7eUKbfe5jZluDeJiUvmm04xkx4qGvHjVNS7dzTYZ06uSoUaHBri5YCwp69ybNZq5erVhdjD3z54v2+U4sWSJ/QZBVli+XrSBrhIc/Gqli9qxdm62zirOFLI4Ak05cnBhH9Shx7Fi2jnR76GgaOXOm3Nb28fHxbN26NV944QUmZyKkHhoqljyhoR4Q9xDQNOFTGzxYvXqktLh7V6w9P/rI/RLoP2n0ZHkhYF/kIamNTIaak5L0hNdcuZQYYe2gedo00Tf15k19UOpjjymVe2PT67w4L1tWDLs4c8aucEoBNI2a/cQuq1GeTQ0hli5dyqCgIH73TxKTNc0Wg9a8vcVCXWVq1iRfecUW2lHaeEijeEBpzWlgaPYMj5rmR00vaWj2FDI1Hzp0iIGBgQTAbt26MTWT6wNjP3sGd5ofxOjxxiOKl1cW3rxmDfDFF+Lnbt2A4cOzRVNGuNV88qT+87BhwJEj4udZs4AqVTyiKz0cNC9dCmzcCDz3HBAZCQQFAb/+Cvj7S9PnjJcXgIQEodOeGzeA9u2BFSsAHx8p2txy+TK8rl3TH+fMCaxeDQwY8NA/Ki4uDh988AESEhIQGxub9f/g/HkgIgLw94fX778Dr7/+0DU+NBITgVq1gAULbN93lq4ZniZnTrdPK605DQzNnuFR0/yo6QUMzZ5CpuYGDRpg5cqV8Pf3xx9//IHevXvDbDZn+HvGfvYMD1vzI2v0ZJrLl4HXXhM/V6sGzJun1jf/xhvA0aPA8uXA1KniuYEDgZdflqvLmVu3gF27xM8XL4p/P/1UCcPMhY0bxaLXnly5gJUrgU8+Uev737BB/7l4cWDnTuD557Plo8aOHYvbt2+jQoUK+PDDD7P+H+zaJQzdNWuArl0fvsCHiaYJg8fXV7YSAwMDAwOFadOmDX7//Xf4+vpi8eLFGDhwIDRNky3LIBv49xk9JBASIn5OSBCRnehosehdsQLInVuuPnvOnBGRnXffBfr2Fc/VqwdMmiRVllt+/931uY8/BooVA3bv9rye9Fi50vFxhQrAvn1Ap05S5KTL+vXi35o1gf37gccey5aPOXfuHL7//nsAwA8//IAcOXJk/T85fRrYtAlo1eohq8sGcuZUKgJpYGBgYKAuHTt2xOLFi+Ht7Y3Zs2djyJAhIClblsFD5t9n9Bw+LNLXSODNN/X0sfnzRaRHJRYsEP/u2QPExAiD7PffgcBAubrcsXSp63OlSwN//QU8+aTn9aSFySTSw6y0bAkcPChSnVQjORnYtg149llxDJQpky0fQxKDBw+GyWRC+/bt0b59+3/2H33yCdC06cMVZ2BgYGBgoADdu3fHvHnz4OXlhQULFuD69euyJRk8ZP59uR+zZokFevHiwOLF4rkPPxQRH5Uwm4FFixyfS00F+vQBmjcHRo8G/PzkaHPm6lVhONjTurXYv4UKydGUFrt3A/fvi5/ffRf47jt1U5z27AFeegmYOTNboxLLly/Hli1bEBAQgB9++OGf/0dFiz48UQYGBgYGBorRq1cvmM1m1K5dG+XLl5ctx+Aho+hq8B8SFwcsWSLy+b/7Tjz3zDPAuHFydbljyxY9Dc9KUhKQkiJS3VQxeADgt98cH48aJTaVGgJYWblS7Lvp00W9lMpUqOCRGrNbt27B398fI0eORIUKFbL1swwMDAwMDB5l+vTp4/A4PDwchQsXlqTG4GHy70pvW7ZMGD725M4NzJgBXLokR1NaWFPb7Bk+XBSLq+ZdsKa25c8PrF0LjBmjpsFDirqY7dvVN3gAoFw5jzRVeP/993H69GmMGDEi2z/LwMDAwMDg38KBAwdQpUoVTJkyRbYUg4fAvyvSM3u263N//SWaGLz6quf1pEVMDPDnn/rjwoWFEdS2rTxNaXHuHHDiBPD446LDXLlyshWlTUyMqIkqXVq2EuWoXLmybAkGBgYGBgaPFJs3b0ZUVBSGDBmCwMBAvPnmm7IlGTwA/55Iz+nTwstvT968ou5k8WIgXz4pstzyxx96S+WWLYHjx9U0eAARPRswQNTKqGzwAOL7NgweAKJ5wdChQ3Ho0CHZUgwMDAwMDB5JPv74YwwbNgwAMHDgQCxcuFCyIoMH4d9j9DhHeZ5+WkQoevaUoyc9fvkF8PYGvvxSzJQpUUK2orRp21bUx6jYUc4gTf744w9MmjQJzZs3R0REhGw5BgYGBgYGjxxeXl6YMGECBg0aBJLo3bs3fnc3wsPgkeDfkd6WlARYrW8/P2DsWGDYMDXrTq5fB65dA3bsUKvVc1o0aSJbgUEWiY+Px9ChQwEAw4YNQ8GCBSUrMjAwMDAweDTx8vLClClTkJSUhDlz5qBnz54IDAxEhw4dZEszyCL/jkjPihWiTXG1aiLFbcQINQ0eAAgPB44dezQMHoNHknHjxiE4OBjlypXDyJEjZcsxMDAwMDB4pPH29saMGTPQs2dPmEwmTJ8+3Rhe+gjy7zB6Zs8GBg0CjhzJton27rh9PhZbvj+RtV9q2BAoUCB7BLnhzOqrOL8zzGOf9084PPckYiNSZMtIl+MLToKauhc4asSVNedw4cIFfPvttwCAyZMnIygoSLIynfjQWMReUftYTLx+F0xW+1hkWLjoVKgysbGyFaQPKQYZPyKo/nVbS1RVhQTu3JGtIn0SEoDr19T+oi8dj0f49XjZMtJl/5SDSEk0Z8v/7ePjg19++QXjx4/H8uXL4fUPuq+ak0048OYc0Kxlg8KHw/19F3Cy+5digPq/jEfD6Env5hQVJVLZpk4FPLjAO3mCOPTYADz5QSOcfXe6xz43Kxzdlwxzl24o8Ewd3Ji9SbYctxxcfRfl+rbEnVINEbbtjGw5btk79Shqv14fh0t0RPTFu7LluGXvO7+ibIea+F+DVkhNTcXzzz+Pjh07ypblwKmeX4OVKmH9cxOVXcRdavkmQnJVxtpeS2VLcY+m4U7FZjibuxHWfrhDthr3xMQgIX8J7M/fDusnnZWtxi2mE2cQl6MQNud/Ebv+UNMQj5j5B0JyVsaqHC/j0gU1F0h3PpmKi/kaYmneN5W1c28O/Aqncz+BNVWGIjVVthr3XH7tc9zKVxPbmn0Cc/as1x+YU11GoWD90vir3U/QFDwcaTJjb4uP0WhIE6x+4qtsu8f4+vpixIgRyJEjh/hcEsHBwZn6XbOJ2F17IBrP6odjNV5V0sGWEE9caTsIdX7/DPdK1AY3Oq0dzWbpxlDo7X9+kjwaRo93OjLz5QPatfOYFADYtAmY1Xg2OicuQQBSQOchowpw+DCw55lPUMd8HEW0u0gKV++OdOkiEdHtTRRCBMokXUCqpt7heOdmKnK/3xc+0FA64hh8c+eQLcmF6JvRqDRjGLaCOBQXDH9/f/zwww//yAuVXSRdCkbdbZOQB7HwuXndE+OJsoz50FHUufYXSppuIikkUrYct/DvdSgRdwk14g8jOlq2mjRYvhxB5jg0itqA8OS8stW4xXfTOuQyRaNh1CZcj84vW45b8lw+ihIJl1E16TiOHFPv2ggAuUIuokr0YVRKPYc1a2SrcU+O0GuoHb8fZeNOY8kS2Wrc43/7GqqknkXFO7vdTt5QAd+bV1AAkWh9fgp++FZB69HbG4G3LsMbRKcTYzD7jb3Z/pEkMXLkSNSuXRtHjx7N8P3mZBO848Ra7LGLS3GhcnswJtb6nwGXL2en3EyRcD8JYX4lAQCF7l+CV5vWMHd/GbCuc+/dA15/HTIt39g7cRm/KS2oMNHR0QTA6Oho2VJszJtH1vc5wQQEkgAj6zYnTSbZshw4eJDsFLSRFKcRQzv1ly3JhbAwcljh+TaNwe9/J1uSCyYTObPC1zaNV3/4S7Ykt2yr9x4JMBneHDtgFCdOnChbkgsXmvQiAUYjN89suytbjlvCm3UiAd5EKR7clSRbjlsSn3qOBHgU9bh5kyZbjlu05s1JgBvQikuXylaTBi1akACXoysnT5YtJg3atSMBLkEPDh0qW0wavPIKCXAFOrFzZ9li0mDYMBLgfjRi9eqk2SxbkCva9BkkwAQEski+ZIaHy1bkinbwkO1e2NN7CQ8ckK3IldSw+7ybo4y4X6McF/wYJV64fDlbPi8pKYlPPfUUAbBAgQI8depUxr8Tb+KacoNs+/J6kQbU7oaRFy+SDRsqcYDGxZEjn9jO06hh06nlzk1+/z159Kh47r33SE3OPehBbAPD6MkkmkaOGUPmQgzPowoJMLVAYfL2bdnSHDhwgKyQO4whKCYuomWriiNYIRISyE71bzAKeUiA92o9rcSJ7sy0IReYiAAS4KXHe8iW45bzv59kKnxIgDsee0+2HLeYDh2lGV4kwPlVx8mW4x7rhRzgJwWmybqWp8+pUzaNvTCf9+/LFuSGa9dsGl/FQm7YIFuQG6KjSV9fEmBfzOKoUbIFpUExcQ3/EOPZvLlsMWnQujUJcBb6MiBA7FrlGDuWBHgeVQiQK1fKFuSG06dt501j7GPfvrIFuSelyVMkwANoyPLlNEZFUdrCNy0SNu6iCd7CYeD1Mjes18jHHxfXpmwgOjqajRo1IgAWKVKE58+fz/B3kpM0Lqr6he6YzluZ2tAPxOOZM7NFZ1Yxmch3BqRwGCYwDkE2rSxSRP9ZkoPVMHqymZQU8o03SEDjQrwqrF4vL6p2R9+/n8yTW+MqdCABmn39xGJOIUwmslsXMzdCeKxTAnKSV67IluXCzu1mbkNzEmCMfwFqoepFJ8wmjcfyPMUQgFe8ijAmOEq2JFc0jaG1WopoHkpyz6Z42YrconXsZNM48n01ozzs10/cIFGE1SsoqtGywIxFTgYhjgcPyhbkhj//tN20SyKYgwfLFuSGO3dsGlthA3PnVtIvRD72GAlwPD4kQC5aJFuQG378UTjXfIsQIBs3Vm6dLr7cfPlIgEPxLQFy3z7Zotxgd+40xW6+0TGc2tfjZatyIfaD0Tady317iJ+//jrbPu/+/fusV68eAbBkyZK8kok1TXIyOa3uDJuBZtsKFCDv3cs2rVlB08gJE8jSuMHl6Oqo07otXOhxXYbRk60abM4svoHZ+hf98cfSNLlj3z4yd25yIH6SboWnx/vvk29jqq5xxgzZkly4d4/8MN8Mm8a4n+bLluSWHf0XUgPYGmDRXAW5c+dO2ZJc0Naste3HsZXV3I/2UZ63MI2HDskW5IawMDJARB1H4XN27y5bkBs0jaxcmQQ4D68TIC9dki3KDf37i/SX3LUJkK+9JluQG9atsx2ThXGXAJkJB7LnKVuWBDit3AQCZKdOsgW5YcECEqDJL8B229m+XbYoN1jSGdcHdSFA1q9PmlIVs85MJrJCBRLgSnTkVrTgzRqtZatyJTWV8Y81c1icJ9eom60fGRYWxho1ahAAy5Urx5s3b2b4O8lJGpdV/czVkBgwIFu1ZpUlS8gyfiE8jjquWn19PR4AMIyebOL2bbJuXfG91sJJJvuIOh4+9RSZmipFkzv27hUGTw2cttUa8bnnlHMN/vADWQkXbaFSrU1b5Vxumkb2evaWLfUuqnFr5TSS5P3rUbzrVZR/AARAf39/Xrx4UbYsR1JTGVumhq0GZfVKtWrfbHTubIvyVC2XpOLXTX75JQkwCf4sglBOmCBbkBv27bPdCJ/BFgJkRIRsUU5oGlmqFAlwTU0RnXjhBdmi3PDVVyJaX7wEfUT2qppRlJw5SYCbes4lQPr7K5ji9tdftuOyVqVEAmSbNrJFucF6jucrQi+YORafcP7oq7JV6Zw9K1JsR450WPRe9KrMkydli7Nj8WLy+edt6aH2W+zBs9n60SEhIaxUqRK9vLy4ePHi9N989aqo4XETPdG8vKhUmPzIESYXKOo+0gOI64AHvYWG0ZMNnDpFli5t+T4Ry/DC1cSDQoXIW7c8rictdu8mc+UiA5DIUz4WK7xgQeVqjVasIH1g4m40FSd1vnxK7Ucrk77XuAKdROqdX5C4MCnI5jrvMR5gGYvR88knn8iW5MrMmbaLYr9ym9Q0Jo4ds2l8G1M5YoRsQW5ITiZLlCABzkVvAuSWLbJFueHtt0mA8QVL0wtmAsr1eHGoi5rSbTsBsmlT2aLc0L270Pn886xdW/z4nmole4mJtn15b94qenlJy3ZJnx07bDp//f6ObZ2mTOb34cPknj3CW24Rd7KgaLRRJs99hobKFmhh+3bSz89lwZsEf1avalandDgx0RbNdd4Wlv+MKSnZ+/E3btzgsmXLMvtmcvBgaoGBroZPgwZqXUATEnh9zmZOzfMR96ORa1pe4cIeC+0bRs9DZssWMm9e8T36+Wq80ux/+he7bp1HtaTHrl3C4AHInwLe0zWuWiVbmgP795M5cpDD8Y2uUUG35aFDZA+f320azd99L1uSW84sOcFU+PATi8FTpkwZxscrVisTG8vkgsLTthbtVPy6BV262KI8AUjkkSOyBblh8WLbMVkXxwiQkZGyRTmRlCRy0QEebfcRAXENVY4JE8S+zJOHX3yWQoCsXl22KDdY0gT5ySfs00dPMFCKW7f06/mePXz6afFjhw6yhVmIiBDZDidO6ClOJ8/z6SLn2BAH2EOV3jTBwWRQkItBYYYXvWBmr16yBdrxyy9ujYniuM0+fWSLc2L+fLHwsI9KoRLf6KN51AEXHh7O+xl1nQkNpWnYCMb75nY8Bn5WL/3/zh1RypcXkeyIldxUfTC16pYubxUr0hNWumH0PEQWLtSvPXnzkmeHz9UPwpEjPaYjI3butGUW8MVcev4333pLtjQHLl8WDoBaOMkk+AuN3boplzIWHU0+Vi6CdyBCuCmPNVLLy2LBbNJ4LNeTvAjQ32L0/Pnnn7JlufL55yRAE7zZusQplbJBdeyiPIPwIytWVO6wFIIaNCABXinTggBZqZJsUW6wK3CeNfQsAbJcOdmi3PDMM0Jn16784QfxY9GiskU5ERNDW9hk+XJOm6ZnkCh1SbIzJnj+PKdaSjX9/RUxyg8cIKtUEfW3Vp0dOtDk7ct6OEpvb4VqzizXS/stITCf7aFS5ZqfudagNMVuNX2ZJ0/qDgTL9hgO84svPPPxISEhrFGjBhs1apSpdWxq2H0urfkF70E4kGIDCtB8V73+5bGxIoPQultbtSJjLoSIA2DatGy/kRpGz0NA02wptQREatvFFad1T0GzZsrU8ezYoRs8VfLdZUpBS65l9eqkQh7/e/fEPccPyTzmVV9oLFJEFGUrhKaRL79MzsYbwrvi40u1kpR1tvZZQA1gW4vB06ZNG2qqrdRDQmjOIeq2ZqIfp06VLSgNLFGeEO8SDECiSj4NnT17bBel0fVXEhDHqnJY6qLYsCFHjBA/1q8vW5QTMTG6R2vWLGt9O/39FTN2d+/Wb0SXL3P/fv3h2ewtScgcO3cKb+6WLbqwe/d45w7pBxE9++UX2SIpvtSqVV3ThnLnZsF8JgIK1YvHx9tqzWw6y5VnpUriYe3ayiw/xH7t2dNB66iKiwiIzBPVSksZHU2++KJN60R8QED0t8huTp8+zQIFChAAn3rqKcZlIgcwNZV8vVss38d3vI3i3Falv1rODgupqeSbb+qHQd26nqtYMIyeByQ11TEFtF49MuRSnDAiAFEjk4lOHJ5g+3YRCQfIggU0Rj/5vH7nPn5ctjwbiYnkk08KaV/AzjO0YoVsaS7MmkW2xGZd42efyZbklntXohjqVZQJAJ/zK0R/f39euHBBtixXLCdTHIJYs0AIExJkC3LD8eMOUR5AoRx/e156SSyAKlRg4QJioaZcU8bwcN2YmDLFdiN89lnZwpxYsUI/x4ODuWaN/lAhX5GtxTLz5CHNZiYk2MYKeWShliF//CHSILratbCdO5ds0oSvNLpMQKHmEOPGuaZjtWnDUaP022ZIiGyRFhYtctRZvz7Xr9cffq9StnVionAEW8TdHzrWVhJQv77IdlUKTSMnT6bm68s7fqXoBTP9/DxTG3nkyBHmzZuXAPjcc88xMTExw99JTRVzfwOQyP6YwcEvhihp+GiarecKAWG3e8JfbBg9D0BMjK1TpPV6yJgYkr1760+uXZttn58Vtm3TDZ5ChchbI6boGidNki3Phtms1+E2wEGavS3th5RKThacPk0WCIznZYg2nFq1agpesQWbag4hAabCh9fXnMrULACPc/o0NW9R4DgaozlO0Vmk1gXb/SAR5alUSTFvP0lev05a9mXEqMm2U33bNtnCnLDmNfn6kmFhVjuNL74oW5gTVs9W7dokHYJoavRUmTlT5K/17StEPf207SVrF9EhQ6Sp07l929WQAMjmzW2peH5+VGN47o0brjrHjmV4uJ7E8eGHskVaMJvJRo10nS1bkhTZ4IDo0KpUf6LwcFHDAZB9+3L5cl36u+/KFpcGe/fSVLwUuxXeQUDY7qdPe+Jj9zJnzpwEwBdeeIHJyckZ/k5qqmNA7bXXFEtvtWPRIt3vlScPuXlz9n6eYfT8Q0JChFfCelC98YYYRMr58/UnFbkibtmiX6QLFSIvLD9pm9vBNm2Uak/94YdCViASGFqgmu4CUCLRWyc+nqxRg5wIMQlZ8/ISqSUKcmLhCVu3lN2Nh8qWkzaWRN8QFGOxXLGqfeUCuyjPiKApBJQbuyUYPty22lm5INp2SYqKki3MCetCrWNHkqJbPiBsDGWwa1VtvaafPatf5k+dkqyPFPuxdWuyWjV95RgcTK5da7ODmjWTLdKCtbWp/bZgAe/csdnpnDdPtkgL1jouJ6/Bu+/qxoQy16m9e3Wd3bqRFEkmVmfnK69I1ufMhQtk/vy2sO5bb+nyV66UrC0twsJ4ffKfzCOmUrBMGc9E+7Zv387AwEACYLdu3ZiaiXxFk0kYO9Z9+uqr6ho+W7fqDcB8fbM3xdUwev4BZ86Ig916MH3xhcXTe+aMfoVp2pTZ3t8wE2zerBs8hQuTpw8lkLVq6U/cuSNboo2ff9b36arKQ/UHGzfKluZCv37k4zikt14cNEi2JLeYUjUezfkkLwHs7hXIy8cVTGkjxYFq+b77YSaHD5ctKA0sUZ6kAsUZADG3Q6HMUEFsrG1CO997jx99JH6sXFm2MCfOndPP8eXLSYrOPsr4i/7+W4Qc7FpVWxe9d+7oT+3YIVcmSVuNmW2zriCmTeNPlpnTQUGKLHqs4TzrliePLUfQamO0aydZo5V583Sdvr42nTdu6GmDX30lV6IDr7wiRPXrZ3tq/Hj9T9i6VaI2d2zfLgx1iqy3OpbJGfnzi32sKps369//Y4+JS252s379evr7+7NcuXK8ncmwnclE/s+ugXDPngrVdzlhP+rFElTNlgwKw+jJItu2OVqk8+dbXoiPJ2vWVOqM3bSJtLZwL1LEEop95x39qFqzRrZEG6tX616+t2psF5ETQMzvUIwlS0hfpOgThkuVsuQ1qsfGXqJ5wfMQzQu6dOkiW5IrZrMohgN4GjWYwy9VnVx5e+w6Tv36hIjyVKmiYGqbNU/Iy4u8coWtWinq6bV2xsqXz5YWWr68eOrrryVrI8Vdt1Yt8v33dbe+xZGVlKSYV9r+um7dypUjk5N58KD+lCfScTLku+8cddp1BLA6vnx9FUlxi4nRHZmNGzu89Prr+r1VmdrDGzfETX/YMNtTycl6ALB6dfFYKZYssVnj587pu1uh/k9usU/qef55z2hdv349b2axRtxk0o9VQDSzUXW/3r5tWwrYbPeHHTswjJ4ssHixKF603v82bbJ78Y039G/qr78e2mf+UzZu1A2eokVFEMqh+vadd2RLtHH4sH6hq1M+hqYylpVPxYpUZ2qZ4NIl8d1/BLsiV4WMR3vCLkbyrlcRrrIYPH5+fmo2L7Cb39AOa/nmm7IFpYElQV4rXpzF84soz6efyhblhNksLDGA7NyZmmYbgcNvv5UtjsJwDAsTOq1uvYEDbS9bA1TTp0vUaMW+ytZ6PRo8mOzRg0xMtEXQlUjFctZqJywxUfdKK9EZzb4gCnCYHn/3ru78mjtXokZ7rDlCdoYEKe6p1j9h2jRJ2tzx6ad0Loi0b5Y3YYIkXZnE3phQMnXYDvtu4QMGeN4Bdvz48Ux1YDWZHEvNe/RQ1/CJiRFVF1atbds+XJ+yYfRkAk0Tnkfrl1CypLh327D2LwXIDz544M97UDZs0Et2iha1tCq9c0cU9ADCe6mIa+r6dbKYmEPJggXJqB5v6l5qxWpkkpLIxx8nq+A8ExGgqPtcZ0P1d5kAsJzF6Pnoo49kS3IlIcFWL7EZLentpakz/8IeuyjPubd+sJ3uynUnX7tWvxZt386rVx0eymfBAlEMad8Nbe9eksIOsgZ4f/tNrkyS5Dd2A5HtN8tsq5IlxUMlOmM5D36sWtVhVWOtPx08WKJGKwkJeuVyrVouK8Vnn9UXO0qwcWOaIb1OnfSgmjKLyNhYt0PGX35ZaM2ZU5R7qYw1JcvLy8m5rBia5mhMjB/vuc9etmwZfX19OXz48EwZPmYzbcOKAZFlqkAFhltSUhzjCPXqPbxGHIbRkwGpqcIRad35tWs7XTDOndMH3zRpIv0oWr9eN3iKFRPyaDbrpnNAgCKVtyJ9wdrZOyCAPPOd3aBUBYs63nuP9IKZO/CUbqUpNDfIvh/F0XnHaYI3R1kMntKlS2eqz78niYigzUNthhfr4Si7d5etyj2mLpZZDcWLc+DrCQREyogSqW3Xrul9ky25bEk16pGaxt9+008pD85pThtrS2Vr6KF8eSZeuEHu2cP7wXE2rUosdJzTsABR02XBWhpp7VIv1Y9kVxNHgFy2zOHlfv3E0088IR6HhUnOyG3YUAhy0zl0xgz9ELl3T5xjly97XqINk0lYuG6u9fv26bt88WLx3P37ClwX3Ai4dUvMwgEcuyMq1MfIRmysHrAuWlSMdlKV5GS9AQsgsvU8wcyZMwnL/X306NGZ+h2zWW/waD0OVDV8NE3Uy1u1linzcNJzDaMnHWJjxcwA605/7jmn7kcJCcIKAkQdz/XrDy48C2ia47Xt7791g6d4cfL8ecsLkybpf8SUKR7VaE9qqmWhSxE1adFC9+asnHefLFFCPFGzpsjJkMzduyL1jhQZiwA5AHbdFhYulCvQiT/+EN781BSNR4Ka8TLAAMtFcbmlUFwl3n4pjKacuUmA89GLgKLzbk6etH3nEaMmM39+8XDUKNnCLCxYINx2dgX3s56cz5EjReDZ6vgnxU1P6iLHfoqz3barVA9u3qTZnjpwQASDpDYJmDzZUWfevDTdvM0JE0THpqcsvo+BA0Xn7ddfl6jVvp1c3bq2L9kafZg+XbyUI4cIrJUuLbmT3+DBItoT7joxPiyM9LFMKpg1SxiV0htbpJPDaL2PWYeAtmmjSBtzN9jb8Rs2CHvu/fdlq3LPsWP6eqZVK3FIa5pY56hGVJS+FPT3d7xuZadf9IcffrAZPuMzGWYym3UnCCCytu0NH+kGuxPz5+s+srx5H7wZh2H02GFd4JIiG+zxx/UD4/XX3RQA2k8llVDNumqVnl6zdq1eb1SihOgGSVJcOawvPP+81CN62TLh2Nc0x1aK335L0U/R6t47ckSaRnumTBG77OZNURdRArcY6y0W6WzTRrmrw9tvi8Xt6pdEqsvrlothq1atMhX+9jRLCg4iASZ6BbIUbrJNG9mK7AgL079fy0TuEBRj8XwJtuP26FExQVz67GGrO8yab1WkCD8ZlkRAr4+oUUNkYloXD9IYNszF4DFXrMR83tG21DZAnG8+PpKHP1qjUtZt5kyS4trl6ytq+6xOG0By84XoaF3n6tW2pxcuFI1E7Wd2eHuLek+pl4TFi12GMZlM5KFDIlJi9ZxbHQzSZ7ekc9LYDwG16lauS5qFlBS931LlyqK+x9fXM93H/gnWMV6AWDts3CianSh4O+PNm7rfNn9+kWUTEWEbmZRtjB8/3mb4/PDDD5n6HbOZtkHQgGj+aF3ffvmlevt30yb9euvnp0dVSbFczMo9zTB6LERFiQM2NFQcrOXK6QfEqFFuDoLFi/U3SHCVpKaK1LDPPhN19Fa7pmRJsRAjKVJerPljRYuK0IUkNE1kNJQtqzdtAkQ/Be13u8lkY8ZI0+iMdYSI6HyjcbV3R/FEzpwej+plhmrVyLyIZCiKkAD3FniSwz74gOdtIT91MJ89zxQI9804fERAGG2zZinSAnj8eHLOHIcoz7vQh3wCYlFepIjU00pgn69gyQO4UrM9Z6A/fZDq8JJ0L6m9owgQrtyjRx0cTNbthRcka7Xvod+8ue3O6pxJZt127pQrl7lyifw1u5uVySQMXmet5cpJ1EmSV664PRitjfKskR7rpmJzk8hIkYo3d65unFk3JRpxpMGOHa7Hw7p1slW5R9P0buw+PmSlSuLnPXtkK3PPsWN6CmH58uSIEZ7RO2rUKJvhM2PGjEz9jtksmi9Yj4HOnUWE0ttbgfuEG06c0P16ViNY04Qzb/bszP8/htFjYZBwOnPwYP0C5uMj1j0unD+vH9mNGknpATl7ts2pazN4SpWiYxG4/bQvyVc1dxfajh1J0+1QvcHC448rk2B64YKj1pewTH+QSW+KJ7EOOp8MMTUvBb7sXvM0X3tNiWaCAruUxYQ2nUmAYSjEPIiy7dry5RUZHfXaa8Id3rSp2J+FijEQCS7H8Nq1soXSMaHcsiXlKsBKuOjwdPPmCnjwnGe0/PwzSXHddd63v/8uWevMmbphZtf10L7xnHXz81OgN0yVKm5DDCtXuu7bJk0k6LNH09wODUpM1NOE7Lf//U+CxkzwySfuDeChCs6Avn9frHO6dNFn91k3BUtobdy7J3y29nrfeku2qrRZv97VaM/uSRGapnH48OEEwAF2LeAzwmx2XCZar2u1aysy08uJ4GDH68OAAeLYyJ8/885Hw+ghuX8/HVIrAGHTrF/v5s0JCfoErXz5yKtXH774DIiP18Oo1q1UKadiT/s73XvveVyjMx06OOr19SXnz9OY1K6TvrA4c0a2TBujRulaC+CeLXpyLLAxDx9Q72qwaBFZB6J5wQmAEzCUgPCCKDOX4Z13hCtp507bzn0bU237uVAhuyilbOrWdThgw1u+xA5Yxaeww/a0MiOkKld2WYEfm7zdZSG2b59soSRbt9YFde9us8KWLnXUmj+/bXSPPObO1V2KTnz6qaNepxEuckhjSqamiQCQvd7OnT2sLQucOqXXcticTi/JVuUeTXPsiGXdpEcp02DdOr3vkv32+OOylbmSnCw6o1nHWdhvBQoodF+zIzVVrCet9X7Wzcsr++9tmqZx+fLlWU5l1zRxL3Pex7YZlIoRFeXWz8dXX83c7//njZ6UFN2Gsd/SnBFgHw9cseKh684M7kYyFC4svDh791K4/QsWFC/UqSO9KYD94HUH7x3s2qwqMUREoGlkhQp2xwJ6kwCT4cdji1SY7meH5QLXt4+Zu6A3L/BGEw4cGKlWZ56KFcWi15I3eB5V6IsUAuLGduCAbIEWUlP18KndFocgPoE9BEQqobVhmlTMZtcV4rx5PH9e0UWuNWe0YkWHdnI3bzrqVcKg/OUXcf10E32+eNFRrxLF4OmsAp0j7XajkZTEuYdEhw6yFaVNSgrZrp2j3sqVZatKmwMH9OWBdfP2VmQYrBNxcY4toe03N125pbN4MZknj3u9nj7nUlJSeCCDm2p4uEhcsSQ0OGylS0tfOqZJeLjjGs26bdyY8e/+542eCRPcH6CAmBPgcCFYskR/UVJlZXh42idVjx7kxfNm3QwODFQieuKcxg+QDYreZEqQ5Q956imlYqn2bUifw0bbg/jho2VLc0TTRH4FyaGF5pMA20Pk9Vas+CzNZtm5THaEh7scBJ2wgoBIBVAiTcyKGyvd7OvP57CRgEhlUqbL3J07jlots5giIx0XNApcBgRVqwqD0s0OtE8Zs5tXKY8lS9IV0qyZrlfB5ogu2C/MM9nhVhpms2NQsFUr2YrSJy5O78JtvaYpkqntlvPnRX2t/aVDQi+mTDN/vmvER9XxBrdv23rfOGyBgZ6bcJGcnMwuXbrQ39+fGzZsSOd9ol9LkSLu15QqDrKdM8d9tBIQhlBGacb/aaPn2jXX3FZA9AOfNMlpjsHFi3odT4MG0nIv3nvPVW/r1nad5yZO1F/46ScpGu0JDXV0RHt5ke8M0pjawmKY5cwpiloVwlrfFYQ4XkU5EqBWo4YC+TZOXL5MArz74zKGogj/shg8Pj6+PHv2rGx1jqxZ43LgJiCQN1GKf40+nPHve5Lff3dxg+778E/bw2++kS3Qjv37dZ0vvmgrttc0/bzr00eyRnuKFUvzutSjh9Bbo4YCtUdkhqG8WbP0XS+1y1wmOX5c12sppVKakBA9IvHkk7LVZExYmGOmqV0ZmJLcvu1YHyG9Q14GnDnj2JQjMFBy2/UMWL3atfbv888989mpqans1q0bATBHjhzcnsFk6thY0QTU2iHNuuXLp14EUNPIXbtETbg7wyejGez/WaNH00Q7YvudVa+eCE+6eGgSE8WLgAizSFqkX72qD7IGhGdpyxa7Nxw5or+hY0clVg6ffabrrV1brNE4bZr+pGJtblKOneazeQ8RIGfkGqpbapap8UphWXVpXl5MAFjeYvR8KH2ohRuciyAAxiMHF/XZLFuZK/YFXQA5f76tWWOLFp4PSppMJiYmJrrf/vyTiWXLMrF9eybev+/wWtOmiaxcOZFXrqTxuzK2995jYkKC29dmzUpk2bKJnDpVAZ2Z2MLCElm1aiKbNZOvJbPboEFiH69e/c9+3+Thg//PP3U/46PAlSt60b1d53BliYzU609q1ZKtJmOc093SLENQhNhYkfpqHR1QqJDnGp4kJyfzhRdeIADmzJmTezOxhgkLE451++xulZtcnDsnZg7Z6/X1FXWBafEgRo8XSUJRYmJikDdvXkRHRyNPnjwur//2G9Cjh/i5TRtg+HCgZUvAy8vNf/b228DPP4ufly8HunXLPuHp8NprwOLFQJUqwLhxQoZNb3w88PjjwIULQPHiwMmTQKFCUnRaSUgASpcGEhOB0aOBoUMBvxuXgbp1xYtt2gDr1qWx0+Vwrs8ERM//E781/QHf7W8KL00DBg8GpkyRLc2VV18Ffv0VADAGwOcASgYF4fw77yDXmDFAYKBMdY60bg1s2mR7GIecmNVpLd5b0Vylr1/QrRvw55/i5ylTgMGDMXeuOH5PngTKlPGMDJIIDQ1FVFRU2m+KjgZiY8U57+Pj8NKdO+IQyJ8/e3VmCU0DvL3dvpSSIjSXKuXypyjLvXviX8mX2kxjMgG3bwPFigEBAf/s/8iXLx+KFSsGLw+duP36AQcOAKdOeeTjHpijR4HmzYExY8Q1Q3USE4GePYGVK4HQUKBoUdmKMmb+fLEse+IJYMsW2Woy5sgRoH9/4NgxYPp0YMAAz3xuUlISOnTogM2bNyNv3rzYunUrHnvssQx/78YNsWZbsADw9wcuXvTcfe+fEBoK/PijWKZHRgJNmwK7drm/1WRkG6THI2v0REUBdeoALVoAw4aJn9PE3jp65x2xZyVw7BjQsSMwahTQpw/g6+v0hjffBGbNEj9v3Ai0auVxjc789BOwahUwr/cOlHilOWA2i7vBnj1AvnzA6dNAyZKyZTpwttgzqHF3O5g3L7yio8WZfuYMkCuXbGmOkGLf3bmDqwBqAkgCsDQoCD02bRJnvSpomlh5x8QAAMw5c+OzBuswdkszNRe3VauKq/zYscCnnwIQx3KBAsDLL3tOxp07dxAVFYUiRYogKCjI/SIzNBTImxfIkcPlpVu3xOLW5VqhKCQQHKz2zdWZuDggORkoWFC2kswTEiKMNH//rP0eSSQkJCAsLAz58uVD8eLFs0egE3FxQPfuwN9/e+TjHgqbNgF//SVtuZBlzGZhRLRsqS93VOfMGXE9Xr9euWWEW0wm4UNbtAg4fDhN389DJz4+Hu3atcOuXbtQoEAB7NixA7Vq1crU754+DXz8sbj3zZ+fvTofBnFxwJw5wKRJwEcfuTcuH8ToeWTT244ezeQU9UuX9CTHxx6TWtOxb186KebWHABATDxXhH37LBl2deqI/t/2XSMWLpQtzwUtKpqar69jalOvXqLPp8MAJAWwK7Y/AbAuwJb+/tROnpStzJWzZ/X9mTcvQ1fukz/TJC0SEkQuwgcfOKSHejqj1WQy8ezZs7x37176b0ynWlq1ErTM8Khp1jQ1W+emR0pK1iaYO3Pv3j2ePXvWo6luipV9Zgpl2u9nEk1TqOFJJomLU792ypnr1z1fAxgdHc3GjRszR44c3OJQE5E50l1/Kkhqqli2ubvO/WfT2zIkOVl4y48eBfLkEf9WrPjwhf4TSOGJrlpVuHPr1gXu3wfq1wf278+6Cy87uXpV7LcKFYTWlBSga1eRJqhaXtOff7pPXSxVClizRuxnVfj5Z+Gas2CqUgWRS5agcCZC1x5n3jzgjTdEtGfjRqBBA9mK0ubYMRHWmTlT6vGZlJSEa9euoVy5csjhJopjYCCLxMREXL9+HeXLl0egSim0BgYGaRIZGYmLFy+icePGsqVI5UFsAw8F5yQxbJgwdABg9mx1DB5AJIgOHChi0r16CYMnRw5R36GSwQMA/2/vvMOaOtswfifsDSJOEFRwF611jyp127q+amvr1lrFUa2jWkfd69OqrdaqtYq1bq2jbkVR68SBRUVEQEVBBRGQlYTk+f54ySJBVsI5+r2/68pFcs7Jyc3JyTnv8z5r3z72NyaGGTx2diwhSYwYi59o2JAFlIvJ4AGAM2e0zxs3huWFC+I0eAB2/NzdgdOnxW3wAEC5cizoWiQGeWnlTXA4hYWfkxzO24ebm5uewRMREYHHjx8LqOjt490yel680D7fswdYvZo9DwwE+vQRRlN+7NgBhIQwr4R68PvTT0CtWoLKMora6FGTlQXUrs0SqlJTBZFkFCJWVEGX7t2Bc+eASpWE0ZQfREBICJYCmFe9OrKPHBF3JnVsLDtPGzQQWknBVK789mTRczgcDodTRMLDw9GmTRu0a9cOCQkJQst5a3i3jJ6pU5kHJSYGGDaMLWvQAFi+XFBZBqhUwM6d7PmBA+xvr16sxI3YeP4cuHjRcPmnnwKHD7MEbLHw778sw1fNt9+ycDcHB+E05cedO3iYmIgfLCzwQ3Q0jpw7J7Si/FGpWDbve+8JrYTzlvDw4UNIJBKEhYUV+j1BQUFwdXUVXAeHw+GIHVdXVzg4OODBgwdo3749EhMThZb0VvDuGD2ZmcDu3cD06axMTFoaq9a1a5e4yv4CwKVLLDdGl/PnWdmVvXuF0ZQfBw4wr4Qu8+axYy22amjq0DaplOV0LF8u3hn/06cxvlo1ZCuV+Oijj9CrVy+hFeWPVMpqrHNMhkRSuo/iEBcXh6FDh6JSpUqwtraGt7c3xo0bh5cvXxb4Xi8vLyQkJBS6whAAfP7557h//37xxBaT9957DyNHjjS6bsuWLbCxsUGSuqZ1MTGHMcfhcP6/8fLywunTp1G5cmXcvXsXHTt2xKtXr4SWJXreHaNn/35W6+74cebtAVj5Zz8/QWUZZccOw2VJSUDr1kDPnqUu543ohrY5OTEjaMYM0eRL6HHkCNN4+DALaRQxRwEciImBpaUlVq1axWPsOaIiJiYGjRo1QlRUFLZv344HDx5g7dq1CA4ORvPmzZGcnJzve+VyOSwsLFChQgVYFqHWtp2dHcqVK2cK+YVm2LBh2LFjB7KysgzWbdq0Cd27d0dZkYSdKpVKqFQqoWVwOByRULVqVZw+fRrly5dHWFgYOnfujLTcthIc47w7Rs+WLfqv7e1ZLsfatcwYEgs5Ocz7pIubGxuoz50rLs9Eaqq2a5ivL6sq1727sJry49Ur1rHvwgWgc2eh1byR7OxsjM1tlDp+/HjUqVNHYEUcjj6jR4+GtbU1Tpw4gTZt2qBKlSro0qULTp06hadPn2L69OmabX18fDBv3jwMHDgQzs7O+Prrr42GlR08eBB+fn6wtbVFQEAANm/eDIlEomncmtcjMnv2bDRo0ABbtmyBj48PXFxc0LdvX7x+/VqzzbFjx9CqVSu4urrC3d0dn3zyCaKjowv9f/bv3x9ZWVnYm8fDHhsbi5CQEAzLDZM+cOAAGjZsCFtbW1SrVg1z5sxBTk6OZvuUlBSMGDEC5cuXh62tLerVq4dDhw4hJCQEQ4YMQWpqKiQSCSQSCWbPng2AVWIaOHAg3NzcYG9vjy5duiAqKkqzT/XxOHjwIOrUqQMbGxuetMzhcPSoUaMGTp06BXd3d1y9ehUff/wxMjIyhJYlWt4NoychgZXR1SUzk3WQcnUVVxjW2bP6BRc++IBVmOvaVThN+XH4MKBQAJ06AVevAmIenD9/zgyetyDvZNmyZYiOjkalSpXwww8/CC2Hw9EjOTkZx48fx6hRowxKbVeoUAH9+vXDzp07odvtYNmyZahfvz5u3ryJmTNnGuwzNjYWvXv3Rs+ePXHr1i2MGDFCz3DKj+joaOzfvx+HDh3CoUOHcPbsWSxevFizPiMjAxMmTMC1a9cQHBwMqVSKXr16FdojUrZsWfTo0QMbN27UWx4UFARPT0907NgR58+fx8CBAzFu3DjcvXsX69atQ1BQEBbkVrBUqVTo0qULLly4gD///BN3797F4sWLYWFhgRYtWmDlypVwdnZGQkICEhISMGnSJADA4MGDce3aNRw8eBCXLl0CEaFr165QKBQaHZmZmViyZAk2bNiAO3fulLonjMPhiJ969erhxIkTcHFxgVKp1JuQ4eShhP2DzEqhGxAtW6bfjFLdiLQUmlEWuUHc8OFajV9/TZSVZRZdanR6Mxad3r2JJk8mKsUGdu866enp5ObmRgBo27ZtQsvhmJGsrCy6e/cuZRn5jee9XJn7URQuX75MAGjfvn1G1y9fvpwA0PPnz4mIyNvbm3r27Km3TWxsLAGgmzdvEhHRlClTqF69enrbTJ8+nQDQq1eviIho06ZN5OLiolk/a9Yssre3p7S0NM2yyZMnU9OmTfPVnpiYSAAoPDzcqA5jHDt2jCQSCcXExBARkUqlIm9vb5oxYwYREbVr144WLlyo954tW7ZQxYoViYjo+PHjJJVKKTKfDot5/y8iovv37xMAunDhgmZZUlIS2dnZ0a5duzTvA0BhYWH5ai8ubzo3ORyOeSnRuOwNhIWF6V0vS4JSpjDJfsxBSZqTvhuenryhbePGsYpjvr5m/di/J4bgRJVhyEosZPicXM4KFdjaAkFBwLp1Zi+ycKDTLzjabikoR1m0NyqVwIABwH//a9aQOyLgaN1JuDT1gNk+o6TIMxQ4V20w7m2/WeJ9OTg4IDQ0FNOnT0ffvn1NoI6RHPEc/9QZjvjQpybbp6l5sOUSLnaei1ePXxe8sUDc/WEHQsduRtpLRcEbv+NQEfpWN8rTu0n2hHmzFQq2j8jISDRu3FhvmyZNmhS4Xx8fHzg5OWleV6xYES90POVRUVH44osvUK1aNTg7O8PHxwcACgwDoxwlZHHP8TopG23bdoCnpyc2bdoEAAgODsbjx48xZMgQAMCtW7cwd+5cODo6ah7Dhw9HQkICMjMzERYWBk9PT9QoQrGPiIgIWFpa6vXccHd3R82aNREREQEAUCkUsLa2hle5mlAqxdlDnMJv48GaE9jyQ1TBGwsE3b6DG8tD8Oe0u0JLyRfl/WicmngUf80JF1pKvmQ9fI7dXTbi7PpIoaXkS9KtpzhYbTwizpes+Ig5idx1C5fKdMWzSNO3+6hfv77e9fLAgQPF8vpc/mYbIt2bI+3Bi4I3fst4O4wemSz/df/+C9y6xZ67ubGCBitXAjY2ZpUUtDwZ7y/vj85PNyKm0WeFe9OpU0zj5cvAoEFm1QcAW2dEoPPJiehy+jtc/7iIYVQWFqWSv7N32BF0ufsjmi/piX9/2GP2zysOIf/5GR/Gbobfl43w6Pi9Eu+vevXqmD9/vkmLF9z/bAZaRWyAddMGSH+RabL9mgwiqCZPQYvjsxBTs7NBQUBRIJfDY9l3aLx6MM7XHyO0GsHw9fWFRCLRDL7zEhERATc3N3h4eGiWOeiWhc/MhE3yM/Y0sWSx5VZWVnqvJRKJXuhat27dkJycjN9++w1XrlzBlStXALBiCm/kdRpsnsfB6eFtZKXlYPDgwdi8eTNUKhU2bdqEgIAAVKtWDQCQnp6OOXPmICwsTPMIDw9HVFQUbG1tDUIATQWlZ8LO2hrOTyNgpM6CKHg0cwN8R3eC37wBiIkRWo1xYgb8gIYTA2C5ZD7EWtU34cPP0H55V8Qv2ozcFDdxoVJBVssffY4Nw41J2yDGXHnV6wxYN6yL7rE/4XzPZXgtwrm119EvUPnzlmiRchS3mw5DWqr5boSLFy9Gz549MXDgQCiVhZ/0jg5+iIarBqN2+jWk1muJtFuxZtNYXM6vvV3s974dRs+bDBi1l6dFCyAsDOjRw+xyfl1DcJo4HJ54CgUsUWn97MK9MTsbuHYNqF/frPoAYN8uBWosGAhbyJBi6Y66a8ea/TOLyoWjaWi6aQQAINbJH/Wmm/+7KyqRJx+jxYlZAIDr3v+Bd6fiNY+VyWQIDQ01pTQNCUduosnt3wEA15uMgmM5e7N8Tkl4vfsoajw/DwB41G2sKIv/KTZugUdWHAAg5bOvBVYjHO7u7ujQoQPWrFljUNXs2bNn2Lp1Kz7//PP8jXadktYqW3Yu1qxZE9euXdPbrKS/h5cvXyIyMhIzZsxAu3btULt27UKXbJXkNlXOhB1yJNYYMmQI4uLi8Ndff2Hfvn2aAgYA0LBhQ0RGRsLX19fgIZVK4e/vjydPnuRbbtva2tpg0FG7dm3k5ORojDTd/0dd2ESqYJN9GXBAVpYIfzAAPMF+L3HwwuHDAovJB8/K7K9KBeRJ3RINrg2rAwAqy6Lx3/8KLMYYUily2rMCQR++PoQpUwTWYwSpkwNefTIQAPBl8mqM758ousk1p+rlEPUfdvDap+7Fn01+RkHzM8Wlbt26sLS0xPbt2zF8+PBC5zlWb+eD05+ugRJSeMkeQN64BdLO3zKPyGLS8uvCt0IwwPTRdqajwLg9hYKocmWi778nkstLRdPq1UTD8JsmWD59+sKC31TK/PMP0VzL2RqNaRt3Cy3JgPh4os12I4gAUsCCUoKvCS3JAIWC6IxrTyKAXkscKTn8SbH3NX/+fAJAU6dONaFCIlKpKLJ8ayKA4iSelPgw3bT7NwVKJb2o6E8E0E00oPgnRU2EKwUUCkqv5EsE0BF0pvv3S77LtzWnh4jlnJQtW5Zat25NZ8+epcePH9PRo0epXr165OfnRy9fvtRs6+3tTStWrGAvVCpShYVR7IEDBIBCQm4SEVFMTAxZWVnRd999R5GRkbRz507y9PQkAJSSkkJExnN66tevr6drxYoV5O3tTURESqWS3N3dqX///hQVFUXBwcHUuHFjvXwkozk9KhVRWBhRaCjFh8ZRQgJb3K5dO3JzcyNXV1e97+zYsWNkaWlJs2fPptu3b9Pdu3dp+/btNH36dM02bdu2pXr16tGJEycoJiaGjhw5QkePHiUiogsXLhAAOnXqFCUmJlJGRgYREfXo0YPq1KlD58+fp7CwMOrcuTP5+vqSPPdetmnePHJxdKT40Cf06FERv8ACMFlOT5MmRAAtx3jq2NE02kzOf/5DBNBWfEE+PiJNUZ0yhQigW3iP7OzY/VF07NqluaBUwhM6c0ZoQUZ4+pQUFjZEAC3Gd7RypdCCjKBUUrRfRyKA5LCk2Z0vmS3HZ/fu3SSVSgkAjR49mlRF+KB9g/ZRFtixfG3hTGmHQrQr4+LMoLbwlCSn5+02em7eJDp+vNT0rFpFVAP3KB327IRt0UZ0V9C7d4kCnEJJDkt2svboJ7QkAxQKom/8z2guoPEDpwgtySh7hxzUaLw5eEWx9/Pw4UOys7MjALR161bTCSSix8u1N6K/ept236ZCuWWrRuOC1keFlmOcbds0Gvt6njfJLt9mo4eInbeDBg2i8uXLk5WVFXl5edHYsWMpKSlJbzs9oyclhSg0VGP0nD9/U7PdgQMHyNfXl2xsbKht27b066+/EgDN8Smq0UNEdPLkSapduzbZ2NiQv78/hYSEFGz0ZGQQhYYShYZSRGgaPcmdy9i2bRsBoFGjRhkci2PHjlGLFi3Izs6OnJ2dqUmTJrR+/XrN+pcvX9KQIUPI3d2dbG1tqV69enTo0CHN+pEjR5K7uzsBoFmzZhERUXJyMg0YMIBcXFzIzs6OOnXqRPfV1rZCQZt++IFcHB0pKjSZIiKMfkXFxmRGT8WKRAB9ix/J2pro9WvT6DMpOkYPQPT330ILMsL69ex+DQcCVDRypNCCjJCaSiorKyKAhmMdVa/OfkpiQzn2GzYhDXuqYPGCdGqFiAbV8xeU7FCZCKBH8KK543KvqWlpbIBkQrZs2UISiYQA0KRJk4pk+OwYdZZewYUIoGyJDb3+4y+2YsAAopCQN77XnPz/Gj2lyE8/EVlBRtfQkAggpasb0ePHQsvSIz6eqIZXJt1BbSKAZOUqEyUnCy3LgOnfZlAUqhMB9Kp8DaLMTKElGfDgVjo9hDcRQNEuDUp0IerVqxcBoDZt2hTpglMgmZn0wq4KEUBXrFrQ6zQzTReVBJmMMipWIwLoDNpQyBkRalQqSVW3LhFAIfiQvv3WNLs1eYWse/eIQkPpdehdunXLNLs0OdHRRKGhlBkaTqGhKkp/g+Nx/vz55OnpWXra1MTHE4WGUk7oDQoNVZnci2ISco1HCg2lsFAZ3bhh2opPJjk3ZTIiiYQIoN7YRQBRPgX/hCXX6Dnq2pcAoi5dhBZkhOBgzQxFOTwjCwsyibfZ5LRvTwTQQXxCANGkSUILMsLTp6SytSUCaAkmU+XKRLmFJkVFztl/KEdiQQTQIXSlX1YpiRYtItKZLDEVv/32GwEgADRz5swivfePSbcoHhWIAMqBlNKXryPy8CCqV6/UIqzywqu3mZmffmIF4eZjBj7ADQCAdMNvgJeXwMq0pKWxVj8j4qajDlgCsvWWjaxwgog4cABwWzETvoiGChK47P4dMFMicHFRqYArn8yDNx4xjdvWAkXoLK/L8ePHsW/fPlhYWGD16tUmLV4QN/5HeGSxKlUPx62Eo5MI4/43bIB9AstwXu+zCB+2EaHGgwchuXMHAPuNf/KJwHqMkZkJdWbuC5QT20+GoVRCnYWdDHcAEr3Cj2vWrEFoaChiYmKwZcsWLF26FINKoaCLAep8HitnABIUIce39MhkxUhUllZQwBpKJWuZJioSEqBOmrCt7gkAOHRISEH5kHvN9fVjL48dA4rQv7Z0qF5d8/R9p2golYCRdlfCk3tx7GRxCnbIxPLlgJlSVYtPpUqQjGC5wqPxC+RPX2DI55lQbd8psDB9LD5sCeWCJQCAj3EEr8b+ANmiH1llXxPz1VdfYdWqVQCg1wC6MAxY6o9jMy/iPvxgARUcJowAEhOB27eB1atNrtXsmMEIMxli8PQsX84mYNrhpDZWZNgwwfQYQyZjEzBtcIaUYDNvZCREQ2gePCBq53BJo1EeOFZoSUbZMSNcEx4YERBY7P1kZ2eTn58fAaBvTeU+yEUV94QypSzMcrfjYJLJTLp705CeTjke5YkA2ocetHq10IKMoFIRNWrEvGVoTM5OKpMdS5N6emJjiUJDSR4aRtdClZqQLFGRlKTxTtwKzabQUP2JwPHjx1PFihXJxsaG/Pz8aO7cuaQwcShHgSgUGo3P7yZRaGiptHMrOlFRRKGhpIyMUsul3NQnk2CSc/P8ec09cfGYOAKIKlQoRu86c/Pppyx3tHdfcnFhkidPFlpUHnJyiHJDxw5/8YdmqHH9utDC8hAdrfnOB5f9mwCienVNd800GfHxRLnenv9iEq3HV3S3Rg+hVRmiUpGsSw+9WGSllTW7lpqBN/UrK4gNc57QM5TTj5t2ciJ6+tR0AgsJ9/SYieXLgQkTAHckYbsVqwqCGjVYSWyRQAR89RVw5VQagjAYUhDrTySyEjBZWUDfXjL8nDEUUhAUlX1g9d+FQssyIO6RCl6LAmGFHLyyLoeae4uv8ccff0RUVBQqVKiA2bNnm04kgPhBU2GnysRrOEKyaCGsrU26e9Pw00+wSHwOFSRYYLcAAwYILcgIJ06wiooAFmA6OnaSiO9Y5uQAyckAgESUBUEqTk9PbtW2HDtHyMEqbup6elasWIH4+HhkZ2fj/v37mDlzJiyL6UEtNqna3hgyW2cAEKenJ4OV+pY6OUBdtVt0ZaufPGF/pVK07lMBAPDsGXDjhoCa3oClJTB4MHv+++8iO54WFkDVqgCAjtWi4ckcZ5g2TUBNxqhWDcitLji38d+wghxf3JmORYsE1pWXihWBkSMBAN9KVmI4NsD6fjiOHBFYly5xccCWLbCuUEZvsVQhx/OfdpjlIxs0aKB5npqair/++qvgNxEBy5dj2C8NUR55+va8fg1MnmxakWaGGz35sGwZMHEiABD2un4FD0UCYGUFbNsGODoKLU/D9OmsavdyTIAPHoGkUmDzZkC3b4YIGDMG6B4+XxN6Z7VpvaiOI8B+23u6bUYL5T8AAOV/l0Pi5lrs/Xl7e8PDwwNLly6Fs7OziVQCqouXUfn0nwCAjRWmo9eoiibbt8l4+RK0hLnu/8BANBpUFyY8BKZjwQIAwL94D3+jmzhD25KSAJUKBAkSwXrjiM7okcuhbt6R7eAOgEUVScV2h1EbPQ4OkORaE6IzeuRybSybvb3mu84UW/utOFauGpUqoWlLS7izr12cIW4AQITAQPY0ORnYvVtYOQbkhrhZPo7BnDls0fHjwJkzAmrSJTeUUX2R9Lp1CFcr90Jf7MCCBSzaSRTs3s1amJw4AQCwJNacszpiMKJfOh4+FFCbLq6uwMmTQG5TZF2eLQnCs2fm++j09HS0a9cOn376KTZv3vzmjSUSYNQoYNYswNvbcP22bUBIiFl0mgOx3ZJEwdKlWuN1kc86tEk5wF4sWAB88IFwwvKwZg2waBHwCf7GV2B9WiRTprAfvIjYuBG4tvEWpmIxWzB0KNChg7CijLBnbRIGhLMv/kmNj1D2my9LtL9+/fohKioK/fr1M4U8hkqFVwPHAQBiUBU1fx0vvoElACxZAklaGmSwxmzM1gw2RMX58+wBYCGmARIpunQRWFNeiIAXbHZN7uAGBawhkZi993LRyfVEQSJBli3LI9T18ogCIo1hBhcXjT7RGT0ZOg1dHRw0Ro+oPBOA1ujx8oKFBcspBSC+fj06eZQ1awLt2rHna9YIpCc/cpvhIjoaAwcQZlb4DZZQYOpUrb0hKKtWAU2bsubqABAfjwZPj8BCymz0oUOZU1pwevdmvRDv3jVYVSnlDnr3Zi0TBcfJic1Y//GHwQRwffk1jAm4Y7YGqw4ODmiRO04cOnQodu4sIN/J1pYZPlFRQFAQXpWvqbc6J3CMCJMOjSPG4ZKgLFkCfPcde/65fwSmPJ/AXrRrp3b9iIL9+5n3xB1JCLIazhb6+zNrXESEhQHfjMrBRgyFFXJAFSsCP/4otCwDnj8HZOOnoCxeQi6xRqX9a2CKDpouLi4mLV6Qs3kr3KOvAgA21l6GTj1sTbZvk/HkCbtBAvgVgfBq5QN/f4E1GSPXy5Pg5Ifd6IOmTYFy5QTWlJfUVKi716VYM3F2diL0oKgbkrq6IodYyJrojJ6MDO2oTMxGj9qlY2MDWFpqjJ7sbFZkRTSow9tyY7HUXtJr11iNA7EyahT7e+UKcP26sFoAAD//DKxbB7i4sNf37sGyWxdMUS1EDqxw9Sq73wtOYCD7/Zw7p7fYvSz7GxrKij4JjkQC/PILYGSy8T2E4/p1YPz40peVLwMGsJjQPBPqTe5tRu/e5rElJBIJVq5cia+++goqlQr9+vXDgQMHCn6jlRUwaBDcnt7BieG7cRMNAACW9+4gfdEq0ws1A2K7dQrKokXA1KnseesmMmxVfQFJVhZQpgwLGRPJSOPiReCLLwAiwp+OI+GueM5Oxi1bRDUFnJICfPopMEa2TFP1TvLrr8ytKzJWf/EP+stZu+60wCmQ1q5ZwDuMI5PJ0L59e+zatQtk6um59HTIvmXdnE8jAF1/62UKu8z0zJ0LZGfjNRyxENMwerTQgowQGspiRwDMz/keKliIM7Qt18sDe3uk5rCQVdGFtmVmat0Q7u4aI0J0Ro86tM3KCrC31zN6RDGTrkbt6ckNUba3Zy+JAJlMIE3G0PH0AEDHjtoil6LInVAbZXno/n4cKlViz3/9tRT15EeTJiz/ZGFu/mhyMnD8OOwb1kLTpmzRtGki8KJYWbGwjTx5eI4OwH/+w57PmAE8eCCAtrxIpSxsrEcPvcX9/cMBMBtzyxYhhOWDnx8b2OlMrA/AFgSfyMFXX5nn+iSVSrF27Vr0798fSqUSn332GY4dO1a4N1tYoOP63ghdewNdcAT/oCUkc2Yh6d940ws1MeIYxYuAhQu1SYPNmwMnG38Pi9u32ILffwcqVxZOnA6RkUC3bmzWL9B5Gzqn72Ur5s6FmKbTiVjSqGVMJGZjNlv4+ecGFyExsH+3Ap+fYUmPr9yro+yy74u9rxUrViA4OBjffPMN0tPTTSURACCfuxgOqQlQQor9bVaiRUsRWjyRkezGCOBHTISkXDnNDVFU5A4wsstXwfqs/gCAjz8WUpARsrK04VjlyiEzi33fojN61KFtlpaAs7P4jR5nZ0CiLaetLkUkCogMjB5bHWeuqPJ68hg9rq5Aq1ZskSjyeoYPB4YNgyY5Ij4eGDIElqNHILeiMbZtA169Ek4iAKBZM6BlS4PFklq1sDg3IvzePRYFJTj167NE4jz88gvrjpGdzQoricIjaWUF7NihjWcE8GGZcI0hOWIEEB4ukDZjWFuzZPKjR0HlyqEinqEjTuCPP8xXvtzCwgKbNm1C7969IZfL0atXL4QUIT/n6xES/Gd9F7TGeXRRHcaKLic083SixeS15ExIaZWsnjdPW4GvRQui9L3HtAtGjDDrZxeFhAQiHx8my88ujhROrlrROTlCy9NjyRIiCZR0Hi2ZRnd3ohcvhJZlQHIy0VzHJZrvO+fwsWLv6/Hjx2Rvb08A6I8//jChSiKKiSGFpQ0RQL9iJIWHm3b3JqNPHyKAElGWHJFG06cLLcgI4eGa73tf+9UEEFWubNrmj0QmKAv88CGrVXzzJsmzlWYpXVxiVCqisDAmLLfLZ25/UnGVgpbJNKWq6eVLImLNz9WLBOqxZ0hWllbU69eaxeHhbFFcnKk+ppjn5oMH7HuWybT3yN27NauXLWOLHByIsrNNo7XYrFypX15X/fj5Z3r6lMiSdSWgFSsE1klEtH+/oc5164iIqFMn9tLTUyR9vGUyovfe0+qsWpWIiIKCtIt+/VVgjbq8fk3UvDkTVrYsPXqoInf33HGUH5GAHVHyJyGBlO070PmKfUrlmMpkMvrkk0+oUqVKFBERUeT3b9ig6VNMdeoQPXtmBpE6lMQ2+L83eubM0f5QW7Ykeh39nKg86y1CtWoRZWSY7bOLQloa0fvvM1mWFipKfL8De2FvL7LRBVFICJFUSjQGP2sP7tatQssyyqTesZQBOyKAkjt9XqJ99e7dmwBQq1atSGXiEXR2995EAL2CC43+THzGIxGxUVnu9/0NVpJUSuLsdv/FF6RuKvJ+rUyzzW2UyOhRKFiTjtyRbmqqdiwsqp4YusJyB+mRkexlbKz+pgBo3759b9zdoEGDqEePHkWS4O3tTSt0Rq5GPycxkYm6do0dW2KXdrX0vIPJ4ugwCTq9jnQnsh48YIvu3zfNxxT73IyKInJ0JJo+XXtt//tvop9+Ijp6lO7d0y4+ftw0WouNTk8ZvUdMDBFp5mfIz08EvYWUSiZEV+fZs0REdOOGdtGyZQLrVHPtGpGFBRNVrRoRsfkPtYHm5ET0+LHAGnV59YqoQQMmLiGBjh/XDtI//dT0E14mQakk2dKfKKBRGgFsTLV/v/k+Lisrix6V4Ib9++/aY1q7NpukNxfc6Ckms2drLyatWhGlpaqIPvmELbC2JipBIydTIpcTdeyo1XpxwC/aF2vWCC1Pj/h4ZjP6IIbSJQ5M4yefiPKqcvw40QF0IwIoy8a5RE22Tpw4QQDIwsKCbt26ZUKVxKzI3O97osUKg8GkaOjADPGnVlXIGtkkxJixQKKi2N0DoKSpS/XGbabmjQNLY4Mxcz6KyKBBgwgAjTBiDY4aNYoA0KBBg9gAMjSU6N9/Nb/xu3fZoryDHl1jJDY2lgAYNMtLSUmhV69eFUlrXqMnISGBsvO6GdRWw717mkXZ2UadKkT0ZqNnz549JJVK6Uk+HWJ9fX2L3oxY7b579IgJun1b7/+Kj2eLTXVpKbbRo1QyN07e88venig5mVQqIl9ftmisGHpP16mjr7NuXc2qM2e0i0+eFE6ihrVr9bU+f65Z1bcvW1SmjIg8vVOn6hk9RMw57ejIFnftKrLb/vPnRDVrEp04QUT647/lywXW9gYSE7X2sJ0d0cWLpfO5p06dKvJYZuNGreFTqxa7bpkD3py0iBCxImfqfpGtWwNHjwJOW9Zog5EXLQJ0GjkJBRGLkc0tOY/V46PQfG9uPe1OnTQNuMRATg7Qty/w/Dlhg+RrOFAGi59fu9YkldBMSXo6sLv/AXTH3wAAi0XzocluLSJyuRxjx44FAIwePRr+psytUiohHzUOAHAPNaEKHA0fH9Pt3mQEB7OeAwC+V8yFHDaaKkmiYvFiFnBepgx2u7Pfjq0t8NFHAusSIV5eXtixYweydGolZ2dnY9u2bahSpQq7OKkTItzdNb/xkuT0uLi4wLWEhU4qVKgAG92CLiqVNp9HXSErj76iVHDr3r073N3djfa3OHfuHB48eIBhw4YVTfTTp8CjRwb5POrsdXUel1xe9IR2eW71P5MglQLvvWe4vH9/wM0NEok2N+7QIXaKCEq3bvqvdaqVtGmj6bMpjvLVAwcCHqwPF8qU0T4HMG8eS5lLTmYtNUTBrFlArVp6i7y9oclDOnIE2LpVAF35Ua4ccOoU1HWgZ85kQyiAVey9cEFAbW+gbFng2DEmPyuLndL375v3M8+ePYuPP/4Y7du3x7179wr9viFDWP0IiYTloQUEiLCSoxmMMJNhDk+PSkU0c6bWwv/ww9xZvvBwIhuWM0EdO4rA383QjSIYOTyHVOrYVFdXonxmGoVi8mQmbSg2aEX/9pvQsowyccRregQvIoBe1/qgRDlR+/fvJwBUvnz5Is9SF8i6dZpj2cv2iBjTotiPqlEjIoAeO9chKXLI11c0PyEtjx5pA/nnztWEYnTtap6Pe9s9PT169KB69erRn3/+qVm+detW8vf3px49etCgvn01rhLvKlU0Xgl1ik/duvVp1qxZOv+y1tMDQO/Rpk0bvc9V06ZNGxo9ejSNHj2anJ2dyd3dnWbMmKEXPppveFtaGpFKRY/v3KE+7duTi6Mjubm5Uffu3Sk2NpZUKqbz8uUcCgz8llxcXKhMmTI0efJkGjhw4BvD2yZMmEB+fn5Gj1vTpk2JiOjVq1c0bNgwKlu2LDk5OVFAQACFhYXpbX/w4EFq1KgR2Vhbk7uLC/Vs25YoNJTaNGpkcIzUXqktW/ZQnTp1yNramry9vWlZnpgnb29vmjt3Lg0YMICcnJyYRy4PJQq9/Pprw/Pr3381q0+e1C6+c6fouzcp//yjr/Off/RWr17NFkulIgnHUsfbt2hhsCowUOtUM2foUJG4eJG59nRQKlnkjNozpeOwEgc6147ERCIvNgygSpVEqFWH0FCtk7VqVfOeA8nJydSgQQMCQJUqVaIHDx4U6f2bN2s9PjVqlCiIxijc01NIiIAffmCzJgDQti2bjXC0zGY1oGUyZlYHBYmiPPXatZpWIujWDVjtsxSSS5fYgl9+EU1FOYD1EVi6FKiIeKyyyi272K4dq54jMi5cACqsm4MqiIMKEjhuWVuiUlM9evRAcHAw1q9fX+JZaj1SUpDz/QwAwBF0Qf2pXXQn/8TDX3+x5hwAxqUvhAoWCAwUxU9In6VL2TS5kxPSB4/RdDoXZalqkTB06FBs0ukYvnHjRgwZMoS9UNdPdnTU8+QWxmty9SrrNXXq1CkkJCTgr7/+ynfbzZs3w9LSElevXsVPP/2E5cuXY8OGDQV/SFISFA8eoFOPHnCyt8f5oCBc+OcfODo6onPnzlAo5LCwALZu/RHbtwdh48aN+Oeff5CcnIx9+/a9cdfDhg1DVFQUzun0LElPT8eePXs0Xp4+ffrgxYsXOHr0KK5fv46GDRuiXbt2SM6tdnf48GH06tULXbt2xc1DhxC8Zg2a5Loe/lq8GJ7ly2Pu7NlISEhAfHwCpFIgIuI6Bg36DH379kV4eDhmz56NmTNnIigoSE/fsmXLUL9+fdy8eRMzTV36qX59/ddt2+p5fz78UNtrUfBGpc2asXs6wLyRzZrprR4wgDnVVCpg/XoB9OVl1Cjmes7jQQGYZ8LOjlXwU49hBKd5c+Dbb/UWSaXAhg2se0ZyMpAbBCEedK5VZcsCe/awQm/x8WwYKLq+Xbk0agTs3s2GKrGxzKNqrualbm5uOHnyJOrWrYv4+Hh89NFHePz4caHfP3AgqzYolTKvVNu2zJktCkxrf5kWU3p6VCqiadO0Ez4BAUTp6bkrv/lGu8Icwf3FYP9+TeoBNW1KlHkpjMjKii3o00dUwbJRUUTOzkSAik47d9dOR+UmjIqJrCyibt63SAGWhCkfMUZoSfkzYQLTCEtq7hZBaWlCCzKCQsHipAGK82xGgIpsbTUFssRDQgKRrS07N6dOpX37tD95cxVbeBc8PS9evCAbGxt6+PAhPXz4kGxtbSnxxQvq0a0bDfr4Yzb9+OKFxtui9p6EhhLVq5e/pye/nB5jnp7atWvreXamTJlCtWvX1rzO19MTG0tb5syhmt7epLp6leXzPHtGsthYsrOzo+PHj9OtW0Rly1akmTP/q3m/QqEgT0/PAgsZNGvWTM+L8vvvv5O9vT2lpaXR+fPnydnZ2SC3qHr16rQutypX8+bNqV+/fmxFXJz2wKm9Z56eev9XRARRp05fUuvWHfT2OXnyZKpTp47e8ejZs+cbtZfI03P+vP65tXevwSaffqqNpBCcgQOZmAEDjK4eOZKtLl9eJEVCAgOJli41uur775lWS0uWpiYK8nHpL16sPUUKqF8iOGqPH8DGiWJm40at1k6dzFt5MiEhgfz8/AgAVa9enZ4W0WXz55/acayvr+mqT3JPTwEQsR486t5fH33E4o0dHMBcPT//zFaMHi2Kad/Ll9mMg0rFelb9vUcGu68HsNa85cuzAGSR5MhkZQG9e7N2IkPsdiIg7SBbsXAhULWqsOKMMG+OClMeBcISSsjKVIDVkvnF3tfNmzfxJJ8GeCUmMhKqn9h5uRpj0Hd2LTg5meejSsTmzaw3D4Dx2YsBSPDFFywkXVQsX86aSNjZAd9+q0nd8/cHqlQRVpqY8fDwwMcff4ygoCBs2rQJHwcEoKxCoW0TLpGwBh256M6SmuoS1axZM0h0dta8eXNERUVBWYgp2VtRUXjw5Amc2rSB4/vvw7FqVZSpVw/Z2dmIjo5GZmYqkpISUL9+U817LC0t0ahRowL3PXToUOzZswevc6dbN27ciD59+sDJyQm3bt1Ceno63N3d4ejoqHnExsYiOjoaABAWFoZ26h4ieRo+wtrawPtsZwc8fBiB+vX1e7q0bNnS4HgURn+x0c1Z9PICunc32ER9G71wQdvGSTDUeT1583tyCQxkf58/Bwpw8JUOEyYAdesaXfXdd+znlpPDolZEQT4u/YkTgQ8+YM8DA0XQD+kNjBrFxlwAG7qIos9UPgwZwtoyAqy/9tdfw2y5cxUqVEBwcDB8fHwQHR2N9u3bazzVhaFfP9YEViplTWvbts23Z3Cp8c4bPUTA999rk+vatQP+/ju3y/Xz5+wMAthFRgQZgvfvsxtGVhZLXDt2DPD4Zba2i9aGDVp3vQgYPRq4dQsoi0T8apXrx27eHBgzRlhhRrhxA0hcshEtcREAYPPLCr3E5qIgl8vxxRdfoFatWjh16pQpZQIAaOJESJU5SII7grx+0DTTExVZWZpqIM/e74y9SW0AQHwFDJKTta3Xv/4aqrLlNGE3omtIKkKGDh2KoKAgbN68GUM//ZTFgWRns5UuLoBUCqlEAlKp9IwehdowEgqJBOlZWfigVi2Ebd3KHocOISwsDPfv38eXX36psSuK00yxb9++AIBdu3YhKioKFy5c0IS2paeno2LFiggLC9N7REZGYvJkVojGTrfLrJWV/s6NhC6rN1coCh7kOKgLIZgDZ2fthNbo0YYGG4AuXdhfpZINzASlY0c2w9mxo9HV/v7apqqiKGjg65uvVldXNp4BWGPVsLBSU1VkLC1ZX3dLS9YfdtIkoRXlj0TCwhtr12avBwxgIWRiZcYM1nsXYNkYs2aZ77O8vLxw+vRpeHp64v3334ezs3OR3v/ll6yghVQKREczw0fd11gI3mmjhwiYMgVYsoS97tBBx+BRqYDBg4EXL1jw6bZtgrc6f/YM6NwZePmSXaMPHwaqPbsI/Pe/bIOvvhLcE6U7qPn9d1apAwCO1xoHm7QkNkP5+++iacf+/Dn7q1AAkwYlYjF9x14HdAA+/7zY+125ciUiIyPh4OBg0lnVS5cAHDsGSe6ofAbmY9ICN+gWoxILrxas0UzbzLZibtTGjVnssaj4+WcgPR1KCys86TsJN29qm7SLwLErejp37gy5XA6FQoFO6s7x6lF3aipw4wbKurjiydNnmutDenoaHj2KBZHxGHlra2sAKJS35sqVK3qvL1++DD8/P1gU4hrTsGZNRMXFoZybG3yrVoVv69aoXt0XVav65laKc0HZshVx48YVELF/JycnB9evXy9w305OTujTpw82btyITZs2oUaNGmjdujX73IYN8ezZM1haWsLX11fvUTZ30srf3x/BwcFsZ7qGg709UKYMrK2t9Y6PnR3g41MbYWEXIJezQm9pacCFCxdQo0aNQh0Pk+Hvz3JPvvrK6Ory5YEmTdjzw4fZ6aKeeCx1nJ2ZW+QNE1zqiZpz54Dbt5lNv2NHKekzxhu+yzFjtDbxtGnsb3o68O+/paCriNSvD0ydyp5v3Kgp8InsbG1BRbHg6Ajs3cvGXikpLIJFPbdDVLyJEXMhkTADXX3/mjcPWLfOfJ9XtWpVXL58GX/88QcsjUxyFETfvmyIbWGhNXyKkCJkWkwTYWceihq3p1CQprqVSkU0caI29rFjxzwN6HS7Nf/0k+nFFwKViujoUfY8LY2oYUMmx8KC6MgRYmXlqldnC318SOikDpWKSN1+4sYNbbG772of1B7LBQsE1aiLUknUuDFrQjh/PtEmDCICKMfKpkRd/uLi4sjBwYEA0KZNm0ymNy2NyMNVTrLqtYgAuoX3qH5dRUkKy5mPlBSSO5chAuhxq76ar9+Eh6NkqBP2UlOJ3NyIALr2wXCqXJmof3+m1d29REX7CuRdyOlRk5qayq7DERFEoaHUo00bbU5PRARN+HYKubtXoB07ztH27f9S27Y9ycHBkUaNmqX5KqCT06NQKMjOzo7mz59Pz549o5Tc5iPGcnocHR3p22+/pXv37tG2bdvIwcGB1q5dq9km35yeR48o4/x58qtShdp+8AGdO3SIYmJi6MyZMzRgwFi6ciWO7t0jGjNmMbm4lKGff95Hx45F0PDhw8nJyalQzUnPnz9PAMjNzY0WL16sWa5SqahVq1ZUv359On78OMXGxtKFCxdo2rRpFBoaSkREZ86cIalUSj/88APdvX6d/t2+nRaPGaNpEd+hQwfq3r07PXjwhCIjEykri2jLlusklUpp/Pi5tHdvJP36axDZ2dnpXYfyHg9jlCinh4johx+Ihg594yZz57JTz82NFXxzdS3eR5mEApJ1srOJypVjekeOJPrsM6LevUtJWzH47TftTzskhH0V8+cLrco42dnadkne3mxYM38+y/cQIzt2aI/t11+zZcePa8dqYiI9nahJE6ZVKiU6eJAtT01lqXfmIicnh5YuXUrpmsT4wrFrl7anbdWqrK+TmqL09OHNSXNZu5boxx/Z4Dw3B1yT7KVn8Ny6xZqPAkRdughWFODwYZYDLpNpOxkDLFGNiLQ1KiUSdmUTmAsXmJyTJ1k/MoDIz+MV5VSoxF40aGDerLoicvUqk9WvH1E7yxDtAZ49u0T7/eyzzwgAtWjRgpQmrMt89izRWPyk0RmAYM1FTHTMmEEEVmShOqIIYIOauDiiyEjNuE04Pv2UzYCos2ktLOjn8dF6doCPDxvcdOvGbsym5l0yejTcuaNv9Ny8SSST0atXqdShw+fk4OBM5ct70axZQeTnV58CA2dpLq+6Rg8R0W+//UZeXl4klUrfWLJ61KhRNHLkSHJ2diY3NzeaNm1a4UpW5zb6TDh6lAb27Elly5YlGxsbqlatGg0YMJzOnEml0FCiS5cU1LfvOHJwcCYXF1eaMGFCgSWrdalZsyZZWFhQfJ67dlpaGo0dO5YqVapEVlZW5OXlRf369aPHOrWR9+7dSw0aNCBra2sq6+pK/+nYUbPu0qVL5O/vTzY2NgRoS1YvWbKHqlatQ5aWVuTlVYWW5kl6LxWjZ8+efJt3BwURffQR+13pnoJubsX7KHNz/z67bumOGQCi5s2FVpY/OvVjqFLu7beA2hWCcumStoRx376sztEXXwitKn/GjtWeB5s3E7VuzepHiZEXL7QNge3s2LFeu5aofXvzfaa6QfVHH31EmXqD64LZvVtr+Pj4EMXGMkO4USN2XhcGbvQQs3grVCBq1oxo/HjtCdu5M6vYpSEzUzvtUK6coIXZP/yQyWjZUqt3zpzclceOaRdOmCCYRl2++orJUReRk0qJnn78ldY9deOG0BL1mDUrVy9kdAe1iQBSVPPLc0IUjVOnThEAkkqlBpWnSsqauYmUDFcigPaiFzk6Ei1cyAx5UVRDU7tFnj1jdy2A1mCkwfi6dWuBqyCpVEROTkTt2mmnb/v3p3nzjNsD5ppPKNTAUqUiunbNoHIX3b5Nj2JzDBbfuSNw4cZ//9UXpNOX6u5dw3+jpB2527RpQ+PGjSvemx8/ZiJu3DA4IRUK44e9iBOXpkOpZAIyMoyuTkw01BoaWnxPZYmNnjcMdBQK5mHP+zsrU6Z4H2VudGf2dR/e3kIrM87+/URLlrCxja5eLy+hlb2Zb781NIILO8gtbWQyNpbUHe9YWxMlJQmtzDgPHhB5eDCdZcuy3jgAu8aZg0uXLpGjoyMBoFWrVhX5/Xv2aFvmeXuz8xkg+v33wr2fV28DsGIFi9O/fBlYuZIt69qVVWOxtdXZcNIk4O5d9jwoiFULEIDLl1n8MKDtBDx8OKvFj1evgKFD2cI6dbTNegQkI0Mb46zOT57U4BQqHc7tlzFlCvD++8KIywd1BZaJ+BF1EAEA+FqxBiPG2aI4TcrlcjnG5BZoGDVqFBo0aGAipQzfLbPghhTIYI1JWIb0dG3MtuDV0ORybT+G+fOBzEzILe0wD/o9QKpUYX0PclM2hCE+njUwCA5mOXsA0LIlfGJOoxqi9TYNDGSd2QUjJ4eNAXSxtAR8fWFlYxjXX6mSwIUbdXNwypdnmdW5GKsu6O5ufkkF4ulpcEJaWupJB8ASbQVL65RK2b3I3t7oand3GOT1WVoKmDr5hgNlacnyN/LWZhBJwVEDPv9ce53VJT7e8KcpBurXZ236jh3TXx4Xp73ciYkjR1h+159/6i9/9So3h1WEpKZq09XU4x25nOWliJHq1VnunL09kJTECmIB2nRwU9OsWTMcPnwYkyZNwqhiVC769FNg1y52rXj0iA0fAZZ6l5lpYrF5eCeMnsREwy/X05Pl0+sZPAcPasuzjBunLTEjAOriCrpERwPLlgGZQ8ewK66lJevwpPdPCMOePSxZUo0D0jHyBisf8qpi7VxrTTwkJADXrwNVEYMfwOo7bsWXiKjcHnPnFm9QrlQq0bNnT1SuXBnzTN0dLjwcH0WtBQAsxwTEohoAVlBwwgTTflSxuHMHWLWKzS7kZkwerzkOCaik2cTODjhwQLB5BC337hkuCwxEl79HIQ3ayjNeXgImV6vJa31LJOwOZmNjcI7a2xe72KBpINIaPQ4OBhXG1A0p1Tg5CWz8qkXkU+0yr0Hm4CBwQ903NJuWSJjBq4vgx/YN1KvHKkzpIlajB2CJ4HmLmigUrKiQ2PDxAc6cYdevvBSi/kap06ULK11s7FgK3rzWCHFxrKCUsRodGzeWvp6CIGLjs3XrDIeKe/awcaU5+PDDD7F06VJIi3nR7NGDlTXX5elTbQcZc/FOGD3z5hl2pn3yhM06L1yYO1uTkADklhOFv7+go51799jgMC+XLgE1bu2G/f7c6YSZM7WF7gVGpyk7AGABpqMqHoIkErjtzWtdCs/RowBAWIWxsEM2UuCCf3r+iDNn2AR1cbCzs8OiRYsQFRUF17zTxMXhn3+A0FCACDljxsMCKiSgAhaCTTsuWSKiMp/qu+mECWw04OqKDWW+09skKAgwsfOreEREGC6rXBk7hhxHEjw0i9avZ4WdBCWv0ePtrXGZ5B3UCu7lUUemWFgA1aoZWAh5jR5TeHlCQkKwUu26LyoWFux45nPQnJ31i6bl1V/qFOC2KVNG/zIrZqMHYFW7dFv6iNnokUpZWV11yWI18fHC6CmIatWY4ePpqb9cjEaPRMLmmA8eNPyNidHo8fICLl40fu8NCwNu3ix1SW9EImGGcFiYYU8slYpNpIuNiAigVi3jk/+LF5t3suGtN3qio4G1aw2X163LrNzvvwckpAIGDWJ+P1tb5qMUcJC+dKmh2/yTT4DIkAT0OJbbKa1xY21BfoF58AA4e1b7ugUuYCxWAQAk48axvjwi4/BhoBf24WMcAQBc6LoQa/6qUOyvnXS+MDtTxcCEhLA+Ubt3w/LcaQDA91iEdDhh0SLWiE405L2bvn6NoIt+OIEOcEA6pk8HPvtMGGkG5PX0uLkBx4/jdRlvzaJBg9hsnuDo9rIpX17PK6E7qHVwENjLA2i9PD4+hrFWYAaE+qchler1LBUGD483XuelUv2wUVE2/9VBIgEqVtS+FrvRY23NZsbVtpyYjR6AGcEHDuiHPYrV6AGYQ/jMGX0H4bVrwukpiI8/ZqH8uh6q27dZeJPYsLFh47QTJ4AKFfTX5Z0AFgONGgFXrrBgjLzXsU2btK07xELt2sDp0yylI+9cT2qqeTM63nqjZ/p0/XGDlxf7km/dYo2iJRKwkBx1gfgff8y323FpEB/POtSq8fRkeUcHDxC85g5nJq6tLQtryxsULRBBQdrnlcpk43jlYZCC2HTT/PmC6TLKwYOQZRMuHn+Nn/ENAOBltcb4+OCIYt90z5w5gxYtWuDGjRsmFArg/HkWNtavHwAgFI3wBwZiwQJtbwPRkPduqlQiidwxEH+gXXdHTYdoUaDr6bGzYxZw3bqa779CBWD5cmGkGaD29Li4GEzb6v78BffyAMzoKVfujdaMeibX1VUErboKcf3U9UaZs5+nqdD19ojd6AFYoEJuL1bhz99C4OcH7NypdWKK2egBWB/TM2e0xrAYPT26+PsDV69qezgB4vT2qOnQgfU/0g19/PNPbf8eMWFhwXo43bun34JQJmPGkNjw9GTRFrdvsxwfXX75BXj40EwfXOTSB6VIQRUa1CWJAdZzY/lyI4W5btzQlt/o1k3g0kdEkydri51NnMhK9RER0YYN2n9m5UpBNeqSk0Pk6clktW9PlDbme63O4GCh5Rni70/hU7bQj2ClYlRSKdH168XenVwupzp16hAAGj16tOl0KhREjo565WyuoDFdazSCaOtW032OKZDJtCXecx/K6n5UEU+pTh0RlKfOS8WKTKelZW7DK4a6Qsxff5WODHWFrIx8KnIREVF0NNHt2/mW4QoLY1XRBL5sMeRyVmXsDSQlsapiuW13RI9KRRQezqrivS28fMmOcUkqOmZkZJSselsRyMpi5ZUrVDD7R5mM5cvZtWLePKGVFI5797SXvWfPhFZTMJmZrF0AQNS1q9BqCkalIlq9WtubcOdOoRUVzLFj2tYirq6Ct3kskCtXiAICtMOMfv3y37Yk1dskRGKsT8JIS0uDi4sLUlNT4Zwn+J4IaNeOzRpMmMASogzCPzIymN/v3j02vfvvvyzkQSBSUlieUd26LCSvfv3cFbGxbAokPR0ICABOnRI4o1bL8WOEH7rdQJ9FH2BC2xuQNmvCZnyHD2dmuph4/hyoUAEZNm6wlaXCAioWTFzcnAAAP/74IyZNmgQPDw9ERkbCzVQxOzdvAg0bGi5v2pQlJAkeG6RDXq2+vojZGIJGPSrj6lU22ygaUlO18SlbtgD9+2tWLV3KHFY7d5aOFJVKhaioKFhYWMDDwwPW1taQ5J3ufvSITdPmM20fHc2cK2IPvVIjlwMxMUDNmm/HzD7AKl4plfqhY2KGiIUcV6pUdO8UEUEulyMxMRFKpRJ+fn7FTkQuChcusNnnJ0/M/lEmgQgYPJgdX3XtI7Fz7x7rdL9xI6tcK3ZUKmD2bJZzkpSUb+FCUREeDnz5JQspzFs9T4xkZbG89iVLWK6MKIoivQEiFpQ1dSobdty4Ybwo8Jtsg4KwLHgTcXL6NKvmvH37GxLTJ0zQxvf/8YegBg/ASvQtXcrsBc19RqViV9f0dDay2bRJNAYPADz461+EVBkIu9HXgBbD2OigcmX2j4iN4GAAgIPsFXstkbCT5PRpZmEWMbM6Pj4es2fPBgAsWbLEdAYPwIoY5CUggAWVi22EqxszkZtBG/+wMnbuFJnBA2h/7ytW6Bk8AEuXKU03v1QqRdWqVZGQkID4/OJkFApWsiYf0tPZ5SApyUwizYBcbsbQBDOQk8M0izFkJT9kMlabp7ghhPb29qhSpUqpGDwA0LIl8M03pfJRJkEiYdWwSjBfVurUqsVC3YzdWsSIVArMnQvUqMEKB7RvL7SignnvPTbRPnUqM+DzFpIQG3Z2rNBXv37M+JHLxR0WK5EAHTuyc2HXLmDDBhbqZtLPeFs9PTKZ0VxaLfv2Af/5D3s+caIoSlhoTrhHj5jLRyJhyQXqun2bNjEDSETkzF0Ay1kz2Ey/Oqfl778N63uKgSFD9BOQ1PTowYpXFHEq6csvv8T27dvRrFkzXLhwwbQDhM8+A3bv1rykTz6BZPdu0VXBA8Ca2axdC1StyoovVKmCnBz9yleiISiINSlYuNBglVCaiQg5OTlQ6va4KSSiPc5vgGs2PyoV+1ucS5KFhQUsLS0NvY5m5m07xgDXXFooFKJJYS40Yjcg8kLEjvPbpFmhYBM7ea9zJfH0vLVGzxt5+pSFiyUnsxq6ly8XYCGVMmPGsNi8GjVYpqdMxgbm+/aJLyakRQv9DmIdOgD794vPF03EDMm88ROBgWx6v4hToiEhIQgICIBEIsG1a9fQ0FgoWkm0Vq7MpmoBoG9fURWuMKBJExYDdPYsKwEsZsLDWZMQsf2OOBwOh8PhlJiSGD1m9W13794dVapUga2tLSpWrIgBAwbkH+ZRUlJS2F+VChg4kBk8dnYs/k1MBo9KxYyb775jOmUyFnezfr34BmqJicxg1OXkSRboPH++uNpV379vaPAsWMB8o8WIAdmwYQMAIDAw0LQGD8ByuNQGz/DhrByMWA0euZz9ts6cEb/BA7D4A7H9jjgcDofD4QiOWZ2gAQEBmDZtGipWrIinT59i0qRJ6N27Ny5evGjaD8rIYFmSx46xMLbTrOcJVq5kga5i4upVwzqY69eLoI29EY4eNTRsLCyY5yQwUBhN+aEuSQ4w3/6GDawZSzHZvHkz2rRpg0/z1lI0Beqg64kTWW6UmAfpL1+y31XVqkIr4XA4HA6Hwyk2pRredvDgQfTs2RMymQxWhZjZLrQLa9cuZvTMmcOytnJygF69gL17xTeg/O47wyIAvr7MOFu9Wlyz6Z9/zo6tGhcXlofSoYNwmvKjZ09WBMDRkX3vHTsKrSh/hg9noXgzZojv/ORwOBwOh8MRKW9F9bbk5GRs3boVLVq0yNfgkclkkMlkmtepqakA2D/4RtTdPmfNYn/V3Qdfvy6x7jfxOjELj0JfoF7XQhoqRMCePYbLk5OBkSNZmeKC/tci8uTaM9iVdYC7TxErgikUzNOjxseHGUA1a5pcY+zZR/Bq7gVL62JGW+bkMO+ehwc7vg0aFFvj/v370bFjR9jnyVl6di0OFRp55fOuItK5MzMcTXl+EiHl/nO41qxQ8LYCoUjLgpSUsHBxFFpKvlBKKiTOTqKqoGhARob4O2mKPctX3Q5CzN/zWwSR+OdvCix+JDAqFbttqSvui5GUFBbsIbYCo7pE3ZGjem1r0f60iYDwU8/g30G89+rs1wpE/R2B9770F1qKUdQ2QbF8NkXu7FNEvvvuO7K3tycA1KxZM0pKSsp321mzZhEA/uAP/uAP/uAP/uAP/uAP/uAPo4/o6Ogi2yRFDm+bOnUqlixZ8sZtIiIiUCs3lyYpKQnJycl49OgR5syZAxcXFxw6dMhoucy8np6UlBR4e3vj8ePHcDHoPMr5fyAtLQ1eXl6Ii4srshuT8+7AzwMOPwc4/Bzg8HOAk5qaiipVquDVq1dwLaJrtMhGT2JiIl6+fPnGbapVqwZrI6ENT548gZeXFy5evIjmzZsX+FklidvjvBvwc4AD8POAw88BDj8HOPwc4JRyTo+Hhwc8PDyK+jYAgCq3o5quN4fD4XA4HA6Hw+FwzInZChlcuXIFoaGhaNWqFdzc3BAdHY2ZM2eievXqhfLycDgcDofD4XA4HI4pMFt9C3t7e/z1119o164datasiWHDhsHf3x9nz56FTSFLqNjY2GDWrFmF3p7z7sHPAQ7AzwMOPwc4/Bzg8HOAU7JzoFT79HA4HA6Hw+FwOBxOaSPSSuYcDofD4XA4HA6HYxq40cPhcDgcDofD4XDeabjRw+FwOBwOh8PhcN5puNHD4XA4HA6Hw+Fw3mneKqOne/fuqFKlCmxtbVGxYkUMGDAA8fHxQsvilBIPHz7EsGHDULVqVdjZ2aF69eqYNWsW5HK50NI4pciCBQvQokUL2NvbF7kbM+ft5JdffoGPjw9sbW3RtGlTXL16VWhJnFLk3Llz6NatGypVqgSJRIL9+/cLLYlTyixatAiNGzeGk5MTypUrh549eyIyMlJoWZxS5Ndff4W/vz+cnZ3h7OyM5s2b4+jRo0Xax1tl9AQEBGDXrl2IjIzE3r17ER0djd69ewsti1NK3Lt3DyqVCuvWrcOdO3ewYsUKrF27FtOmTRNaGqcUkcvl6NOnDwIDA4WWwikFdu7ciQkTJmDWrFm4ceMG6tevj06dOuHFixdCS+OUEhkZGahfvz5++eUXoaVwBOLs2bMYPXo0Ll++jJMnT0KhUKBjx47IyMgQWhqnlPD09MTixYtx/fp1XLt2DR999BF69OiBO3fuFHofb3XJ6oMHD6Jnz56QyWSwsrISWg5HAJYuXYpff/0VMTExQkvhlDJBQUEYP348UlJShJbCMSNNmzZF48aNsXr1agCASqWCl5cXxo4di6lTpwqsjlPaSCQS7Nu3Dz179hRaCkdAEhMTUa5cOZw9exYffvih0HI4AlGmTBksXboUw4YNK9T2b5WnR5fk5GRs3boVLVq04AbP/zGpqakoU6aM0DI4HI4ZkMvluH79Otq3b69ZJpVK0b59e1y6dElAZRwOR0hSU1MBgN///09RKpXYsWMHMjIy0Lx580K/760zeqZMmQIHBwe4u7vj8ePHOHDggNCSOALx4MEDrFq1CiNGjBBaCofDMQNJSUlQKpUoX7683vLy5cvj2bNnAqnicDhColKpMH78eLRs2RL16tUTWg6nFAkPD4ejoyNsbGwwcuRI7Nu3D3Xq1Cn0+wU3eqZOnQqJRPLGx7179zTbT548GTdv3sSJEydgYWGBgQMH4i2O0OOg6OcAADx9+hSdO3dGnz59MHz4cIGUc0xFcc4BDofD4fz/MXr0aNy+fRs7duwQWgqnlKlZsybCwsJw5coVBAYGYtCgQbh7926h3y94Tk9iYiJevnz5xm2qVasGa2trg+VPnjyBl5cXLl68WCT3FkdcFPUciI+PR9u2bdGsWTMEBQVBKhXcdueUkOJcB3hOz7uPXC6Hvb099uzZo5fDMWjQIKSkpHBP//8hPKfn/5sxY8bgwIEDOHfuHKpWrSq0HI7AtG/fHtWrV8e6desKtb2lmfUUiIeHBzw8PIr1XpVKBQCQyWSmlMQpZYpyDjx9+hQBAQH44IMPsGnTJm7wvCOU5DrAeXextrbGBx98gODgYM0gV6VSITg4GGPGjBFWHIfDKTWICGPHjsW+ffsQEhLCDR4OAHY/KIoNILjRU1iuXLmC0NBQtGrVCm5uboiOjsbMmTNRvXp17uX5P+Hp06do27YtvL29sWzZMiQmJmrWVahQQUBlnNLk8ePHSE5OxuPHj6FUKhEWFgYA8PX1haOjo7DiOCZnwoQJGDRoEBo1aoQmTZpg5cqVyMjIwJAhQ4SWxikl0tPT8eDBA83r2NhYhIWFoUyZMqhSpYqAyjilxejRo7Ft2zYcOHAATk5Ompw+FxcX2NnZCayOUxp8//336NKlC6pUqYLXr19j27ZtCAkJwfHjxwu9D8HD2wpLeHg4xo0bh1u3biEjIwMVK1ZE586dMWPGDFSuXFloeZxSICgoKN+BzltyGnNMwODBg7F582aD5WfOnEHbtm1LXxDH7KxevRpLly7Fs2fP0KBBA/z8889o2rSp0LI4pURISAgCAgIMlg8aNAhBQUGlL4hT6kgkEqPLN23ahMGDB5euGI4gDBs2DMHBwUhISICLiwv8/f0xZcoUdOjQodD7eGuMHg6Hw+FwOBwOh8MpDjwhgsPhcDgcDofD4bzTcKOHw+FwOBwOh8PhvNNwo4fD4XA4HA6Hw+G803Cjh8PhcDgcDofD4bzTcKOHw+FwOBwOh8PhvNNwo4fD4XA4HA6Hw+G803Cjh8PhcDgcDofD4bzTcKOHw+FwOBwOh8PhvNNwo4fD4XA4HA6Hw+G803Cjh8PhcDgcDofD4bzTcKOHw+FwOBwOh8PhvNNwo4fD4XA4HA6Hw+G80/wPZMxnzekdoAcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenvalues, eigenvectors = np.linalg.eig(matrix)\n",
+    "# Plot the positive and negative eigenvectors so it looks like a line through the origin\n",
+    "# Also reshape them so they are easier to plot\n",
+    "# Multiply by 4 so they take up the whole plot\n",
+    "eigenvectors_to_plot = 4*np.array((eigenvectors,-eigenvectors)).transpose(1,0,2)\n",
+    "ax.plot(*eigenvectors_to_plot,'k--')\n",
+    "ax.set_ylim([-3,3])\n",
+    "ax.set_xlim([-3,3])\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4b233b-ab96-49cf-afe7-21d8ac40ac72",
+   "metadata": {},
+   "source": [
+    "We see that vectors that are along the directions represented by the eigenvectors do not change direction, only change scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83279b35-f120-4a31-88f8-3d6087c90379",
+   "metadata": {},
+   "source": [
+    "Without approximation or loss of generality, we can take *all* of the eigenvectors or mode shapes of a system as column vectors and *stack* them side-by-side to form a matrix.  This matrix forms a coordinate transformation: in the same way we can transform between a local and global coordinate system, we can also transform between *physical* and *modal* degrees of freedom.  Physical degrees of freedom are generally represented by some physical quantity (e.g., Point 1 moves 2.5 centimeters in the vertical direction).  Modal degrees of freedom are generally represented by some *modal degree of freedom* or *modal quantity*.  Motions are then represented by how much of each mode shape is present in the motion (e.g., the system moves with a shape that looks like mode shape 1 scaled up by a factor of 2, plus mode shape 2 scaled down by a factor of 1/2).  We can represent this mathematically by saying physical motions $\\mathbf{x}$ are represented by a linear combination of shapes $\\mathbf{\\phi}$.  We call the coefficient matrix $\\mathbf{q}$ the *modal coefficients* or *modal degrees of freedom*, because they describe how much of each mode shape in $\\mathbf{\\phi}$ is present in $\\mathbf{x}$.\n",
+    "$$\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a4baa0-4a8d-44fc-84a8-14e0885710e3",
+   "metadata": {},
+   "source": [
+    "We can substitute this transformation into our original undamped equations of motion to get equations of motion in terms of the modal degrees of freedom $\\mathbf{q}$.\n",
+    "$$\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{F}$$\n",
+    "\n",
+    "We note that the mode shapes $\\mathbf{\\phi}$ are invariant in time, (they are a function of the matrices $\\mathbf{M}$ and $\\mathbf{K}$, which are also invariant in time).  Therefore taking the derivative of the physical response $\\mathbf{x}$ is simply\n",
+    "$$\\ddot{\\mathbf{x}} = \\mathbf{\\phi}\\ddot{\\mathbf{q}}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c218db3-a489-4015-861e-492ef63345eb",
+   "metadata": {},
+   "source": [
+    "While the transformed equation above is technically valid, we generally do not like this form, because it breaks the symmetry of the mass and stiffness matrices.  For all real structures, the mass, stiffness, and damping matrices should be symmetric.  This is a function of Netwton's third law of equal and opposite reactions: if there is a force developing at degree of freedom 1 due to motion of degree of freedom 2, an equal and opposite force will develop on degree of freedom 2 due to degree of freedom 1 pushing back on it.  Currently, however, in our transformed equations of motion, the new effective mass matrix $\\mathbf{M}\\mathbf{\\phi}$ is not symmetric, nor is the equivalent effective stiffness matrix.  We therefore premultiply the equations of motion by $\\mathbf{\\phi}^T$.\n",
+    "\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "In this final format, the effective mass and stiffness matrices $\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$ and $\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$ are symmetric.  We will often make a substitution\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "$$\\tilde{\\mathbf{K}}=\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$$\n",
+    "where $\\tilde{\\mathbf{M}}$ and $\\tilde{\\mathbf{K}}$ are referred to as the *modal mass* and *modal stiffness* matrices.  The modal system of equations is then\n",
+    "\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "The symmetry of the system matrices is not the only interesting property when we transform into modal coordinates.  It turns out that the mode shapes $\\mathbf{\\phi}$ *diagonalize* the mass and stiffness matrices.  The modal mass and stiffness matrices, formed by pre- and post-multiplying the mass and stiffness matrices by the mode shapes, are diagonal.  This physically means that there is no coupling between modal degrees of freedom; the response of mode 1 does not depend at all on the response of mode 2.  They can be treated as entirely separate equations.  Compare this to a physical mass and stiffness matrix, where there would be coupling between the degrees of freedom.\n",
+    "\n",
+    "We can think about this concept physically.  In the physical domain, if I pluck the end of a beam, all of the degrees of freedom on the beam will eventually vibrate.  This is because the physical degrees of freedom are all connected to one another through the mass and stiffness of a beam.  In the modal domain, however, if I pluck \"mode 1\" (an admittedly hard thing to accomplish in practice, as you would need to deform the beam into its first mode shape and then let it go instantaneously), none of the other modes will be excited at all, because there is no coupling between them.  Note that this still means that the entire beam will vibrate, because the mode shape of the beam contains all the beam degrees of freedom, so if the mode shape vibrates, the whole beam vibrates.  In fact because the modal representation described above is exact, plucking the end of a beam will result in exactly the same beam motion regardless of whether it is represented by modal or physical degrees of freedom.  The modal transformation is simply a different way to represent a dynamic system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0db175db-4400-49c1-915c-f81bbede2855",
+   "metadata": {},
+   "source": [
+    "### An Infinite Number of Mode Shapes\n",
+    "One last concept to describe before we move on is the concept of mode shape scaling.  From the previous figure, we saw that each eigenvector of a matrix mostly represented a particular direction, rather than a specific vector.  Any vector in that direction would be only scaled by the matrix instead of having its direction modified.  Therefore, eigenvectors are only really unique up to a scale factor.  An eigenvector times a scalar is still a valid eigenvector of a matrix, so you could argue there are an infinite number of eigenvectors of any matrix.\n",
+    "\n",
+    "Rather than allowing for any arbitrary scaling of eigenvectors, we will often scale them to some preferred normalization.  One might, for example, scale an eigenvector such that it has a length of 1, i.e. make it a unit vector.  Another might, for example, scale a specific degree of freedom to 1.  The most common scaling on an eigenvector, particularly in the context of structural dynamics where an eigenvector represents a mode shape, is to scale the eigenvectors such that the modal mass matrix $\\tilde{\\mathbf{M}}$ is equal to the identity matrix.  This is known as *mass normalization*.  We have already described how the modal mass matrix is diagonal.  To mass-normalize a mode shape, we only need to scale the mode shape such that the diagonal entries obtained when computing the modal mass matrix end up being 1.  To do this for the $r$th mode, we can simply compute the modal mass matrix with a single unscaled mode shape $\\tilde{\\mathbf{\\phi}}_r$, then divide the mode shapes by the square-root of the diagonal entry of the modal mass matrix corresponding to that mode shape.\n",
+    "\n",
+    "$$\\mathbf{\\phi}_r = \\frac{\\tilde{\\mathbf{\\phi}}_r}{\\sqrt{{\\tilde{\\mathbf{\\phi}}_r}^T\\mathbf{M}\\tilde{\\mathbf{\\phi}}_r}}$$\n",
+    "\n",
+    "When we compute the modal stiffness using mass normalized modes, the entries on the diagonal of the modal stiffness matrix will be equal to the natural frequency squared.  For example, for the $r$th mode shape:\n",
+    "\n",
+    "$${\\mathbf{\\phi}_r}^T\\mathbf{K}\\mathbf{\\phi}_r = {\\omega_r}^2$$\n",
+    "\n",
+    "### Units with Scaled Mode Shapes\n",
+    "Note that when we mass-normalize the mode shapes, the mode shapes will have a unit of $\\frac{1}{\\sqrt{m}}$.  The mass matrix has dimensions of $m$ where $m$ is the unit of mass, and to equal the dimensionless identity matrix when the modal mass matrix is assembled, the mode shape units from pre- and post-multiplying must cancel out the units of the mass matrix.  The modal coefficient $\\mathbf{q}$ must then have units of ${l}{\\sqrt{m}}$ where $l$ is a length dimension, and one must recall that taking a derivative over time adds a $\\frac{1}{t}$ where $t$ is the unit representing time.\n",
+    "\n",
+    "As an example, we will assume we have a meter/kilogram/newton/second unit system.  The modal transformation equation must have consistent units.\n",
+    "$$ \\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$\n",
+    "$$ \\left[m\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right]$$\n",
+    "\n",
+    "Similarly, the equations of motion must have consistent units.\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{N}{m}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[N\\right]$$\n",
+    "\n",
+    "We can substitute $\\left[N\\right] = \\left[\\frac{kg{\\,}m}{s^2}\\right]$\n",
+    "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{m{\\,}s^2}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{s^2}\\right]$$\n",
+    "\n",
+    "Then with a good deal of cancelling units\n",
+    "$$\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{m\\sqrt{kg}}{s^2}\\right] = \\left[\\frac{m\\sqrt{kg}}{s^2}\\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd03536b-95a6-405b-8249-153b7887f208",
+   "metadata": {},
+   "source": [
+    "### Adding Damping\n",
+    "Eventually, we will need to consider the damping values that we simply disregarded previously.  If we bring back our original damped system of equations, and transform to modal space, we end up with the following equations of motion:\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "Here the matrix $\\tilde{\\mathbf{C}} = \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$ is referred to as the modal damping matrix.\n",
+    "\n",
+    "The important consideration that we must make to determine if the mode shapes that we have computed are still valid mode shapes is to understand if the modal damping matrix is also diagonalized by the mode shape matrix.  If it is, then our mode shapes are still valid: they represent independent, uncoupled degrees of freedom in our system.  However if the modal damping matrix is not diagonal, that means there is coupling between the degrees of freedom, and the degrees of freedom represented by the equation are not actually modal degrees of freedom.\n",
+    "\n",
+    "The mode shapes can diagonalize the damping matrix, but only in a very limited case.  Since the mode shapes only decouple the mass and stiffness matrices from which they were computed, the damping matrix will only be decoupled completely if the physical damping matrix is a linear combination of the physical mass and stiffness matrices.\n",
+    "\n",
+    "$$\\mathbf{C} = \\alpha\\mathbf{M} + \\beta\\mathbf{K}$$\n",
+    "\n",
+    "This is known as *proportional damping*, where the damping is proportional to the mass and stiffness of the system.  Transforming to modal coordinates will diagonalize this matrix.  If we do have proportional damping, and we use mass normalized modes for the modal transformation, the modal damping matrix will be diagonal, and the entry corresponding to the $r$th mode will be equal to\n",
+    "\n",
+    "$$\\tilde{\\mathbf{C}}_{rr} = {\\mathbf{\\phi}_r}^T\\mathbf{C}\\mathbf{\\phi}_r = 2\\zeta_r\\omega_r = \\alpha+\\beta\\omega_r^2$$\n",
+    "\n",
+    "where $\\omega_r$ is the natural frequency and $\\zeta_r$ is the fraction of critical damping for mode $r$.\n",
+    "\n",
+    "We only get a diagonal modal damping matrix in this case.  For any other case, we will still have off-diagonal terms.  Luckily for us, these diagonal terms are often small, so using the real mode shapes derived from the mass and stiffness of the system is not a terrible approximation.  However, there are cases where the approximation is not good, and in those cases, we need to turn to a different toolset to help us analyze the system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3590f2c-04ea-443e-903d-cb05fdeabd13",
+   "metadata": {},
+   "source": [
+    "## Complex Modes -- The General Solution\n",
+    "A more general solution to decoupling damped vibration problems requires us to rearange our equations of motion.  Rather than treating the system of equations as $N$ equations with a second order derivative, we will instead treat the system of equations as $2N$ first-order equations using a simple introducton of a new variable $\\mathbf{v} = \\dot{\\mathbf{x}}$ to represent velocity.  In this way, we can eliminate the double-derivative in the acceleration term ($\\ddot{\\mathbf{x}} = \\dot{\\mathbf{v}}$)\n",
+    "\n",
+    "$$\\mathbf{M}\\dot{\\mathbf{v}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "However, we now need to add some relationship between $v$ and $x$.  We could simply write $\\mathbf{v} = \\dot{\\mathbf{x}}$ as we showed above.  However, it will be useful to keep our matrices symmetric, so we will construct the equations in a slightly more complex way.\n",
+    "\n",
+    "$$\\mathbf{M}\\dot{\\mathbf{x}} - \\mathbf{M}\\mathbf{v} = \\mathbf{0}$$\n",
+    "\n",
+    "We can combine these two sets of equations into one set of equations of the form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "Here we note that our coefficient matrices are both symmetric, the first term contains all of the derivatives, the second term contains no derivatives, and the right-hand side of the equation contains our input forces."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e95a290-9795-4fa3-8b31-d71776e2db66",
+   "metadata": {},
+   "source": [
+    "We can perform a similar operation as previous to try to understand the free response of the system.  We will make the following assumptions.\n",
+    "\n",
+    "- $\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$ -- The force into the system is zero.\n",
+    "- $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$ -- The response is a complex sinusoid.\n",
+    "\n",
+    "In this case, the first assumption is essentially identical to the first assumption in the real mode shape case.  The second assumption is essentially a complex generalization of the second assumption made in the real mode shape case.  In this generalization, instead of a \"real\" shape where all responses are either in-phase or 180$^\\circ$ out-of-phase, the shape can now be complex, meaning there can be any phasing between different values in the shape.  The exponential $e$ now represents an oscillatory portion and decaying or growing portion of the response ($\\lambda$ is in general a complex number.  The imaginary portion of it will result in sinusoidal motion of the form $e^{j\\theta} = \\cos{\\theta} + j\\sin{\\theta}$ where $j = \\sqrt{-1}$.  The real part will result in exponential decay or exponential growth of the form $e^{x}$).  The term $\\lambda$ appears on the first row of the second assumption due to the chain rule when differentiating $\\mathbf{v} = \\dot{x}$.\n",
+    "\n",
+    "Using the chain rule for differentiation, we can also say $\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} = \\lambda\\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e824ad9-e3a4-4638-8b43-ace3c75bc707",
+   "metadata": {},
+   "source": [
+    "We can then apply these assumptions to our equations of motion:\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can again recognize that the term $e^{\\lambda t}$ is in general nonzero and therefore can be cancelled.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can then subtract $\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$ from both sides of the equation and we end up with\n",
+    "\n",
+    "$$ \\lambda \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = - \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b05928-6886-4d5a-b714-081a5d10abfd",
+   "metadata": {},
+   "source": [
+    "While a little more complex, this equation is also a [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9aa2bf5c-7063-4df0-97eb-4d0ad1cfb50e",
+   "metadata": {},
+   "source": [
+    "Transforming the eigenvalues and eigenvectors into natural frequencies and mode shapes is not as straightforward in the present case with complex modes.  In general, we will solve for $2N$ complex eigenvalues; however, due to the fact that the matrices are real, the eigenvalues will be complex conjugates of one another.  We will often hear these values called *poles* because they are zeros of the denominator polynomials in the frequency response function formulation that we will discuss later.\n",
+    "\n",
+    "The eigenvalues $\\lambda$ are often expressed as a function of the critical damping ratio $\\zeta$ and the natural frequency $\\omega$.  For the $r$th mode,\n",
+    "\n",
+    "$$\\lambda_r = -\\zeta_r \\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$$\n",
+    "\n",
+    "The real part $-\\zeta_r \\omega_r$ drives the exponential decay of the sinusoid.  The imaginary part drives the oscillatory behavior.  Because of this, the term $\\omega_r\\sqrt{1-\\zeta_r}$ is often referred to as the *damped natural frequency* $\\omega_d$\n",
+    "\n",
+    "$$\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$$\n",
+    "\n",
+    "To recover the natural frequency $\\omega_r$, we can simply take the magnitude of the eigenvalue.\n",
+    "\n",
+    "$$\\left|\\lambda_r\\right| = \\sqrt{\\Re{(\\lambda_r)}^2 + \\Im{(\\lambda_r)}^2} = \\sqrt{(\\zeta_r^2 \\omega_r^2) + (\\omega_r^2 (1-\\zeta_r^2))} = \\sqrt{\\omega_r^2 + \\omega_r^2\\zeta_r^2 - \\omega_r^2\\zeta_r^2} = \\omega_r$$\n",
+    "\n",
+    "To recover the damping ratio, we can divide the negative real part of the eigenvalue by the magnitude of the eigenvalue.\n",
+    "\n",
+    "$$- \\frac{\\Re{(\\lambda_r)}}{\\left|\\lambda_r\\right|} = \\frac{\\zeta_r\\omega_r}{\\omega_r} = \\zeta_r$$ \n",
+    "\n",
+    "Note that for complex modes, the damping is part of the solution to the free vibration problem and appears in the solution, whereas in the real mode case where damping was ignored, it was not.\n",
+    "\n",
+    "We will also solve for $2N$ eigenvectors.  These will also appear in complex conjugate pairs.  They will be of the form\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$\n",
+    "\n",
+    "We can extract the mode shape from the eigenvector simply by selecting the bottom partition of the vector.\n",
+    "\n",
+    "The equations to compute the modal mass and modal damping matrices are the same as those used in the case of real modes.  However, in general, the modal mass and modal damping matrices are not diagonal and have complex elements.\n",
+    "\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "$$\\tilde{\\mathbf{C}}=\\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$$\n",
+    "\n",
+    "Recall that the eigenvalue problem we solved for was with respect to the generalized form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.  Therefore the eigenvectors will diagonalize these matrices.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{A}}$$\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{B}}$$\n",
+    "\n",
+    "We often refer to these matrices $\\tilde{\\mathbf{A}}$ and $\\tilde{\\mathbf{B}}$ as the *Modal A* and *Modal B* matrices.\n",
+    "\n",
+    "Modal mass is defined in different ways for complex modes.  Some references will treat the diagonal entries in Modal A $\\tilde{\\mathbf{A}}_{rr}$ directly as the modal mass values.  Other references define the modal mass for the $r$th mode $m_r$ as:\n",
+    "\n",
+    "$$\\tilde{\\mathbf{A}}_{rr} = 2\\lambda_r m_r + 2\\zeta_r\\omega_r m_r = 2j\\omega_{dr} m_r$$\n",
+    "$$m_r = \\frac{\\tilde{\\mathbf{A}}_{rr}}{2j\\omega_{dr}}$$\n",
+    "\n",
+    "It doesn't really matter which definition is used as long as the definition is consistently used when reconstructing responses from modal parameters.  This document will proceed with the modal mass defined as the diagonal terms of $\\tilde{\\mathbf{A}}$.\n",
+    "\n",
+    "We can also see that if we premultiply the eigenvalue equation by our eigenvectors, we get a relationship between the Modal A and Modal B matrices.\n",
+    "\n",
+    "$$\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f2a6ce4-2692-4839-8826-5ef45900ff85",
+   "metadata": {},
+   "source": [
+    "## An Example Problem\n",
+    "Before we get too far, let's illustrate some of these concepts with a small example problem.  We will generate a 3-degree-of-freedom problem that we will simulate.  We will explore undamped, proportionally damped, and generally damped cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6eb06f7-2c41-44fc-bf7b-d160b7bd4303",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.linalg as la\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "def pretty_print_table(df):\n",
+    "    return display( HTML( df.to_html().replace(\"\\\\n\",\"<br>\") ) )\n",
+    "\n",
+    "# Set up physical matrices\n",
+    "K = np.array([[ 6000, -2000,     0],\n",
+    "              [-2000,  4000, -2000],\n",
+    "              [    0, -2000,  6000]])\n",
+    "M = np.array([[2, 0, 0],\n",
+    "              [0, 3, 0],\n",
+    "              [0, 0, 2]])\n",
+    "alpha = 1.36/2\n",
+    "beta = 0.00154865/2\n",
+    "C_proportional = alpha*M+beta*K\n",
+    "\n",
+    "C_general = np.array([[14, -2, 0],\n",
+    "                      [-2, 10, -2],\n",
+    "                      [0, -2, 10]])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "457c045b-1adb-4923-a5b9-a64cd6294ba4",
+   "metadata": {},
+   "source": [
+    "For the complex mode cases, let's construct the $\\mathbf{A}$ and $\\mathbf{B}$ matrices.  The $\\mathbf{A}$ matrix will depend on damping, the $\\mathbf{B}$ matrix will not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0065867a-cd5c-4548-8cf2-dd6580fbf2ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For convenience, construct a matrix of zeros\n",
+    "Z = np.zeros(M.shape)\n",
+    "# Assemble State Space Matrices\n",
+    "A_undamped = np.block([[Z, M],\n",
+    "                       [M, Z]])\n",
+    "A_proportional = np.block([[Z, M],\n",
+    "                           [M, C_proportional]])\n",
+    "A_general = np.block([[Z, M],\n",
+    "                      [M, C_general]])\n",
+    "B = np.block([[-M, Z],\n",
+    "              [ Z, K]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6031364-f521-4438-a584-335ff3e83900",
+   "metadata": {},
+   "source": [
+    "Now let's compute the eigenvalues and eigenvectors.  For the 2nd-order eigenvalue using the mass and stiffness matrices directly, we can use the `eigh` function which assumes Hermetian, positive definite matrices.  For the complex mode cases, we will have to use the more general `eig` function.\n",
+    "\n",
+    "When using an eigenvalue solver, be wary of the order that matrices occur in the function call.  For example, in the [scipy.linalg.eig documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html), it states the following:\n",
+    "\n",
+    "> **scipy.linalg.eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False)**\n",
+    ">\n",
+    "> Solve an ordinary or generalized eigenvalue problem of a square matrix.\n",
+    "> \n",
+    "> Find eigenvalues w and right or left eigenvectors of a general matrix:\n",
+    ">\n",
+    "> ```python\n",
+    ">  a   vr[:,i] = w[i]        b   vr[:,i]\n",
+    ">  a.H vl[:,i] = w[i].conj() b.H vl[:,i]\n",
+    ">   ```\n",
+    ">\n",
+    "> where `.H` is the Hermitian conjugation.\n",
+    "\n",
+    "SciPy uses the definition $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$ for its eigenvalue problem, and our equation we have defined as $- \\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\lambda \\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$. Note that what SciPy calls `a` in its function call is what we have defined as $-\\mathbf{B}$, and what SciPy calls `b` is what we have defined as $\\mathbf{A}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "954836de-ba9e-4019-8e02-5e730de1fdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute Eigenvalues and Eigenvectors\n",
+    "lam_2ndOrder,E_2ndOrder = la.eigh(K,M)\n",
+    "lam_undamped, E_undamped = la.eig(-B,A_undamped)\n",
+    "lam_proportional, E_proportional = la.eig(-B,A_proportional)\n",
+    "lam_general, E_general = la.eig(-B,A_general)\n",
+    "\n",
+    "# Since the half the complex eigenvalues and eigenvectors are complex conjugates of one another,\n",
+    "# we will only keep those corresponding to the eigenvalues with imaginary part > 0\n",
+    "keep_undamped = lam_undamped.imag > 0\n",
+    "keep_proportional = lam_proportional.imag > 0\n",
+    "keep_general = lam_general.imag > 0\n",
+    "\n",
+    "# Now reduce to just those we want to keep\n",
+    "lam_undamped = lam_undamped[keep_undamped]\n",
+    "E_undamped = E_undamped[:,keep_undamped]\n",
+    "lam_proportional = lam_proportional[keep_proportional]\n",
+    "E_proportional = E_proportional[:,keep_proportional]\n",
+    "lam_general = lam_general[keep_general]\n",
+    "E_general = E_general[:,keep_general]\n",
+    "\n",
+    "# Let's also sort the eigenvalues so they are all ascending\n",
+    "for evals,evects in zip([lam_2ndOrder, lam_undamped, lam_proportional, lam_general],\n",
+    "                        [E_2ndOrder, E_undamped, E_proportional, E_general]):\n",
+    "    isort = np.argsort(np.abs(evals))\n",
+    "    evals[...] = evals[isort]\n",
+    "    evects[...] = evects[:,isort]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c785b0f3-4103-44b9-a3f6-99c149557c88",
+   "metadata": {},
+   "source": [
+    "Now we can extract the natural frequencies and damping ratios from the eigenvalues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "99f8881b-af4f-4ac1-9131-a90cd1fb0566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute natural frequencies\n",
+    "omega_2ndOrder = np.sqrt(lam_2ndOrder)\n",
+    "omega_undamped = np.abs(lam_undamped)\n",
+    "omega_proportional = np.abs(lam_proportional)\n",
+    "omega_general = np.abs(lam_general)\n",
+    "\n",
+    "# Compute damping ratios\n",
+    "zeta_undamped = -np.real(lam_undamped)/np.abs(lam_undamped)\n",
+    "zeta_proportional = -np.real(lam_proportional)/np.abs(lam_proportional)\n",
+    "zeta_general = -np.real(lam_general)/np.abs(lam_general)\n",
+    "\n",
+    "# Compute damped natural frequencies\n",
+    "omega_d_undamped = omega_undamped*np.sqrt(1-zeta_undamped**2)\n",
+    "omega_d_proportional = omega_proportional*np.sqrt(1-zeta_proportional**2)\n",
+    "omega_d_general = omega_general*np.sqrt(1-zeta_general**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5ea9635-f9d9-49fd-b4a2-88f8540f2f24",
+   "metadata": {},
+   "source": [
+    "Let's plot these datasets in a table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8643d599-fbb1-42d8-991f-50e46bc40f3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Undamped</th>\n",
+       "      <th>Proportional Damping</th>\n",
+       "      <th>General Damping</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mass Matrix</th>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stiffness Matrix</th>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Damping Matrix</th>\n",
+       "      <td>[[0. 0. 0.]<br> [0. 0. 0.]<br> [0. 0. 0.]]</td>\n",
+       "      <td>[[ 6.00595 -1.54865  0.     ]<br> [-1.54865  5.1373  -1.54865]<br> [ 0.      -1.54865  6.00595]]</td>\n",
+       "      <td>[[ 7. -1.  0.]<br> [-1.  5. -1.]<br> [ 0. -1.  5.]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poles</th>\n",
+       "      <td>[[0.+27.2518998j ]<br> [0.+54.77225575j]<br> [0.+59.92217695j]]</td>\n",
+       "      <td>[[-0.62753244+27.24467371j]<br> [-1.5014875 +54.75167153j]<br> [-1.73017173+59.89719356j]]</td>\n",
+       "      <td>[[-0.73753202+27.24242803j]<br> [-1.50037636+54.76028673j]<br> [-1.59542495+59.89042233j]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Natural Frequencies</th>\n",
+       "      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>\n",
+       "      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>\n",
+       "      <td>[[27.25240977]<br> [54.78083727]<br> [59.91166888]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Damping Ratios</th>\n",
+       "      <td>[[-0.]<br> [-0.]<br> [-0.]]</td>\n",
+       "      <td>[[0.02302711]<br> [0.02741329]<br> [0.02887365]]</td>\n",
+       "      <td>[[0.027063  ]<br> [0.02738871]<br> [0.02662962]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Undamped','Proportional Damping','General Damping'],\n",
+    "                  index=['Mass Matrix','Stiffness Matrix','Damping Matrix','Poles','Natural Frequencies','Damping Ratios'],\n",
+    "                  dtype=object)\n",
+    "df.at['Mass Matrix', 'Undamped'] = str(M)\n",
+    "df.at['Mass Matrix', 'Proportional Damping'] = str(M)\n",
+    "df.at['Mass Matrix', 'General Damping'] = str(M)\n",
+    "df.at['Stiffness Matrix', 'Undamped'] = str(K)\n",
+    "df.at['Stiffness Matrix', 'Proportional Damping'] = str(K)\n",
+    "df.at['Stiffness Matrix', 'General Damping'] = str(K)\n",
+    "df.at['Damping Matrix', 'Undamped'] = str(np.zeros(C_proportional.shape))\n",
+    "df.at['Damping Matrix', 'Proportional Damping'] = str(C_proportional)\n",
+    "df.at['Damping Matrix', 'General Damping'] = str(C_general)\n",
+    "df.at['Poles', 'Undamped'] = str(lam_undamped[:,np.newaxis])\n",
+    "df.at['Poles', 'Proportional Damping'] = str(lam_proportional[:,np.newaxis])\n",
+    "df.at['Poles', 'General Damping'] = str(lam_general[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'Undamped'] = str(omega_undamped[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'Proportional Damping'] = str(omega_proportional[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'General Damping'] = str(omega_general[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'Undamped'] = str(zeta_undamped[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'Proportional Damping'] = str(zeta_proportional[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'General Damping'] = str(zeta_general[:,np.newaxis])\n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c1b1019-07d2-47a9-b59d-834e6fb3e08a",
+   "metadata": {},
+   "source": [
+    "We will now look at the eigenvectors.  To start out, we will normalize the eigenvectors **to a value that is NOT equal to unity modal mass or unity Modal A**.  This is useful while we are learning, because if we have unity modal mass, it allows us to be sloppy and simply drop or ignore effects of the modal mass.  This could result in significant errors down the road if we assume that the formulae we developed for the unity modal mass case also apply to the non-unity modal mass case.  It is therefore better to develop and check all the formulae with non-unity modal mass to ensure that we understand its effects and have accounted for it successfully.  We will deliberately normalize to a value not equal to 1, because many eigenvector solvers will automatically normalize for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aa9e0cb3-b626-4b88-9a55-b67e71cfab75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize Eigenvectors using einsum to pull out the m_ii terms from the modal normalization matrix\n",
+    "# We will multiply by 2 to ensure non-unity modal mass.\n",
+    "E_2ndOrder = 2*E_2ndOrder/np.sqrt(np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder))\n",
+    "E_undamped = 2*E_undamped/np.sqrt(np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped))\n",
+    "E_proportional = 2*E_proportional/np.sqrt(np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional))\n",
+    "E_general = 2*E_general/np.sqrt(np.einsum('ji,jk,ki->i',E_general,A_general,E_general))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8a0830-55f4-48b3-bfee-8d3fff1c6ec1",
+   "metadata": {},
+   "source": [
+    "Now that we have the eigenvectors normalized to a non-unity modal mass, we will compute the Modal Mass and Modal A matrices to extract the modal mass.\n",
+    "\n",
+    "We will use `np.einsum` in the same way we used previously but here we will describe it a bit better.  Numpy's `einsum` function implements a generalized version of Einstein Summation Notation.  While we can represent matrix operations like multiplication simply by using symbols for the matrices\n",
+    "$$\\mathbf{C} = \\mathbf{A}\\mathbf{B}$$\n",
+    "we can also represent those operations with the indices\n",
+    "$$C_{ik} = A_{ij}B_{jk}$$\n",
+    "In the previous equation, we have the index $i$ representing the rows of $\\mathbf{A}$, and the index $j$ representing the columns.  Similarly for $\\mathbf{B}$, we have the indices $j$ representing the rows and $k$ representing the columns.  When we do matrix multiplication, we sum over the inner dimension, e.g., the $j$ indices, and that index drops out.  We are left with rows $i$ and columns $k$ of our output matrix $\\mathbf{C}$.  This operation would be represented in Numpy's `einsum` function as\n",
+    "```python\n",
+    "C = np.einsum('ij,jk->ik',A,B)\n",
+    "```\n",
+    "The first argument `'ij,jk->ik'` is a string that gives the index operations to be performed.  We see that there is an arrow `->` that separates input indices and output indices, and the different input indices are separated by a comma, and there is only one set of output indices.  The remaining arguments are the actual matrices corresponding to the input indices passed in the first argument.  Therefore `ij` will be associated with `A`, and `jk` will be associated with `B`.  The output indices on the right side of the arrow `ik` will be associated with the function's output `C`.  We note that any index not listed in the output indices will be summed over and drop out.\n",
+    "\n",
+    "This notation is very useful for writing complex matrix expressions in a compact way.  For example, if instead of $\\mathbf{C} = \\mathbf{A}\\mathbf{B}$ we wanted to do $\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$, we can simply swap the indices on $\\mathbf{A}$, which now means we are summing across the rows of $\\mathbf{A}$, which is equivalent to matrix multiplication by a transpose.\n",
+    "$$\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$$\n",
+    "$$C_{ik} = A_{ji}B_{jk}$$\n",
+    "\n",
+    "We can therefore represent a more complex product like\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "as\n",
+    "$$\\tilde{{M}}_{il}={\\phi}_{ji}{M}_{jk}{\\phi}_{kl}$$\n",
+    "In the previous equation, we can see that the $j$ index corresponds to the rows of the mass matrix and the rows of the mode shape matrix.  Because we sum over $j$, this is equivalent to multiplying the matrix $\\mathbf{M}$ by $\\mathbf{\\phi}^T$.  We can indeed improve upon this if, for example, we wanted a 1D array of modal mass values, rather than a 2D matrix that we will then need to extract the diagonals from.  We can replace the $l$ index with another $i$ index and have the output specify just a single $i$, and we will get a 1D array of modal mass values.  As an aside, this is a syntax that is not acceptable in true Einstein Summation Notation, where a repeated index is automatically summed over.  In Numpy's generalized version, it subverts some of these very strict rules to give more flexibility to the user.\n",
+    "$$\\tilde{{M}}_{i}={\\phi}_{ji}{M}_{jk}{\\phi}_{ki}$$\n",
+    "\n",
+    "We can then perform these operations in code to solve for the modal mass and modal A quantities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0e7c1966-278c-4e88-b4cd-bcb8246b282b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mm_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder)\n",
+    "ma_undamped = np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped)\n",
+    "ma_proportional = np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional)\n",
+    "ma_general = np.einsum('ji,jk,ki->i',E_general,A_general,E_general)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7184e6a9-807d-473a-a1d2-5a12854ea046",
+   "metadata": {},
+   "source": [
+    "The last thing we will do is to pull out the mode shapes from the eigenvectors.  For the 2nd Order case, the mode shapes are the eigenvectors.  But for the complex case, the mode shapes are the lower half of the eigenvector matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "806b85b5-ef45-441e-bd98-d0cb087350b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = E_2ndOrder\n",
+    "psi_undamped = E_undamped[E_undamped.shape[0]//2:,:]\n",
+    "psi_proportional = E_proportional[E_proportional.shape[0]//2:,:]\n",
+    "psi_general = E_general[E_general.shape[0]//2:,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f52d5276-14d0-4ae8-a0f0-924b20ecddb3",
+   "metadata": {},
+   "source": [
+    "We should be able to check that the top half of the eigenvector matrix is the bottom half multiplied by the eigenvalue.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2533c3db-1a36-4fd3-aca6-770889ed201e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,\n",
+       "        -3.16635239-3.71568777j],\n",
+       "       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n",
+       "         2.00036437+2.07501753j],\n",
+       "       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n",
+       "        -3.52944456-3.37788878j]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "E_general[:E_general.shape[0]//2,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "87a69d80-b352-43e3-9c7f-55f1fe6c272b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,\n",
+       "        -3.16635239-3.71568777j],\n",
+       "       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n",
+       "         2.00036437+2.07501753j],\n",
+       "       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n",
+       "        -3.52944456-3.37788878j]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lam_general*psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6db7704-a5c2-4394-8d5d-422e23ae82d6",
+   "metadata": {},
+   "source": [
+    "One final thing to note is that in general, the mode shapes extracted from a complex analysis will not diagonalize the orignal mass, stiffness, or damping matrices, as these were not the matrices the eigensolution was performed on.  There will be off-diagonal terms, and they will be complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3e036cbb-b641-4aac-ac0b-2769f764020d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-6.13061679e-08-7.34176433e-02j, -1.43810774e-04+9.83528782e-06j,\n",
+       "         1.15410477e-04+2.13846966e-06j],\n",
+       "       [-1.43810774e-04+9.83528782e-06j, -1.24812005e-07-3.65342888e-02j,\n",
+       "        -1.36140173e-04+1.88235958e-06j],\n",
+       "       [ 1.15410477e-04+2.13846966e-06j, -1.36140173e-04+1.88235958e-06j,\n",
+       "         1.86118173e-07-3.33825843e-02j]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@M@psi_general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6ce545ce-0d31-4d53-a589-4bd891eea2b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.00014982-0.10829239j,  0.00048468+0.01181488j,\n",
+       "         0.00045558-0.01005105j],\n",
+       "       [ 0.00048468+0.01181488j, -0.00125664-0.1096167j ,\n",
+       "        -0.00020565+0.01561439j],\n",
+       "       [ 0.00045558-0.01005105j, -0.00020565+0.01561439j,\n",
+       "         0.00140645-0.10654111j]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@C_general@psi_general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bed4c0f0-266e-4434-a7e4-65002b1bfeed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-6.49635076e-05-5.45227499e+01j,  2.15177460e-01-2.97517095e-03j,\n",
+       "        -1.87975974e-01-1.36004490e-02j],\n",
+       "       [ 2.15177460e-01-2.97517095e-03j, -1.51087546e-03-1.09568400e+02j,\n",
+       "         4.46495302e-01+1.79584080e-02j],\n",
+       "       [-1.87975974e-01-1.36004490e-02j,  4.46495302e-01+1.79584080e-02j,\n",
+       "         1.57583896e-03-1.19907950e+02j]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@K@psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb5b9b23-80f7-4940-bd63-759731824312",
+   "metadata": {},
+   "source": [
+    "## Frequency Response Functions and Modal Parameters\n",
+    "Now that we have familiarized ourselves with the modal parameters for real and complex modes, let's discuss the relationship to the frequency response function.  This is of utmost importance, because we eventually would like to fit the modal parameters from the frequency response functions, as well as reconstruct frequency response functions from the modal parameters.\n",
+    "\n",
+    "### Real Modes\n",
+    "\n",
+    "Starting with the modal system of equations, and assuming proportional damping, we have\n",
+    "\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "We can also substitute in the diagonal values for the modal parameters.  *Note the inclusion of the modal mass* $m_r$ *in all terms, because we have not assumed mass normalized modes in this case*.  We will represent the generalized modal force $\\mathbf{\\phi}^T\\mathbf{F}$ as $\\mathbf{Q}$.\n",
+    "$$\\left[m_r\\right]\\ddot{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]\\dot{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "\n",
+    "We can then use the *Laplace Transform* to make this differential equation into an algebraic one.\n",
+    "$$\\left[m_r\\right]s^2{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]s{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "We can collect terms of $\\mathbf{q}$ to create an equation of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{b}$.\n",
+    "$$\\left[m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "\n",
+    "Because the equation is diagonal, inverting the coefficient matrix is trival.\n",
+    "$${\\mathbf{q}} = \\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{Q}$$\n",
+    "\n",
+    "This is now a representation of a *modal* transfer function.  If we apply a set of *modal* forces $\\mathbf{Q}$, we get out a set of *modal* responses $\\mathbf{q}$.  To transform to physical space, we need to transform our modal quantities to physical quantities.  We know the substitution for modal forces $\\mathbf{Q} = \\mathbf{\\phi}^T\\mathbf{F}$, and we know our equation for physical response from modal responses $\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$.  Therefore we can make the subsitution for modal force, and pre-multiply the entire equation by the mode shape matrix.\n",
+    "\n",
+    "$$\\mathbf{x} = \\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}\\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "If instead of the entire Laplace domain we only consider sinusoidal responses, we can substitute $s=j\\omega$ to get the familiar form for the frequency response function.\n",
+    "\n",
+    "$$\\mathbf{H} = \\mathbf{\\phi}\\left[\\frac{1}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T$$\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$\n",
+    "\n",
+    "To pull out a specific entry in the FRF matrix, we can index the mode shapes.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96ab4e96-9288-4414-bf16-82a4534ad509",
+   "metadata": {},
+   "source": [
+    "### Complex Modes\n",
+    "The derivation for the frequency response function in terms of complex modes is similar.  Recall that our physical vector for the complex state space formulation includes velocities and displacements.  Similarly our eigenvectors consisted of pairs of shapes, the shape and its complex conjugate.  Therefore, in the case of complex modes, the projection from modal space to physical space is\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "Applying the transformation to the complex state space equation\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "\n",
+    "Gives\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "\n",
+    "And then pre-multiplying by the eigenvector matrix transpose completes the transformation into modal space.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "$$= \\begin{bmatrix}\\lambda \\mathbf{\\psi}^T & \\mathbf{\\psi}^T \\\\ \\lambda^* {\\mathbf{\\psi}^*}^T & {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$\n",
+    "\n",
+    "Transforming into the modal matrices\n",
+    "$$\\tilde{\\mathbf{A}}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\tilde{\\mathbf{B}}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "We recognize that the diagonal terms of $\\tilde{\\mathbf{A}}$ are simply what we've defined as the modal mass, and the diagonal terms of $\\tilde{\\mathbf{B}}$ are simply the negative modal mass times the eigenvector $\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & \\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e520d365-73f4-45dc-a5f0-24158348da43",
+   "metadata": {},
+   "source": [
+    "Once again, transforming to the Laplace Domain,\n",
+    "\n",
+    "$$\\begin{bmatrix}s\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & s\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "$$\\begin{bmatrix}(s-\\lambda)\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & (s-\\lambda^*)\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "Once again, because this matrix is diagonal, it is easy to invert.\n",
+    "\n",
+    "$$\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "This is the modal transfer function.\n",
+    "\n",
+    "To compute the transfer functions between the force $\\mathbf{F}$ and the response $\\mathbf{x}$ (we ignore $\\mathbf{v}$ here), we can use the identities $\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$, which we defined previously, and the last row of $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$, which is $\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$. \n",
+    "\n",
+    "$$\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4afe2217-3136-4cef-a027-a4be65bc7982",
+   "metadata": {},
+   "source": [
+    "The transfer function in the $s$-domain is then\n",
+    "\n",
+    "$$\\mathbf{H} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}$$\n",
+    "\n",
+    "Because it is diagonal, we can take it out of matrix form and put it into summation form, which reveals the usual partial fraction form of the transfer function.\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "We can extract a specific transfer function $\\mathbf{H}_{ij}$\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_{ir}\\mathbf{\\psi}_{jr}}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_{ir}^*\\mathbf{\\psi}_{jr}^*}{m_r(s-\\lambda_r^*)}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17543413-bd30-4a68-80b1-d1b7f117cf34",
+   "metadata": {},
+   "source": [
+    "To translate into more intuitive variables, we can make the following substitutions.\n",
+    "\n",
+    "- $\\lambda_r = -\\sigma_r + j\\omega_{dr} = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$  - Substitute the poles for the natural frequency and critical damping ratio.\n",
+    "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I$ - Split the mode shapes into real and imaginary parts.\n",
+    "\n",
+    "From these, it follows that the complex conjugate versions simply have the sign flipped on their imaginary components.\n",
+    "\n",
+    "- $\\lambda_r^* = -\\sigma_r - j\\omega_{dr} = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$\n",
+    "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I$\n",
+    "\n",
+    "Substituting these into the previous equation, we get\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left(\\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R + j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r + j\\omega_{dr}\\right))} + \\frac{\\left(\\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R - j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r - j\\omega_{dr}\\right))}\\right)$$\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s + \\sigma_r - j\\omega_{dr}\\right)} + \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)}\\right)$$\n",
+    "\n",
+    "If we put the two terms over a common denominator, we get\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "  \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n",
+    "  \\left(s+ \\sigma_r + j\\omega_{dr}\\right)\n",
+    "+ \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n",
+    "  \\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n",
+    "{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)\\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n",
+    "\\right)$$\n",
+    "\n",
+    "We can expand and reduce this equation\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s+\\sigma_r\\right)^2 + \\omega_{dr}^2\\right)\n",
+    "}\n",
+    "\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97b4be2f-c392-4c79-950e-00d0e32a038f",
+   "metadata": {},
+   "source": [
+    "### What are Real Modes in the Complex Mode Framework?\n",
+    "\n",
+    "One might naively believe that a real mode is simply a complex mode where the imaginary part is zero.  However, it isn't clear whether or not that should be true, because the eigenvalue problem was posed differently.  What is clear, however, is that real modes should certainly be some subset of complex modes; in other words, we should be able to represent a real mode as a complex mode.  In this section, we will investigate that relationship.\n",
+    "\n",
+    "Starting with the previous equation, we can further expand the denominator with natural frequency and damping ratio, where $\\sigma_r = \\zeta_r\\omega_r$ and $\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s+-\\zeta_r\\omega_r\\right)^2 + \\left(\\omega_r\\sqrt{1-\\zeta_r^2}\\right)^2\\right)\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s^2+2s\\zeta_r\\omega_r + \\omega_r^2\\zeta_r^2\\right) + \\left(\\omega_r^2 - \\omega_r^2\\zeta_r^2\\right)\\right)\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_rs^2+2\\zeta_r\\omega_rm_rs + m_r\\omega_r^2\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "The denominator in the previous equation is identical to that from the real modes equation.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}$$\n",
+    "\n",
+    "The numerators should also be equivalent.\n",
+    "\n",
+    "$$2\\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\\right) = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "We note that there is no terms with $s$ on the real modes side of the equation, so any term containing $s$ on the complex modes side of the equation must be zero in the case of real modes.\n",
+    "\n",
+    "$$\\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I = 0$$\n",
+    "\n",
+    "This then leaves the remainder of the equation\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "We must find a solution for these two equations simultaneously.  Since the modeshape at location $i$ and the mode shape at location $j$ are generally independent of one another, we can treat them as independent.  In other words, $\\psi_{ir}$ cannot be a function $\\psi_{jr}$.  This means for the first equation, there are two possibilities; $\\psi_{ir}^I = -\\psi_{ir}^R$ or $\\psi_{ir}^I = \\psi_{ir}^R$.\n",
+    "\n",
+    "To figure out which, we will look at the drive point transfer function where $i = j$, as the relationship should hold for that point as much as any other point.\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R \\right)\\omega_{dr} = \\phi_{ir}^2$$\n",
+    "$$\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "\n",
+    "The since term $\\phi_{ir}^2$ cannot be negative and $\\omega_{dr}$ is also non-negative, the term $\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R$ must be negative.  This will be the case if $\\psi_{ir}^I = -\\psi_{ir}^R$, but will not be the case if $\\psi_{ir}^I = \\psi_{ir}^R$.\n",
+    "\n",
+    "Substituting that expression,\n",
+    "\n",
+    "$$-{\\psi_{ir}^R}^2 + -{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "$$-2{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "$${\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{4\\omega_{dr}}$$\n",
+    "$${\\psi_{ir}^R} = \\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}} = -\\psi_{ir}^I$$\n",
+    "\n",
+    "To verify that this is the general expression, let's plug it into the original equation.\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}} + \n",
+    "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-2\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$\\phi_{ir}\\phi_{jr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "Therefore for a real mode represented in a complex mode, we should expect the imaginary part to be equal in magnitude to the real part, with phase angle of -45$^\\circ$ instead of zero degrees, which may have been expected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b59fa20c-70e7-44b3-ab9b-b584f3b15400",
+   "metadata": {},
+   "source": [
+    "### Back to our Example Problem\n",
+    "\n",
+    "Let's return to our example problem to compute frequency response functions from the matrices as well as from modal parameters.  We will start with computing the frequency response functions from dynamic stiffness matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ae0359c0-8d92-45f2-ab35-1815657a7f89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get frequency lines\n",
+    "omegas = np.linspace(0,np.max(omega_2ndOrder)*1.5,1000)\n",
+    "# Set up omega for broadcasting the FRF matrix, the first dimension will be frequency line.\n",
+    "# The second two dimensions will be i,j degrees of freedom.\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Construct Dynamic Stiffness\n",
+    "Z_undamped = M*(1j*omegas_bc)**2 + K\n",
+    "Z_proportional = M*(1j*omegas_bc)**2 + C_proportional*1j*omegas_bc + K\n",
+    "Z_general = M*(1j*omegas_bc)**2 + C_general*1j*omegas_bc + K\n",
+    "# Invert to get frequency response functions\n",
+    "H_undamped = np.linalg.inv(Z_undamped)\n",
+    "H_proportional = np.linalg.inv(Z_proportional)\n",
+    "H_general = np.linalg.inv(Z_general)\n",
+    "# Plot the FRFs\n",
+    "fig,axes = plt.subplots(H_undamped.shape[1]*2,H_undamped.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_u,f_p,f_g in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                          H_undamped.reshape(omegas.size,-1).T,\n",
+    "                          H_proportional.reshape(omegas.size,-1).T,\n",
+    "                          H_general.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_u)*180/np.pi)\n",
+    "    ax[0].plot(omegas,np.angle(f_p)*180/np.pi)\n",
+    "    ax[0].plot(omegas,np.angle(f_g)*180/np.pi)\n",
+    "    ax[1].plot(omegas,np.abs(f_u))\n",
+    "    ax[1].plot(omegas,np.abs(f_p))\n",
+    "    ax[1].plot(omegas,np.abs(f_g))\n",
+    "    ax[1].set_yscale('log')\n",
+    "ax[1].legend(['Undamped','Proportional Damping','General Damping'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8819a537-c842-495b-b0c3-484a69f959e8",
+   "metadata": {},
+   "source": [
+    "Now let's reconstruct the frequency response functions from modal parameters and see if they match those constructed from the dynamic stiffness matrices.\n",
+    "\n",
+    "For the second-order case,\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "25f2ed27-2e2d-4d85-9092-8da2c49e1a95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2nd Order Case\n",
+    "# First we need to find the modal damping ratios from the damping matrix\n",
+    "zeta_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,C_proportional,E_2ndOrder)/(2*mm_2ndOrder*omega_2ndOrder)\n",
+    "\n",
+    "# Now Construct the FRF matrix from modal parameters\n",
+    "num_modes = omega_2ndOrder.size\n",
+    "H_2ndOrder_fromModes = np.sum([\n",
+    "    phi[:,r,np.newaxis]@phi[:,r,np.newaxis].T/ # The newaxis turns the array from a 1D array back into a 2D array that can be transposed\n",
+    "    (-mm_2ndOrder[r]*omegas_bc**2 + 2j*zeta_2ndOrder[r]*omega_2ndOrder[r]*mm_2ndOrder[r]*omegas_bc + omega_2ndOrder[r]**2*mm_2ndOrder)\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_proportional.reshape(omegas.size,-1).T,\n",
+    "                      H_2ndOrder_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "031edfd9-54c1-44e0-9ddb-851744aca873",
+   "metadata": {},
+   "source": [
+    "We see we get identical FRFs.\n",
+    "\n",
+    "For the complex mode cases:\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "Note that for a frequency response function, which assumes a single frequency line excitation, the Laplace variable $s = j\\omega$.  The pole $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "Here is the proportional damped case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "43e01c80-adc6-41e4-b882-09c24e203d50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_proportional.size\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "# Compute FRFs\n",
+    "H_proportional_fromModes = np.sum([\n",
+    "    psi_proportional[:,r,np.newaxis]@psi_proportional[:,r,np.newaxis].T/\n",
+    "    (ma_proportional[r]*(s-\n",
+    "        (-zeta_proportional[r]*omega_proportional[r] + 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n",
+    "    + psi_proportional[:,r,np.newaxis].conj()@psi_proportional[:,r,np.newaxis].T.conj()/\n",
+    "    (ma_proportional[r]*(s-\n",
+    "        (-zeta_proportional[r]*omega_proportional[r] - 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_proportional.reshape(omegas.size,-1).T,\n",
+    "                      H_proportional_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "811671ce-53fa-48a5-8851-bfad6a333687",
+   "metadata": {},
+   "source": [
+    "And here is the general damped case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4c6bbc32-5c2a-4f85-af36-18c8856b976a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general.size\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "H_general_fromModes = np.sum([\n",
+    "    psi_general[:,r,np.newaxis]@psi_general[:,r,np.newaxis].T/\n",
+    "    (ma_general[r]*(s-\n",
+    "        (-zeta_general[r]*omega_general[r] + 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n",
+    "    + psi_general[:,r,np.newaxis].conj()@psi_general[:,r,np.newaxis].T.conj()/\n",
+    "    (ma_general[r]*(s-\n",
+    "        (-zeta_general[r]*omega_general[r] - 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_general.shape[1]*2,H_general.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general.reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "335edf60-cc3b-4bcf-8671-5f36dec4965b",
+   "metadata": {},
+   "source": [
+    "We can see that we understand the relationships between the modal parameters and the frequency response functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0912a4-8590-4b7e-83b4-860e436cd0ac",
+   "metadata": {},
+   "source": [
+    "## Solving for Modal Parameters from FRFs\n",
+    "The process of Experimental Modal Analysis involves measuring FRFs and then fitting modal parameters to those FRFs.  Many mode fitting software packages utilize a two-step process.  First, the poles (frequency and damping) are identified, then the shape information is found.  Some curve fitters identify modal participation factors in the first step, which are essentially the mode shapes at the reference degrees of freedom.\n",
+    "\n",
+    "This section of the document will follow the implementation of the paper [Peeters, Bart et al. ‘The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation?’ 1 Jan. 2004 : 395 – 409](https://content.iospress.com/articles/shock-and-vibration/sav00272), which implements the popular PolyMax modal parameter estimator.  This is the algorithm implemented in SDynPy's `PolyPy` fitter.  Other modal parameter estimators may give similar results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99330d1c-fe11-4cb4-91c8-339a638b8c7e",
+   "metadata": {},
+   "source": [
+    "### Setting up the System of Equations\n",
+    "\n",
+    "Let us consider the frequency response function that we may measure during a modal test.  We understand that we will only measure a finite bandwidth during the test, therefore there may be contributions to the measured FRFs from modes outside the bandwidth.  We will turn our typical FRF matrix equation\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "into a \"measured\" version.  We will replace the value $s$ with $j\\omega$.  We will also introduce lower and upper \"residuals\" ($\\mathbf{R}_l$ and $\\mathbf{R}_u$, respectively), to try to fit portions of the FRFs from out-of-band modes.  We will explicitly add the notation to show that the frequency response function matrix is a function of frequency $\\omega$.  Additionally, we will call the mode shapes of the references the *modal participation factor* $\\mathbf{l}_r$ and the mode shapes corresponding to the responses $\\mathbf{v}$.  Finally, We will remove the modal mass term, which will make the assumption that the modes we fit from the algorithm will be mass normalized.  Note that the format of the residual terms implies that the frequency response functions we are investigating here are *admittance* frequency response functions, which relate the force at a location to the displacement at a location.\n",
+    "\n",
+    "$$\\mathbf{H}(\\omega) = \\sum_{r=1}^N\\left( \\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}\\right) + \\frac{\\mathbf{R}_l}{(j\\omega)^2} + \\mathbf{R}_u$$\n",
+    "\n",
+    "Recall the poles $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$ and $\\lambda_r^* = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "The PolyMax approach assumes that the frequency response function can be represented by a right matrix-fraction model.\n",
+    "\n",
+    "$$ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$$\n",
+    "\n",
+    "Note that these are not the same $\\mathbf{A}$ and $\\mathbf{B}$ matrices from the previous sections, which represent the state space formulation.  However, this document attempts to match the notation from the referenced paper, so we will override those previous definitions and apologize for any confusion it may cause.\n",
+    "\n",
+    "We can see similarities between the two formulations.  The FRF equation has a numerator and a denominator, as well as a summation over a number of modes.  Similarly, the right matrix-fraction model has a matrix that is not inverted (representing something like a numerator), a matrix that is inverted (representing something like a denominator), and a summation operation inherent in the matrix multiplication that is performed.  Terms in $\\mathbf{B}$ will relate to the shape information in the numerator of the FRF equation, and terms in $\\mathbf{A}$ will relate to the pole information in the denominator of the FRF equation.\n",
+    "\n",
+    "The numerator matrix $\\mathbf{B}$ will have a number of rows equal to the number of responses or outputs $N_o$ and number of columns equal to the number of references or inputs $N_i$.  The denominator matrix $\\mathbf{A}$ will have a number of rows and columns equal to the number of inputs $N_i$, and is therefore square.\n",
+    "\n",
+    "We can split up the above equation and look at an individual row of the FRF matrix, which we will denote $\\mathbf{H}_o$ to represent a single output.  This will involve selecting a single row from $\\mathbf{B}_o$.\n",
+    "\n",
+    "$$ \\mathbf{H}_o(\\omega) = \\mathbf{B}_o(\\omega)\\mathbf{A}(\\omega)^{-1}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23ba4d5b-112a-42e4-8034-3e2616448aad",
+   "metadata": {},
+   "source": [
+    "Each entry in the $\\mathbf{B}_o$ and $\\mathbf{A}$ matrices is represented as a set of coefficients multiplied by a set of basis functions, similar to how we represent displacements in modal analysis by a set of modal coefficients multiplied by a set of mode shapes.  The basis functions $\\Omega_r(\\omega)$ are a set of functions that depend on the frequency $\\omega$.  The coefficients are constant with respect to $\\mathbf{\\omega}$.  We will call the coefficient of the $\\mathbf{B}$ matrix corresponding to the $o$th row and the $r$th basis function $\\beta_{or}$.  Similarly, we will call the coefficient of the $\\mathbf{A}$ matrix for the $r$th basis function $\\alpha_r$.  The number of coefficients and basis functions will be equal to the polynomial order $p+1$.\n",
+    "\n",
+    "$$\\mathbf{B}_o = \\sum_{r=0}^p \\Omega_r(\\omega)\\beta_{or}$$\n",
+    "$$\\mathbf{A} = \\sum_{r=0}^p \\Omega_r(\\omega)\\alpha_{r}$$\n",
+    "\n",
+    "The secret of PolyMax is that instead of using a polynomial basis function for $\\Omega_r(\\omega)$, it instead transforms to the $z$-domain.  For larger model orders, polynomials will have large exponents, which when applied to large frequency ranges will result in huge differences between the largest and smallest values, which is a sure-fire way to run into numerical issues.  Instead, by using the $z$-domain, the PolyMax approach avoids these numerical issues and generally achieves better results.  For a test where the minimum measured frequency is $\\omega_0$ and the maximum measured frequency is $\\omega_{end}$, this frequency range is mapped onto a half-unit circle.\n",
+    "\n",
+    "$$\\Omega_r(\\omega) = e^{-j\\omega_z \\Delta t_z r}$$\n",
+    "where $\\omega_z = \\omega - \\omega_0$ and $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.  If instead of angular frequencies, the frequency range is represented in Hz with minimum measured frequency $f_0$ and maximum measured frequency $f_{end}$, then the values are instead $\\omega_z = 2\\pi(f - f_0)$ and $\\Delta t_z = \\frac{1}{2(f_{end}-f_0)}$ where $f=\\frac{\\omega}{2\\pi}$.\n",
+    "\n",
+    "The polynomnial coefficients are stacked into matrices.\n",
+    "\n",
+    "$$\\beta_o = \\begin{bmatrix}\\beta_{o0}\\\\\\beta_{o1}\\\\\\beta_{o2}\\\\\\vdots\\\\\\beta_{op}\\end{bmatrix}$$\n",
+    "\n",
+    "$$\\alpha = \\begin{bmatrix}\\alpha_{0}\\\\\\alpha_{1}\\\\\\alpha_{2}\\\\\\vdots\\\\\\alpha_{p}\\end{bmatrix}$$\n",
+    "\n",
+    "Here $\\beta_o$ has number of rows equal to the polynomial order $p+1$ and number of columns equal to the number of references or inputs $N_i$.  Each row of $\\beta_o$ corresponds to the coefficients of one polynomial basis function for all of the references.\n",
+    "\n",
+    "$\\alpha$ also has a number of columns equal to the number of references or inputs $N_i$.  However, each row partition $\\alpha_r$ of $\\alpha$ has a number of rows equal to the number of inputs $N_i$ as well.  This means after stacking $p+1$ of these matrices on top of one another, the final number of rows of $\\alpha$ is equal to $N_i(p+1)$.\n",
+    "\n",
+    "We finally stack all of these coefficients into a single coefficient matrix $\\theta$, recalling that there is one matrix $\\beta_o$ for each of the $N_o$ outputs of the system.\n",
+    "\n",
+    "$$\\theta = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\beta_{3}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "Each row partition in $\\theta$ has columns equal to the number of inputs $N_i$.  Each of the $\\beta_o$ row partions has number of rows equal to the polynomial order $p+1$, and there are $N_o$ of those partitions, resulting in the $\\beta$ portion of the $\\theta$ matrix having $N_o(p+1)$ rows.  Added to the $N_i(p+1)$ rows of the $\\alpha$ partition, the final $\\theta$ matrix has $(N_i+N_o)(p+1)$ rows.\n",
+    "\n",
+    "At this point, we can represent the FRF matrix as a function of frequency $\\omega$ as well as a function of the coefficients $\\theta$, $\\mathbf{H}(\\omega,\\theta)$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81e2b187-5adc-488c-a62d-9ce63f2ddc6f",
+   "metadata": {},
+   "source": [
+    "### Selecting Coefficients that Minimize Errors\n",
+    "It may be obvious, but the goal is to eventually find the parameters $\\theta$ that best fit some measured frequency response functions.  In a real test, we will have measured frequency response functions $\\hat{\\mathbf{H}}$ at $N_f$ discrete frequency values $\\omega_k$.  We would like to find the coefficients $\\theta$ that minimize the error between the fit frequency response functions and the measured frequency response functions.  These coefficients can be found by minimizing the weighted non-linear least-squares error for the $o$th output $\\varepsilon_o^{NLS}$\n",
+    "\n",
+    "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{H}_o(\\omega_k,\\theta) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n",
+    "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o)\\mathbf{A}^{-1}(\\omega_k,\\alpha) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n",
+    "\n",
+    "where $w_o(\\omega_k)$ is a weighting function as a function of the output degree of freedom and the frequency line that allows taking into account data quality in the least-squares solution.  The previous equation is a matrix equation, and we would ideally like to transform it into a scalar equation that can be minimized which is only a function of the parameters $\\theta$.\n",
+    "\n",
+    "$$l^{NLS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "We multiply the value $\\varepsilon_o^{NLS}(\\omega_k,\\theta)$ complex-conjugate-transpose ($\\bullet^H$) by itself, and compute the trace ($\\mathrm{tr}(\\bullet)$).  We then sum over all frequency lines and all outputs.\n",
+    "\n",
+    "This equation is solved by setting the derivatives of the equation with respect to the unknown modal coefficients to zero.  It is reasonably clear that this leads to nonlinear equations due to the matrix inverse in the error equation.  We can construct a linear set of equations that approximates the original equations by simply post-multiplying the equation by the matrix $\\mathbf{A}$.\n",
+    "\n",
+    "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o) - \\hat{\\mathbf{H}}_o(\\omega_k)\\mathbf{A}(\\omega_k,\\alpha)\\right)$$\n",
+    "Substituting for the basis functions and coefficients:\n",
+    "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\sum_{r=0}^p\\left(\n",
+    "\\Omega_r(\\omega_k)\\beta_{or}\n",
+    "- \\Omega_r(\\omega_k)\\hat{\\mathbf{H}}_o(\\omega_k)\\alpha_r\\right)$$\n",
+    "\n",
+    "We can stack this equation for all frequency lines ($\\omega_1$, $\\omega_2$, ... $\\omega_{N_f}$)\n",
+    "\n",
+    "$$E_o^{LS}(\\theta) = \\begin{bmatrix}\n",
+    "\\varepsilon_o^{LS}(\\omega_1,\\theta) \\\\\n",
+    "\\varepsilon_o^{LS}(\\omega_2,\\theta) \\\\\n",
+    "\\vdots \\\\\n",
+    "\\varepsilon_o^{LS}(\\omega_{N_f},\\theta) \\\\\n",
+    "\\end{bmatrix} = \\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o \\end{bmatrix}\\begin{bmatrix}\\beta_o\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "This will result in $E_o^{LS}(\\theta)$ having a number of rows equal to the number of frequency lines in the test, and the number of columns equal to the number of references or inputs $N_i$.\n",
+    "\n",
+    "The $\\mathbf{X}_o$ matrix can be constructed from pulling off the coefficients of the $\\beta$ terms, which are generally the basis functions $\\Omega_r$ evaluated at each frequency line $\\omega_k$.  Each row will correspond to a separate frequency line, and each column will correspond to a basis function order.\n",
+    "\n",
+    "$$\\mathbf{X}_o = \\begin{bmatrix}\n",
+    "w_o(\\omega_1)\\Omega_0(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1) & \\cdots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\\\\n",
+    "w_o(\\omega_2)\\Omega_0(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2) & \\cdots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\\\\n",
+    "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f}) & \\cdots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\\\\n",
+    "\\end{bmatrix} $$\n",
+    "\n",
+    "The $\\mathbf{Y}_o$ matrix can be constructed by pulling off the coefficients of the $\\alpha$ terms, which are generally the basis functions somehow multiplied by the FRFs.  We will explore this expression a bit more here.  The relevant portion of $\\varepsilon_o^{LS}(\\omega_k,\\theta)$ containing the $\\alpha$ terms is \n",
+    "\n",
+    "$$\\mathbf{Y}_o \\alpha = -\\begin{bmatrix}\n",
+    "    \\sum_{r=0}^pw_o(\\omega_1)\\Omega_r(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_r \\\\\n",
+    "    \\sum_{r=0}^pw_o(\\omega_2)\\Omega_r(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_r \\\\\n",
+    "    \\vdots \\\\\n",
+    "    \\sum_{r=0}^pw_o(\\omega_{N_f})\\Omega_r(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_r\\end{bmatrix}$$\n",
+    "\n",
+    "Expanding the equation out in terms of the summation can help visualize which terms go where in the $\\mathbf{Y}_o$ matrix.\n",
+    "\n",
+    "$$\\mathbf{Y}_o \\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix} = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1) \\left( \\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_0 + \\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_1 + \\dots + \\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_p \\right) \\\\\n",
+    "    w_o(\\omega_2) \\left( \\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_0 + \\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_1 + \\dots + \\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_p \\right) \\\\\n",
+    "    \\vdots \\\\\n",
+    "    w_o(\\omega_{N_f}) \\left( \\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_0 + \\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_1 + \\dots + \\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_p \\right) \\\\\n",
+    "    \\end{bmatrix}$$\n",
+    "or in matrix form\n",
+    "$$\\mathbf{Y}_o \\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix} = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1)\\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & \\dots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) \\\\\n",
+    "    w_o(\\omega_2)\\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & \\dots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) \\\\\n",
+    "    \\vdots & \\vdots & \\ddots & \\vdots \\\\ \n",
+    "    w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & \\dots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) \\\\\n",
+    "    \\end{bmatrix}\\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix}$$\n",
+    "\n",
+    "We can recognize the pattern $\\begin{bmatrix}\\Omega_0\\hat{\\mathbf{H}}_o & \\Omega_1\\hat{\\mathbf{H}}_o & \\dots & \\Omega_p\\hat{\\mathbf{H}}_o\\end{bmatrix}$ as the [Kronecker Product](https://en.wikipedia.org/wiki/Kronecker_product) $\\mathbf{\\Omega} \\otimes \\hat{\\mathbf{H}}_o$ where the matrix $\\mathbf{\\Omega}$ has only one row.  Therefore, \n",
+    "\n",
+    "$$\\mathbf{X}_o = \\begin{bmatrix}\n",
+    "w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\\\\n",
+    "w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\\\\n",
+    "\\vdots \\\\\n",
+    "w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\\\\n",
+    "\\end{bmatrix} $$\n",
+    "$$\\mathbf{Y}_o = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\otimes\\hat{\\mathbf{H}}_o(\\omega_1)\\\\\n",
+    "    w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\otimes\\hat{\\mathbf{H}}_o(\\omega_2)\\\\\n",
+    "    \\vdots\\\\ \n",
+    "    w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\otimes\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\\\\n",
+    "    \\end{bmatrix}$$\n",
+    "\n",
+    "where $\\mathbf{\\Omega}(\\omega) = \\begin{bmatrix}\\Omega_0(\\omega)&\\Omega_1(\\omega)&\\dots&\\Omega_p(\\omega)\\end{bmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "873cdaf8-293e-46d6-837b-29eedf756c3b",
+   "metadata": {},
+   "source": [
+    "Similarly to the nonlinear least squares solution, we can construct an error cost function to minimize based on the linearized equation.\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "or \n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\\left(E_o^{LS}(\\omega_k,\\theta)\\right)^HE_o^{LS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "If we substitute the matrix equations, we get\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\n",
+    "\\begin{bmatrix}\\beta_o^T & \\alpha^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o^H \\\\ \\mathbf{Y}_o^H\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o\\end{bmatrix}\\begin{bmatrix}\\beta_o \\\\ \\alpha\\end{bmatrix}\\right)$$\n",
+    "\n",
+    "Ideally, we would like to remove the summation in the previous equation and end up with an equation of the form $l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$.  We recognize that we can stack the equations for $E_o^{LS}$ for all outputs and arrive at an equation\n",
+    "\n",
+    "$$\\begin{bmatrix}E_1^{LS} \\\\ E_2^{LS} \\\\ \\vdots \\\\ E_{N_o}^{LS} \\end{bmatrix} = \n",
+    "\\begin{bmatrix}\n",
+    "    \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{Y}_1\\end{bmatrix}\\begin{bmatrix}\\beta_1 \\\\ \\alpha\\end{bmatrix} \\\\ \n",
+    "    \\begin{bmatrix}\\mathbf{X}_2 & \\mathbf{Y}_2\\end{bmatrix}\\begin{bmatrix}\\beta_2 \\\\ \\alpha\\end{bmatrix} \\\\\n",
+    "    \\vdots \\\\\n",
+    "    \\begin{bmatrix}\\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o}\\end{bmatrix}\\begin{bmatrix}\\beta_{N_o} \\\\ \\alpha\\end{bmatrix}\\end{bmatrix} = \n",
+    "\\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n",
+    "               \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n",
+    "               \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "               \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$\\mathbf{J} = \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n",
+    "               \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n",
+    "               \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "               \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}$$\n",
+    "and as we've seen previously,\n",
+    "$$\\mathbf{\\theta} = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "Therefore our cost function can be simplified to\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347b489b-ba1c-4564-a8ef-ad96fa26c986",
+   "metadata": {},
+   "source": [
+    "In general, the quantity $\\mathbf{J}^H\\mathbf{J}$ will be conjugate-symmetric or [Hermitian](https://en.wikipedia.org/wiki/Hermitian_matrix).  This means that its upper triangle will be equal to the complex conjugate of the lower triangle transposed, and the diagonal will be real valued.  While the matrix $\\mathbf{J}^H\\mathbf{J}$ is therefore complex, in the previous equation, because it multiplies by real valued coefficients $\\mathbf{\\theta}$ and we take the trace, which should only select the real-valued diagonal, we can replace the quantity $\\mathbf{J}^H\\mathbf{J}$ with its real part $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$\n",
+    "\n",
+    "Computing $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$ gives\n",
+    "\n",
+    "$$\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right) = \\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n",
+    "                                                          \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n",
+    "                                                          \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n",
+    "                                                          \\end{bmatrix} $$\n",
+    "where\n",
+    "$$\\mathbf{R}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{X}_o\\right)$$\n",
+    "$$\\mathbf{S}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{Y}_o\\right)$$\n",
+    "$$\\mathbf{T}_o = \\Re\\left(\\mathbf{Y}_o^H\\mathbf{Y}_o\\right)$$\n",
+    "\n",
+    "We minimize the cost function by setting the derivatives of the cost function with respect to the coefficients $\\mathbf{\\theta}$ to zero.\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n",
+    "                                                          \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n",
+    "                                                          \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n",
+    "                                                          \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}\\right)$$\n",
+    "Expanding the multiplication gives\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\n",
+    "    \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{R}_o\\beta_o\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{S}_o^T\\beta_o\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{S}_o\\alpha\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{T}_o\\alpha\\right)\\right)$$\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\sum_{o=1}^{N_o}\\left(\n",
+    "    \\beta_o^T\\mathbf{R}_o\\beta_o + \n",
+    "    \\alpha^T\\mathbf{S}_o^T\\beta_o + \n",
+    "    \\beta_o^T\\mathbf{S}_o\\alpha + \n",
+    "    \\alpha^T\\mathbf{T}_o\\alpha\\right)$$\n",
+    "\n",
+    "We can therefore compute the partial derivatives with respect to the unknown coefficients and set them to zero.\n",
+    "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\beta_o} = \n",
+    "    2\\mathbf{R}_o\\beta_o + \n",
+    "    2\\mathbf{S}_o\\alpha = \\mathbf{0}$$\n",
+    "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\alpha} = \n",
+    "    \\sum_{o=1}^{N_o}\\left( \n",
+    "    2\\mathbf{S}_o^T\\beta_o + \n",
+    "    2\\mathbf{T}_o\\alpha\\right) =\\mathbf{0}$$\n",
+    "\n",
+    "As a first pass, we only care about the poles, which come from the denominator terms $\\alpha$.  We can eliminate the $\\beta_o$ terms using the first of these constraints\n",
+    "$$\\beta_o = -\\mathbf{R}_o^{-1}\\mathbf{S}_o\\alpha$$\n",
+    "\n",
+    "We can then plug this into the second constraint to get an equation for $\\mathbf{\\alpha}$.\n",
+    "\n",
+    "$$\\left[2\\sum_{o=1}^{N_o}\\left( \n",
+    "    \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)\\right]\\alpha= \\mathbf{M}\\alpha= \\mathbf{0}$$\n",
+    "\n",
+    "This matrix $\\mathbf{M}$ is a square matrix with number of rows and columns equal to the number of inputs times the polynomial order $N_i(p+1) \\times N_i(p+1)$, and depends only on the measured FRFs from the test.  Note, however, that this equation is not unique.  For example, the trival solution $\\alpha = \\mathbf{0}$ satisfies this equation.  Additionally, one may see that in the right matrix-fraction formulation $ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$, one could multiply both $\\mathbf{A}$ and $\\mathbf{B}$ by the same matrix, and the equation would still be satisfied.  Therefore, there is not a unique value for $\\alpha$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc9b2e09-e498-4663-9717-d1ffa3085ee7",
+   "metadata": {},
+   "source": [
+    "### Solving the Constrained Problem\n",
+    "To solve a problem of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{0}$ for the non-trivial $\\mathbf{x}$, we must apply some constraint to the problem.\n",
+    "\n",
+    "To solve the problem, we will partition the matrix $\\mathbf{M}$ as follows\n",
+    "\n",
+    "$$\\mathbf{M} = \\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n",
+    "                              \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}$$\n",
+    "\n",
+    "where the $a$ indices are the first $N_ip$ rows or columns and the $b$ indices are the last $p$ columns, recalling the size of $\\mathbf{M}$ is $N_i(p+1)$.  Therefore the matrix $\\mathbf{M}_{aa}$ matrix will have size $N_ip\\times N_ip$, the $\\mathbf{M}_{ab}$ matrix will have size $N_ip\\times p$, the $\\mathbf{M}_{ba}$ matrix will have size $p\\times N_ip$, and the $\\mathbf{M}_{bb}$ matrix will have size $p\\times p$.\n",
+    "\n",
+    "Because we must apply constraints to our problem, we will make the assumption that our solution matrix $\\mathbf{\\alpha}$ will have the form\n",
+    "\n",
+    "$$\\mathbf{\\alpha} = \\begin{bmatrix}\\hat{\\mathbf{\\alpha}}  \\\\ \n",
+    "                                   \\mathbf{I}\\end{bmatrix}$$\n",
+    "\n",
+    "We can see that if we plug this version of $\\mathbf{\\alpha}$ into the equation, we get an expression for the portion of $\\mathbf{\\alpha}$ that was not constrained.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n",
+    "                 \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}\n",
+    "  \\begin{bmatrix}\\hat{\\mathbf{\\alpha}}  \\\\ \n",
+    "                 \\mathbf{I}\\end{bmatrix} =\n",
+    "  \\begin{bmatrix}\\mathbf{M}_{aa}\\hat{\\alpha} + \\mathbf{M}_{ab}\\\\\n",
+    "                 \\mathbf{M}_{ba}\\hat{\\alpha} + \\mathbf{M}_{bb}\\end{bmatrix} = \n",
+    "   \\begin{bmatrix}\\mathbf{0} \\\\\n",
+    "                  \\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can then simply take the top partition to solve for $\\hat{\\alpha}$.\n",
+    "\n",
+    "$$\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38d7b7fb-bb03-4710-b443-d5f1989876a8",
+   "metadata": {},
+   "source": [
+    "### Extracting Poles and Participation Factors\n",
+    "Now that we have the polynomial coefficients $\\mathbf{\\alpha}$ for the right matrix-fraction model, we would like to extract the roots from it.  The roots of this polynomial are the poles of the frequency response function.  We will do this by setting up a [Companion Matrix](https://en.wikipedia.org/wiki/Companion_matrix).  A Companion matrix of a polynomial is a matrix whose characteristic equation is equal to that polynomial.  Then the eigenvalues of the matrix (which are the roots of the characteristic equation) are also the roots of the polynomial.  In our case, the eigenvectors are also useful, as they are related to the participation factors.\n",
+    "\n",
+    "For a polynomial $p(x) = \\alpha_p x^p + \\alpha_{p-1} x^{p-1} + \\dots + \\alpha_2 x^2 + \\alpha_1 x + \\alpha_0$, the companion matrix will be \n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} 0 & 1 & 0 & \\dots & 0\\\\\n",
+    "                                0 & 0 & 1 & \\dots & 0\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                0 & 0 & 0 & \\dots & 1\\\\\n",
+    "                                -\\frac{\\alpha_0}{\\alpha_p} & -\\frac{\\alpha_1}{\\alpha_p} & -\\frac{\\alpha_2}{\\alpha_p} & \\dots & -\\frac{\\alpha_{p-1}}{\\alpha_p} \n",
+    "                                \\end{bmatrix}$$\n",
+    "\n",
+    "Readers can verify that $\\mathrm{det}(\\mathbf{I}x - \\mathbf{C}) = p(x)$.\n",
+    "\n",
+    "Our present case is slightly different, because our coefficients are matrices rather than scalars.\n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\alpha_0^T{\\alpha_p^T}^{-1} & -\\alpha_1^T{\\alpha_p^T}^{-1} & -\\alpha_2^T{\\alpha_p^T}^{-1} & \\dots & -\\alpha_{p-1}^T{\\alpha_p^T}^{-1}\n",
+    "                                \\end{bmatrix}$$\n",
+    "However, because we applied the constraint such that $\\alpha_p = \\mathbf{I}$ when solving for $\\mathbf{\\alpha}$, the quantity $\\alpha_p^{-1} = \\mathbf{I}$, and the remaining terms are those in $\\hat{\\mathbf{\\alpha}}$.\n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n",
+    "                                \\end{bmatrix}$$\n",
+    "\n",
+    "The $\\mathbf{C}$ matrix will be a square matrix with size $N_ip \\times N_ip$ where $N_i$ is the number of inputs or references, and $p$ is the order of the polynomial.\n",
+    "\n",
+    "We can therefore notate the companion matrix compactly as\n",
+    "$$\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{I}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b34449b0-790f-426e-9f61-7590aa486efc",
+   "metadata": {},
+   "source": [
+    "We can then solve for the eigenvalues and eigenvectors of this matrix.  The companion matrix is set up such that its characteristic polynomial is identical to the polynomial it represents.  Therefore, its eigenvalues, which are roots of the characteristic polynomial, will be roots of the polynomial it represents.\n",
+    "\n",
+    "$$\\mathbf{C}\\mathbf{V} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n",
+    "                                \\end{bmatrix}\\mathbf{V}= \\mathbf{\\Lambda}\\mathbf{V}$$\n",
+    "The diagonals of the eigenvector matrix $\\bar{\\lambda}_r = \\mathbf{\\Lambda}_{rr}$ are the eigenvalues of the companion matrix.  These are the poles of the system; however, we must remember they are represented in the $z$-domain, so we must return to the $s$-domain $\\lambda_r$ in order to solve for the natural frequency and damping ratio.\n",
+    "\n",
+    "$$\\bar{\\lambda}_r = e^{-\\lambda_r \\Delta t_z}$$\n",
+    "$$\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$$\n",
+    "\n",
+    "where $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.\n",
+    "\n",
+    "We can then solve for the natural frequency and damping ratio from the poles.  Recall that the frequency was shifted by $\\omega_0$ during the mapping, so we will need to take that into account.\n",
+    "\n",
+    "$${\\omega_z}_r = \\left|\\lambda_r\\right|$$\n",
+    "$${\\omega_r} = {\\omega_z}_r + \\omega_0$$\n",
+    "$$\\zeta_r = -\\frac{\\Re\\left({\\lambda_r}\\right)}{\\omega_r}$$\n",
+    "\n",
+    "Note that there will be in general $N_ip$ eigenvalues.  Half of them will be complex conjugates of the other half, so we can ignore those.  Similarly, we can ignore non-physical eigenvalues.  A criteria that is successfully used is to only keep poles where the real part is less than zero and the imaginary part is greater than zero.\n",
+    "\n",
+    "The modal participation factors $\\mathbf{l}_r$ can be determined from the eigenvectors of the companion matrix.  They will be the last $N_i$ rows of the eigenvector matrix.  One must also be sure that for each eigenvalue discarded for being non-physical or complex conjugate, the corresponding eigenvector should also be discarded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724700d5-1b64-4f54-8c17-505f115c560f",
+   "metadata": {},
+   "source": [
+    "### Stabilization Diagrams\n",
+    "Solving for a polynomial of a particular order will in general result in a large number of poles.  For a typical problem, only a subset of these poles will correspond to true modes of the structure.  For a typical modal test, it may not be obvious how many modes are in a given bandwidth anyways.  Therefore, we need some kind of metric to determine if a pole is valid or not, and for this we utilize the stabilization diagram.\n",
+    "\n",
+    "A stabilization diagram is constructed by computing the poles over a large number of polynomial orders.  As we increase the polynomial orders, more poles may be found in the system.  We declare a *stable pole* to be one which does not change significantly as the polynomial order changes.  Computational poles may bounce around the measurement bandwidth.  Real poles of the system, on the other hand, will generally result in a similar frequency, damping ratio, and set of participation factors regardless of the polynomial order.  We then construct a final set of poles by selecting from the stable poles in the stabilization diagram."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e84ee11-8387-4196-8c61-dc3e1c944e31",
+   "metadata": {},
+   "source": [
+    "### Back to the Example Problem\n",
+    "To demonstrate the equations shown here, we will implement the previous equations in code.  We will demonstrate solution of one polynomial order to demonstrate the process, and then rely on the implementation in SDynPy to perform the rest of the analysis.  We will initially focus only on the general damping case rather than repeating the analysis for all the damping matrices.\n",
+    "\n",
+    "To start, we will pull in the measured frequency response functions and corresponding frequency values.  We will shape the frequency response function such that it is a 3D array with shape $N_o \\times N_i \\times N_f$.  We will also assume that we only peformed measurements with inputs at the first and second degrees of freedom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0e5df1a5-cdc8-4e07-a7f9-e041e22c4a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "H = H_general.transpose(1,2,0)[:,:2,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c834190f-3f0d-41fe-866f-e616e36e9cfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 2, 1000)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20e3730a-73f0-4316-9c23-6545c19572aa",
+   "metadata": {},
+   "source": [
+    "We will also assume that we only performed measurements over a limited bandwidth from 10 rad/s to 80 rad/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a274ff4c-0012-48b5-9e62-48351fac2513",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frequency_lines_to_keep = (omegas > 10) & (omegas < 80)\n",
+    "H = H[:,:,frequency_lines_to_keep]\n",
+    "omegas = omegas[frequency_lines_to_keep]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b873134-0289-4846-8bcc-824d57174fc3",
+   "metadata": {},
+   "source": [
+    "Now that we have truncated the frequency domain, we will define some helper variables to make the code a bit more intuitive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f7dcec4f-fbf1-4afd-bccb-e9f1b84364b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Outputs: 3\n",
+      "Num Inputs: 2\n",
+      "Num Frequencies: 778\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_output, num_input, num_freq = H.shape\n",
+    "print('Num Outputs: {:}\\nNum Inputs: {:}\\nNum Frequencies: {:}'.format(num_output, num_input, num_freq))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f5f1720-01be-4a77-bfd2-074f1d23d420",
+   "metadata": {},
+   "source": [
+    "We will also set our polynomial order.  In this case, we know that there are 3 modes of the structure in the bandwidth.  However, let's select 5 as the polynomial order, as we generally would like to overfit the model.  Keep in mind that this will result in 6 polynomial coefficients: 0, 1, 2, 3, 4, and 5.  Therefore, you shouldn't be confused when dimensions of arrays associated with the polynomial order have length 6 instead of length 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3c9da069-ffcd-4640-a8c7-fa281bd33fa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae930b28-a34b-4a65-a03a-05977c5809d7",
+   "metadata": {},
+   "source": [
+    "We will assume no weighting, or rather the weighting is identically 1 across all outputs and across all frequencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4df3d9a0-b12a-4ba5-b2e3-11d818d4cca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weighting = np.ones((num_freq,num_output))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e27b2ab-82df-4618-bae9-7dfb65ec472a",
+   "metadata": {},
+   "source": [
+    "The first thing we will do is to set up our $\\mathbf{\\Omega}_r(\\omega) = e^{-j\\omega_z\\Delta t_z r}$ vectors.  We will set this up as an array with number of rows equal to number of frequency lines and number of columns equal to the number of polynomial coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4942b941-1e13-4e88-97b1-f03a17335542",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(778, 6)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "omega_z = omegas - omegas[0] # Perform the shift\n",
+    "deltat = np.pi/(omegas[-1] - omegas[0]) # Compute the scale factor\n",
+    "Omega = np.exp(-1j * omega_z[:,np.newaxis] * deltat * np.arange(order+1)) # Compute Omega\n",
+    "Omega.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ea75991-908b-41c4-b425-29b5e46c286a",
+   "metadata": {},
+   "source": [
+    "In the previous code, we use broadcasting to set up our $\\mathbf{\\Omega}_r$ array.  We add a `newaxis` to our shifted frequency vector $\\omega_z$ to make it a `num_freq` $\\times$ 1 array.  This is then multiplied by an array `[0,1,2,3,4,5]` constructed from the `arange` function.  Per [Numpy's Broadcasting Rules](https://numpy.org/doc/stable/user/basics.broadcasting.html), a `num_freq` $\\times$ 1 array and a `order+1` array will be broadcast to a `num_freq` $\\times$ `order+1` array.  Multiplying by scalars `deltat` and `-1j` get performed elementwise, as does the exponential function `exp`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b46fbf38-0e64-49fa-b8c2-4fcb128ef1a3",
+   "metadata": {},
+   "source": [
+    "The next step will be to assemble the $\\mathbf{R}_o$, $\\mathbf{S}_o$, and $\\mathbf{T}_o$ matrices.  These are built from the $\\mathbf{X}_o$ and $\\mathbf{Y}_o$ matrices.\n",
+    "\n",
+    "Before we do this let's think about the dimensions of the arrays.  The matrix $\\mathbf{R}_o$ for each output will have dimension `order+1` $\\times$ `order+1`.  The matrix $\\mathbf{S}_o$ for each output will have dimension `order + 1` $\\times$ `num_input*(order+1)`.  The $\\mathbf{T}_o$ matrix for each output will have dimension `num_input*(order+1)` $\\times$ `num_input*(order+1)`.  Because we will have `num_output` of each of these matrices, we will consider assembling them into a 3D array with dimension `num_output` $\\times$ `...` $\\times$ `...` ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "97c9d6a9-be8a-4e44-a440-7ff0ac71a911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  R: (3, 6, 6)\n",
+      "  S: (3, 6, 12)\n",
+      "  T: (3, 12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "R = np.zeros([num_output, order + 1, order + 1])\n",
+    "S = np.zeros([num_output,order + 1, num_input * (order + 1)])\n",
+    "T = np.zeros([num_output, num_input * (order + 1), num_input * (order + 1)])\n",
+    "print('Shapes:\\n  R: {:}\\n  S: {:}\\n  T: {:}'.format(R.shape,S.shape,T.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f86ddef-0627-4f7d-8554-66900ad82bc1",
+   "metadata": {},
+   "source": [
+    "We will then loop through each of the outputs and assemble the corresponding portions of the matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c050e1b7-3872-4bfe-9344-9758c8403463",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accumulating Data: Progress = 0.00%\n",
+      "Accumulating Data: Progress = 33.33%\n",
+      "Accumulating Data: Progress = 66.67%\n",
+      "Accumulating Data: Progress = 100.00%\n",
+      "Shapes:\n",
+      "  Xo: (778, 6)\n",
+      "  Yo: (778, 12)\n",
+      "  Ro: (6, 6)\n",
+      "  So: (6, 12)\n",
+      "  To: (12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for o in range(num_output):\n",
+    "    print('Accumulating Data: Progress = {:0.2f}%'.format(o / num_output * 100.0))\n",
+    "    # The Xo matrix depends only on the weighting and Omega.  We use\n",
+    "    # broadcasting to extend the weighting across all polynomial coefficients\n",
+    "    Xo = weighting[:, o][..., np.newaxis] * Omega\n",
+    "    # The Yo matrix will need the measured frequency response functions\n",
+    "    # at the output o.\n",
+    "    Ho = H[o, ...]\n",
+    "    # We note that rather than re-multiplying the weighting by Omega in the\n",
+    "    # Yo matrix, we can simply use Xo.  Here we must loop over all frequency\n",
+    "    # lines to perform the kronecker product.  The frequency lines are the\n",
+    "    # rows of X, and the columns of Ho, so we transpose Ho.\n",
+    "    # Xk is then the polynomial coefficients at the k frequency line and\n",
+    "    # Hk is the FRF for each input at that frequency line.\n",
+    "    Yo = np.array([-np.kron(Xk, Hk) for Xk, Hk in zip(Xo, Ho.transpose())])\n",
+    "    # We then construct the R, S, and T matrices for that output from their\n",
+    "    # definitions\n",
+    "    Ro = np.real(Xo.conjugate().transpose() @ Xo)\n",
+    "    So = np.real(Xo.conjugate().transpose() @ Yo)\n",
+    "    To = np.real(Yo.conjugate().transpose() @ Yo)\n",
+    "    # We stick the matrices into the 3D arrays for future usage.\n",
+    "    R[o, :, :] = Ro\n",
+    "    S[o, :, :] = So\n",
+    "    T[o, :, :] = To\n",
+    "print('Accumulating Data: Progress = {:0.2f}%'.format((o+1) / num_output * 100.0))\n",
+    "print('Shapes:\\n  Xo: {:}\\n  Yo: {:}\\n  Ro: {:}\\n  So: {:}\\n  To: {:}'.format(\n",
+    "    Xo.shape,Yo.shape,Ro.shape,So.shape,To.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7004a057-06f0-42bc-882c-39cdb34aa075",
+   "metadata": {},
+   "source": [
+    "We will now compute the $\\mathbf{M}$ matrix, which is equal to $\\sum_{o=1}^{N_o}\\left(\\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)$.  Note that we dropped the factor of 2 in front of the expression because the final answer $\\alpha$ is only defined up to a matrix product.  Multiplying the answer by 2 will not result in any meaningful change.\n",
+    "\n",
+    "We will compute the contribution $\\mathbf{M}_o = \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o$ for each output and then sum them together to construct $\\mathbf{M}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "cbee950b-972c-45aa-8ead-6d65fdd1afc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  M: (12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Because S is a 3D array, to transpose it, we will only want to\n",
+    "# swap the last two dimensions and leave the first output dimension\n",
+    "# out front.\n",
+    "# We compute R^-1 using np.linalg.solve, which solves a problem.\n",
+    "# np.linalg.solve(R,S) is equivalent to R^-1@S, but is faster and\n",
+    "# better conditioned.\n",
+    "M = np.sum(T - S.transpose(0,2,1) @ np.linalg.solve(R, S),axis=0)\n",
+    "print('Shapes:\\n  M: {:}'.format(M.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26545d69-262d-459a-919c-72a6fcfc2dc6",
+   "metadata": {},
+   "source": [
+    "Now we will solve for the parameters $\\mathbf{\\alpha}$ and construct the companion matrix.  Recall that in our solution we will assume $\\mathbf{\\alpha}_p$ is equal to the identity matrix and drops out of the companion matrix computation, so we really only need to compute $\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "40d56445-b74b-4f9b-af2e-1e98251a22ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  Maa: (10, 10)\n",
+      "  Mbb: (10, 2)\n",
+      "  alpha: (10, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Maa = M[:order * num_input, :order * num_input]\n",
+    "Mbb = -M[:order * num_input, order * num_input:]\n",
+    "alpha = np.linalg.solve(Maa, Mbb)\n",
+    "print('Shapes:\\n  Maa: {:}\\n  Mbb: {:}\\n  alpha: {:}'.format(\n",
+    "    Maa.shape,Mbb.shape,alpha.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f24809a1-e67f-4446-b842-19f139beadd7",
+   "metadata": {},
+   "source": [
+    "We will then construct the companion matrix $\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{1}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$.  The partition $\\mathbf{0}$ will have shape `(order-1)*num_input` $\\times$ `num_input`, and the partition $\\mathbf{I}$ will have shape `(order-1)*num_input` $\\times$ `(order-1)*num_input`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "788bc574-8350-4be2-b8db-acfab146d5b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  C: (10, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "C_top_left = np.zeros([(order - 1) * num_input, num_input])\n",
+    "C_top_right = np.eye((order - 1) * num_input)\n",
+    "C_bottom = -alpha.transpose()\n",
+    "C = np.concatenate(\n",
+    "    (np.concatenate((C_top_left, C_top_right), axis=1),\n",
+    "     C_bottom), axis=0)\n",
+    "print('Shapes:\\n  C: {:}'.format(\n",
+    "    C.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab985b8c-3070-4041-a128-e24fa02ba8ec",
+   "metadata": {},
+   "source": [
+    "Finally, we can compute the eigenvalues and eigenvectors from this matrix, which should be the solutions to the polynomial.  Remember that the eigenvalues will be in the $z$-domain, and must be transformed back to the $s$ domain for us to extract natural frequency and damping ratio: $\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5f2a5fec-6a50-4f5e-9286-ab7ab4b95ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zpoles, V = np.linalg.eig(C)\n",
+    "spoles = -np.log(zpoles)/deltat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be16c045-b456-42dd-acf1-c4704cc460fb",
+   "metadata": {},
+   "source": [
+    "We will immediately discard any poles where the imaginary part is less than zero or the real part is greater than zero, ensuring that we also discard the corresponding eigenvectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9070751d-05f8-4510-8fbf-41d66fb9942f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keep_poles = (np.imag(spoles) > 0) & (np.real(spoles) < 0)\n",
+    "spoles = spoles[keep_poles]\n",
+    "V = V[:,keep_poles]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbc83e04-6f70-44ce-8720-e4cd0bf0a96b",
+   "metadata": {},
+   "source": [
+    "We can then extract the (shifted) natural frequency and damping ratios from the poles.  We unshift the natural frequency from the pole to recover the natural frequency of the structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b06e5f64-ca4f-4ffe-8e8e-56304150103d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omega_general_fit = abs(spoles) + omegas[0]\n",
+    "zeta_general_fit = -np.real(spoles)/omega_general_fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f11f8a6a-7f07-4e72-bd4f-bbf181f354cf",
+   "metadata": {},
+   "source": [
+    "The modal participation factors $\\mathbf{l}_r$ are then the last `num_input` rows of the eigenvectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "cd6279de-eb9f-401c-9baf-12ba4ebdc193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = V[-num_input:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681c6a98-1297-4e84-b1a2-c787d2853b53",
+   "metadata": {},
+   "source": [
+    "Let's compare the fit natural frequencies to those from the test problem.  We will sort them ascending so they match those computed directly from the eigenvalues of the state space formulation.  Make sure that we also sort the damping and participation factors identically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "74d9e3ed-63cc-4595-860f-2d2cad63f99c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sort_indices = np.argsort(omega_general_fit)\n",
+    "omega_general_fit = omega_general_fit[sort_indices]\n",
+    "zeta_general_fit = zeta_general_fit[sort_indices]\n",
+    "lr = lr[:,sort_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5091ad-1e98-4063-a1c7-7cd06a1053b5",
+   "metadata": {},
+   "source": [
+    "Let's now set up a table to show the differences.  We will compare the participation factors against the same degrees of freedom from the corresponding mode shapes.  Note that the participation factors may be scaled differently and rotated differently than the mode shapes, so we must rescale and rotate them to judge how well they fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "71fa8ee2-5eab-48e8-a0cb-6933f8b9d67e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frequency From System Matrices</th>\n",
+       "      <th>Frequency From FRF Fits</th>\n",
+       "      <th>Frequency Error</th>\n",
+       "      <th>Damping From System Matrices</th>\n",
+       "      <th>Damping From FRF Fits</th>\n",
+       "      <th>Damping Error</th>\n",
+       "      <th>Participation From System Matrices</th>\n",
+       "      <th>Participation From FRF Fits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.25</td>\n",
+       "      <td>27.30</td>\n",
+       "      <td>0.19%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>2.60%</td>\n",
+       "      <td>-3.90%</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "      <td>[-0.04318693+0.04408044j -0.09857685+0.09838141j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.78</td>\n",
+       "      <td>54.86</td>\n",
+       "      <td>0.15%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>2.76%</td>\n",
+       "      <td>0.67%</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "      <td>[0.06534612-0.06998435j 0.00118367+0.00286075j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>59.91</td>\n",
+       "      <td>59.88</td>\n",
+       "      <td>-0.06%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>2.69%</td>\n",
+       "      <td>1.02%</td>\n",
+       "      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>\n",
+       "      <td>[-0.06070207+0.05458388j  0.03320861-0.0344524j ]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n",
+    "                             'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n",
+    "                             'Participation From System Matrices','Participation From FRF Fits'],\n",
+    "                  dtype=object)\n",
+    "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n",
+    "    omega_general,omega_general_fit,\n",
+    "    zeta_general, zeta_general_fit,\n",
+    "    psi_general[:2,:].T, lr.T)):\n",
+    "    df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n",
+    "    df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n",
+    "    df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n",
+    "    df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n",
+    "    df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n",
+    "    df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n",
+    "    # Find the maximum participation degree of freedom from the\n",
+    "    # system matrices\n",
+    "    check_index = np.argmax(np.abs(lm))\n",
+    "    # Compute the angle of that index\n",
+    "    check_angle = np.angle(lm[check_index])\n",
+    "    # Compute the same angle from the fit\n",
+    "    this_angle = np.angle(lf[check_index])\n",
+    "    rotation = check_angle - this_angle\n",
+    "    # Compute the magnitudes of the vectors\n",
+    "    check_magnitude = np.linalg.norm(np.abs(lm))\n",
+    "    this_magnitude = np.linalg.norm(np.abs(lf))\n",
+    "    # Correct rotation and scaling\n",
+    "    lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n",
+    "    df.at[i,'Participation From System Matrices'] = str(lm)\n",
+    "    df.at[i,'Participation From FRF Fits'] = str(lf)\n",
+    "    \n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4b3d62-f721-4cd0-93ad-60ca93746ee3",
+   "metadata": {},
+   "source": [
+    "We can verify that we get very good agreement between the quantities derived from the system matrices and those fit by the FRFs.  However, we may have simply been lucky: our system of equations actually solved for `order*num_inputs` or 10 poles, and it just so happened that 3 of them ended up being physically realizable with positive damping and frequency.  It is possible that more of them would have been physically realizable, in which case we would have had to go through and try to figure out which poles are \"real\" and which are \"computational\", and without *a priori* knowledge of the true pole information, it could be very difficult to do this.\n",
+    "\n",
+    "Instead, we use a stabilization diagram.  We solve for a range of different polynomial orders and we plot the resulting frequencies on a figure.  We then identify poles that appear in many different model orders to be \"stable\" and we accept these as real poles.\n",
+    "\n",
+    "Rather than code all this from scratch, we will simply rely on SDynPy's `PolyPy` implementation to help us out.  `PolyPy` relies on the FRF being structured as a `TransferFunctionArray`, so we must first create one from our data.  Note that `PolyPy` works with frequencies in Hz rather than in radians per second, so we must convert our `omegas` variable by dividing by $2\\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "547b6ff7-d71a-4f9c-9950-26f4d3cf9066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sdynpy as sdpy\n",
+    "tfarray = sdpy.data_array(sdpy.data.FunctionTypes.FREQUENCY_RESPONSE_FUNCTION,\n",
+    "                          abscissa = omegas/(2*np.pi), ordinate = H, \n",
+    "                          coordinate = sdpy.coordinate.outer_product(\n",
+    "                              sdpy.coordinate_array([1,2,3],'X+'),\n",
+    "                              sdpy.coordinate_array([1,2],'X+')))\n",
+    "pp = sdpy.PolyPy(tfarray, displacement_derivative = 0) # We tell PolyPy these are displacement FRFs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c771ea7-f4a6-46ab-a8e9-1e71f32fd5e1",
+   "metadata": {},
+   "source": [
+    "We can then tell `PolyPy` which polynomial orders to solve for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "eefd5ef6-b2a6-4ba8-912b-d8428a3f7925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accumulating Data: Progress = 0.00%\n",
+      "Accumulating Data: Progress = 33.33%\n",
+      "Accumulating Data: Progress = 66.67%\n",
+      "Solving for 29 roots (1 of 13)\n",
+      "Solving for 27 roots (2 of 13)\n",
+      "Solving for 25 roots (3 of 13)\n",
+      "Solving for 23 roots (4 of 13)\n",
+      "Solving for 21 roots (5 of 13)\n",
+      "Solving for 19 roots (6 of 13)\n",
+      "Solving for 17 roots (7 of 13)\n",
+      "Solving for 15 roots (8 of 13)\n",
+      "Solving for 13 roots (9 of 13)\n",
+      "Solving for 11 roots (10 of 13)\n",
+      "Solving for 9 roots (11 of 13)\n",
+      "Solving for 7 roots (12 of 13)\n",
+      "Solving for 5 roots (13 of 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pp.compute_poles(np.arange(5,30,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d394d513-7cd6-4323-8ae9-fa540a477736",
+   "metadata": {},
+   "source": [
+    "We can then plot the stabilization diagram.  The stabilization diagram will have different markers to denote the stability of a given pole.  We generally overlay these poles with a mode indicator function to help us figure out which poles are \"real\".  In this case, a red X denotes an unstable pole, a blue triangle denotes that the frequency has stablized, a blue square means that the frequency and damping have stabilized, and a green circle means that the frequency, damping, and participation factor have stabilized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a5ff5306-cdbf-46db-95cc-b539e9adc423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Polynomial Order')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmif_axis, order_axis = pp.plot_stability()\n",
+    "cmif_axis.set_ylabel('CMIF')\n",
+    "cmif_axis.set_xlabel('Frequency (Hz)')\n",
+    "order_axis.set_ylabel('Polynomial Order')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e544c5-0ad6-4d0d-853e-5f91a09066ae",
+   "metadata": {},
+   "source": [
+    "We can see that as we move up to a higher model order, we start to see more computational poles appear, as shown by the red Xs throughout the frequency band.  However we see the three main columns of green circles denoting three stable poles in our fits.  We would then select these three stable poles.\n",
+    "\n",
+    "In `PolyPy` we can select them by index, and we can determine the indices by plotting the stabilization diagram with labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "56fd5c29-a2d3-4dd3-84f4-5d21ad3e38ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Polynomial Order')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmif_axis, order_axis = pp.plot_stability(label_poles=True)\n",
+    "cmif_axis.set_ylabel('CMIF')\n",
+    "cmif_axis.set_xlabel('Frequency (Hz)')\n",
+    "order_axis.set_ylabel('Polynomial Order')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96f1fb64-126c-4b63-9cd3-c2ea7bb2bf84",
+   "metadata": {},
+   "source": [
+    "We can then extract a final pole list from those indices.  For example we may wish to select poles `(4,0)`, `(4,1)`, and `(4,2)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "550a58ed-8f09-4a2c-a4d9-bb7d532ffe4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pole_indices = [(4,0), (4,1), (4,2)]\n",
+    "pole_list = pp.pole_list_from_indices(pole_indices)\n",
+    "omega_general_stab = pole_list['omega']\n",
+    "zeta_general_stab = pole_list['zeta']\n",
+    "lr_stab = pole_list['Lr_complex']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e01bd62-660d-4575-9f35-3d5295933814",
+   "metadata": {},
+   "source": [
+    "The stabilization diagram actually reveals that for our test case of `order=5`, our poles may not have yet stabilized, and therefore our results may be inaccurate!  Let's compare the same quantities extracted from the stabilization diagram with those computed from the system matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "43de5b7e-a171-48b8-a89a-94fcddfe309f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frequency From System Matrices</th>\n",
+       "      <th>Frequency From FRF Fits</th>\n",
+       "      <th>Frequency Error</th>\n",
+       "      <th>Damping From System Matrices</th>\n",
+       "      <th>Damping From FRF Fits</th>\n",
+       "      <th>Damping Error</th>\n",
+       "      <th>Participation From System Matrices</th>\n",
+       "      <th>Participation From FRF Fits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.25</td>\n",
+       "      <td>27.26</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>-0.02%</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.78</td>\n",
+       "      <td>54.79</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>59.91</td>\n",
+       "      <td>59.92</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>\n",
+       "      <td>[-0.06059006+0.05448316j  0.03373322-0.03429902j]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n",
+    "                             'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n",
+    "                             'Participation From System Matrices','Participation From FRF Fits'],\n",
+    "                  dtype=object)\n",
+    "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n",
+    "    omega_general,omega_general_stab,\n",
+    "    zeta_general, zeta_general_stab,\n",
+    "    psi_general[:2,:].T, lr_stab)):\n",
+    "    df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n",
+    "    df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n",
+    "    df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n",
+    "    df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n",
+    "    df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n",
+    "    df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n",
+    "    # Find the maximum participation degree of freedom from the\n",
+    "    # system matrices\n",
+    "    check_index = np.argmax(np.abs(lm))\n",
+    "    # Compute the angle of that index\n",
+    "    check_angle = np.angle(lm[check_index])\n",
+    "    # Compute the same angle from the fit\n",
+    "    this_angle = np.angle(lf[check_index])\n",
+    "    rotation = check_angle - this_angle\n",
+    "    # Compute the magnitudes of the vectors\n",
+    "    check_magnitude = np.linalg.norm(np.abs(lm))\n",
+    "    this_magnitude = np.linalg.norm(np.abs(lf))\n",
+    "    # Correct rotation and scaling\n",
+    "    lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n",
+    "    df.at[i,'Participation From System Matrices'] = str(lm)\n",
+    "    df.at[i,'Participation From FRF Fits'] = str(lf)\n",
+    "    \n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef16ad2f-08bb-458e-bf22-5143e52820b4",
+   "metadata": {},
+   "source": [
+    "We now see that we have extracted nearly identical modes to those that were computed directly from the system matrices, meaning the fit has significantly improved by using a higher-order polynomial.\n",
+    "\n",
+    "Note that this section has used the code-based `PolyPy` implementation.  However, in SDynPy there is also a `PolyPy_GUI` which can be used to interactively select poles."
+   ]
+  },
+  {
+   "attachments": {
+    "188f4ca3-1e6a-465f-8085-438e9f6aac2b.png": {
+     "image/png": 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"
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "31e870ab-8990-4de7-8137-d3c2324bb9ed",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "ppgui = sdpy.PolyPy_GUI(tfarray)\n",
+    "```\n",
+    "![image.png](attachment:188f4ca3-1e6a-465f-8085-438e9f6aac2b.png)\n",
+    "![image.png](attachment:628e4999-feea-4f9c-888c-9e86b6bd0f32.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "278d5451-ab7a-4f7e-8a54-c4f12ace5031",
+   "metadata": {},
+   "source": [
+    "## Fitting Mode Shapes to FRF Data\n",
+    "Once the poles and participation factors are known, we can go ahead and fit the mode shapes of the system.  Looking again at the frequency response function equation\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n",
+    "containing the frequency response function $\\mathbf{H}$, the participation factors $\\mathbf{l}_r$, the frequency lines $\\omega$, and the poles $\\lambda_r$.  We wish to solve for the mode shapes $\\mathbf{v}_r$ and the residuals $\\mathbf{R}_u$ and $\\mathbf{R}_l$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "913fad3e-aa05-4d5e-a13c-8c08d3f5cc48",
+   "metadata": {},
+   "source": [
+    "We want to pose this problem in an entirely real set of equations $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$.\n",
+    "\n",
+    "The mode shapes will generally be complex $\\mathbf{v}_r = \\mathbf{v}_r^R + j \\mathbf{v}_r^I$, so we will want to solve simultaneously for the real and imaginary parts.  Similarly, the frequency response function is complex, so we will need to split that up into its real and imaginary parts.  Ignoring the residuals for now:\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{H}^R\\\\\\mathbf{H}^I\\end{bmatrix} = \\mathbf{P}\\begin{bmatrix}\\mathbf{v}^R\\\\\\mathbf{v}^I\\end{bmatrix}$$\n",
+    "\n",
+    "The goal is to then find the coefficient matrix $\\mathbf{P}$ which allows us to solve for the mode shape coefficients.\n",
+    "\n",
+    "$$\\mathbf{P} = \\begin{bmatrix}\\mathbf{P}_{RR} & \\mathbf{P}_{RI}\\\\\\mathbf{P}_{IR} & \\mathbf{P}_{II}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25037b11-5fbc-4520-83d5-b2ae1d418a15",
+   "metadata": {},
+   "source": [
+    "We will start with the term\n",
+    "\n",
+    "$$\\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}$$\n",
+    "\n",
+    "We will substitute each term with its real and imaginary parts to ensure that all of the variables in the expression are real.\n",
+    "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right)}{j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right)}{- j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega}$$\n",
+    "\n",
+    "As we wish to collect real and imaginary parts, we will want to multiply each term's numerator and denominator by the denominator's complex conjugate.\n",
+    "\n",
+    "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(- j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}$$\n",
+    "\n",
+    "We would then like to collect real and imaginary terms, as well as collecting terms that have the real and imaginary part of the mode shapes.  $\\mathbf{P}_{RR}$ will contain real coefficients of the real part of the mode shapes.  $\\mathbf{P}_{RI}$ will contain real coefficients of the imaginary part of the mode shapes.  $\\mathbf{P}_{IR}$ will contain imaginary coefficients of the real part of the mode shapes.  Finally, $\\mathbf{P}_{II}$ will contain imaginary coefficients of the imaginary part of the mode shapes.\n",
+    "\n",
+    "$$j v^{I}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + v^{I}_{ir} \\left(\\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n",
+    "$$+ v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + j v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n",
+    "\n",
+    "We can substitute this into a matrix form where the real equations are on the top row and the imaginary equations are on the bottom row.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\j H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\j \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) & j \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n",
+    "\n",
+    "Cancelling out the $j$ terms in the bottom row reveals the final matrix equation for the contribution to the frequency response function from one mode shape $H_r$.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n",
+    "\n",
+    "We can then solve this equation in a least squares sense by using the pseudoinverse.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right]^+ \\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "600deb13-308c-4883-b3d3-66147f5f863e",
+   "metadata": {},
+   "source": [
+    "Note that this equation must be generalized to solve for all modal coefficients simultaneously in terms of all frequency response functions for all degrees of freedom at all frequency lines.  For this we will give some thought as to how to structure the matrices.\n",
+    "\n",
+    "At the end of the computation, the matrix $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ should have real and imaginary parts for each degree of freedom for each mode being solved for.  We will solve for these terms over all frequency lines simultaneously.  However, because there is no frequency line dimension in the mode shape matrix, this reveals that the frequency line must be on the inner dimension of the matrix product.  The frequency lines must correspond to the rows of $\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$ and $\\mathbf{P}$ (which will be the columns of $\\mathbf{P}^+$).  We also see that the columns of the FRF matrix corresponding to the references at each frequency line do not appear in the final mode shape matrix (they are related to the participation factors, which are known).  Therefore this dimension must also be encoded in the inner dimensions of the matrix product.\n",
+    "\n",
+    "We can therefore start to envision what the structure of each term in $\\mathbf{P}$ should look like.  The columns of $\\mathbf{P}$ (the rows of $\\mathbf{P}^+$) must correspond to the mode shape matrix, and they should correspond to the mode index rather than the response degree of freedom index because we need matrix multiplication of $\\mathbf{P}\\mathbf{v}$ to sum over modes, and summation occurs on the inner dimensions of the matrices in matrix multiplication.  The rows of $\\mathbf{P}$ (the columns of $\\mathbf{P}^+$) must correspond to both the mode participation factor (reference degrees of freedom) and frequency lines, separated into real and imaginary parts.  If we compute each partition of $\\mathbf{P}$ as a `num_input` $\\times$ `num_freq` $\\times$ `num_modes` array, and then reshape it into a `num_input*num_freq` $\\times$ `num_modes` array, this should produce the correct size matrix.\n",
+    "\n",
+    "Each partition of the mode shape matrix $v^{R}_{ir}$ and $v^{I}_{ir}$ will then be revealed as a matrix with number of rows equal to the number of modes and columns equal to the number of response degrees of freedom.\n",
+    "\n",
+    "We can similarly construct the dimensions of the partitions $H_r^{R}$ and $H_r^{I}$, as they must now be consistent with the rest of the terms in the equation.  The number of rows of these partitions must match the number of rows of partitions in $\\mathbf{P}$, meaning that the FRF must be similarly structured with mode participation factor (reference degrees of freedom) and frequency lines along its rows and response degrees of freedom along its columns.\n",
+    "\n",
+    "This generalization into matrix form can be somewhat abstract to visualize, but should become more obvious when applied to real data arrays."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c129812-1273-49d1-b18f-5d2851a401f0",
+   "metadata": {},
+   "source": [
+    "### Fitting Residuals for Out-of-Band Modes\n",
+    "We note that the terms $v_{ir}$ are not the only unknowns in the frequency response function formulation.  In general, we can never fit all of the modes of a structure, so there will always be some contributions from out-of-band modes in our frequency response functions.  As we try to solve for the mode shapes of the structure, these out-of-band modes can contaminate the response.  Therefore we also include terms to try to match the contributions from these out-of-band modes, and we call these terms residuals.\n",
+    "\n",
+    "We will in general have a lower residual $R_{Lij}$ and an upper residual $R_{Uij}$, and as the expression suggests, there can be one term in the residual for each pair of input and output degrees of freedom.  We must therefore solve for these terms simultaneously with the mode shapes.  We can think of these terms as \"extra modes\" of the structure, which therefore should imply that the $\\mathbf{P}$ matrix should have columns added corresponding to the coefficients of the residual variables, and the $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ matrix should have rows appended corresponding to the real and imaginary parts of these terms.\n",
+    "\n",
+    "Indeed, the coefficients are easily picked directly from the frequency response function.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]\n",
+    "= \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & -\\frac{1}{\\omega^2} & 0 & 1 & 0 \\\\\n",
+    "\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & 0 & -\\frac{1}{\\omega^2} & 0  & 1\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\\\R^R_{Lij}\\\\R^I_{Lij}\\\\R^R_{Uij}\\\\R^I_{Uij}\\end{matrix}\\right]$$\n",
+    "\n",
+    "When generalizing to multiple references, responses, and frequency lines, each residual term $R^R_{Lij}$, $R^I_{Lij}$, $R^R_{Uij}$ and $R^I_{Uij}$ will have a number of rows equal to the number of references and a number of columns equal to the number of responses.  Similarly the coefficient terms ($0$, $1$, $-\\frac{1}{\\omega}$) will have a number of rows equal to the number of `num_freq*num_input` and number of columns equal to `num_input`.  Because of the structure of the matrices, they are easily constructed using Kronecker products of identity matrices multiplied by either $1$ or $-\\frac{1}{\\omega^2}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533195b0-3bd2-4e9a-8314-dcf73de1d52d",
+   "metadata": {},
+   "source": [
+    "### A Note on Types of FRFs\n",
+    "Note that the mode shape derivations described in the previous sections are valid for *receptance* FRFs, meaning FRFs between displacement and force.  When transforming to from *receptance* to *mobility* (velocity over force) to *accelerance* (acceleration over force) FRFs, we multiply the FRF expression by $j\\omega$ at each step.  This will clearly change which terms are real and which terms are imaginary, as well as the coefficients on each term.  As this document is already quite long, these will not be derived, but the same approach as described above can be used to find the coefficient matrices for those cases; simply substitute for real and imaginary parts of each variable, collect imaginary and real coefficients for imaginary and real parts of each term of interest, and place them into a matrix form."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22c07fae-f815-4be9-8a10-04fec78dd6c2",
+   "metadata": {},
+   "source": [
+    "### Normalizing Mode Shapes\n",
+    "Once we solve for the mode shapes, our job is not quite done.  Due to how the problem is formulated, the particpation factors have some arbitrary scaling and rotation applied to them.  This means when the shapes are found, they will also have an arbitrary scaling and rotation applied to them.  There is no constraint that the participation factors are identical to the mode shapes at the input locations.\n",
+    "\n",
+    "To perform the normalization, we compute the residue matrices for each mode\n",
+    "\n",
+    "$$R_{rij} = \\psi_{ir}l_{jr}$$\n",
+    "\n",
+    "We recognize the at the drive point, the residue is equal to the mode shape squared or the participation factor squared if the mode shapes are scaled such that the participation factors are the mode shapes.\n",
+    "$$R_{rii} = \\psi_{ir}l_{ir} = \\psi_{ir}^2 = l_{ir}^2$$\n",
+    "\n",
+    "However, we currently do not have such a scenario.  In our case, the fit shapes $\\bar{\\psi}_{ir}$ are scaled by some complex coefficient $c$, and the participation factors $\\bar{l}_{jr}$ are scaled by some factor ${\\frac{1}{c}}$.\n",
+    "\n",
+    "$$\\psi_{ir} = c\\bar{\\psi}_{ir}$$\n",
+    "$$l_{jr} = \\frac{1}{c}\\bar{l}_{jr}$$\n",
+    "\n",
+    "We note that due to the reciprocal scale factors, we can still compute the residuals.  We can therefore set up a set of equations to solve for the scale factor that gets applied to the mode shapes.\n",
+    "\n",
+    "$$R_{rii} = \\psi_{ir}l_{ir} = c\\bar{\\psi}_{ir}\\frac{1}{c}\\bar{l}_{ir} = \\bar{\\psi}_{ir}\\bar{l}_{ir}$$\n",
+    "$$R_{rii} = \\psi_{ir}^2 = c^2\\bar{\\psi}_{ir}^2$$\n",
+    "$$\\sqrt{R_{rii}} = c \\bar{\\psi}_{ir}$$\n",
+    "\n",
+    "Since we have participation factors and shapes at multiple degrees of freedom for a multiple-input solution, we can solve for the scale factor $c$ in a least-squares sense for each mode.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = \\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}c$$\n",
+    "\n",
+    "$$\\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}^+\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = c$$\n",
+    "\n",
+    "**BE AWARE:** when taking the square root of the residue matrix terms, there are two values that can be obtained (e.g. $\\sqrt{4} = \\pm2$).  In the case of real numbers, most square root algorithms will only return the positive root.  However for complex numbers, it is not so obvious which sign the root will have (positive real part?  positive imaginary part?).  Flipping a sign on one term in the matrix $\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix}$ will make the least squares solution incorrect, therefore we must make sure the signs are consistent between the mode shapes $\\bar{\\psi}_{ir}$ and the residue terms $\\sqrt{R_{rii}}$.\n",
+    "\n",
+    "We can do this by dividing the termes $\\sqrt{R_{rii}}$ by $\\bar{\\psi}_{ir}$ and looking at the direction of the complex vectors.  If the vectors are all pointing nominally the same direction, then the least squares problem will solve for that direction successfully.  If some are pointing opposite due to the sign of the output of the square root, then the scale factor found by the least squares solution will be incorrect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13f2483d-e733-4d8d-94e5-b4cfbfcfde11",
+   "metadata": {},
+   "source": [
+    "### Back to the Example Problem\n",
+    "While the symbolic mathematics in the previous sections was straightforward (though perhaps a bit tedious), the discussion of the construction of a system of equations to solve for each coefficient of the mode shape matrix was perhaps a bit abstract.  We will therefore make it more concrete by applying it to our specific example problem.  Note that we will take advantage heavily of broadcasting and dimensional manipulation to help us put these equations together.  Readers are encouraged to run these problems themselves to understand exactly what operations are being performed at each step, and the resulting shapes of all of the arrays.\n",
+    "\n",
+    "We will first generate the $\\mathbf{P}$ matrix described above.  We will start with only the mode shape coefficients, then show how it can be expanded to include residuals.  This will involve translating our symbolic code into numeric Numpy code.  To help us repeat this process for different sets of modes and frequency ranges when we introduce the residuals later, we will define the process to generate the $\\mathbf{P}$ matrix as a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9cc21035-44bd-4128-ac92-0709ab740be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def P_modal(omegas, poles, participation_factors):\n",
+    "    '''Construct the mode shape coefficient matrix\n",
+    "\n",
+    "    Constructs the coefficients from the system poles,\n",
+    "    participation factors, and frequency lines.\n",
+    "\n",
+    "    Arguments should be passed as arrays and will be broadcast\n",
+    "    together.\n",
+    "    '''\n",
+    "    # We want the output array to be n_input x n_freq*2 x n_modes*2\n",
+    "    # So let's adjust the terms so they have the right shapes\n",
+    "    # We want frequency lines to be the middle dimension\n",
+    "    omegas = omegas[np.newaxis,:,np.newaxis]\n",
+    "    # We want inputs to be the first dimension and modes the\n",
+    "    # last dimension\n",
+    "    participation_factors = participation_factors.T[:,np.newaxis,:]\n",
+    "    # Split up terms into real and imaginary parts\n",
+    "    pr = poles.real\n",
+    "    pi = poles.imag\n",
+    "    lr = participation_factors.real\n",
+    "    li = participation_factors.imag\n",
+    "    P_blocks = np.array([\n",
+    "        [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n",
+    "         (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n",
+    "        [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n",
+    "         (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n",
+    "    return np.block([[P_blocks[0,0],P_blocks[0,1]],\n",
+    "                     [P_blocks[1,0],P_blocks[1,1]]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cef05146-3d81-4003-86fe-1500592485de",
+   "metadata": {},
+   "source": [
+    "We can now create our pole vector from the extracted frequencies and damping ratios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "0693d32a-2136-4c72-8f6e-fb67515ce6f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  omegas: (778,)\n",
+      "  poles: (3,)\n",
+      "  participation_factors: (3, 2)\n",
+      "  P: (2, 1556, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute poles from the extracted data\n",
+    "poles = -omega_general_stab*zeta_general_stab + 1j*omega_general_stab*np.sqrt(1-zeta_general_stab**2)\n",
+    "# Compute P with our function\n",
+    "P = P_modal(omegas,poles, lr_stab)\n",
+    "\n",
+    "# Check shapes\n",
+    "print('Shapes:\\n  omegas: {:}\\n  poles: {:}\\n  participation_factors: {:}\\n  P: {:}'.format(\n",
+    "    omegas.shape, poles.shape, lr_stab.shape, P.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68336074-9c72-4186-9c32-76c4597712f8",
+   "metadata": {},
+   "source": [
+    "We must now set up the frequency response functions to perform the least squares computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "b9a0bf64-820f-4786-b4e6-f78f7f209dad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  H_LS: (2, 1556, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "H_LS = H.transpose(1,2,0)\n",
+    "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n",
+    "\n",
+    "print('Shapes:\\n  H_LS: {:}'.format(\n",
+    "    H_LS.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0af6fc0-5e43-4fe6-838d-d30266bfb2f5",
+   "metadata": {},
+   "source": [
+    "To construct the least-squares set of equations, we simply squash the first two dimensions into a single dimension, in which case the least-squares solution will solve over all frequency lines and all inputs simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0ef24c44-cb79-4317-9aae-5a3b99ec51ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n",
+    "                            H_LS.reshape(-1,H_LS.shape[-1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818bdad7-6a69-422e-9d1e-f5d2480acd83",
+   "metadata": {},
+   "source": [
+    "We can reconstruct the FRF by muliplying the output shapes by the coefficient matrix and extracting real and imaginary parts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "570bd80c-83d0-4f5e-a6ca-289637763d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reconstructed_frfs = P.reshape(-1,P.shape[-1])@shapes_LS\n",
+    "H_recon = reconstructed_frfs.reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_recon = H_recon[:H_recon.shape[0]//2] + 1j*H_recon[H_recon.shape[0]//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a6e4c568-2976-4d5b-a4cb-76547020ee30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general_stab.size\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(num_output*2,num_input, sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n",
+    "                      H_recon.reshape(omegas.size,-1).T,\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['Truth','Reconstructed from\\nLeast-Squares Solution'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "669682c7-558a-4ab6-995c-c24c8dccdea1",
+   "metadata": {},
+   "source": [
+    "We have now solved in a least-squares sense for our modal coefficients.  We should extract the shape arrays from the matrix.  The top partition will be the real part of the shape, and the bottom part will be the imaginary part of the shape.  Remember also that the terms are transposed from the typical mode shape matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "aa810667-ba40-42ca-8b2b-fab6ecfcbf3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_stab = (shapes_LS[:shapes_LS.shape[0]//2]+1j*shapes_LS[shapes_LS.shape[0]//2:]).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39a5a890-5f7e-478a-b0b2-985454864c4a",
+   "metadata": {},
+   "source": [
+    "Note that because the participation factors were arbitrarily rotated, these shapes will also be arbitrarily rotated.  Therefore we need to normalize the mode shapes.  We will do this by computing the residue matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "09077e1b-1ba5-4527-8898-cf6539d8c7b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residues = np.einsum('ik,jk->kij',psi_stab,lr_stab.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a8143f5-66d3-41b2-a024-931218b1a1ba",
+   "metadata": {},
+   "source": [
+    "We can get the drive point response indices corresponding to the participation factors and pull out the drive point residues.  In general, the drive point residues should have a negative imaginary part, so we can use this to discard 'bad' drive point data where the drive point does not excite the mode well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d6a3f830-7704-46ef-a7a0-a6388757dfd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drive_indices_response = np.array([0,1])\n",
+    "drive_indices_input = np.array([0,1])\n",
+    "drive_residues = residues[:,drive_indices_response,drive_indices_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56cd126e-1a06-4234-99ae-8929d52116a7",
+   "metadata": {},
+   "source": [
+    "We will now go through each mode and compute the correct shape scaling using only the good drive points where the imaginary part is negative and not close to zero.  First, let's go through and make sure that our participation factors and mode shapes that we solved for are actually scaled versions of the true mode shapes.  I'm going to mass normalize my mode shapes for better comparisions to the fit data without having to carry around the modal mass term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "883ccaa0-1f86-41ae-947e-2d0796702b0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mass normalize the mode shapes\n",
+    "psi_general /= np.sqrt(ma_general)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aed6d5e9-2bb7-43a1-957b-b76d1bda87d6",
+   "metadata": {},
+   "source": [
+    "Let's find the scale factor between the participation factors and the mode shapes at the participation factors.  We will average over all of the degrees of freedom for each mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "bcf8a804-ecc4-45d6-8f55-e1e0874bfde2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_scaling = np.mean(psi_general[:2,:]/lr_stab.T,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a6b122a4-87ab-476a-8d7a-510fb951cd0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.18452662-0.18416078j,  0.08928666-0.0956242j ,\n",
+       "       -0.093857  +0.08439714j])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83c5bce4-a5cb-4e7d-9214-83b2800f6bf2",
+   "metadata": {},
+   "source": [
+    "We can then check to see if we reconstruct the mode shape by applying the scaling to the participation factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "aa40a7de-d7e4-47e3-985a-f4f24fe5e3ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267784-0.03499731j,\n",
+       "        -0.03029502+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.01686661-0.01714951j]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_stab.T*lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e887ee89-7ad0-4fae-bee5-be261d25caa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,\n",
+       "        -0.03029503+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.0168666 -0.01714951j]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general[:2,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2245282f-b4cc-4448-8e05-72d35a076f38",
+   "metadata": {},
+   "source": [
+    "Now we will do the same thing for the mode shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5458c528-b3dd-449f-87b7-01181b91c076",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_scaling = np.mean(psi_general/psi_stab,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "349fc0a1-850a-41f4-aab0-8c1198a09e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,\n",
+       "       -5.88158134-5.30751768j])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "805218db-fbff-419b-a61f-248fad8c7c63",
+   "metadata": {},
+   "source": [
+    "Let's reconstruct the mode shapes by scaling the fit mode shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "ec397820-33e5-4bd5-b968-8d6ea7d63cc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02159971+0.02202864j,  0.03316166-0.03473379j,\n",
+       "        -0.03033178+0.02722321j],\n",
+       "       [-0.04928856+0.04919178j,  0.00094139+0.00090236j,\n",
+       "         0.0168663 -0.01716533j],\n",
+       "       [-0.02186329+0.02176496j, -0.03539605+0.03236945j,\n",
+       "        -0.02735936+0.03018341j]])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_stab*psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3e49f237-ee6c-48e2-8854-e52abd728337",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,\n",
+       "        -0.03029503+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.0168666 -0.01714951j],\n",
+       "       [-0.02186239+0.02176446j, -0.03496025+0.03263839j,\n",
+       "        -0.02739619+0.03019566j]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0854c79-1cc4-492b-bf1c-c43526067a1a",
+   "metadata": {},
+   "source": [
+    "Since the particpation factors times the mode shapes should be equal to the scaled mode shapes squared, we should be able to see that the scale factors are reciprocals of one another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7b80d0c2-c92a-479c-97a9-823c368e0cad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,\n",
+       "       -5.88158134-5.30751768j])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "87376ad2-6f5c-4c78-8b9b-f92a5191f2a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.71501354+2.70963081j,  5.2165319 +5.58679996j,\n",
+       "       -5.89109483-5.29733029j])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050511f9-7ba5-4c09-a5e9-1f92d713f291",
+   "metadata": {},
+   "source": [
+    "This was a sanity check to make sure the mode shapes and participation factors were identified consistently.  Unfortunately in a real situation we don't have the true mode shapes, so we need to compute them from the residue matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "5676966d-8021-4f4b-8da1-9fdff6a48330",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scale for mode 1: (-2.709418738512416-2.7149722671668157j)\n",
+      "Scale for mode 2: (5.211812790651124+5.590252990269536j)\n",
+      "Scale for mode 3: (5.886705065831037+5.299694143958719j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "psi_stab_normalized = []\n",
+    "for k,drive_residue in enumerate(drive_residues):\n",
+    "    # Throw away non-negative imaginary parts\n",
+    "    bad_indices_positive = drive_residue.imag > 0\n",
+    "    # Throw away small values compared to the average value (only considering negative imaginary parts)\n",
+    "    bad_indices_small = np.abs(drive_residue) < 0.01*np.mean(np.abs(drive_residue[~bad_indices_positive]))\n",
+    "    # Combine into a single criteria\n",
+    "    bad_indices = bad_indices_positive | bad_indices_small\n",
+    "    # Get the good indices that are remaining\n",
+    "    remaining_indices = np.where(~bad_indices)[0]\n",
+    "    # We will then construct the least squares solution\n",
+    "    shape_coefficients = psi_stab[drive_indices_response[remaining_indices],k][:,np.newaxis]\n",
+    "    residue_coefficients = np.sqrt(drive_residue[remaining_indices])[:,np.newaxis]\n",
+    "    # Before we compute the scale, we need to make sure that we have all of the signs the same way.\n",
+    "    # This is because the square root can give you +/- root where root**2 = complex number\n",
+    "    # This will mess up the least squares at it will try to find something between the\n",
+    "    # two vectors.\n",
+    "    scale_vector = (residue_coefficients/shape_coefficients).flatten()\n",
+    "    sign_vector = np.array((scale_vector.real,scale_vector.imag))\n",
+    "    # Get the signs\n",
+    "    signs = np.sign(np.dot(sign_vector[:,0],sign_vector))\n",
+    "    residue_coefficients = residue_coefficients*signs[:,np.newaxis]\n",
+    "    # Now compute the least-squares solution\n",
+    "    scale = np.linalg.lstsq(shape_coefficients,residue_coefficients)[0].squeeze()\n",
+    "    print('Scale for mode {:}: {:}'.format(k+1,scale))\n",
+    "    psi_stab_normalized.append(psi_stab[:,k]*scale)\n",
+    "psi_stab_normalized = np.array(psi_stab_normalized).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab437cbd-420d-4421-8cff-040a1f1bf4cb",
+   "metadata": {},
+   "source": [
+    "Now that we have our mode shapes, we should resynthesize frequency response functions from them to make sure they fit our original data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "576a0ff3-1f51-4d50-85a6-a9b9985a4a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general_stab.size\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "H_general_fromUnNormModes = np.sum([\n",
+    "    psi_stab[:,r,np.newaxis]@lr_stab.T[:,r,np.newaxis].T/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + psi_stab[:,r,np.newaxis].conj()@lr_stab.T[:,r,np.newaxis].T.conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "H_general_fromFitModes = np.sum([\n",
+    "    psi_stab_normalized[:,r,np.newaxis]@psi_stab_normalized[drive_indices_input,r,np.newaxis].T/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + psi_stab_normalized[:,r,np.newaxis].conj()@psi_stab_normalized[drive_indices_input,r,np.newaxis].T.conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "H_general_fromFitResidues = np.sum([\n",
+    "    residues[r]/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + residues[r].conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_general_fromFitResidues.shape[1]*2,H_general_fromFitResidues.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_t,f_m,f_r,f_u in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromFitModes[:,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromFitResidues.reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromUnNormModes.reshape(omegas.size,-1).T\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_u)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas,np.angle(f_r)*180/np.pi,linewidth=1)\n",
+    "    ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_u),linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_r),linewidth=1)\n",
+    "ax[1].legend(['Truth','Unnormalized','Normalized','Residues'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c801ba95-827a-4b4d-9bdd-d1205b84c327",
+   "metadata": {},
+   "source": [
+    "The last demonstration will be to understand how residuals can be implemented.  To do this, we will truncate the frequency domain to remove the first mode.  We will then use residuals to try to fit the effect of this mode while only truly fitting the second two modes.  We will assume we have correctly identified the poles and participation factors from the first analysis, and only consider solving the shape problem here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036eaad3-1f6a-4cb8-a107-5ddc8a079dea",
+   "metadata": {},
+   "source": [
+    "We will update our function to solve for the $\\mathbf{P}$ matrix to also solve for residual terms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "fd95b767-a897-4fa4-b46f-23fb79831fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def P_modal_w_residuals(omegas, poles, participation_factors, lower_residuals = False, upper_residuals = False):\n",
+    "    '''Construct the mode shape coefficient matrix\n",
+    "\n",
+    "    Constructs the coefficients from the system poles,\n",
+    "    participation factors, and frequency lines.\n",
+    "\n",
+    "    Arguments should be passed as arrays and will be broadcast\n",
+    "    together.\n",
+    "    '''\n",
+    "    # Get the number of modes and number of inputs from the shape\n",
+    "    # of the participation factors.\n",
+    "    n_modes,n_inputs = participation_factors.shape\n",
+    "    n_freq, = omegas.shape\n",
+    "    # We want the output array to be n_input x n_freq*2 x n_modes*2\n",
+    "    # So let's adjust the terms so they have the right shapes\n",
+    "    # We want frequency lines to be the middle dimension\n",
+    "    omegas = omegas[np.newaxis,:,np.newaxis]\n",
+    "    # We want inputs to be the first dimension and modes the\n",
+    "    # last dimension\n",
+    "    participation_factors = participation_factors.T[:,np.newaxis,:]\n",
+    "    # Split up terms into real and imaginary parts\n",
+    "    pr = poles.real\n",
+    "    pi = poles.imag\n",
+    "    lr = participation_factors.real\n",
+    "    li = participation_factors.imag\n",
+    "    P_blocks = np.array([\n",
+    "        [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n",
+    "         (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n",
+    "        [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n",
+    "         (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n",
+    "    if lower_residuals:\n",
+    "        RL_block = np.concatenate([\n",
+    "            np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n",
+    "            np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n",
+    "    if upper_residuals: \n",
+    "        RU_block = np.concatenate([\n",
+    "            np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n",
+    "            np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n",
+    "    P = np.block([[P_blocks[0,0],P_blocks[0,1]],\n",
+    "                  [P_blocks[1,0],P_blocks[1,1]]])\n",
+    "    if lower_residuals:\n",
+    "        P = np.concatenate((P,RL_block),axis=-1)\n",
+    "    if upper_residuals:\n",
+    "        P = np.concatenate((P,RU_block),axis=-1)\n",
+    "    return P"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43af6e4a-d977-4586-af15-07d6fe168447",
+   "metadata": {},
+   "source": [
+    "We can then call the function with a subset of the modes that we aim to solve for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "917fedde-e85a-4b05-8c3e-f253ab9dc214",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  P: (2, 778, 12)\n",
+      "  H_LS: (2, 778, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_freq = 45\n",
+    "mode_indices = slice(1,None)\n",
+    "P = P_modal_w_residuals(omegas[omegas > min_freq],\n",
+    "                        poles[mode_indices],\n",
+    "                        lr_stab[mode_indices],\n",
+    "                        lower_residuals = True, upper_residuals = True)\n",
+    "\n",
+    "H_LS = H[...,omegas>min_freq].transpose(1,2,0)\n",
+    "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n",
+    "\n",
+    "print('Shapes:\\n  P: {:}\\n  H_LS: {:}'.format(P.shape,H_LS.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6151d42f-0920-4794-adb0-e66fd8d34f8d",
+   "metadata": {},
+   "source": [
+    "We can then solve the least squares problem over all frequency lines and inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "c6ee7e46-ed84-434a-aa91-5b438cc0e8d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n",
+    "                            H_LS.reshape(-1,H_LS.shape[-1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08ee6d5a-4f87-464b-a35b-53c94ab7282f",
+   "metadata": {},
+   "source": [
+    "Now at this point, we can reconstruct the portions of the FRFs from the modes and those from the residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "6aaeb549-5e36-4667-87e2-8f32afbfe4ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P_full = P.reshape(-1,P.shape[-1])\n",
+    "P_modes = P_full[...,:2*poles[mode_indices].size]\n",
+    "P_residuals = P_full[...,2*poles[mode_indices].size:]\n",
+    "\n",
+    "shapes_modes = shapes_LS[:2*poles[mode_indices].size]\n",
+    "shapes_residuals = shapes_LS[2*poles[mode_indices].size:]\n",
+    "\n",
+    "H_modes = (P_modes@shapes_modes).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_modes = H_modes[:H_modes.shape[0]//2] + 1j*H_modes[H_modes.shape[0]//2:]\n",
+    "\n",
+    "H_residuals = (P_residuals@shapes_residuals).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_residuals = H_residuals[:H_residuals.shape[0]//2] + 1j*H_residuals[H_residuals.shape[0]//2:]\n",
+    "\n",
+    "H_full = (P_full@shapes_LS).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_full = H_full[:H_full.shape[0]//2] + 1j*H_full[H_full.shape[0]//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bba9570d-2dcf-43bf-9c9f-b0ddb08213b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_full.shape[1]*2,H_full.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_t in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n",
+    "for ax,f_r,f_m,f_f in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_residuals.reshape(omegas[omegas>min_freq].size,-1).T,\n",
+    "                      H_modes.reshape(omegas[omegas>min_freq].size,-1).T,\n",
+    "                      H_full.reshape(omegas[omegas>min_freq].size,-1).T\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_f)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_r)*180/np.pi,linewidth=1)\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_m)*180/np.pi,linewidth=1)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_f),linewidth=2)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_r),linewidth=1)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_m),linewidth=1)\n",
+    "ax[1].legend(['Truth','Fit','Residual Contribution','Mode Contribution'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6f0d0b-7662-4165-b20b-9e2d1a111635",
+   "metadata": {},
+   "source": [
+    "We see that the fit matches the measured data very due to the inclusion of the residual terms.  If we were to just include the modal contributions, it would not fit as well, but by adding in residual contributions we can get a much better match."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "739c7f46-ba2e-4b8a-be4a-01b7fa034e55",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "In this document, we have extensively studied complex modes.  We first explored the real-modes case to understand the limitations of that approach.  We then derived the state space system of equations from the typical 2nd order differential equation to construct the eigenvalue equation to solve for the complex modes.\n",
+    "\n",
+    "We then related the modal parameters to the frequency response function, which allowed us to compute a frequency response function given the modal parameters.\n",
+    "\n",
+    "We finally spent a good deal of time discussing how to fit modal parameters to frequency response functions using the PolyMax approach, which is implemented in SDynPy as PolyPy."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png
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diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png
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diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png
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diff --git a/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png b/.doctrees/nbsphinx/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png
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index 529409c..3550104 100644
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diff --git a/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html b/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html
index 70a2372..68c6367 100644
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+</tr>
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+<dl class="field-list simple">
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+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="#sdynpy.core.sdynpy_coordinate.CoordinateArray" title="sdynpy.core.sdynpy_coordinate.CoordinateArray">CoordinateArray</a></p>
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index 1345e47..65b6e16 100644
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+  <title>sdynpy.core.sdynpy_coordinate.parse_coordinate_string &mdash; SDynPy 0.14.0 documentation</title>
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html b/_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html
index 60e9a56..c2c334e 100644
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+  <title>sdynpy.core.sdynpy_data.CPSDPlot &mdash; SDynPy 0.14.0 documentation</title>
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html b/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html
index 0156f7f..d123623 100644
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-  <title>sdynpy.core.sdynpy_data.CoherenceArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.CoherenceArray &mdash; SDynPy 0.14.0 documentation</title>
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@@ -79,6 +79,7 @@
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+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.CoherenceArray.from_time_data"><code class="docutils literal notranslate"><span class="pre">CoherenceArray.from_time_data()</span></code></a></li>
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 </ul>
 </li>
@@ -172,6 +173,9 @@ <h1>sdynpy.core.sdynpy_data.CoherenceArray<a class="headerlink" href="#sdynpy-co
 <col style="width: 90%" />
 </colgroup>
 <tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.CoherenceArray.from_time_data" title="sdynpy.core.sdynpy_data.CoherenceArray.from_time_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_time_data</span></code></a>(response_data[, ...])</p></td>
+<td><p>Computes coherence from reference and response time histories</p></td>
+</tr>
 </tbody>
 </table>
 <p class="rubric">Attributes</p>
@@ -186,6 +190,40 @@ <h1>sdynpy.core.sdynpy_data.CoherenceArray<a class="headerlink" href="#sdynpy-co
 </tr>
 </tbody>
 </table>
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.CoherenceArray.from_time_data">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#CoherenceArray.from_time_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.CoherenceArray.from_time_data" title="Permalink to this definition"></a></dt>
+<dd><p>Computes coherence from reference and response time histories</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>response_data</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><em>TimeHistoryArray</em></a>) – Time data to be used as responses</p></li>
+<li><p><strong>samples_per_average</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of samples used to split up the signals into averages.  The
+default is None, meaning the data is treated as a single measurement
+frame.</p></li>
+<li><p><strong>overlap</strong> (<em>float</em><em>, </em><em>optional</em>) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).
+The default is 0.0, meaning no overlap is used.</p></li>
+<li><p><strong>window</strong> (<em>np.ndarray</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – A 1D ndarray with length samples_per_average that specifies the
+coefficients of the window.  A Hann window is applied if not specified.
+If a string is specified, then the window will be obtained from scipy.</p></li>
+<li><p><strong>reference_data</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><em>TimeHistoryArray</em></a>) – Time data to be used as reference.  If not specified, the response
+data will be used as references, resulting in a square coherence matrix.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if reference and response functions do not have consistent
+    abscissa</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A PSD array computed from the specified reference and
+response signals.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray">PowerSpectralDensityArray</a></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.CoherenceArray.function_type">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">function_type</span></span><a class="headerlink" href="#sdynpy.core.sdynpy_data.CoherenceArray.function_type" title="Permalink to this definition"></a></dt>
@@ -207,7 +245,7 @@ <h1>sdynpy.core.sdynpy_data.CoherenceArray<a class="headerlink" href="#sdynpy-co
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html b/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html
index 0c79ee1..3babaf9 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.ComplexType.html
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-  <title>sdynpy.core.sdynpy_data.ComplexType &mdash; SDynPy 0.11.0 documentation</title>
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@@ -242,7 +242,7 @@ <h1>sdynpy.core.sdynpy_data.ComplexType<a class="headerlink" href="#sdynpy-core-
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html b/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html
index fc03168..ee93c47 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.CorrelationArray.html
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-  <title>sdynpy.core.sdynpy_data.CorrelationArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.CorrelationArray &mdash; SDynPy 0.14.0 documentation</title>
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@@ -207,7 +207,7 @@ <h1>sdynpy.core.sdynpy_data.CorrelationArray<a class="headerlink" href="#sdynpy-
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html b/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html
index 296439d..c84c844 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.DecayedSineTable &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.DecayedSineTable &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -224,7 +224,7 @@ <h1>sdynpy.core.sdynpy_data.DecayedSineTable<a class="headerlink" href="#sdynpy-
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed088430>
+<jinja2.runtime.BlockReference object at 0x7f5a291229e0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html b/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html
index 911db2c..c70fe53 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.FunctionTypes &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.FunctionTypes &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -458,7 +458,7 @@ <h1>sdynpy.core.sdynpy_data.FunctionTypes<a class="headerlink" href="#sdynpy-cor
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed122a70>
+<jinja2.runtime.BlockReference object at 0x7f5a29079ae0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html b/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html
index f1e86c3..c155559 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.GUIPlot &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.GUIPlot &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -181,9 +181,6 @@ <h1>sdynpy.core.sdynpy_data.GUIPlot<a class="headerlink" href="#sdynpy-core-sdyn
 arguments.  Position arguments will be labelled generically in the
 legend.  Keyword arguments will be labelled with their keywords with
 <cite>_</cite> replaced by ` <a href="#id1"><span class="problematic" id="id2">`</span></a>.</p>
-<p>Note that all datasets will be reshaped so that the coordinates match
-the first dataset.  If not all coordinates exist in subsequent datasets
-an error will be thrown.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -191,6 +188,32 @@ <h1>sdynpy.core.sdynpy_data.GUIPlot<a class="headerlink" href="#sdynpy-core-sdyn
 labeled generically in the legend</p></li>
 <li><p><strong>**labeled_data_to_plot</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray" title="sdynpy.core.sdynpy_data.NDDataArray"><em>NDDataArray</em></a>) – Data to visualize.  Data passed by keyword argument will be
 labeled with its keyword</p></li>
+<li><p><strong>abscissa_markers</strong> (<em>np.ndarray</em>) – Abscissa values at which markers will be placed.  If not specified,
+no markers will be added.  Markers will be added to all plotted
+curves if this argument is passed.  To add markers to just a
+specific plotted data, pass the argument <cite>abscissa_markers_*</cite> where
+<cite>*</cite> is replaced with either the index of the data that was passed
+via a positional argument, or the keyword of the data that was
+passed via a keyword argument.  Must be passed as a keyword argument.</p></li>
+<li><p><strong>abscissa_marker_labels</strong> (<em>iterable</em>) – Labels that will be applied to the markers.  If not specified, no
+label will be applied.  If a single string is passed, it will be
+passed to the <cite>.format</cite> method with keyword arguments <cite>index</cite> and
+<cite>abscissa</cite>.  This marker label will be used for all plotted
+curves if this argument is passed.  To add markers to just a
+specific plotted data, pass the argument <cite>abscissa_marker_labels_*</cite> where
+<cite>*</cite> is replaced with either the index of the data that was passed
+via a positional argument, or the keyword of the data that was
+passed via a keyword argument.  Must be passed as a keyword argument.</p></li>
+<li><p><strong>abscissa_marker_type</strong> (<em>str:</em>) – The type of marker that will be applied.  Can be ‘vline’ for a
+vertical line across the axis, or it can be a pyqtgraph symbol specifier
+(e.g. ‘x’, ‘o’, ‘star’, etc.) which will be placed on the plotted curves.
+If not specified, a vertical line will be used.
+This marker type will be used for all plotted
+curves if this argument is passed.  To add markers to just a
+specific plotted data, pass the argument <cite>abscissa_marker_type_*</cite> where
+<cite>*</cite> is replaced with either the index of the data that was passed
+via a positional argument, or the keyword of the data that was
+passed via a keyword argument.  Must be passed as a keyword argument.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Return type</dt>
@@ -379,7 +402,7 @@ <h1>sdynpy.core.sdynpy_data.GUIPlot<a class="headerlink" href="#sdynpy-core-sdyn
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecebdd50>
+<jinja2.runtime.BlockReference object at 0x7f5a273c2650>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html b/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html
index 41fbc0a..8281b0a 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -336,7 +336,7 @@ <h2>Paramters<a class="headerlink" href="#paramters" title="Permalink to this he
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecd93ca0>
+<jinja2.runtime.BlockReference object at 0x7f5a2728a0b0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html b/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html
index 5651d4f..614f123 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.MPLCanvas.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.MPLCanvas &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.MPLCanvas &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -199,7 +199,7 @@ <h1>sdynpy.core.sdynpy_data.MPLCanvas<a class="headerlink" href="#sdynpy-core-sd
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eccb1b70>
+<jinja2.runtime.BlockReference object at 0x7f5a27343ca0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html b/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html
index a86eea6..7777285 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.MPLMultiCanvas &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.MPLMultiCanvas &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -199,7 +199,7 @@ <h1>sdynpy.core.sdynpy_data.MPLMultiCanvas<a class="headerlink" href="#sdynpy-co
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecdd9c90>
+<jinja2.runtime.BlockReference object at 0x7f5a272080d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html b/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html
index 794de20..80953ad 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -207,7 +207,7 @@ <h1>sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray<a class="headerlink" href
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eccfacb0>
+<jinja2.runtime.BlockReference object at 0x7f5a270cd8a0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html b/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html
index 8aaf794..f1cbc53 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.MultipleCoherenceArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.MultipleCoherenceArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -89,6 +89,7 @@
 <li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.core.sdynpy_data.MultipleCoherenceArray</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray"><code class="docutils literal notranslate"><span class="pre">MultipleCoherenceArray</span></code></a><ul>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray.__init__"><code class="docutils literal notranslate"><span class="pre">MultipleCoherenceArray.__init__()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data"><code class="docutils literal notranslate"><span class="pre">MultipleCoherenceArray.from_time_data()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray.function_type"><code class="docutils literal notranslate"><span class="pre">MultipleCoherenceArray.function_type</span></code></a></li>
 </ul>
 </li>
@@ -172,6 +173,9 @@ <h1>sdynpy.core.sdynpy_data.MultipleCoherenceArray<a class="headerlink" href="#s
 <col style="width: 90%" />
 </colgroup>
 <tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data" title="sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_time_data</span></code></a>(response_data[, ...])</p></td>
+<td><p>Computes coherence from reference and response time histories</p></td>
+</tr>
 </tbody>
 </table>
 <p class="rubric">Attributes</p>
@@ -186,6 +190,40 @@ <h1>sdynpy.core.sdynpy_data.MultipleCoherenceArray<a class="headerlink" href="#s
 </tr>
 </tbody>
 </table>
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#MultipleCoherenceArray.from_time_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data" title="Permalink to this definition"></a></dt>
+<dd><p>Computes coherence from reference and response time histories</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>response_data</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><em>TimeHistoryArray</em></a>) – Time data to be used as responses</p></li>
+<li><p><strong>samples_per_average</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of samples used to split up the signals into averages.  The
+default is None, meaning the data is treated as a single measurement
+frame.</p></li>
+<li><p><strong>overlap</strong> (<em>float</em><em>, </em><em>optional</em>) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).
+The default is 0.0, meaning no overlap is used.</p></li>
+<li><p><strong>window</strong> (<em>np.ndarray</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – A 1D ndarray with length samples_per_average that specifies the
+coefficients of the window.  A Hann window is applied if not specified.
+If a string is specified, then the window will be obtained from scipy.</p></li>
+<li><p><strong>reference_data</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><em>TimeHistoryArray</em></a>) – Time data to be used as reference.  If not specified, the response
+data will be used as references, resulting in a square coherence matrix.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if reference and response functions do not have consistent
+    abscissa</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A PSD array computed from the specified reference and
+response signals.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray">PowerSpectralDensityArray</a></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.MultipleCoherenceArray.function_type">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">function_type</span></span><a class="headerlink" href="#sdynpy.core.sdynpy_data.MultipleCoherenceArray.function_type" title="Permalink to this definition"></a></dt>
@@ -207,7 +245,7 @@ <h1>sdynpy.core.sdynpy_data.MultipleCoherenceArray<a class="headerlink" href="#s
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecbbd540>
+<jinja2.runtime.BlockReference object at 0x7f5a26f95450>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html b/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html
index de8aeb0..d600e32 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.NDDataArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.NDDataArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -100,6 +100,8 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.from_uff"><code class="docutils literal notranslate"><span class="pre">NDDataArray.from_uff()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.from_unv"><code class="docutils literal notranslate"><span class="pre">NDDataArray.from_unv()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.function_type"><code class="docutils literal notranslate"><span class="pre">NDDataArray.function_type</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.get_drive_points"><code class="docutils literal notranslate"><span class="pre">NDDataArray.get_drive_points()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data"><code class="docutils literal notranslate"><span class="pre">NDDataArray.get_reciprocal_data()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.idx_by_ab"><code class="docutils literal notranslate"><span class="pre">NDDataArray.idx_by_ab</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.idx_by_el"><code class="docutils literal notranslate"><span class="pre">NDDataArray.idx_by_el</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.interpolate"><code class="docutils literal notranslate"><span class="pre">NDDataArray.interpolate()</span></code></a></li>
@@ -110,6 +112,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.num_coordinates"><code class="docutils literal notranslate"><span class="pre">NDDataArray.num_coordinates</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.num_elements"><code class="docutils literal notranslate"><span class="pre">NDDataArray.num_elements</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.plot"><code class="docutils literal notranslate"><span class="pre">NDDataArray.plot()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.plot_image"><code class="docutils literal notranslate"><span class="pre">NDDataArray.plot_image()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.reference_coordinate"><code class="docutils literal notranslate"><span class="pre">NDDataArray.reference_coordinate</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix"><code class="docutils literal notranslate"><span class="pre">NDDataArray.reshape_to_matrix()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.response_coordinate"><code class="docutils literal notranslate"><span class="pre">NDDataArray.response_coordinate</span></code></a></li>
@@ -224,6 +227,12 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.from_unv" title="sdynpy.core.sdynpy_data.NDDataArray.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_data_dict[, squeeze])</p></td>
 <td><p>Create a data array from a unv dictionary from read_unv</p></td>
 </tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.get_drive_points" title="sdynpy.core.sdynpy_data.NDDataArray.get_drive_points"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_drive_points</span></code></a>([return_indices])</p></td>
+<td><p>Returns data arrays where the reference is equal to the response</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data" title="sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_reciprocal_data</span></code></a>([return_indices])</p></td>
+<td><p>Gets reciprocal pairs of data from an NDDataArray.</p></td>
+</tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.interpolate" title="sdynpy.core.sdynpy_data.NDDataArray.interpolate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interpolate</span></code></a>(interpolated_abscissa[, kind])</p></td>
 <td><p>Interpolates the NDDataArray using SciPy's interp1d.</p></td>
 </tr>
@@ -239,31 +248,34 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.min" title="sdynpy.core.sdynpy_data.NDDataArray.min"><code class="xref py py-obj docutils literal notranslate"><span class="pre">min</span></code></a>([reduction])</p></td>
 <td><p>Returns the minimum ordinate in the data array</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.plot" title="sdynpy.core.sdynpy_data.NDDataArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, plot_kwargs])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.plot" title="sdynpy.core.sdynpy_data.NDDataArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, ...])</p></td>
 <td><p>Plot the data array</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix" title="sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape_to_matrix</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.plot_image" title="sdynpy.core.sdynpy_data.NDDataArray.plot_image"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_image</span></code></a>([ax, reduction_function, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix" title="sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape_to_matrix</span></code></a>([error_if_missing])</p></td>
 <td><p>Reshapes a data array to a matrix with response coordinates along the rows and reference coordinates along the columns</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.save" title="sdynpy.core.sdynpy_data.NDDataArray.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(filename)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.save" title="sdynpy.core.sdynpy_data.NDDataArray.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(filename)</p></td>
 <td><p>Save the array to a numpy file</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct" title="sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_imat_struct</span></code></a>([Version, SetRecord, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct" title="sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_imat_struct</span></code></a>([Version, SetRecord, ...])</p></td>
 <td><p>Creates a Matlab structure that can be read the IMAT toolbox.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct_array" title="sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_imat_struct_array</span></code></a>([Version, SetRecord, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct_array" title="sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_imat_struct_array</span></code></a>([Version, SetRecord, ...])</p></td>
 <td><p>Creates a Matlab structure that can be read the IMAT toolbox.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.to_shape_array" title="sdynpy.core.sdynpy_data.NDDataArray.to_shape_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_shape_array</span></code></a>([abscissa_values])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.to_shape_array" title="sdynpy.core.sdynpy_data.NDDataArray.to_shape_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_shape_array</span></code></a>([abscissa_values])</p></td>
 <td><p>Converts an NDDataArray to a ShapeArray</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.transform_coordinate_system" title="sdynpy.core.sdynpy_data.NDDataArray.transform_coordinate_system"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform_coordinate_system</span></code></a>(...[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.transform_coordinate_system" title="sdynpy.core.sdynpy_data.NDDataArray.transform_coordinate_system"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform_coordinate_system</span></code></a>(...[, ...])</p></td>
 <td><p>Performs coordinate system transformations on the data</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.validate_common_abscissa" title="sdynpy.core.sdynpy_data.NDDataArray.validate_common_abscissa"><code class="xref py py-obj docutils literal notranslate"><span class="pre">validate_common_abscissa</span></code></a>(**allclose_kwargs)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.validate_common_abscissa" title="sdynpy.core.sdynpy_data.NDDataArray.validate_common_abscissa"><code class="xref py py-obj docutils literal notranslate"><span class="pre">validate_common_abscissa</span></code></a>(**allclose_kwargs)</p></td>
 <td><p>Returns True if all functions have the same abscissa</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.zero_pad" title="sdynpy.core.sdynpy_data.NDDataArray.zero_pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_pad</span></code></a>([num_samples, update_abscissa, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.NDDataArray.zero_pad" title="sdynpy.core.sdynpy_data.NDDataArray.zero_pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zero_pad</span></code></a>([num_samples, update_abscissa, ...])</p></td>
 <td><p>Add zeros to the beginning or end of a signal</p></td>
 </tr>
 </tbody>
@@ -467,6 +479,56 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 <dd><p>Returns the function type of the data array as a FunctionTypes Enum</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.get_drive_points">
+<span class="sig-name descname"><span class="pre">get_drive_points</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.get_drive_points"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.get_drive_points" title="Permalink to this definition"></a></dt>
+<dd><p>Returns data arrays where the reference is equal to the response</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>return_indices</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will return a set of indices into the original array
+that extract the drive point functions. If False, then the
+drive point functions are returned directly.  The default is False.</p>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – If the data does not have reference and response coordinates, the
+    method will raise a ValueError.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>If return_indices is True, this will return the indices into the
+original array that extract the drive point data.  If return_indices
+is False, this will return the drive point NDDataArrays directly.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>np.ndarray or NDDataArray subclass</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data">
+<span class="sig-name descname"><span class="pre">get_reciprocal_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.get_reciprocal_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data" title="Permalink to this definition"></a></dt>
+<dd><p>Gets reciprocal pairs of data from an NDDataArray.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>return_indices</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, it will return a set of indices into the original array
+that extract the reciprocal functions. If False, then the
+reciprocal functions are returned directly.  The default is False.</p>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – If the data does not have reference and response coordinates, the
+    method will raise a ValueError.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>If return_indices is True, this will return the indices into the
+original array that extract the reciprocal data.  If return_indices
+is False, this will return the reciprocal NDDataArrays directly.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>np.ndarray or NDDataArray subclass</p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.idx_by_ab">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">idx_by_ab</span></span><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.idx_by_ab" title="Permalink to this definition"></a></dt>
@@ -610,7 +672,7 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.plot" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_markers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vline'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.plot" title="Permalink to this definition"></a></dt>
 <dd><p>Plot the data array</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -622,6 +684,16 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 <li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib subplots function to create the
 figure and axes. The default is {}.</p></li>
 <li><p><strong>plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib plot function. The default is {}.</p></li>
+<li><p><strong>abscissa_markers</strong> (<em>ndarray</em><em>, </em><em>optional</em>) – Array containing abscissa values to mark on the plot to denote
+significant events.</p></li>
+<li><p><strong>abscissa_marker_labels</strong> (<em>str</em><em> or </em><em>ndarray</em>) – Array of strings to label the abscissa_markers with, or
+alternatively a format string that accepts index and abscissa
+inputs (e.g. ‘{index:}: {abscissa:0.2f}’).  By default no label
+will be applied.</p></li>
+<li><p><strong>abscissa_marker_type</strong> (<em>str</em>) – The type of marker to use.  This can either be the string ‘vline’
+or a valid matplotlib symbol specifier (e.g. ‘o’, ‘x’, ‘.’).</p></li>
+<li><p><strong>abscissa_marker_plot_kwargs</strong> (<em>dict</em>) – Additional keyword arguments used when plotting the abscissa label
+markers.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -633,6 +705,11 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.plot_image">
+<span class="sig-name descname"><span class="pre">plot_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colorbar_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.plot_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.plot_image" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.reference_coordinate">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reference_coordinate</span></span><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.reference_coordinate" title="Permalink to this definition"></a></dt>
@@ -641,15 +718,22 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix">
-<span class="sig-name descname"><span class="pre">reshape_to_matrix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.reshape_to_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">reshape_to_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">error_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#NDDataArray.reshape_to_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix" title="Permalink to this definition"></a></dt>
 <dd><p>Reshapes a data array to a matrix with response coordinates along the
 rows and reference coordinates along the columns</p>
 <dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p><strong>output_array</strong> – 2D Array of NDDataArray</p>
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>error_if_missing</strong> (<em>bool</em>) – If True, an error will be thrown if there are missing data objects
+when trying to make a matrix of functions (i.e. if a response
+degree of freedom is missing from one reference).  If False,
+response coordinates will simply be discarded if they do not exist
+for all references.  Default is True.</p>
 </dd>
-<dt class="field-even">Return type</dt>
-<dd class="field-even"><p>Data Aarray</p>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>output_array</strong> – 2D Array of NDDataArray</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>Data Aarray</p>
 </dd>
 </dl>
 </dd></dl>
@@ -911,7 +995,7 @@ <h1>sdynpy.core.sdynpy_data.NDDataArray<a class="headerlink" href="#sdynpy-core-
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec8bb280>
+<jinja2.runtime.BlockReference object at 0x7f5a26dd15d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html b/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html
index 40ed68d..092811a 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.PowerSpectralDensityArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.PowerSpectralDensityArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -92,10 +92,12 @@
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray</span></code></a><ul>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.__init__"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.__init__()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.angle"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.angle()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.bandwidth_average()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.coherence()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.compare_asds"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.compare_asds()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.error_summary()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.eye"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.eye()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.from_time_data()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.function_type"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.function_type</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.generate_time_history"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.generate_time_history()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.get_asd"><code class="docutils literal notranslate"><span class="pre">PowerSpectralDensityArray.get_asd()</span></code></a></li>
@@ -191,18 +193,24 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectralDensityArray<a class="headerlink" href=
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.angle" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.angle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">angle</span></code></a>()</p></td>
 <td><p>Computes the angle of a PSD matrix</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coherence</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bandwidth_average</span></code></a>(band_lb, band_ub)</p></td>
+<td><p>Integrates the PSD over frequency to get the power spectrum for each frequency bin (line)</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">coherence</span></code></a>()</p></td>
 <td><p>Computes the coherence of a PSD matrix</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.compare_asds" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.compare_asds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compare_asds</span></code></a>([figure_kwargs, linewidth])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.compare_asds" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.compare_asds"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compare_asds</span></code></a>([figure_kwargs, linewidth])</p></td>
 <td><p>Plot the diagonals of the CPSD matrix, as well as the level</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">error_summary</span></code></a>([figure_kwargs, linewidth, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary"><code class="xref py py-obj docutils literal notranslate"><span class="pre">error_summary</span></code></a>([figure_kwargs, linewidth, ...])</p></td>
 <td><p>Plots an error summary compared to the current array</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.eye" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.eye"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eye</span></code></a>(frequencies, coordinates[, rms, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.eye" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.eye"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eye</span></code></a>(frequencies, coordinates[, rms, ...])</p></td>
 <td><p>Computes a diagonal CPSD matrix</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_time_data</span></code></a>(response_data[, ...])</p></td>
+<td><p>Computes a PSD matrix from reference and response time histories</p></td>
+</tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.generate_time_history" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.generate_time_history"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_time_history</span></code></a>([time_length, ...])</p></td>
 <td><p>Generates a time history from a CPSD matrix</p></td>
 </tr>
@@ -231,7 +239,7 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectralDensityArray<a class="headerlink" href=
 <td><p>Sets the coherence and phase of a PSD matrix while maintaining the ASDs</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.svd" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.svd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svd</span></code></a>([full_matrices, compute_uv, as_matrix])</p></td>
-<td><p>Compute the SVD of the provided FRF matrix</p></td>
+<td><p>Compute the SVD of the provided CPSD matrix</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.to_rattlesnake_specification" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.to_rattlesnake_specification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_rattlesnake_specification</span></code></a>([filename, ...])</p></td>
 <td><p></p></td>
@@ -265,6 +273,34 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectralDensityArray<a class="headerlink" href=
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average">
+<span class="sig-name descname"><span class="pre">bandwidth_average</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">band_lb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">band_ub</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#PowerSpectralDensityArray.bandwidth_average"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average" title="Permalink to this definition"></a></dt>
+<dd><p>Integrates the PSD over frequency to get the power spectrum for each
+frequency bin (line)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>band_lb</strong> (<em>ndarray</em>) – (n_bands,1) array of bandwidth lower bounds</p></li>
+<li><p><strong>band_ub</strong> (<em>ndarray</em>) – (n_bands,1) array of bandwidth upper bounds</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><em>PowerSpectralDensityArray with abscissa given by the mean of band_lb</em></p></li>
+<li><p><em>and band_ub</em></p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Determines which freq bins (lines) contribute to each band. Contribute
+means the freq bin is at least partially within the band limits</p>
+<p>The portion of the bin which contributes to the band is computed based
+multiplied by the fraction of the contributing frequency to get how
+much bin PS adds to the band PS</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence">
 <span class="sig-name descname"><span class="pre">coherence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#PowerSpectralDensityArray.coherence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence" title="Permalink to this definition"></a></dt>
@@ -366,6 +402,40 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectralDensityArray<a class="headerlink" href=
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_asds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#PowerSpectralDensityArray.from_time_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data" title="Permalink to this definition"></a></dt>
+<dd><p>Computes a PSD matrix from reference and response time histories</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>response_data</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><em>TimeHistoryArray</em></a>) – Time data to be used as responses</p></li>
+<li><p><strong>samples_per_average</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of samples used to split up the signals into averages.  The
+default is None, meaning the data is treated as a single measurement
+frame.</p></li>
+<li><p><strong>overlap</strong> (<em>float</em><em>, </em><em>optional</em>) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).
+The default is 0.0, meaning no overlap is used.</p></li>
+<li><p><strong>window</strong> (<em>np.ndarray</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – A 1D ndarray with length samples_per_average that specifies the
+coefficients of the window.  A Hann window is applied if not specified.
+If a string is specified, then the window will be obtained from scipy.</p></li>
+<li><p><strong>reference_data</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><em>TimeHistoryArray</em></a>) – Time data to be used as reference.  If not specified, the response
+data will be used as references, resulting in a square CPSD matrix.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if reference and response functions do not have consistent
+    abscissa</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A PSD array computed from the specified reference and
+response signals.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray" title="sdynpy.core.sdynpy_data.PowerSpectralDensityArray">PowerSpectralDensityArray</a></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py property">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.function_type">
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">function_type</span></span><a class="headerlink" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.function_type" title="Permalink to this definition"></a></dt>
@@ -614,7 +684,7 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectralDensityArray<a class="headerlink" href=
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.PowerSpectralDensityArray.svd">
 <span class="sig-name descname"><span class="pre">svd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_matrices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_uv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#PowerSpectralDensityArray.svd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.svd" title="Permalink to this definition"></a></dt>
-<dd><p>Compute the SVD of the provided FRF matrix</p>
+<dd><p>Compute the SVD of the provided CPSD matrix</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -657,7 +727,7 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectralDensityArray<a class="headerlink" href=
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecd92140>
+<jinja2.runtime.BlockReference object at 0x7f5a26989030>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.PowerSpectrumArray.html b/_autosummary/sdynpy.core.sdynpy_data.PowerSpectrumArray.html
index 33c400a..f1bc555 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.PowerSpectrumArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.PowerSpectrumArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.PowerSpectrumArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.PowerSpectrumArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -207,7 +207,7 @@ <h1>sdynpy.core.sdynpy_data.PowerSpectrumArray<a class="headerlink" href="#sdynp
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecfba080>
+<jinja2.runtime.BlockReference object at 0x7f5a2686bc40>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html b/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html
index 3f777c4..2bc6053 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.ShockResponseSpectrumArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.ShockResponseSpectrumArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -173,7 +173,7 @@ <h1>sdynpy.core.sdynpy_data.ShockResponseSpectrumArray<a class="headerlink" href
 <col style="width: 90%" />
 </colgroup>
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot" title="sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, plot_kwargs])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot" title="sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, ...])</p></td>
 <td><p>Plot the shock response spectrum</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.sum_decayed_sines" title="sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.sum_decayed_sines"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum_decayed_sines</span></code></a>(sample_rate, block_size[, ...])</p></td>
@@ -201,7 +201,7 @@ <h1>sdynpy.core.sdynpy_data.ShockResponseSpectrumArray<a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#ShockResponseSpectrumArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_markers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vline'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#ShockResponseSpectrumArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot" title="Permalink to this definition"></a></dt>
 <dd><p>Plot the shock response spectrum</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -213,6 +213,16 @@ <h1>sdynpy.core.sdynpy_data.ShockResponseSpectrumArray<a class="headerlink" href
 <li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib subplots function to create the
 figure and axes. The default is {}.</p></li>
 <li><p><strong>plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib plot function. The default is {}.</p></li>
+<li><p><strong>abscissa_markers</strong> (<em>ndarray</em><em>, </em><em>optional</em>) – Array containing abscissa values to mark on the plot to denote
+significant events.</p></li>
+<li><p><strong>abscissa_marker_labels</strong> (<em>str</em><em> or </em><em>ndarray</em>) – Array of strings to label the abscissa_markers with, or
+alternatively a format string that accepts index and abscissa
+inputs (e.g. ‘{index:}: {abscissa:0.2f}’).  By default no label
+will be applied.</p></li>
+<li><p><strong>abscissa_marker_type</strong> (<em>str</em>) – The type of marker to use.  This can either be the string ‘vline’
+or a valid matplotlib symbol specifier (e.g. ‘o’, ‘x’, ‘.’).</p></li>
+<li><p><strong>abscissa_marker_plot_kwargs</strong> (<em>dict</em>) – Additional keyword arguments used when plotting the abscissa label
+markers.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -365,7 +375,7 @@ <h1>sdynpy.core.sdynpy_data.ShockResponseSpectrumArray<a class="headerlink" href
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed182560>
+<jinja2.runtime.BlockReference object at 0x7f5a26832170>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html b/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html
index f675d5f..33326dc 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.SpecificDataType &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.SpecificDataType &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -368,7 +368,7 @@ <h1>sdynpy.core.sdynpy_data.SpecificDataType<a class="headerlink" href="#sdynpy-
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eed5c640>
+<jinja2.runtime.BlockReference object at 0x7f5a266c0e50>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html b/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html
index 2985628..3462829 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.SpectrumArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.SpectrumArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -182,8 +182,8 @@ <h1>sdynpy.core.sdynpy_data.SpectrumArray<a class="headerlink" href="#sdynpy-cor
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.SpectrumArray.interpolate_by_zero_pad" title="sdynpy.core.sdynpy_data.SpectrumArray.interpolate_by_zero_pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interpolate_by_zero_pad</span></code></a>(...[, ...])</p></td>
 <td><p>Interpolates a spectrum by zero padding or truncating its time response</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.SpectrumArray.plot" title="sdynpy.core.sdynpy_data.SpectrumArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, plot_kwargs])</p></td>
-<td><p>Plot the transfer functions</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.SpectrumArray.plot" title="sdynpy.core.sdynpy_data.SpectrumArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, ...])</p></td>
+<td><p>Plot the spectra</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.SpectrumArray.plot_spectrogram" title="sdynpy.core.sdynpy_data.SpectrumArray.plot_spectrogram"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_spectrogram</span></code></a>([abscissa, axis, ...])</p></td>
 <td><p>Plots a spectrogram</p></td>
@@ -270,8 +270,8 @@ <h1>sdynpy.core.sdynpy_data.SpectrumArray<a class="headerlink" href="#sdynpy-cor
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.SpectrumArray.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#SpectrumArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.SpectrumArray.plot" title="Permalink to this definition"></a></dt>
-<dd><p>Plot the transfer functions</p>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_markers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vline'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#SpectrumArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.SpectrumArray.plot" title="Permalink to this definition"></a></dt>
+<dd><p>Plot the spectra</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><ul class="simple">
@@ -282,6 +282,16 @@ <h1>sdynpy.core.sdynpy_data.SpectrumArray<a class="headerlink" href="#sdynpy-cor
 <li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib subplots function to create the
 figure and axes. The default is {}.</p></li>
 <li><p><strong>plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib plot function. The default is {}.</p></li>
+<li><p><strong>abscissa_markers</strong> (<em>ndarray</em><em>, </em><em>optional</em>) – Array containing abscissa values to mark on the plot to denote
+significant events.</p></li>
+<li><p><strong>abscissa_marker_labels</strong> (<em>str</em><em> or </em><em>ndarray</em>) – Array of strings to label the abscissa_markers with, or
+alternatively a format string that accepts index and abscissa
+inputs (e.g. ‘{index:}: {abscissa:0.2f}’).  By default no label
+will be applied.</p></li>
+<li><p><strong>abscissa_marker_type</strong> (<em>str</em>) – The type of marker to use.  This can either be the string ‘vline’
+or a valid matplotlib symbol specifier (e.g. ‘o’, ‘x’, ‘.’).</p></li>
+<li><p><strong>abscissa_marker_plot_kwargs</strong> (<em>dict</em>) – Additional keyword arguments used when plotting the abscissa label
+markers.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -334,7 +344,7 @@ <h1>sdynpy.core.sdynpy_data.SpectrumArray<a class="headerlink" href="#sdynpy-cor
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eecb51b0>
+<jinja2.runtime.BlockReference object at 0x7f5a265e0c70>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html b/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html
index de0cdce..62a3066 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.TimeHistoryArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.TimeHistoryArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -114,6 +114,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.rms"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.rms()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.shift_signal"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.shift_signal()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_signal"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.sine_signal()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.sine_sweep_signal()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.split_into_frames()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.srs"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.srs()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.stft"><code class="docutils literal notranslate"><span class="pre">TimeHistoryArray.stft()</span></code></a></li>
@@ -244,16 +245,19 @@ <h1>sdynpy.core.sdynpy_data.TimeHistoryArray<a class="headerlink" href="#sdynpy-
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_signal" title="sdynpy.core.sdynpy_data.TimeHistoryArray.sine_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sine_signal</span></code></a>(dt, signal_length, coordinates, ...)</p></td>
 <td><p>Creates a sinusoidal signal with the specified parameters</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames" title="sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_into_frames</span></code></a>([samples_per_frame, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal" title="sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sine_sweep_signal</span></code></a>(dt, coordinates, ...[, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames" title="sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_into_frames</span></code></a>([samples_per_frame, ...])</p></td>
 <td><p>Splits a time history into measurement frames with a given overlap and window function applied.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.srs" title="sdynpy.core.sdynpy_data.TimeHistoryArray.srs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">srs</span></code></a>([min_frequency, max_frequency, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.srs" title="sdynpy.core.sdynpy_data.TimeHistoryArray.srs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">srs</span></code></a>([min_frequency, max_frequency, ...])</p></td>
 <td><p>Compute a shock response spectrum (SRS) from the time history</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.stft" title="sdynpy.core.sdynpy_data.TimeHistoryArray.stft"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stft</span></code></a>([samples_per_frame, frame_length, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.stft" title="sdynpy.core.sdynpy_data.TimeHistoryArray.stft"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stft</span></code></a>([samples_per_frame, frame_length, ...])</p></td>
 <td><p>Computes a Short-Time Fourier Transform (STFT)</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.to_rattlesnake_specification" title="sdynpy.core.sdynpy_data.TimeHistoryArray.to_rattlesnake_specification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_rattlesnake_specification</span></code></a>(filename[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.to_rattlesnake_specification" title="sdynpy.core.sdynpy_data.TimeHistoryArray.to_rattlesnake_specification"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_rattlesnake_specification</span></code></a>(filename[, ...])</p></td>
 <td><p></p></td>
 </tr>
 </tbody>
@@ -571,14 +575,14 @@ <h1>sdynpy.core.sdynpy_data.TimeHistoryArray<a class="headerlink" href="#sdynpy-
 estimation. The available options are:</p>
 <blockquote>
 <div><ul>
-<li><p>single_frame - this method performs the Fourier deconvolution</p></li>
-</ul>
-<p>via an FFT on a single frame that encompases the entire time
-signal.
-- COLA - this method performs the Fourier deconvolution via a
+<li><p>single_frame - this method performs the Fourier deconvolution
+via an FFT on a single frame that encompases the entire time
+signal.</p></li>
+<li><p>COLA - this method performs the Fourier deconvolution via a
 series of FFTs on relatively small frames of the time signal
 using a “constant overlap and add” method. This method may be
-faster than the single_frame method.</p>
+faster than the single_frame method.</p></li>
+</ul>
 </div></blockquote>
 </p></li>
 <li><p><strong>cola_frame_length</strong> (<em>float</em><em>, </em><em>optional</em>) – The frame length (in samples) if the COLA method is being used. The
@@ -904,6 +908,11 @@ <h1>sdynpy.core.sdynpy_data.TimeHistoryArray<a class="headerlink" href="#sdynpy-
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sine_sweep_signal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_breakpoints</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sweep_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sweep_rates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amplitudes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phases</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment3</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment4</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment5</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TimeHistoryArray.sine_sweep_signal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames">
 <span class="sig-name descname"><span class="pre">split_into_frames</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">samples_per_frame</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frame_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_cola</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_fractional_frames</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TimeHistoryArray.split_into_frames"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames" title="Permalink to this definition"></a></dt>
@@ -1068,7 +1077,7 @@ <h1>sdynpy.core.sdynpy_data.TimeHistoryArray<a class="headerlink" href="#sdynpy-
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ef0c3340>
+<jinja2.runtime.BlockReference object at 0x7f5a26f2c3d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html b/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html
index 71959ab..f55c116 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.TransferFunctionArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.TransferFunctionArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -110,6 +110,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.plot()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_cond_num"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.plot_cond_num()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_singular_values"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.plot_singular_values()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.plot_with_coherence()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.substructure_by_constraint_matrix()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_coordinate"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.substructure_by_coordinate()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.svd"><code class="docutils literal notranslate"><span class="pre">TransferFunctionArray.svd()</span></code></a></li>
@@ -214,7 +215,7 @@ <h1>sdynpy.core.sdynpy_data.TransferFunctionArray<a class="headerlink" href="#sd
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.interpolate_by_zero_pad" title="sdynpy.core.sdynpy_data.TransferFunctionArray.interpolate_by_zero_pad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">interpolate_by_zero_pad</span></code></a>(irf_padded_length[, ...])</p></td>
 <td><p>Interpolates a transfer function by zero padding or truncating its impulse response</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot" title="sdynpy.core.sdynpy_data.TransferFunctionArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, subplots_kwargs, plot_kwargs])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot" title="sdynpy.core.sdynpy_data.TransferFunctionArray.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([one_axis, part, subplots_kwargs, ...])</p></td>
 <td><p>Plot the transfer functions</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_cond_num" title="sdynpy.core.sdynpy_data.TransferFunctionArray.plot_cond_num"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_cond_num</span></code></a>([number_retained_values, ...])</p></td>
@@ -223,13 +224,16 @@ <h1>sdynpy.core.sdynpy_data.TransferFunctionArray<a class="headerlink" href="#sd
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_singular_values" title="sdynpy.core.sdynpy_data.TransferFunctionArray.plot_singular_values"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_singular_values</span></code></a>([rcond, ...])</p></td>
 <td><p>Plot the singular values of an FRF matrix with a visualization of the rcond tolerance</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix" title="sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_constraint_matrix</span></code></a>(dofs, ...)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence" title="sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_with_coherence</span></code></a>(coherence[, part, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix" title="sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_constraint_matrix</span></code></a>(dofs, ...)</p></td>
 <td><p>Performs frequency based substructuring using the</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_coordinate" title="sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_coordinate</span></code></a>(dof_pairs)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_coordinate" title="sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_coordinate</span></code></a>(dof_pairs)</p></td>
 <td><p>Performs frequency based substructuring by constraining pairs of degrees of freedom</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.svd" title="sdynpy.core.sdynpy_data.TransferFunctionArray.svd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svd</span></code></a>([full_matrices, compute_uv, as_matrix])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.svd" title="sdynpy.core.sdynpy_data.TransferFunctionArray.svd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">svd</span></code></a>([full_matrices, compute_uv, as_matrix])</p></td>
 <td><p>Compute the SVD of the provided FRF matrix</p></td>
 </tr>
 </tbody>
@@ -377,15 +381,15 @@ <h1>sdynpy.core.sdynpy_data.TransferFunctionArray<a class="headerlink" href="#sd
 (used for data processing). The current options are:</p>
 <blockquote>
 <div><ul>
-<li><p>exponential_taper (default) - this applies a exponential taper</p></li>
-</ul>
-<p>to the end of a boxcar window on the IRF.
-- boxcar - this applies a boxcar (uniform) window to the IRF
-with the cuttoff at a specified sample.
-- exponential - this applies an exponential window to the IRF
+<li><p>exponential_taper (default) - this applies a exponential taper
+to the end of a boxcar window on the IRF.</p></li>
+<li><p>boxcar - this applies a boxcar (uniform) window to the IRF
+with the cuttoff at a specified sample.</p></li>
+<li><p>exponential - this applies an exponential window to the IRF
 with the 40 dB down point (of the window) at a specified sample.
 Care should be taken when using this window type, since it can
-lead to erratic behavior.</p>
+lead to erratic behavior.</p></li>
+</ul>
 </div></blockquote>
 </p></li>
 <li><p><strong>window_parameter</strong> (<em>int</em><em>, </em><em>optional</em>) – <p>This is a parameter that defines the window for the causality
@@ -393,16 +397,16 @@ <h1>sdynpy.core.sdynpy_data.TransferFunctionArray<a class="headerlink" href="#sd
 if it isn’t provided. The behaviors for the options are:</p>
 <blockquote>
 <div><ul>
-<li><p>boxcar - the window_paramter is the sample after which the</p></li>
-</ul>
-<p>IRF is set to zero. It is the same as the end_of_ringdown
-parameter for this window type.
-- exponential - the window_parameter is where the 40 dB down
+<li><p>boxcar - the window_paramter is the sample after which the
+IRF is set to zero. It is the same as the end_of_ringdown
+parameter for this window type.</p></li>
+<li><p>exponential - the window_parameter is where the 40 dB down
 point is for the window. It is the same as the end_of_ringdown
-parameter for this window type.
-- exponential_taper - the window_parameter is where the end point
+parameter for this window type.</p></li>
+<li><p>exponential_taper - the window_parameter is where the end point
 of the window (where the amplitude is 0.001), as defined by the
-number of samples after the uniform section of the window.</p>
+number of samples after the uniform section of the window.</p></li>
+</ul>
 </div></blockquote>
 </p></li>
 <li><p><strong>end_of_ringdown</strong> (<em>int</em><em>, </em><em>optional</em>) – This is a parameter that defines the end of the uniform section of
@@ -442,7 +446,7 @@ <h1>sdynpy.core.sdynpy_data.TransferFunctionArray<a class="headerlink" href="#sd
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.TransferFunctionArray.from_time_data">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">timedata2frf_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TransferFunctionArray.from_time_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.from_time_data" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_model_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">timedata2frf_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TransferFunctionArray.from_time_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.from_time_data" title="Permalink to this definition"></a></dt>
 <dd><p>Computes a transfer function from reference and response time histories</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -455,12 +459,14 @@ <h1>sdynpy.core.sdynpy_data.TransferFunctionArray<a class="headerlink" href="#sd
 <li><p><strong>overlap</strong> (<em>float</em><em>, </em><em>optional</em>) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).
 The default is 0.0, meaning no overlap is used.</p></li>
 <li><p><strong>method</strong> (<em>str</em><em>, </em><em>optional</em>) – The method for creating the frequency response function. ‘H1’ is
-default if not specified.</p></li>
+default if not specified. samples_per_average, overlap, and window
+are not used if method==’LRM’.</p></li>
 <li><p><strong>window</strong> (<em>np.ndarray</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – A 1D ndarray with length samples_per_average that specifies the
 coefficients of the window.  No window is applied if not specified.
 If a string is specified, then the window will be obtained from scipy.</p></li>
 <li><p><strong>**timedata2frf_kwargs</strong> (<em>various</em>) – Additional keyword arguments that may be passed into the
-timedata2frf function in sdynpy.frf.</p></li>
+timedata2frf function in sdynpy.frf.  If method==’LRM’, see also
+frf_local_model in sdynpy.lrm for more options.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Raises</dt>
@@ -548,7 +554,7 @@ <h2>Paramters<a class="headerlink" href="#paramters" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.TransferFunctionArray.plot">
-<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TransferFunctionArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">one_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">part</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_markers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'vline'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa_marker_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TransferFunctionArray.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot" title="Permalink to this definition"></a></dt>
 <dd><p>Plot the transfer functions</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -557,9 +563,23 @@ <h2>Paramters<a class="headerlink" href="#paramters" title="Permalink to this he
 data on multiple subplots.  one_axis can also be set to a
 matplotlib axis to plot data on an existing axis.  The default is
 True.</p></li>
+<li><p><strong>part</strong> (<em>str</em><em>, </em><em>optional</em>) – The part of the FRF to plot.  This can be, ‘real’, ‘imag’ or
+‘imaginary’, ‘mag’ or ‘magnitude’, or ‘phase’.  If not specified,
+magnitude and phase will be plotted if <cite>one_axis</cite> is True, and
+magnitude will be plotted if <cite>one_axis</cite> is False.</p></li>
 <li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib subplots function to create the
 figure and axes. The default is {}.</p></li>
 <li><p><strong>plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Keywords passed to the matplotlib plot function. The default is {}.</p></li>
+<li><p><strong>abscissa_markers</strong> (<em>ndarray</em><em>, </em><em>optional</em>) – Array containing abscissa values to mark on the plot to denote
+significant events.</p></li>
+<li><p><strong>abscissa_marker_labels</strong> (<em>str</em><em> or </em><em>ndarray</em>) – Array of strings to label the abscissa_markers with, or
+alternatively a format string that accepts index and abscissa
+inputs (e.g. ‘{index:}: {abscissa:0.2f}’).  By default no label
+will be applied.</p></li>
+<li><p><strong>abscissa_marker_type</strong> (<em>str</em>) – The type of marker to use.  This can either be the string ‘vline’
+or a valid matplotlib symbol specifier (e.g. ‘o’, ‘x’, ‘.’).</p></li>
+<li><p><strong>abscissa_marker_plot_kwargs</strong> (<em>dict</em>) – Additional keyword arguments used when plotting the abscissa label
+markers.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -614,6 +634,11 @@ <h2>Paramters<a class="headerlink" href="#paramters" title="Permalink to this he
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence">
+<span class="sig-name descname"><span class="pre">plot_with_coherence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coherence</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">part</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TransferFunctionArray.plot_with_coherence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix">
 <span class="sig-name descname"><span class="pre">substructure_by_constraint_matrix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dofs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_matrix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_data.html#TransferFunctionArray.substructure_by_constraint_matrix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix" title="Permalink to this definition"></a></dt>
@@ -700,7 +725,7 @@ <h2>Paramters<a class="headerlink" href="#paramters" title="Permalink to this he
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee4f6590>
+<jinja2.runtime.BlockReference object at 0x7f5a291239d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html b/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html
index 2be7104..86c7a25 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.TransmissibilityArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.TransmissibilityArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -207,7 +207,7 @@ <h1>sdynpy.core.sdynpy_data.TransmissibilityArray<a class="headerlink" href="#sd
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee254760>
+<jinja2.runtime.BlockReference object at 0x7f5a28edb1c0>
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   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html b/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html
index 11c232a..626cea0 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.TypeQual.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.TypeQual &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.TypeQual &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -206,7 +206,7 @@ <h1>sdynpy.core.sdynpy_data.TypeQual<a class="headerlink" href="#sdynpy-core-sdy
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99edb93d90>
+<jinja2.runtime.BlockReference object at 0x7f5a28df09d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.data_array.html b/_autosummary/sdynpy.core.sdynpy_data.data_array.html
index 260735f..e93e416 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.data_array.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.data_array.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.data_array &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.data_array &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -193,7 +193,7 @@ <h1>sdynpy.core.sdynpy_data.data_array<a class="headerlink" href="#sdynpy-core-s
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99edf29570>
+<jinja2.runtime.BlockReference object at 0x7f5a28f24a90>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html b/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html
index a255d42..c4822fc 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.decayed_sine_table &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.decayed_sine_table &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -194,7 +194,7 @@ <h1>sdynpy.core.sdynpy_data.decayed_sine_table<a class="headerlink" href="#sdynp
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ede66050>
+<jinja2.runtime.BlockReference object at 0x7f5a28fab9d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html b/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html
index 74f43ff..6b466c7 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html
+++ b/_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data.frf_from_time_data &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data.frf_from_time_data &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -153,7 +153,7 @@
 <h1>sdynpy.core.sdynpy_data.frf_from_time_data<a class="headerlink" href="#sdynpy-core-sdynpy-data-frf-from-time-data" title="Permalink to this heading"></a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_data.frf_from_time_data">
-<span class="sig-name descname"><span class="pre">frf_from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">timedata2frf_kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sdynpy.core.sdynpy_data.frf_from_time_data" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">frf_from_time_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray" title="sdynpy.core.sdynpy_data.TimeHistoryArray"><span class="pre">TimeHistoryArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_model_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">timedata2frf_kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sdynpy.core.sdynpy_data.frf_from_time_data" title="Permalink to this definition"></a></dt>
 <dd><p>Computes a transfer function from reference and response time histories</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -166,12 +166,14 @@ <h1>sdynpy.core.sdynpy_data.frf_from_time_data<a class="headerlink" href="#sdynp
 <li><p><strong>overlap</strong> (<em>float</em><em>, </em><em>optional</em>) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).
 The default is 0.0, meaning no overlap is used.</p></li>
 <li><p><strong>method</strong> (<em>str</em><em>, </em><em>optional</em>) – The method for creating the frequency response function. ‘H1’ is
-default if not specified.</p></li>
+default if not specified. samples_per_average, overlap, and window
+are not used if method==’LRM’.</p></li>
 <li><p><strong>window</strong> (<em>np.ndarray</em><em> or </em><em>str</em><em>, </em><em>optional</em>) – A 1D ndarray with length samples_per_average that specifies the
 coefficients of the window.  No window is applied if not specified.
 If a string is specified, then the window will be obtained from scipy.</p></li>
 <li><p><strong>**timedata2frf_kwargs</strong> (<em>various</em>) – Additional keyword arguments that may be passed into the
-timedata2frf function in sdynpy.frf.</p></li>
+timedata2frf function in sdynpy.frf.  If method==’LRM’, see also
+frf_local_model in sdynpy.lrm for more options.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Raises</dt>
@@ -201,7 +203,7 @@ <h1>sdynpy.core.sdynpy_data.frf_from_time_data<a class="headerlink" href="#sdynp
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee618ee0>
+<jinja2.runtime.BlockReference object at 0x7f5a28c49930>
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   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html b/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html
index ebb343f..6f66b38 100644
--- a/_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -90,6 +92,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.camera_visualization"><code class="docutils literal notranslate"><span class="pre">Geometry.camera_visualization()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.compress_ids"><code class="docutils literal notranslate"><span class="pre">Geometry.compress_ids()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.copy"><code class="docutils literal notranslate"><span class="pre">Geometry.copy()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_excel_template"><code class="docutils literal notranslate"><span class="pre">Geometry.from_excel_template()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_exodus"><code class="docutils literal notranslate"><span class="pre">Geometry.from_exodus()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_imat_struct"><code class="docutils literal notranslate"><span class="pre">Geometry.from_imat_struct()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_uff"><code class="docutils literal notranslate"><span class="pre">Geometry.from_uff()</span></code></a></li>
@@ -109,6 +112,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.reduce"><code class="docutils literal notranslate"><span class="pre">Geometry.reduce()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.rigid_body_shapes"><code class="docutils literal notranslate"><span class="pre">Geometry.rigid_body_shapes()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.save"><code class="docutils literal notranslate"><span class="pre">Geometry.save()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.write_excel_template"><code class="docutils literal notranslate"><span class="pre">Geometry.write_excel_template()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.write_to_unv"><code class="docutils literal notranslate"><span class="pre">Geometry.write_to_unv()</span></code></a></li>
 </ul>
 </li>
@@ -229,63 +233,69 @@ <h1>sdynpy.core.sdynpy_geometry.Geometry<a class="headerlink" href="#sdynpy-core
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.copy" title="sdynpy.core.sdynpy_geometry.Geometry.copy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code></a>()</p></td>
 <td><p>Return's a copy of the current Geometry</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_exodus" title="sdynpy.core.sdynpy_geometry.Geometry.from_exodus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_exodus</span></code></a>(exo[, blocks, local, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_excel_template" title="sdynpy.core.sdynpy_geometry.Geometry.from_excel_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_excel_template</span></code></a>(path_to_xlsx)</p></td>
+<td><p>Create a geometry from Excel file template</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_exodus" title="sdynpy.core.sdynpy_geometry.Geometry.from_exodus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_exodus</span></code></a>(exo[, blocks, local, ...])</p></td>
 <td><p>Generate a geometry from exodus file data</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_imat_struct" title="sdynpy.core.sdynpy_geometry.Geometry.from_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_imat_struct</span></code></a>(imat_fem_struct)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_imat_struct" title="sdynpy.core.sdynpy_geometry.Geometry.from_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_imat_struct</span></code></a>(imat_fem_struct)</p></td>
 <td><p>Constructs a Geometry from an imat_fem class saved to a Matlab structure</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_uff" title="sdynpy.core.sdynpy_geometry.Geometry.from_uff"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff</span></code></a>(unv_dict)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_uff" title="sdynpy.core.sdynpy_geometry.Geometry.from_uff"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff</span></code></a>(unv_dict)</p></td>
 <td><p>Create a geometry from universal file format data from readunv</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_unv" title="sdynpy.core.sdynpy_geometry.Geometry.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_dict)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.from_unv" title="sdynpy.core.sdynpy_geometry.Geometry.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_dict)</p></td>
 <td><p>Create a geometry from universal file format data from readunv</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.global_deflection" title="sdynpy.core.sdynpy_geometry.Geometry.global_deflection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_deflection</span></code></a>(coordinate_array)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.global_deflection" title="sdynpy.core.sdynpy_geometry.Geometry.global_deflection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_deflection</span></code></a>(coordinate_array)</p></td>
 <td><p>Direction of local deflection in the global coordinate system</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.global_node_coordinate" title="sdynpy.core.sdynpy_geometry.Geometry.global_node_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_node_coordinate</span></code></a>([node_ids])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.global_node_coordinate" title="sdynpy.core.sdynpy_geometry.Geometry.global_node_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_node_coordinate</span></code></a>([node_ids])</p></td>
 <td><p>Position of the Geometry's nodes in the global coordinate system</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.load" title="sdynpy.core.sdynpy_geometry.Geometry.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(filename)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.load" title="sdynpy.core.sdynpy_geometry.Geometry.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(filename)</p></td>
 <td><p>Loads a geometry from a numpy .npz or .unv file</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.map_ids" title="sdynpy.core.sdynpy_geometry.Geometry.map_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">map_ids</span></code></a>([node_id_map, traceline_id_map, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.map_ids" title="sdynpy.core.sdynpy_geometry.Geometry.map_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">map_ids</span></code></a>([node_id_map, traceline_id_map, ...])</p></td>
 <td><p>Maps id numbers from an original set of ids to a new set of ids.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.modify_ids" title="sdynpy.core.sdynpy_geometry.Geometry.modify_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modify_ids</span></code></a>([node_change, traceline_change, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.modify_ids" title="sdynpy.core.sdynpy_geometry.Geometry.modify_ids"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modify_ids</span></code></a>([node_change, traceline_change, ...])</p></td>
 <td><p>Shifts the id numbers in the geometry</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position" title="sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position"><code class="xref py py-obj docutils literal notranslate"><span class="pre">node_by_global_position</span></code></a>(global_position_array)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position" title="sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position"><code class="xref py py-obj docutils literal notranslate"><span class="pre">node_by_global_position</span></code></a>(global_position_array)</p></td>
 <td><p>Select node by closest position</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.overlay_geometries" title="sdynpy.core.sdynpy_geometry.Geometry.overlay_geometries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">overlay_geometries</span></code></a>(geometries[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.overlay_geometries" title="sdynpy.core.sdynpy_geometry.Geometry.overlay_geometries"><code class="xref py py-obj docutils literal notranslate"><span class="pre">overlay_geometries</span></code></a>(geometries[, ...])</p></td>
 <td><p>Combines several geometries, offsetting the id numbers to avoid conflicts</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot" title="sdynpy.core.sdynpy_geometry.Geometry.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([node_size, line_width, opacity, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot" title="sdynpy.core.sdynpy_geometry.Geometry.plot"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot</span></code></a>([node_size, line_width, opacity, ...])</p></td>
 <td><p>Plots the geometry in an interactive 3D window.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_coordinate" title="sdynpy.core.sdynpy_geometry.Geometry.plot_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_coordinate</span></code></a>([coordinates, arrow_scale, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_coordinate" title="sdynpy.core.sdynpy_geometry.Geometry.plot_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_coordinate</span></code></a>([coordinates, arrow_scale, ...])</p></td>
 <td><p>Plots coordinate arrows on the geometry</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_deflection_shape" title="sdynpy.core.sdynpy_geometry.Geometry.plot_deflection_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_deflection_shape</span></code></a>(deflection_shape_data)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_deflection_shape" title="sdynpy.core.sdynpy_geometry.Geometry.plot_deflection_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_deflection_shape</span></code></a>(deflection_shape_data)</p></td>
 <td><p>Plot deflection shapes shapes on the geometry</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_shape" title="sdynpy.core.sdynpy_geometry.Geometry.plot_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_shape</span></code></a>(shape[, plot_kwargs, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_shape" title="sdynpy.core.sdynpy_geometry.Geometry.plot_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_shape</span></code></a>(shape[, plot_kwargs, ...])</p></td>
 <td><p>Plot mode shapes on the geometry</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_transient" title="sdynpy.core.sdynpy_geometry.Geometry.plot_transient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_transient</span></code></a>(displacement_data[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.plot_transient" title="sdynpy.core.sdynpy_geometry.Geometry.plot_transient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_transient</span></code></a>(displacement_data[, ...])</p></td>
 <td><p>Create a TransientPlotter object to plot displacements over time</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.reduce" title="sdynpy.core.sdynpy_geometry.Geometry.reduce"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce</span></code></a>(node_list)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.reduce" title="sdynpy.core.sdynpy_geometry.Geometry.reduce"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reduce</span></code></a>(node_list)</p></td>
 <td><p>Reduce the geometry to only contain nodes in <cite>node_list</cite></p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.rigid_body_shapes" title="sdynpy.core.sdynpy_geometry.Geometry.rigid_body_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rigid_body_shapes</span></code></a>(coordinates[, mass, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.rigid_body_shapes" title="sdynpy.core.sdynpy_geometry.Geometry.rigid_body_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rigid_body_shapes</span></code></a>(coordinates[, mass, ...])</p></td>
 <td><p>Creates a set of shapes corresponding to the rigid body motions</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.save" title="sdynpy.core.sdynpy_geometry.Geometry.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(filename)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.save" title="sdynpy.core.sdynpy_geometry.Geometry.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(filename)</p></td>
 <td><p>Saves the geometry to a numpy .npz file</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.write_excel_template" title="sdynpy.core.sdynpy_geometry.Geometry.write_excel_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_excel_template</span></code></a>(path_to_xlsx)</p></td>
+<td><p>Writes an Excel File Template for Creating Geometry</p></td>
+</tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry.write_to_unv" title="sdynpy.core.sdynpy_geometry.Geometry.write_to_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_to_unv</span></code></a>(filename[, write_nodes, ...])</p></td>
 <td><p>Write the geometry to a unversal file format file</p></td>
 </tr>
@@ -394,6 +404,69 @@ <h1>sdynpy.core.sdynpy_geometry.Geometry<a class="headerlink" href="#sdynpy-core
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.Geometry.from_excel_template">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_excel_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_xlsx</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_geometry.html#Geometry.from_excel_template"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.Geometry.from_excel_template" title="Permalink to this definition"></a></dt>
+<dd><p>Create a geometry from Excel file template</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>path_to_xlsx</strong> (<em>string</em>) – Path to xlsx Excel file containing geometry information</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Geometry object created from the Excel file</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.Geometry" title="sdynpy.core.sdynpy_geometry.Geometry">Geometry</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>To use this function, first save out an excel template file using the
+<cite>write_excel_template</cite> function.  This will construct an excel
+workbook with four worksheets on which the different portions of the
+Geometry are defined.</p>
+<p>On the Coordinate Systems tab, users will define the various global and
+local coordinate systems in their geometry.  Each geometry requires
+an ID number.   A Name can optionally be given.  The Color should be
+specified as an integer corresponding to the Ideas color map.  The
+Type of the coordinate system should be an integer:</p>
+<blockquote>
+<div><p>0 - Cartesian
+1 - Polar
+2 - Spherical</p>
+</div></blockquote>
+<p>The origin of the coordinate system can be specified with the X Location,
+Y Location, Z Location columns.  Then rotations of the coordinate system
+can be specified using rotations about axes.  Up to three axes and angles
+can be specified to create arbitrary compound rotations.  The rotation
+axes should be X, Y, or Z, and the rotation angles are in degrees.</p>
+<p>On the Nodes tab, all tabs must be filled out.  ID numbers must be unique.
+Colors should be an integer corresponding to the Ideas color map.  The
+position of the node is specified using the X Location, Y Location, Z
+Location columns.  Each node has a displacement coordinate system in which
+its position is defined, and a a definition coordinate system in which
+its displacements are defined.  These columns should consist of integers
+corresponding to ID numbers from the Coordinate Systems tab.</p>
+<p>On the Elements tab, elements connecting nodes are defined.  The ID
+column must consist of unique integer identifiers.  The Color tab should
+be specified as an integer corresponding to the Ideas color map.  The
+type can be a string (hex, quad, tet, tri, beam, etc.) or an integer
+consisting of a universal file format element type.  If the type column
+is empty, an element type based on the number of connections given will
+be used. Defult element types for connection length of 2 is “Type 21 - Linear Beam”,
+for a connection length of 3 is “Type 41 - Plane Stress Linear Triangle”,
+and for a connection length of 4 is “Type 44 - Plane Stress Linear Quadrilateral”
+The columns Node 1 through Node 20 contain the nodes in each element.  Only the required
+number of nodes must be filled out (e.g. a tri element would only contain
+3 nodes, so only columns Node 1, Node 2, and Node 3 would be filled).</p>
+<p>On the Trace Lines tab, lines connecting the nodes are defined.  The
+ID column must consist of unique integer identifiers.  The Description
+column contains a string description of the line.  The Color column
+should consist of an integer corresponding to the Ideas color map.  The
+Node 1 through Node 20 columns should contain the nodes for each line.
+Only the number of nodes in the line must be filled out, so if a line
+only connects 5 nodes, only Node 1 though Node 5 must be filled.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.Geometry.from_exodus">
 <em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_exodus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exo</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus" title="sdynpy.fem.sdynpy_exodus.Exodus"><span class="pre">Exodus</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preferred_local_orientation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([0.,</span> <span class="pre">0.,</span> <span class="pre">1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">secondary_preferred_local_orientation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([1.,</span> <span class="pre">0.,</span> <span class="pre">0.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_geometry.html#Geometry.from_exodus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.Geometry.from_exodus" title="Permalink to this definition"></a></dt>
@@ -917,6 +990,23 @@ <h1>sdynpy.core.sdynpy_geometry.Geometry<a class="headerlink" href="#sdynpy-core
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.Geometry.write_excel_template">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">write_excel_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_xlsx</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_geometry.html#Geometry.write_excel_template"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.Geometry.write_excel_template" title="Permalink to this definition"></a></dt>
+<dd><p>Writes an Excel File Template for Creating Geometry</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>path_to_xlsx</strong> (<em>string</em>) – Path to write xlsx Excel file</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>Nothing</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>See documentation for from_excel_template for instructions on filling
+out the template to create a geometry.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.Geometry.write_to_unv">
 <span class="sig-name descname"><span class="pre">write_to_unv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_nodes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_coordinate_systems</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_tracelines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">write_elements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_2412_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataset_2420_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_geometry.html#Geometry.write_to_unv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.Geometry.write_to_unv" title="Permalink to this definition"></a></dt>
@@ -966,7 +1056,7 @@ <h1>sdynpy.core.sdynpy_geometry.Geometry<a class="headerlink" href="#sdynpy-core
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed23d660>
+<jinja2.runtime.BlockReference object at 0x7f5a27824b80>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html b/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html
index 32009f1..eca98e4 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.GeometryPlotter &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.GeometryPlotter &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.coordinate_system_array.html">sdynpy.core.sdynpy_geometry.coordinate_system_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -220,7 +222,7 @@ <h1>sdynpy.core.sdynpy_geometry.GeometryPlotter<a class="headerlink" href="#sdyn
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed375390>
+<jinja2.runtime.BlockReference object at 0x7f5a289554e0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.NodeArray.html b/_autosummary/sdynpy.core.sdynpy_geometry.NodeArray.html
index e9b791f..b25228f 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.NodeArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.NodeArray.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.NodeArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.NodeArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.coordinate_system_array.html">sdynpy.core.sdynpy_geometry.coordinate_system_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -379,7 +381,7 @@ <h1>sdynpy.core.sdynpy_geometry.NodeArray<a class="headerlink" href="#sdynpy-cor
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed2f7bb0>
+<jinja2.runtime.BlockReference object at 0x7f5a2690f6d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html b/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html
index 2c6059e..2359d40 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.ShapePlotter &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.ShapePlotter &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.coordinate_system_array.html">sdynpy.core.sdynpy_geometry.coordinate_system_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -94,6 +96,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.next_shape"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.next_shape()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.play_animation"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.play_animation()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.prev_shape"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.prev_shape()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.reset_shape()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.save_animation()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_all_shapes"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.save_animation_all_shapes()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_from_action"><code class="docutils literal notranslate"><span class="pre">ShapePlotter.save_animation_from_action()</span></code></a></li>
@@ -229,67 +232,70 @@ <h1>sdynpy.core.sdynpy_geometry.ShapePlotter<a class="headerlink" href="#sdynpy-
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.prev_shape" title="sdynpy.core.sdynpy_geometry.ShapePlotter.prev_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prev_shape</span></code></a>()</p></td>
 <td><p>Decreases the index of the shape being plotted</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation" title="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_animation</span></code></a>([filename, frames, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape" title="sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reset_shape</span></code></a>()</p></td>
+<td><p>Resets a shape if the value of the current shape is changed</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation" title="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_animation</span></code></a>([filename, frames, ...])</p></td>
 <td><p>Saves the current shape animation to a file</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_all_shapes" title="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_all_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_animation_all_shapes</span></code></a>([filename_base, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_all_shapes" title="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_all_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_animation_all_shapes</span></code></a>([filename_base, ...])</p></td>
 <td><p>Helper function to easily save animations for all shapes</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_from_action" title="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_from_action"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_animation_from_action</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_from_action" title="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation_from_action"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_animation_from_action</span></code></a>()</p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_complex" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_complex</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_complex" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_complex</span></code></a>()</p></td>
 <td><p>Adjusts how complex shapes are plotted</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_loop" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_loop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_loop</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_loop" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_loop"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_loop</span></code></a>()</p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p25" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_0p25</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p25" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_0p25</span></code></a>()</p></td>
 <td><p>Adjusts the current shape scaling by 0.25x</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p5" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_0p5</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p5" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p5"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_0p5</span></code></a>()</p></td>
 <td><p>Adjusts the current shape scaling by 0.5x</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p8" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p8"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_0p8</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p8" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_0p8"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_0p8</span></code></a>()</p></td>
 <td><p>Adjusts the current shape scaling by 0.8x</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_1</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_1</span></code></a>()</p></td>
 <td><p>Resets shape scaling to 1</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1p25" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1p25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_1p25</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1p25" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_1p25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_1p25</span></code></a>()</p></td>
 <td><p>Adjusts the current shape scaling by 1.25x</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_2p0" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_2p0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_2p0</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_2p0" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_2p0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_2p0</span></code></a>()</p></td>
 <td><p>Adjusts the current shape scaling by 2x</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_4p0" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_4p0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_4p0</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_4p0" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_scaling_4p0"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_scaling_4p0</span></code></a>()</p></td>
 <td><p>Adjusts the current shape scaling by 4x</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_shape" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_shape</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_shape" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_shape</span></code></a>()</p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_0p8" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_0p8"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_speed_0p8</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_0p8" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_0p8"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_speed_0p8</span></code></a>()</p></td>
 <td><p>Adjusts the current animation speed by 0.8</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_speed_1</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_speed_1</span></code></a>()</p></td>
 <td><p>Resets animation speed to default quantities</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1p25" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1p25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_speed_1p25</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1p25" title="sdynpy.core.sdynpy_geometry.ShapePlotter.select_speed_1p25"><code class="xref py py-obj docutils literal notranslate"><span class="pre">select_speed_1p25</span></code></a>()</p></td>
 <td><p>Adjusts the current animation speed by 1.25</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.show_comment" title="sdynpy.core.sdynpy_geometry.ShapePlotter.show_comment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show_comment</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.show_comment" title="sdynpy.core.sdynpy_geometry.ShapePlotter.show_comment"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show_comment</span></code></a>()</p></td>
 <td><p>Shows or hides the shape comment</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.stop_animation" title="sdynpy.core.sdynpy_geometry.ShapePlotter.stop_animation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stop_animation</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.stop_animation" title="sdynpy.core.sdynpy_geometry.ShapePlotter.stop_animation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stop_animation</span></code></a>()</p></td>
 <td><p>Stops the animation from playing</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.toggle_undeformed" title="sdynpy.core.sdynpy_geometry.ShapePlotter.toggle_undeformed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">toggle_undeformed</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.toggle_undeformed" title="sdynpy.core.sdynpy_geometry.ShapePlotter.toggle_undeformed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">toggle_undeformed</span></code></a>()</p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape" title="sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_shape</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape" title="sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_shape</span></code></a>()</p></td>
 <td><p>Updates the shape that is being plotted</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape_mode" title="sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_shape_mode</span></code></a>([phase])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape_mode" title="sdynpy.core.sdynpy_geometry.ShapePlotter.update_shape_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_shape_mode</span></code></a>([phase])</p></td>
 <td><p>Updates the mode that is being plotted</p></td>
 </tr>
 </tbody>
@@ -344,6 +350,12 @@ <h1>sdynpy.core.sdynpy_geometry.ShapePlotter<a class="headerlink" href="#sdynpy-
 <dd><p>Decreases the index of the shape being plotted</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape">
+<span class="sig-name descname"><span class="pre">reset_shape</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_geometry.html#ShapePlotter.reset_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape" title="Permalink to this definition"></a></dt>
+<dd><p>Resets a shape if the value of the current shape is changed</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation">
 <span class="sig-name descname"><span class="pre">save_animation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frames</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frame_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">individual_images</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_geometry.html#ShapePlotter.save_animation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.ShapePlotter.save_animation" title="Permalink to this definition"></a></dt>
@@ -519,7 +531,7 @@ <h1>sdynpy.core.sdynpy_geometry.ShapePlotter<a class="headerlink" href="#sdynpy-
   <hr/>
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html b/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html
index 08c6ee6..1864e49 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.TracelineArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.TracelineArray &mdash; SDynPy 0.14.0 documentation</title>
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@@ -68,6 +68,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
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@@ -243,7 +245,7 @@ <h1>sdynpy.core.sdynpy_geometry.TracelineArray<a class="headerlink" href="#sdynp
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.TransientPlotter.html b/_autosummary/sdynpy.core.sdynpy_geometry.TransientPlotter.html
index dba2b9d..22414bc 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.TransientPlotter.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.TransientPlotter.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.TransientPlotter &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.TransientPlotter &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
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@@ -493,7 +495,7 @@ <h1>sdynpy.core.sdynpy_geometry.TransientPlotter<a class="headerlink" href="#sdy
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.coordinate_system_array.html b/_autosummary/sdynpy.core.sdynpy_geometry.coordinate_system_array.html
index 4e68eaa..ee12b69 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.coordinate_system_array.html
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-  <title>sdynpy.core.sdynpy_geometry.coordinate_system_array &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.coordinate_system_array &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -71,6 +71,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -82,6 +83,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -186,7 +188,7 @@ <h1>sdynpy.core.sdynpy_geometry.coordinate_system_array<a class="headerlink" hre
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99edd126e0>
+<jinja2.runtime.BlockReference object at 0x7f5a271bfc40>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.element_array.html b/_autosummary/sdynpy.core.sdynpy_geometry.element_array.html
index d793e09..4316a87 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.element_array.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.element_array.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
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-  <title>sdynpy.core.sdynpy_geometry.element_array &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.element_array &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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 </head>
 
@@ -71,6 +71,7 @@
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.element_array"><code class="docutils literal notranslate"><span class="pre">element_array()</span></code></a></li>
 </ul>
 </li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -82,6 +83,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -269,13 +271,13 @@ <h1>sdynpy.core.sdynpy_geometry.element_array<a class="headerlink" href="#sdynpy
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 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.html
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+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.core.sdynpy_geometry.from_excel_template</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.from_excel_template"><code class="docutils literal notranslate"><span class="pre">from_excel_template()</span></code></a></li>
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+             
+  <section id="sdynpy-core-sdynpy-geometry-from-excel-template">
+<h1>sdynpy.core.sdynpy_geometry.from_excel_template<a class="headerlink" href="#sdynpy-core-sdynpy-geometry-from-excel-template" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.from_excel_template">
+<span class="sig-name descname"><span class="pre">from_excel_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_xlsx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.from_excel_template" title="Permalink to this definition"></a></dt>
+<dd><p>Create a geometry from Excel file template</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>path_to_xlsx</strong> (<em>string</em>) – Path to xlsx Excel file containing geometry information</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>Geometry object created from the Excel file</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry" title="sdynpy.core.sdynpy_geometry.Geometry">Geometry</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>To use this function, first save out an excel template file using the
+<cite>write_excel_template</cite> function.  This will construct an excel
+workbook with four worksheets on which the different portions of the
+Geometry are defined.</p>
+<p>On the Coordinate Systems tab, users will define the various global and
+local coordinate systems in their geometry.  Each geometry requires
+an ID number.   A Name can optionally be given.  The Color should be
+specified as an integer corresponding to the Ideas color map.  The
+Type of the coordinate system should be an integer:</p>
+<blockquote>
+<div><p>0 - Cartesian
+1 - Polar
+2 - Spherical</p>
+</div></blockquote>
+<p>The origin of the coordinate system can be specified with the X Location,
+Y Location, Z Location columns.  Then rotations of the coordinate system
+can be specified using rotations about axes.  Up to three axes and angles
+can be specified to create arbitrary compound rotations.  The rotation
+axes should be X, Y, or Z, and the rotation angles are in degrees.</p>
+<p>On the Nodes tab, all tabs must be filled out.  ID numbers must be unique.
+Colors should be an integer corresponding to the Ideas color map.  The
+position of the node is specified using the X Location, Y Location, Z
+Location columns.  Each node has a displacement coordinate system in which
+its position is defined, and a a definition coordinate system in which
+its displacements are defined.  These columns should consist of integers
+corresponding to ID numbers from the Coordinate Systems tab.</p>
+<p>On the Elements tab, elements connecting nodes are defined.  The ID
+column must consist of unique integer identifiers.  The Color tab should
+be specified as an integer corresponding to the Ideas color map.  The
+type can be a string (hex, quad, tet, tri, beam, etc.) or an integer
+consisting of a universal file format element type.  If the type column
+is empty, an element type based on the number of connections given will
+be used. Defult element types for connection length of 2 is “Type 21 - Linear Beam”,
+for a connection length of 3 is “Type 41 - Plane Stress Linear Triangle”,
+and for a connection length of 4 is “Type 44 - Plane Stress Linear Quadrilateral”
+The columns Node 1 through Node 20 contain the nodes in each element.  Only the required
+number of nodes must be filled out (e.g. a tri element would only contain
+3 nodes, so only columns Node 1, Node 2, and Node 3 would be filled).</p>
+<p>On the Trace Lines tab, lines connecting the nodes are defined.  The
+ID column must consist of unique integer identifiers.  The Description
+column contains a string description of the line.  The Color column
+should consist of an integer corresponding to the Ideas color map.  The
+Node 1 through Node 20 columns should contain the nodes for each line.
+Only the number of nodes in the line must be filled out, so if a line
+only connects 5 nodes, only Node 1 though Node 5 must be filled.</p>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.core.sdynpy_geometry.element_array.html" class="btn btn-neutral float-left" title="sdynpy.core.sdynpy_geometry.element_array" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.core.sdynpy_geometry.from_exodus.html" class="btn btn-neutral float-right" title="sdynpy.core.sdynpy_geometry.from_exodus" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f5a271bdab0>
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+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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+   
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+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
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+</html>
\ No newline at end of file
diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html
index 985f873..35d2288 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.from_exodus.html
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-  <title>sdynpy.core.sdynpy_geometry.from_exodus &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.from_exodus &mdash; SDynPy 0.14.0 documentation</title>
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-    <link rel="prev" title="sdynpy.core.sdynpy_geometry.element_array" href="sdynpy.core.sdynpy_geometry.element_array.html" /> 
+    <link rel="prev" title="sdynpy.core.sdynpy_geometry.from_excel_template" href="sdynpy.core.sdynpy_geometry.from_excel_template.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
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 </ul>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
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@@ -188,14 +190,14 @@ <h1>sdynpy.core.sdynpy_geometry.from_exodus<a class="headerlink" href="#sdynpy-c
            </div>
           </div>
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-        <a href="sdynpy.core.sdynpy_geometry.element_array.html" class="btn btn-neutral float-left" title="sdynpy.core.sdynpy_geometry.element_array" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.core.sdynpy_geometry.from_excel_template.html" class="btn btn-neutral float-left" title="sdynpy.core.sdynpy_geometry.from_excel_template" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="sdynpy.core.sdynpy_geometry.from_imat_struct.html" class="btn btn-neutral float-right" title="sdynpy.core.sdynpy_geometry.from_imat_struct" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_imat_struct.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_imat_struct.html
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-  <title>sdynpy.core.sdynpy_geometry.from_imat_struct &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.from_imat_struct &mdash; SDynPy 0.14.0 documentation</title>
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_uff.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_uff.html
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-  <title>sdynpy.core.sdynpy_geometry.from_uff &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.from_uff &mdash; SDynPy 0.14.0 documentation</title>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.from_unv.html b/_autosummary/sdynpy.core.sdynpy_geometry.from_unv.html
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-  <title>sdynpy.core.sdynpy_geometry.from_unv &mdash; SDynPy 0.11.0 documentation</title>
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       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -68,6 +68,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.global_deflection.html b/_autosummary/sdynpy.core.sdynpy_geometry.global_deflection.html
index 4c94357..2a5b08a 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.global_deflection.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.global_deflection.html
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-  <title>sdynpy.core.sdynpy_geometry.global_deflection &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.global_deflection &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.coordinate_system_array.html">sdynpy.core.sdynpy_geometry.coordinate_system_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -82,6 +83,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -183,7 +185,7 @@ <h1>sdynpy.core.sdynpy_geometry.global_deflection<a class="headerlink" href="#sd
   <hr/>
 
   <div role="contentinfo">
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+<jinja2.runtime.BlockReference object at 0x7f5a2785b160>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.html b/_autosummary/sdynpy.core.sdynpy_geometry.html
index d06512e..9f92e90 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.html
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@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="current reference internal" href="#">sdynpy.core.sdynpy_geometry</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.coordinate_system_array.html">sdynpy.core.sdynpy_geometry.coordinate_system_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
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@@ -79,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -168,39 +170,45 @@
 <tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html#sdynpy.core.sdynpy_geometry.element_array" title="sdynpy.core.sdynpy_geometry.element_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">element_array</span></code></a>([id, type, color, ...])</p></td>
 <td><p>Creates an array that specifies elements in the geometry</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html#sdynpy.core.sdynpy_geometry.from_exodus" title="sdynpy.core.sdynpy_geometry.from_exodus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_exodus</span></code></a>(exo[, blocks, local, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html#sdynpy.core.sdynpy_geometry.from_excel_template" title="sdynpy.core.sdynpy_geometry.from_excel_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_excel_template</span></code></a>(path_to_xlsx)</p></td>
+<td><p>Create a geometry from Excel file template</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html#sdynpy.core.sdynpy_geometry.from_exodus" title="sdynpy.core.sdynpy_geometry.from_exodus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_exodus</span></code></a>(exo[, blocks, local, ...])</p></td>
 <td><p>Generate a geometry from exodus file data</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html#sdynpy.core.sdynpy_geometry.from_imat_struct" title="sdynpy.core.sdynpy_geometry.from_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_imat_struct</span></code></a>(imat_fem_struct)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html#sdynpy.core.sdynpy_geometry.from_imat_struct" title="sdynpy.core.sdynpy_geometry.from_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_imat_struct</span></code></a>(imat_fem_struct)</p></td>
 <td><p>Constructs a Geometry from an imat_fem class saved to a Matlab structure</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html#sdynpy.core.sdynpy_geometry.from_uff" title="sdynpy.core.sdynpy_geometry.from_uff"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff</span></code></a>(unv_dict)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html#sdynpy.core.sdynpy_geometry.from_uff" title="sdynpy.core.sdynpy_geometry.from_uff"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff</span></code></a>(unv_dict)</p></td>
 <td><p>Create a geometry from universal file format data from readunv</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_unv.html#sdynpy.core.sdynpy_geometry.from_unv" title="sdynpy.core.sdynpy_geometry.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_dict)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_unv.html#sdynpy.core.sdynpy_geometry.from_unv" title="sdynpy.core.sdynpy_geometry.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_dict)</p></td>
 <td><p>Create a geometry from universal file format data from readunv</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.global_coord.html#sdynpy.core.sdynpy_geometry.global_coord" title="sdynpy.core.sdynpy_geometry.global_coord"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_coord</span></code></a>(coordinate_system, local_coord)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.global_coord.html#sdynpy.core.sdynpy_geometry.global_coord" title="sdynpy.core.sdynpy_geometry.global_coord"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_coord</span></code></a>(coordinate_system, local_coord)</p></td>
 <td><p>Compute global coordinates from local coordinates</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.global_deflection.html#sdynpy.core.sdynpy_geometry.global_deflection" title="sdynpy.core.sdynpy_geometry.global_deflection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_deflection</span></code></a>(coordinate_system, ...[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.global_deflection.html#sdynpy.core.sdynpy_geometry.global_deflection" title="sdynpy.core.sdynpy_geometry.global_deflection"><code class="xref py py-obj docutils literal notranslate"><span class="pre">global_deflection</span></code></a>(coordinate_system, ...[, ...])</p></td>
 <td><p>Compute deflections in the global coordinate system</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.load.html#sdynpy.core.sdynpy_geometry.load" title="sdynpy.core.sdynpy_geometry.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(filename)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.load.html#sdynpy.core.sdynpy_geometry.load" title="sdynpy.core.sdynpy_geometry.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a>(filename)</p></td>
 <td><p>Loads a geometry from a numpy .npz or .unv file</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.local_coord.html#sdynpy.core.sdynpy_geometry.local_coord" title="sdynpy.core.sdynpy_geometry.local_coord"><code class="xref py py-obj docutils literal notranslate"><span class="pre">local_coord</span></code></a>(coordinate_system, global_coord)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.local_coord.html#sdynpy.core.sdynpy_geometry.local_coord" title="sdynpy.core.sdynpy_geometry.local_coord"><code class="xref py py-obj docutils literal notranslate"><span class="pre">local_coord</span></code></a>(coordinate_system, global_coord)</p></td>
 <td><p>Compute local coordinates from global coordinates</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html#sdynpy.core.sdynpy_geometry.node_array" title="sdynpy.core.sdynpy_geometry.node_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">node_array</span></code></a>([id, coordinate, color, def_cs, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html#sdynpy.core.sdynpy_geometry.node_array" title="sdynpy.core.sdynpy_geometry.node_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">node_array</span></code></a>([id, coordinate, color, def_cs, ...])</p></td>
 <td><p>Creates an array that specifies nodes in the geometry</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html#sdynpy.core.sdynpy_geometry.split_list" title="sdynpy.core.sdynpy_geometry.split_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_list</span></code></a>(seq, value)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html#sdynpy.core.sdynpy_geometry.split_list" title="sdynpy.core.sdynpy_geometry.split_list"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split_list</span></code></a>(seq, value)</p></td>
 <td><p>Splits a list by a value that exists in the list</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html#sdynpy.core.sdynpy_geometry.traceline_array" title="sdynpy.core.sdynpy_geometry.traceline_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">traceline_array</span></code></a>([id, description, color, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html#sdynpy.core.sdynpy_geometry.traceline_array" title="sdynpy.core.sdynpy_geometry.traceline_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">traceline_array</span></code></a>([id, description, color, ...])</p></td>
 <td><p>Creates an array that specifies tracelines in the geometry</p></td>
 </tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html#sdynpy.core.sdynpy_geometry.write_excel_template" title="sdynpy.core.sdynpy_geometry.write_excel_template"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_excel_template</span></code></a>(path_to_xlsx)</p></td>
+<td><p>Writes an Excel File Template for Creating Geometry</p></td>
+</tr>
 </tbody>
 </table>
 <p class="rubric">Classes</p>
@@ -255,7 +263,7 @@
   <hr/>
 
   <div role="contentinfo">
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+<jinja2.runtime.BlockReference object at 0x7f5a29f26a10>
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.id_map.html b/_autosummary/sdynpy.core.sdynpy_geometry.id_map.html
index 35d9c72..3ac28ca 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.id_map.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.id_map.html
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.id_map &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.id_map &mdash; SDynPy 0.14.0 documentation</title>
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@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
@@ -79,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.write_excel_template.html">sdynpy.core.sdynpy_geometry.write_excel_template</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
@@ -194,7 +196,7 @@ <h1>sdynpy.core.sdynpy_geometry.id_map<a class="headerlink" href="#sdynpy-core-s
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ef21d480>
+<jinja2.runtime.BlockReference object at 0x7f5a28955000>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_geometry.load.html b/_autosummary/sdynpy.core.sdynpy_geometry.load.html
index b3f25b8..e39ec01 100644
--- a/_autosummary/sdynpy.core.sdynpy_geometry.load.html
+++ b/_autosummary/sdynpy.core.sdynpy_geometry.load.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry.load &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry.load &mdash; SDynPy 0.14.0 documentation</title>
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       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -68,6 +68,7 @@
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a><ul class="current">
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+<li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_coordinate.html">sdynpy.core.sdynpy_coordinate</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_data.html">sdynpy.core.sdynpy_data</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.element_array.html">sdynpy.core.sdynpy_geometry.element_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_excel_template.html">sdynpy.core.sdynpy_geometry.from_excel_template</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_exodus.html">sdynpy.core.sdynpy_geometry.from_exodus</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_imat_struct.html">sdynpy.core.sdynpy_geometry.from_imat_struct</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_uff.html">sdynpy.core.sdynpy_geometry.from_uff</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.from_unv.html">sdynpy.core.sdynpy_geometry.from_unv</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.global_coord.html">sdynpy.core.sdynpy_geometry.global_coord</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.global_deflection.html">sdynpy.core.sdynpy_geometry.global_deflection</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.load.html">sdynpy.core.sdynpy_geometry.load</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.local_coord.html">sdynpy.core.sdynpy_geometry.local_coord</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.node_array.html">sdynpy.core.sdynpy_geometry.node_array</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.split_list.html">sdynpy.core.sdynpy_geometry.split_list</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.traceline_array.html">sdynpy.core.sdynpy_geometry.traceline_array</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.core.sdynpy_geometry.write_excel_template</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.core.sdynpy_geometry.write_excel_template"><code class="docutils literal notranslate"><span class="pre">write_excel_template()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.CoordinateSystemArray.html">sdynpy.core.sdynpy_geometry.CoordinateSystemArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html">sdynpy.core.sdynpy_geometry.DeflectionShapePlotter</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ElementArray.html">sdynpy.core.sdynpy_geometry.ElementArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.Geometry.html">sdynpy.core.sdynpy_geometry.Geometry</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.GeometryPlotter.html">sdynpy.core.sdynpy_geometry.GeometryPlotter</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.NodeArray.html">sdynpy.core.sdynpy_geometry.NodeArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.ShapePlotter.html">sdynpy.core.sdynpy_geometry.ShapePlotter</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.TracelineArray.html">sdynpy.core.sdynpy_geometry.TracelineArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.TransientPlotter.html">sdynpy.core.sdynpy_geometry.TransientPlotter</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_geometry.id_map.html">sdynpy.core.sdynpy_geometry.id_map</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_matrix.html">sdynpy.core.sdynpy_matrix</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_shape.html">sdynpy.core.sdynpy_shape</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_system.html">sdynpy.core.sdynpy_system</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.demo.html">sdynpy.demo</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.doc.html">sdynpy.doc</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+</ul>
+
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+    </nav>
+
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+          <a href="../index.html">SDynPy</a>
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+        <div class="rst-content">
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
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+          <li class="breadcrumb-item"><a href="sdynpy.core.sdynpy_geometry.html">sdynpy.core.sdynpy_geometry</a></li>
+      <li class="breadcrumb-item active">sdynpy.core.sdynpy_geometry.write_excel_template</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-core-sdynpy-geometry-write-excel-template">
+<h1>sdynpy.core.sdynpy_geometry.write_excel_template<a class="headerlink" href="#sdynpy-core-sdynpy-geometry-write-excel-template" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_geometry.write_excel_template">
+<span class="sig-name descname"><span class="pre">write_excel_template</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_xlsx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sdynpy.core.sdynpy_geometry.write_excel_template" title="Permalink to this definition"></a></dt>
+<dd><p>Writes an Excel File Template for Creating Geometry</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>path_to_xlsx</strong> (<em>string</em>) – Path to write xlsx Excel file</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>Nothing</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>See documentation for from_excel_template for instructions on filling
+out the template to create a geometry.</p>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.core.sdynpy_geometry.traceline_array.html" class="btn btn-neutral float-left" title="sdynpy.core.sdynpy_geometry.traceline_array" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+
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+<jinja2.runtime.BlockReference object at 0x7f5a27ee1d80>
+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
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\ No newline at end of file
diff --git a/_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html b/_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html
index 7463da2..bd68dd1 100644
--- a/_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html
+++ b/_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_matrix.Matrix &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_matrix.Matrix &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -308,7 +308,7 @@ <h1>sdynpy.core.sdynpy_matrix.Matrix<a class="headerlink" href="#sdynpy-core-sdy
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee86b8e0>
+<jinja2.runtime.BlockReference object at 0x7f5a27aa56c0>
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   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_matrix.html b/_autosummary/sdynpy.core.sdynpy_matrix.html
index 5a46e81..a93a0ce 100644
--- a/_autosummary/sdynpy.core.sdynpy_matrix.html
+++ b/_autosummary/sdynpy.core.sdynpy_matrix.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_matrix &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_matrix &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -168,7 +168,7 @@
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eeaa5b40>
+<jinja2.runtime.BlockReference object at 0x7f5a27843700>
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   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_matrix.matrix.html b/_autosummary/sdynpy.core.sdynpy_matrix.matrix.html
index 00ea1fd..6f4f8e2 100644
--- a/_autosummary/sdynpy.core.sdynpy_matrix.matrix.html
+++ b/_autosummary/sdynpy.core.sdynpy_matrix.matrix.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_matrix.matrix &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_matrix.matrix &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -155,7 +155,7 @@ <h1>sdynpy.core.sdynpy_matrix.matrix<a class="headerlink" href="#sdynpy-core-sdy
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee94a5f0>
+<jinja2.runtime.BlockReference object at 0x7f5a2776e950>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html b/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html
index a89adf1..ac46b8a 100644
--- a/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html
+++ b/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_shape.ShapeArray &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_shape.ShapeArray &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -28,7 +28,7 @@
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="sdynpy.core.sdynpy_system" href="sdynpy.core.sdynpy_system.html" />
+    <link rel="next" title="sdynpy.core.sdynpy_shape.ShapeCommentTable" href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html" />
     <link rel="prev" title="sdynpy.core.sdynpy_shape.shape_comparison_table" href="sdynpy.core.sdynpy_shape.shape_comparison_table.html" /> 
 </head>
 
@@ -86,6 +86,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.complex_data_dtype"><code class="docutils literal notranslate"><span class="pre">ShapeArray.complex_data_dtype()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.compute_frf"><code class="docutils literal notranslate"><span class="pre">ShapeArray.compute_frf()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.concatenate_dofs"><code class="docutils literal notranslate"><span class="pre">ShapeArray.concatenate_dofs()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.edit_comments"><code class="docutils literal notranslate"><span class="pre">ShapeArray.edit_comments()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.expand"><code class="docutils literal notranslate"><span class="pre">ShapeArray.expand()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_exodus"><code class="docutils literal notranslate"><span class="pre">ShapeArray.from_exodus()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_imat_struct"><code class="docutils literal notranslate"><span class="pre">ShapeArray.from_imat_struct()</span></code></a></li>
@@ -95,6 +96,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.mode_table"><code class="docutils literal notranslate"><span class="pre">ShapeArray.mode_table()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.modeshape"><code class="docutils literal notranslate"><span class="pre">ShapeArray.modeshape</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.ndof"><code class="docutils literal notranslate"><span class="pre">ShapeArray.ndof</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.normalize"><code class="docutils literal notranslate"><span class="pre">ShapeArray.normalize()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.optimize_degrees_of_freedom"><code class="docutils literal notranslate"><span class="pre">ShapeArray.optimize_degrees_of_freedom()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.overlay_shapes"><code class="docutils literal notranslate"><span class="pre">ShapeArray.overlay_shapes()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.plot_frequency"><code class="docutils literal notranslate"><span class="pre">ShapeArray.plot_frequency()</span></code></a></li>
@@ -105,6 +107,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.shape_alignment"><code class="docutils literal notranslate"><span class="pre">ShapeArray.shape_alignment()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.system"><code class="docutils literal notranslate"><span class="pre">ShapeArray.system()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.time_integrate"><code class="docutils literal notranslate"><span class="pre">ShapeArray.time_integrate()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_complex"><code class="docutils literal notranslate"><span class="pre">ShapeArray.to_complex()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_real"><code class="docutils literal notranslate"><span class="pre">ShapeArray.to_real()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.transform_coordinate_system"><code class="docutils literal notranslate"><span class="pre">ShapeArray.transform_coordinate_system()</span></code></a></li>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.write_to_unv"><code class="docutils literal notranslate"><span class="pre">ShapeArray.write_to_unv()</span></code></a></li>
@@ -112,6 +115,7 @@
 </li>
 </ul>
 </li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html">sdynpy.core.sdynpy_shape.ShapeCommentTable</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_system.html">sdynpy.core.sdynpy_system</a></li>
@@ -187,27 +191,33 @@ <h1>sdynpy.core.sdynpy_shape.ShapeArray<a class="headerlink" href="#sdynpy-core-
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.concatenate_dofs" title="sdynpy.core.sdynpy_shape.ShapeArray.concatenate_dofs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">concatenate_dofs</span></code></a>(shape_arrays)</p></td>
 <td><p>Combines the degrees of freedom from multiple shapes into one set of shapes</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.expand" title="sdynpy.core.sdynpy_shape.ShapeArray.expand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expand</span></code></a>(initial_geometry, expansion_geometry, ...)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.edit_comments" title="sdynpy.core.sdynpy_shape.ShapeArray.edit_comments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">edit_comments</span></code></a>([geometry])</p></td>
+<td><p>Opens up a table where the shape comments can be edited</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.expand" title="sdynpy.core.sdynpy_shape.ShapeArray.expand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expand</span></code></a>(initial_geometry, expansion_geometry, ...)</p></td>
 <td><p>Perform SEREP expansion on shape data</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_exodus" title="sdynpy.core.sdynpy_shape.ShapeArray.from_exodus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_exodus</span></code></a>(exo[, x_disp, y_disp, z_disp, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_exodus" title="sdynpy.core.sdynpy_shape.ShapeArray.from_exodus"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_exodus</span></code></a>(exo[, x_disp, y_disp, z_disp, ...])</p></td>
 <td><p>Reads shape data from displacements in an Exodus file</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_imat_struct" title="sdynpy.core.sdynpy_shape.ShapeArray.from_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_imat_struct</span></code></a>(imat_shp_struct)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_imat_struct" title="sdynpy.core.sdynpy_shape.ShapeArray.from_imat_struct"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_imat_struct</span></code></a>(imat_shp_struct)</p></td>
 <td><p>Constructs a ShapeArray from an imat_shp class saved to a Matlab structure</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_uff" title="sdynpy.core.sdynpy_shape.ShapeArray.from_uff"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff</span></code></a>(unv_data_dict[, combine])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_uff" title="sdynpy.core.sdynpy_shape.ShapeArray.from_uff"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff</span></code></a>(unv_data_dict[, combine])</p></td>
 <td><p>Load ShapeArrays from universal file data from read_unv</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_unv" title="sdynpy.core.sdynpy_shape.ShapeArray.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_data_dict[, combine])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.from_unv" title="sdynpy.core.sdynpy_shape.ShapeArray.from_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_unv</span></code></a>(unv_data_dict[, combine])</p></td>
 <td><p>Load ShapeArrays from universal file data from read_unv</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.is_complex" title="sdynpy.core.sdynpy_shape.ShapeArray.is_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_complex</span></code></a>()</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.is_complex" title="sdynpy.core.sdynpy_shape.ShapeArray.is_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_complex</span></code></a>()</p></td>
 <td><p>Returns true if the shape is a complex shape, False if shape is real</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.mode_table" title="sdynpy.core.sdynpy_shape.ShapeArray.mode_table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mode_table</span></code></a>([table_format, frequency_format, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.mode_table" title="sdynpy.core.sdynpy_shape.ShapeArray.mode_table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mode_table</span></code></a>([table_format, frequency_format, ...])</p></td>
 <td><p>Generates a table of modal information including frequency and damping</p></td>
 </tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.normalize" title="sdynpy.core.sdynpy_shape.ShapeArray.normalize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normalize</span></code></a>(system_or_matrix[, ...])</p></td>
+<td><p>Computes A-normalized or mass-normalized shapes</p></td>
+</tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.optimize_degrees_of_freedom" title="sdynpy.core.sdynpy_shape.ShapeArray.optimize_degrees_of_freedom"><code class="xref py py-obj docutils literal notranslate"><span class="pre">optimize_degrees_of_freedom</span></code></a>(sensors_to_keep)</p></td>
 <td><p>Creates a reduced set of shapes using optimal degrees of freedom</p></td>
 </tr>
@@ -238,13 +248,16 @@ <h1>sdynpy.core.sdynpy_shape.ShapeArray<a class="headerlink" href="#sdynpy-core-
 <tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.time_integrate" title="sdynpy.core.sdynpy_shape.ShapeArray.time_integrate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">time_integrate</span></code></a>(forces, dt[, responses, ...])</p></td>
 <td><p>Integrate equations of motion created from shapes</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_real" title="sdynpy.core.sdynpy_shape.ShapeArray.to_real"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_real</span></code></a>([force_angle])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_complex" title="sdynpy.core.sdynpy_shape.ShapeArray.to_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_complex</span></code></a>()</p></td>
+<td><p>Creates complex shapes from real shapes</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_real" title="sdynpy.core.sdynpy_shape.ShapeArray.to_real"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_real</span></code></a>([force_angle])</p></td>
 <td><p>Creates real shapes from complex shapes by collapsing the complexity</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.transform_coordinate_system" title="sdynpy.core.sdynpy_shape.ShapeArray.transform_coordinate_system"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform_coordinate_system</span></code></a>(...[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.transform_coordinate_system" title="sdynpy.core.sdynpy_shape.ShapeArray.transform_coordinate_system"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform_coordinate_system</span></code></a>(...[, ...])</p></td>
 <td><p>Performs coordinate system transformations on the shape</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.write_to_unv" title="sdynpy.core.sdynpy_shape.ShapeArray.write_to_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_to_unv</span></code></a>(filename[, specific_data_type, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray.write_to_unv" title="sdynpy.core.sdynpy_shape.ShapeArray.write_to_unv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">write_to_unv</span></code></a>(filename[, specific_data_type, ...])</p></td>
 <td><p>Writes shape data to a unverisal file</p></td>
 </tr>
 </tbody>
@@ -327,6 +340,34 @@ <h1>sdynpy.core.sdynpy_shape.ShapeArray<a class="headerlink" href="#sdynpy-core-
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeArray.edit_comments">
+<span class="sig-name descname"><span class="pre">edit_comments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeArray.edit_comments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeArray.edit_comments" title="Permalink to this definition"></a></dt>
+<dd><p>Opens up a table where the shape comments can be edited</p>
+<p>If a geometry is also passed, it will also open up a mode shape plotter
+window where you can visualize the modes you are looking at.</p>
+<p>Edited comments will be stored back into the ShapeArray object when the
+OK button is pressed.  Comments will not be stored if the Cancel button
+is pressed.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>geometry</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry" title="sdynpy.core.sdynpy_geometry.Geometry"><em>Geometry</em></a><em>, </em><em>optional</em>) – A geometry on which the shapes will be plotted.  If not specified,
+a table will just open up.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A ShapeCommentTable displaying the modal information where comments
+can be edited.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable" title="sdynpy.core.sdynpy_shape.ShapeCommentTable">ShapeCommentTable</a></p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Due to how Python handles garbage collection, the table may be
+immediately closed if not assigned to a variable, as Python things it
+is no longer in use.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeArray.expand">
 <span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initial_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expansion_geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expansion_shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_id_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expansion_coordinates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_coefficients</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeArray.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeArray.expand" title="Permalink to this definition"></a></dt>
@@ -521,6 +562,28 @@ <h1>sdynpy.core.sdynpy_shape.ShapeArray<a class="headerlink" href="#sdynpy-core-
 <dd><p>The number of degrees of freedom in the shape</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeArray.normalize">
+<span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">system_or_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_modal_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeArray.normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeArray.normalize" title="Permalink to this definition"></a></dt>
+<dd><p>Computes A-normalized or mass-normalized shapes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>system_or_matrix</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System" title="sdynpy.core.sdynpy_system.System"><em>System</em></a><em> or </em><em>np.ndarray</em>) – A System object or a mass matrix for real modes or A-matrix for
+complex modes.</p></li>
+<li><p><strong>return_modal_matrix</strong> (<em>bool</em><em>, </em><em>optional</em>) – If true, it will return the modal mass or modal-A matrix computed
+from the normalized mode shapes.  The default is False.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A copy of the original shape array with normalized shape coefficients</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray" title="sdynpy.core.sdynpy_shape.ShapeArray">ShapeArray</a></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeArray.optimize_degrees_of_freedom">
 <span class="sig-name descname"><span class="pre">optimize_degrees_of_freedom</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sensors_to_keep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ei'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeArray.optimize_degrees_of_freedom"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeArray.optimize_degrees_of_freedom" title="Permalink to this definition"></a></dt>
@@ -729,14 +792,28 @@ <h1>sdynpy.core.sdynpy_shape.ShapeArray<a class="headerlink" href="#sdynpy-core-
 </dd>
 <dt class="field-odd">Returns</dt>
 <dd class="field-odd"><p><ul class="simple">
-<li><p><strong>response_array</strong> (<em>TimeHistoryArray</em>) – Input forces assembled into a TimeHistoryArray.</p></li>
-<li><p><strong>reference_array</strong> (<em>TimeHistoryArray</em>) – Responses assembled into a TimeHistoryArray.</p></li>
+<li><p><strong>response_array</strong> (<em>TimeHistoryArray</em>) – Responses assembled into a TimeHistoryArray.</p></li>
+<li><p><strong>reference_array</strong> (<em>TimeHistoryArray</em>) – Input forces assembled into a TimeHistoryArray.</p></li>
 </ul>
 </p>
 </dd>
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeArray.to_complex">
+<span class="sig-name descname"><span class="pre">to_complex</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeArray.to_complex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_complex" title="Permalink to this definition"></a></dt>
+<dd><p>Creates complex shapes from real shapes</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Complex shapes compute from the real shape coefficients</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeArray" title="sdynpy.core.sdynpy_shape.ShapeArray">ShapeArray</a></p>
+</dd>
+</dl>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeArray.to_real">
 <span class="sig-name descname"><span class="pre">to_real</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">force_angle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-0.7853981633974483</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeArray.to_real"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeArray.to_real" title="Permalink to this definition"></a></dt>
@@ -825,13 +902,13 @@ <h1>sdynpy.core.sdynpy_shape.ShapeArray<a class="headerlink" href="#sdynpy-core-
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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-        <a href="sdynpy.core.sdynpy_system.html" class="btn btn-neutral float-right" title="sdynpy.core.sdynpy_system" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed2ffc70>
+<jinja2.runtime.BlockReference object at 0x7f5a2b5577c0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html b/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html
new file mode 100644
index 0000000..ad8a903
--- /dev/null
+++ b/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html
@@ -0,0 +1,242 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable"><code class="docutils literal notranslate"><span class="pre">ShapeCommentTable</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__"><code class="docutils literal notranslate"><span class="pre">ShapeCommentTable.__init__()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.accept"><code class="docutils literal notranslate"><span class="pre">ShapeCommentTable.accept()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode"><code class="docutils literal notranslate"><span class="pre">ShapeCommentTable.update_mode()</span></code></a></li>
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+             
+  <section id="sdynpy-core-sdynpy-shape-shapecommenttable">
+<h1>sdynpy.core.sdynpy_shape.ShapeCommentTable<a class="headerlink" href="#sdynpy-core-sdynpy-shape-shapecommenttable" title="Permalink to this heading"></a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeCommentTable">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ShapeCommentTable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plotter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeCommentTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">QDialog</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plotter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeCommentTable.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__" title="Permalink to this definition"></a></dt>
+<dd><p>Creates a table window that allows editing of comments on the mode
+shapes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shapes</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray" title="sdynpy.core.sdynpy_shape.ShapeArray"><em>ShapeArray</em></a>) – The shapes for which the comments need to be modified.</p></li>
+<li><p><strong>plotter</strong> (<a class="reference internal" href="sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter" title="sdynpy.core.sdynpy_geometry.ShapePlotter"><em>ShapePlotter</em></a><em>, </em><em>optional</em>) – A shape plotter that is to be linked to the table.  It should have
+the same modes used for the table plotted on it.  The plotter will
+automatically update the mode being displayed as different rows of
+the table are selected. If not specified, there will be no mode
+shape display linked to the table.</p></li>
+<li><p><strong>parent</strong> (<em>QWidget</em><em>, </em><em>optional</em>) – Parent widget for the window. The default is No parent.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>None.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__" title="sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(shapes[, plotter, parent])</p></td>
+<td><p>Creates a table window that allows editing of comments on the mode shapes.</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.accept" title="sdynpy.core.sdynpy_shape.ShapeCommentTable.accept"><code class="xref py py-obj docutils literal notranslate"><span class="pre">accept</span></code></a>(self)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode" title="sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update_mode</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeCommentTable.accept">
+<span class="sig-name descname"><span class="pre">accept</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeCommentTable.accept"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.accept" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode">
+<span class="sig-name descname"><span class="pre">update_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#ShapeCommentTable.update_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.core.sdynpy_shape.ShapeArray.html" class="btn btn-neutral float-left" title="sdynpy.core.sdynpy_shape.ShapeArray" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.core.sdynpy_system.html" class="btn btn-neutral float-right" title="sdynpy.core.sdynpy_system" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f5a29666bc0>
+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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diff --git a/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html b/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html
index 876a6cb..e2ceb91 100644
--- a/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html
+++ b/_autosummary/sdynpy.core.sdynpy_shape.concatenate_dofs.html
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-  <title>sdynpy.core.sdynpy_shape.concatenate_dofs &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_shape.concatenate_dofs &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -84,6 +84,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeArray.html">sdynpy.core.sdynpy_shape.ShapeArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html">sdynpy.core.sdynpy_shape.ShapeCommentTable</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_system.html">sdynpy.core.sdynpy_system</a></li>
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diff --git a/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html b/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html
index 4f0d420..e8f89e2 100644
--- a/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html
+++ b/_autosummary/sdynpy.core.sdynpy_shape.from_exodus.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_shape.from_exodus &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_shape.from_exodus &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -84,6 +84,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeArray.html">sdynpy.core.sdynpy_shape.ShapeArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html">sdynpy.core.sdynpy_shape.ShapeCommentTable</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_system.html">sdynpy.core.sdynpy_system</a></li>
@@ -187,7 +188,7 @@ <h1>sdynpy.core.sdynpy_shape.from_exodus<a class="headerlink" href="#sdynpy-core
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-<jinja2.runtime.BlockReference object at 0x7f99ed2fef80>
+<jinja2.runtime.BlockReference object at 0x7f5a29480820>
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diff --git a/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html b/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html
index 05c2822..34a4145 100644
--- a/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html
+++ b/_autosummary/sdynpy.core.sdynpy_shape.from_imat_struct.html
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-  <title>sdynpy.core.sdynpy_shape.from_imat_struct &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_shape.from_imat_struct &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -84,6 +84,7 @@
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeArray.html">sdynpy.core.sdynpy_shape.ShapeArray</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html">sdynpy.core.sdynpy_shape.ShapeCommentTable</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.core.sdynpy_system.html">sdynpy.core.sdynpy_system</a></li>
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diff --git a/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html b/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html
index 0151f82..b392d68 100644
--- a/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html
+++ b/_autosummary/sdynpy.core.sdynpy_shape.from_unv.html
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+<span class="sig-name descname"><span class="pre">rigid_body_check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rigid_shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_yscale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residuals_to_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_shape_diagnostics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_figures</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rigid_shape_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/core/sdynpy_shape.html#rigid_body_check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.core.sdynpy_shape.rigid_body_check" title="Permalink to this definition"></a></dt>
 <dd><p>Performs rigid body checks, both looking at the complex plane and residuals</p>
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diff --git a/_autosummary/sdynpy.core.sdynpy_shape.shape_array.html b/_autosummary/sdynpy.core.sdynpy_shape.shape_array.html
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@@ -84,6 +84,7 @@
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diff --git a/_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html b/_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html
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diff --git a/_autosummary/sdynpy.core.sdynpy_system.System.html b/_autosummary/sdynpy.core.sdynpy_system.System.html
index b884d5c..505205a 100644
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@@ -95,6 +95,7 @@
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.reduce_craig_bampton"><code class="docutils literal notranslate"><span class="pre">System.reduce_craig_bampton()</span></code></a></li>
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@@ -243,37 +244,40 @@ <h1>sdynpy.core.sdynpy_system.System<a class="headerlink" href="#sdynpy-core-sdy
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+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.remove_transformation" title="sdynpy.core.sdynpy_system.System.remove_transformation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remove_transformation</span></code></a>()</p></td>
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+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.set_proportional_damping" title="sdynpy.core.sdynpy_system.System.set_proportional_damping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_proportional_damping</span></code></a>(mass_fraction, ...)</p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.simulate_test" title="sdynpy.core.sdynpy_system.System.simulate_test"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulate_test</span></code></a>(bandwidth, frame_length, ...)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.simulate_test" title="sdynpy.core.sdynpy_system.System.simulate_test"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulate_test</span></code></a>(bandwidth, frame_length, ...)</p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.spy" title="sdynpy.core.sdynpy_system.System.spy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">spy</span></code></a>([subplots_kwargs, spy_kwargs])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.spy" title="sdynpy.core.sdynpy_system.System.spy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">spy</span></code></a>([subplots_kwargs, spy_kwargs])</p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.substructure_by_coordinate" title="sdynpy.core.sdynpy_system.System.substructure_by_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_coordinate</span></code></a>(dof_pairs[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.substructure_by_coordinate" title="sdynpy.core.sdynpy_system.System.substructure_by_coordinate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_coordinate</span></code></a>(dof_pairs[, ...])</p></td>
 <td><p>Constrain the system by connecting the specified degree of freedom pairs</p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.substructure_by_position" title="sdynpy.core.sdynpy_system.System.substructure_by_position"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_position</span></code></a>(systems, geometries)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.substructure_by_position" title="sdynpy.core.sdynpy_system.System.substructure_by_position"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_position</span></code></a>(systems, geometries)</p></td>
 <td><p>Applies constraints to systems by constraining colocated nodes together</p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.substructure_by_shape" title="sdynpy.core.sdynpy_system.System.substructure_by_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_shape</span></code></a>(constraint_shapes, ...)</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.substructure_by_shape" title="sdynpy.core.sdynpy_system.System.substructure_by_shape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">substructure_by_shape</span></code></a>(constraint_shapes, ...)</p></td>
 <td><p>Constrain the system using a set of shapes in a least-squares sense.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.time_integrate" title="sdynpy.core.sdynpy_system.System.time_integrate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">time_integrate</span></code></a>(forces[, dt, responses, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.time_integrate" title="sdynpy.core.sdynpy_system.System.time_integrate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">time_integrate</span></code></a>(forces[, dt, responses, ...])</p></td>
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 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.to_state_space" title="sdynpy.core.sdynpy_system.System.to_state_space"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_state_space</span></code></a>([output_displacement, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.to_state_space" title="sdynpy.core.sdynpy_system.System.to_state_space"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_state_space</span></code></a>([output_displacement, ...])</p></td>
 <td><p>Compute the state space representation of the system</p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.transformation_matrix_at_coordinates" title="sdynpy.core.sdynpy_system.System.transformation_matrix_at_coordinates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transformation_matrix_at_coordinates</span></code></a>(coordinates)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.transformation_matrix_at_coordinates" title="sdynpy.core.sdynpy_system.System.transformation_matrix_at_coordinates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transformation_matrix_at_coordinates</span></code></a>(coordinates)</p></td>
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+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.core.sdynpy_system.System.transformation_shapes" title="sdynpy.core.sdynpy_system.System.transformation_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transformation_shapes</span></code></a>([shape_indices])</p></td>
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@@ -699,6 +703,11 @@ <h1>sdynpy.core.sdynpy_system.System<a class="headerlink" href="#sdynpy-core-sdy
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@@ -964,7 +973,7 @@ <h1>sdynpy.core.sdynpy_system.System<a class="headerlink" href="#sdynpy-core-sdy
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diff --git a/_autosummary/sdynpy.core.sdynpy_system.html b/_autosummary/sdynpy.core.sdynpy_system.html
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@@ -29,7 +29,7 @@
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-    <link rel="prev" title="sdynpy.core.sdynpy_shape.ShapeArray" href="sdynpy.core.sdynpy_shape.ShapeArray.html" /> 
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+        <a href="sdynpy.core.sdynpy_shape.ShapeCommentTable.html" class="btn btn-neutral float-left" title="sdynpy.core.sdynpy_shape.ShapeCommentTable" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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   <hr/>
 
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diff --git a/_autosummary/sdynpy.core.sdynpy_system.substructure_by_position.html b/_autosummary/sdynpy.core.sdynpy_system.substructure_by_position.html
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-  <title>sdynpy.core.sdynpy_system.substructure_by_position &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_system.substructure_by_position &mdash; SDynPy 0.14.0 documentation</title>
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@@ -159,7 +159,7 @@ <h1>sdynpy.core.sdynpy_system.substructure_by_position<a class="headerlink" href
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diff --git a/_autosummary/sdynpy.demo.beam_airplane.create_models.html b/_autosummary/sdynpy.demo.beam_airplane.create_models.html
index e016f0e..e975169 100644
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-  <title>sdynpy.demo.beam_airplane.create_models &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.demo.beam_airplane.create_models &mdash; SDynPy 0.14.0 documentation</title>
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       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -150,7 +150,7 @@ <h1>sdynpy.demo.beam_airplane.create_models<a class="headerlink" href="#sdynpy-d
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diff --git a/_autosummary/sdynpy.demo.beam_airplane.html b/_autosummary/sdynpy.demo.beam_airplane.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.demo.beam_airplane &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.demo.beam_airplane &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -149,7 +149,7 @@
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diff --git a/_autosummary/sdynpy.demo.beam_plate.create_models.html b/_autosummary/sdynpy.demo.beam_plate.create_models.html
index 22b4fab..b96351a 100644
--- a/_autosummary/sdynpy.demo.beam_plate.create_models.html
+++ b/_autosummary/sdynpy.demo.beam_plate.create_models.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.demo.beam_plate.create_models &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.demo.beam_plate.create_models &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -132,7 +132,7 @@ <h1>sdynpy.demo.beam_plate.create_models<a class="headerlink" href="#sdynpy-demo
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed5c59f0>
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   </div>
diff --git a/_autosummary/sdynpy.demo.beam_plate.html b/_autosummary/sdynpy.demo.beam_plate.html
index ded1b5a..6157a4b 100644
--- a/_autosummary/sdynpy.demo.beam_plate.html
+++ b/_autosummary/sdynpy.demo.beam_plate.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.demo.beam_plate &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.demo.beam_plate &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -149,7 +149,7 @@
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+<jinja2.runtime.BlockReference object at 0x7f5a265a2b30>
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diff --git a/_autosummary/sdynpy.demo.html b/_autosummary/sdynpy.demo.html
index c32c5b2..82b3e12 100644
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+  <title>sdynpy.demo &mdash; SDynPy 0.14.0 documentation</title>
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@@ -148,7 +148,7 @@
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+<jinja2.runtime.BlockReference object at 0x7f5a2686b5e0>
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diff --git a/_autosummary/sdynpy.doc.html b/_autosummary/sdynpy.doc.html
index df78b7d..198e0ca 100644
--- a/_autosummary/sdynpy.doc.html
+++ b/_autosummary/sdynpy.doc.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -148,7 +148,7 @@
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-<jinja2.runtime.BlockReference object at 0x7f99ed5dbdf0>
+<jinja2.runtime.BlockReference object at 0x7f5a264885b0>
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diff --git a/_autosummary/sdynpy.doc.sdynpy_latex.create_latex_summary.html b/_autosummary/sdynpy.doc.sdynpy_latex.create_latex_summary.html
index b894344..c597fa8 100644
--- a/_autosummary/sdynpy.doc.sdynpy_latex.create_latex_summary.html
+++ b/_autosummary/sdynpy.doc.sdynpy_latex.create_latex_summary.html
@@ -4,7 +4,7 @@
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-  <title>sdynpy.doc.sdynpy_latex.create_latex_summary &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_latex.create_latex_summary &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -28,7 +28,7 @@
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="sdynpy.doc.sdynpy_ppt" href="sdynpy.doc.sdynpy_ppt.html" />
+    <link rel="next" title="sdynpy.doc.sdynpy_latex.figure" href="sdynpy.doc.sdynpy_latex.figure.html" />
     <link rel="prev" title="sdynpy.doc.sdynpy_latex" href="sdynpy.doc.sdynpy_latex.html" /> 
 </head>
 
@@ -68,6 +68,8 @@
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.doc.sdynpy_latex.create_latex_summary"><code class="docutils literal notranslate"><span class="pre">create_latex_summary()</span></code></a></li>
 </ul>
 </li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.figure.html">sdynpy.doc.sdynpy_latex.figure</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.table.html">sdynpy.doc.sdynpy_latex.table</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.doc.sdynpy_ppt.html">sdynpy.doc.sdynpy_ppt</a></li>
@@ -126,13 +128,13 @@ <h1>sdynpy.doc.sdynpy_latex.create_latex_summary<a class="headerlink" href="#sdy
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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   <hr/>
 
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-<jinja2.runtime.BlockReference object at 0x7f99ec6bf9d0>
+<jinja2.runtime.BlockReference object at 0x7f5a267e0970>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.doc.sdynpy_latex.figure.html b/_autosummary/sdynpy.doc.sdynpy_latex.figure.html
new file mode 100644
index 0000000..cb1706f
--- /dev/null
+++ b/_autosummary/sdynpy.doc.sdynpy_latex.figure.html
@@ -0,0 +1,228 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+<li class="toctree-l3"><a class="reference internal" href="sdynpy.core.html">sdynpy.core</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.demo.html">sdynpy.demo</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="sdynpy.doc.html">sdynpy.doc</a><ul class="current">
+<li class="toctree-l4 current"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.html">sdynpy.doc.sdynpy_latex</a><ul class="current">
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.create_latex_summary.html">sdynpy.doc.sdynpy_latex.create_latex_summary</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.doc.sdynpy_latex.figure</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.doc.sdynpy_latex.figure"><code class="docutils literal notranslate"><span class="pre">figure()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.table.html">sdynpy.doc.sdynpy_latex.table</a></li>
+</ul>
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+</ul>
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+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fem.html">sdynpy.fem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
+</li>
+</ul>
+</li>
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+
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+          <li class="breadcrumb-item"><a href="sdynpy.doc.sdynpy_latex.html">sdynpy.doc.sdynpy_latex</a></li>
+      <li class="breadcrumb-item active">sdynpy.doc.sdynpy_latex.figure</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_autosummary/sdynpy.doc.sdynpy_latex.figure.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-doc-sdynpy-latex-figure">
+<h1>sdynpy.doc.sdynpy_latex.figure<a class="headerlink" href="#sdynpy-doc-sdynpy-latex-figure" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.doc.sdynpy_latex.figure">
+<span class="sig-name descname"><span class="pre">figure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figures</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure_caption</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graphics_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'width=0.7\\linewidth'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">animate_graphics_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'width=0.7\\linewidth,loop'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure_placement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subfigure_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'[t]{0.45\\linewidth}'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subfigure_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subfigure_captions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subfigures_per_page</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_subfigures_first_page</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure_save_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latex_root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/doc/sdynpy_latex.html#figure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.doc.sdynpy_latex.figure" title="Permalink to this definition"></a></dt>
+<dd><p>Adds figures, subfigures, and animations to a running latex document.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>figures</strong> (<em>list</em>) – Figure or figures that can be inserted into a latex document.  See
+note for various figure types and configurations that can be used.</p></li>
+<li><p><strong>figure_label</strong> (<em>str</em><em>, </em><em>optional</em>) – The label that will be used for the figure in the latex document.
+If not specified, the figure will not be labeled.</p></li>
+<li><p><strong>figure_caption</strong> (<em>str</em><em>, </em><em>optional</em>) – The caption that will be used for the figure in the latex document.
+If not specified, the figure will only be captioned with the figure
+number.</p></li>
+<li><p><strong>graphics_options</strong> (<em>str</em><em>, </em><em>optional</em>) – Graphics options that will be used for the figure.  If not specified
+this will be r’width=0.7linewidth’.</p></li>
+<li><p><strong>animate_graphics_options</strong> (<em>str</em><em>, </em><em>optional</em>) – Graphics options that will be used for an animation.  If not specified
+this will be r’width=0.7linewidth,loop’.</p></li>
+<li><p><strong>figure_placement</strong> (<em>str</em><em>, </em><em>optional</em>) – Specify the placement of the figure with strings such as ‘[t]’, ‘[b]’,
+or ‘[h]’.  If not specified, the figure will have no placement
+specified.</p></li>
+<li><p><strong>subfigure_options</strong> (<em>str</em><em>, </em><em>optional</em>) – The options that will be applied to each subfigure in the figure, if
+subfigures are specified.  By default, this will be r’[t]{0.45linewidth}’</p></li>
+<li><p><strong>subfigure_labels</strong> (<em>str</em><em>, </em><em>optional</em>) – Labels to apply to the subfigure.  This can either be a list of strings
+the same size as the list of figures, or a string with a format specifier
+accepting the subfigure index.  If not specified, the subfigures will
+not be labeled.</p></li>
+<li><p><strong>subfigure_captions</strong> (<em>list</em><em>, </em><em>optional</em>) – A list of strings the same length as the list of figures to use as
+captions for the subfigures.  If not specified, the subfigures will
+only be captioned with the subfigure number.</p></li>
+<li><p><strong>max_subfigures_per_page</strong> (<em>int</em><em>, </em><em>optional</em>) – The maximum number of subfigures on a page.  Longer figures will be
+broken up into multiple figures using ContinuedFloat.  If not specified,
+a single figure environment will be generated.</p></li>
+<li><p><strong>max_subfigures_first_page</strong> (<em>int</em><em>, </em><em>optional</em>) – The maximum number of subfigures on the first page.  Longer figures will be
+broken up into multiple figures using ContinuedFloat.  If not specified,
+the max_subfigures_per_page value will be used if specified, otherwise
+a single figure environment will be generated.</p></li>
+<li><p><strong>figure_save_names</strong> (<em>str</em><em> or </em><em>list</em><em> of </em><em>str</em><em>, </em><em>optional</em>) – File names to save the figures as.  This can be specified as a string
+with a format specifier in it that will accept the figure index, or
+a list of strings the same length as the list of figures.
+If not specified, files will be specified as ‘figure_0’,
+‘figure_1’, etc.  If file names are not present, then the file name
+will be automatically selected for the type of figure given.</p></li>
+<li><p><strong>latex_root</strong> (<em>str</em><em>, </em><em>optional</em>) – Directory in which the latex .tex file will be constructed.  This is
+used to create relative paths to the save_figure_names within the latex
+document.  If not specified, then the current directory will be assumed.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>latex_string</strong> – The latex source code to insert the figures into the document.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>The <cite>figures</cite> argument must be a list of figures.  If only one entry
+is present in the list, a figure will be made in the latex document.  If
+multiple entries are present, a figure will be made and subfigures will be
+made for each entry in the list.  If an entry in the list is also a list,
+then that figure or subfigure will be made into an animation.</p>
+<p>The list of figures can contain many types of objects that a figure will be
+made from, including:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>A 2D numpy array</p></li>
+<li><p>A Matplotlib figure</p></li>
+<li><p>A pyqtgraph plotitem</p></li>
+<li><p>A bytes object that represents an image</p></li>
+<li><p>A string to a file name</p></li>
+</ul>
+</div></blockquote>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.doc.sdynpy_latex.create_latex_summary.html" class="btn btn-neutral float-left" title="sdynpy.doc.sdynpy_latex.create_latex_summary" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.doc.sdynpy_latex.table.html" class="btn btn-neutral float-right" title="sdynpy.doc.sdynpy_latex.table" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f5a2a185540>
+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/_autosummary/sdynpy.doc.sdynpy_latex.html b/_autosummary/sdynpy.doc.sdynpy_latex.html
index 385e34f..3e9dfc1 100644
--- a/_autosummary/sdynpy.doc.sdynpy_latex.html
+++ b/_autosummary/sdynpy.doc.sdynpy_latex.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc.sdynpy_latex &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_latex &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -65,6 +65,8 @@
 <li class="toctree-l3 current"><a class="reference internal" href="sdynpy.doc.html">sdynpy.doc</a><ul class="current">
 <li class="toctree-l4 current"><a class="current reference internal" href="#">sdynpy.doc.sdynpy_latex</a><ul>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.create_latex_summary.html">sdynpy.doc.sdynpy_latex.create_latex_summary</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.figure.html">sdynpy.doc.sdynpy_latex.figure</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.table.html">sdynpy.doc.sdynpy_latex.table</a></li>
 </ul>
 </li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.doc.sdynpy_ppt.html">sdynpy.doc.sdynpy_ppt</a></li>
@@ -134,6 +136,12 @@
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 <td><p></p></td>
 </tr>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.doc.sdynpy_latex.figure.html#sdynpy.doc.sdynpy_latex.figure" title="sdynpy.doc.sdynpy_latex.figure"><code class="xref py py-obj docutils literal notranslate"><span class="pre">figure</span></code></a>(figures[, figure_label, ...])</p></td>
+<td><p>Adds figures, subfigures, and animations to a running latex document.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.doc.sdynpy_latex.table.html#sdynpy.doc.sdynpy_latex.table" title="sdynpy.doc.sdynpy_latex.table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">table</span></code></a>(table[, justification_string, ...])</p></td>
+<td><p></p></td>
+</tr>
 </tbody>
 </table>
 </section>
@@ -149,7 +157,7 @@
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-<jinja2.runtime.BlockReference object at 0x7f99ed5cac20>
+<jinja2.runtime.BlockReference object at 0x7f5a2a361db0>
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diff --git a/_autosummary/sdynpy.doc.sdynpy_latex.table.html b/_autosummary/sdynpy.doc.sdynpy_latex.table.html
new file mode 100644
index 0000000..1617ced
--- /dev/null
+++ b/_autosummary/sdynpy.doc.sdynpy_latex.table.html
@@ -0,0 +1,159 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>sdynpy.doc.sdynpy_latex.table &mdash; SDynPy 0.14.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/copybutton.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/custom.css" />
+
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+        <script src="../_static/jquery.js"></script>
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+        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../_static/doctools.js"></script>
+        <script src="../_static/sphinx_highlight.js"></script>
+        <script src="../_static/clipboard.min.js"></script>
+        <script src="../_static/copybutton.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="sdynpy.doc.sdynpy_ppt" href="sdynpy.doc.sdynpy_ppt.html" />
+    <link rel="prev" title="sdynpy.doc.sdynpy_latex.figure" href="sdynpy.doc.sdynpy_latex.figure.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
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+<li class="toctree-l1"><a class="reference internal" href="../usage.html">Usage</a></li>
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+<li class="toctree-l1 current"><a class="reference internal" href="../modules.html">SDynPy Programming Interface</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="sdynpy.html">sdynpy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.core.html">sdynpy.core</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.demo.html">sdynpy.demo</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="sdynpy.doc.html">sdynpy.doc</a><ul class="current">
+<li class="toctree-l4 current"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.html">sdynpy.doc.sdynpy_latex</a><ul class="current">
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.create_latex_summary.html">sdynpy.doc.sdynpy_latex.create_latex_summary</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.doc.sdynpy_latex.figure.html">sdynpy.doc.sdynpy_latex.figure</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.doc.sdynpy_latex.table</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.doc.sdynpy_latex.table"><code class="docutils literal notranslate"><span class="pre">table()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.doc.sdynpy_ppt.html">sdynpy.doc.sdynpy_ppt</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fem.html">sdynpy.fem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+          <a href="../index.html">SDynPy</a>
+      </nav>
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../modules.html">SDynPy Programming Interface</a></li>
+          <li class="breadcrumb-item"><a href="sdynpy.html">sdynpy</a></li>
+          <li class="breadcrumb-item"><a href="sdynpy.doc.html">sdynpy.doc</a></li>
+          <li class="breadcrumb-item"><a href="sdynpy.doc.sdynpy_latex.html">sdynpy.doc.sdynpy_latex</a></li>
+      <li class="breadcrumb-item active">sdynpy.doc.sdynpy_latex.table</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_autosummary/sdynpy.doc.sdynpy_latex.table.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-doc-sdynpy-latex-table">
+<h1>sdynpy.doc.sdynpy_latex.table<a class="headerlink" href="#sdynpy-doc-sdynpy-latex-table" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.doc.sdynpy_latex.table">
+<span class="sig-name descname"><span class="pre">table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">table</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">justification_string</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">table_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">table_caption</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">longtable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">horizontal_lines</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/doc/sdynpy_latex.html#table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.doc.sdynpy_latex.table" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.doc.sdynpy_latex.figure.html" class="btn btn-neutral float-left" title="sdynpy.doc.sdynpy_latex.figure" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.doc.sdynpy_ppt.html" class="btn btn-neutral float-right" title="sdynpy.doc.sdynpy_ppt" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f5a26969cf0>
+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
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+</html>
\ No newline at end of file
diff --git a/_autosummary/sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide.html b/_autosummary/sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide.html
index eda1f38..0008829 100644
--- a/_autosummary/sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide.html
+++ b/_autosummary/sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -140,7 +140,7 @@ <h1>sdynpy.doc.sdynpy_ppt.add_geometry_overview_slide<a class="headerlink" href=
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eca818d0>
+<jinja2.runtime.BlockReference object at 0x7f5a26ea1120>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.doc.sdynpy_ppt.add_section_header_slide.html b/_autosummary/sdynpy.doc.sdynpy_ppt.add_section_header_slide.html
index cddd61a..91fa0ac 100644
--- a/_autosummary/sdynpy.doc.sdynpy_ppt.add_section_header_slide.html
+++ b/_autosummary/sdynpy.doc.sdynpy_ppt.add_section_header_slide.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc.sdynpy_ppt.add_section_header_slide &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_ppt.add_section_header_slide &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -140,7 +140,7 @@ <h1>sdynpy.doc.sdynpy_ppt.add_section_header_slide<a class="headerlink" href="#s
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee9f54e0>
+<jinja2.runtime.BlockReference object at 0x7f5a2a46e260>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_animation_slides.html b/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_animation_slides.html
index f69df8d..fc098e4 100644
--- a/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_animation_slides.html
+++ b/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_animation_slides.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc.sdynpy_ppt.add_shape_animation_slides &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_ppt.add_shape_animation_slides &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -140,7 +140,7 @@ <h1>sdynpy.doc.sdynpy_ppt.add_shape_animation_slides<a class="headerlink" href="
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecad4a90>
+<jinja2.runtime.BlockReference object at 0x7f5a26a00640>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_comparison_animation_slides.html b/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_comparison_animation_slides.html
index 709f936..b6c7f48 100644
--- a/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_comparison_animation_slides.html
+++ b/_autosummary/sdynpy.doc.sdynpy_ppt.add_shape_comparison_animation_slides.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-<li class="toctree-l8"><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_b"><code class="docutils literal notranslate"><span class="pre">Sdynpy_UFF_Dataset_55.real_modal_b</span></code></a></li>
 <li class="toctree-l8"><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.time"><code class="docutils literal notranslate"><span class="pre">Sdynpy_UFF_Dataset_55.time</span></code></a></li>
 <li class="toctree-l8"><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.time_step_number"><code class="docutils literal notranslate"><span class="pre">Sdynpy_UFF_Dataset_55.time_step_number</span></code></a></li>
 <li class="toctree-l8"><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.write_string"><code class="docutils literal notranslate"><span class="pre">Sdynpy_UFF_Dataset_55.write_string()</span></code></a></li>
@@ -188,10 +184,10 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_5
 <col style="width: 90%" />
 </colgroup>
 <tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.buckling_eigenvalue" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.buckling_eigenvalue"><code class="xref py py-obj docutils literal notranslate"><span class="pre">buckling_eigenvalue</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.dataset_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.dataset_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset_number</span></code></a></p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.dataset_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.dataset_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dataset_number</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.eigenvalue" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.eigenvalue"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eigenvalue</span></code></a></p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.frequency" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.frequency"><code class="xref py py-obj docutils literal notranslate"><span class="pre">frequency</span></code></a></p></td>
@@ -203,37 +199,25 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_5
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.id_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.id_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">id_number</span></code></a></p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_eigenvalue" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_eigenvalue"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imaginary_eigenvalue</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_case_number</span></code></a></p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_a" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imaginary_modal_a</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_a" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_a</span></code></a></p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_b" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imaginary_modal_b</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_b" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_b</span></code></a></p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_case_number</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_hysteretic_damping" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_hysteretic_damping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_hysteretic_damping</span></code></a></p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_hysteretic_damping" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_hysteretic_damping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_hysteretic_damping</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_mass" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_mass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_mass</span></code></a></p></td>
 <td><p></p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_mass" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_mass"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_mass</span></code></a></p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_viscous_damping" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_viscous_damping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_viscous_damping</span></code></a></p></td>
 <td><p></p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_viscous_damping" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_viscous_damping"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modal_viscous_damping</span></code></a></p></td>
-<td><p></p></td>
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-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.mode_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.mode_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mode_number</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_eigenvalue" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_eigenvalue"><code class="xref py py-obj docutils literal notranslate"><span class="pre">real_eigenvalue</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_a" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_a"><code class="xref py py-obj docutils literal notranslate"><span class="pre">real_modal_a</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_b" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_b"><code class="xref py py-obj docutils literal notranslate"><span class="pre">real_modal_b</span></code></a></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.mode_number" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.mode_number"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mode_number</span></code></a></p></td>
 <td><p></p></td>
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@@ -245,13 +229,13 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_5
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+<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.dataset_number">
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-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.dataset_number">
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+<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.eigenvalue">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">eigenvalue</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.eigenvalue" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -275,23 +259,18 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_5
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-<dd></dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_a">
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+<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_case_number</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_b">
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+<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_a">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">modal_a</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_a" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.load_case_number">
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+<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_b">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">modal_b</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_b" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -314,21 +293,6 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_5
 <em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">mode_number</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.mode_number" title="Permalink to this definition"></a></dt>
 <dd></dd></dl>
 
-<dl class="py property">
-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_eigenvalue">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">real_eigenvalue</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_eigenvalue" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_a">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">real_modal_a</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_a" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
-<dl class="py property">
-<dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_b">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">real_modal_b</span></span><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_b" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
-
 <dl class="py property">
 <dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.time">
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@@ -359,7 +323,7 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_5
   <hr/>
 
   <div role="contentinfo">
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.html
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+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55 &mdash; SDynPy 0.14.0 documentation</title>
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-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.read &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.read &mdash; SDynPy 0.14.0 documentation</title>
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.html
index 31436ef..e0eac27 100644
--- a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.html
+++ b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.html
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-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58 &mdash; SDynPy 0.14.0 documentation</title>
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@@ -157,8 +157,8 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_5
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.__init__" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(idline1, idline2, idline3, idline4, ...)</p></td>
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-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.from_uff_data_array" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.from_uff_data_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff_data_array</span></code></a>(data)</p></td>
-<td><p></p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.from_uff_data_array" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.from_uff_data_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_uff_data_array</span></code></a>(block_lines, is_binary, ...)</p></td>
+<td><p>Extract function at nodal DOF - data-set 58.</p></td>
 </tr>
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@@ -184,8 +184,18 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_5
 
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-<dd></dd></dl>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_uff_data_array</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">block_lines</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_binary</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">byte_ordering</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">floating_point_format</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_ascii_lines_following</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_bytes_following</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_58.html#Sdynpy_UFF_Dataset_58.from_uff_data_array"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.from_uff_data_array" title="Permalink to this definition"></a></dt>
+<dd><p>Extract function at nodal DOF - data-set 58.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Object with attributes as the dataset fields and values containing the
+data from the universal file in those dataset fields.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>ds_58</p>
+</dd>
+</dl>
+</dd></dl>
 
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.html
index e1432c9..36f64bf 100644
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@@ -154,7 +154,7 @@
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+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read" title="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read</span></code></a>(data[, is_binary, byte_ordering, ...])</p></td>
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@@ -184,7 +184,7 @@
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.is_abscissa_even.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.is_abscissa_even.html
index 47e5d20..4e34345 100644
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.html
index 6759c27..a67a446 100644
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@@ -134,7 +134,7 @@
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 <dt class="sig sig-object py" id="sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read">
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+<span class="sig-name descname"><span class="pre">read</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_binary</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">byte_ordering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">floating_point_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_ascii_lines_following</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_bytes_following</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_58.html#read"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read" title="Permalink to this definition"></a></dt>
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@@ -150,7 +150,7 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read<a class="header
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_82.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_82.html
index 44dee96..09ee6c8 100644
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-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_82 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_82 &mdash; SDynPy 0.14.0 documentation</title>
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@@ -206,7 +206,7 @@ <h1>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_8
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.html
index e838bcd..b4aa534 100644
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+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82 &mdash; SDynPy 0.14.0 documentation</title>
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@@ -180,7 +180,7 @@
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diff --git a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.read.html b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.read.html
index 7f76241..d004261 100644
--- a/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.read.html
+++ b/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.read.html
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-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.read &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.read &mdash; SDynPy 0.14.0 documentation</title>
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 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.modal.sdynpy_ccmif.html b/_autosummary/sdynpy.modal.sdynpy_ccmif.html
index afc3de2..bfa1728 100644
--- a/_autosummary/sdynpy.modal.sdynpy_ccmif.html
+++ b/_autosummary/sdynpy.modal.sdynpy_ccmif.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_ccmif &mdash; SDynPy 0.11.0 documentation</title>
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@@ -71,6 +71,7 @@
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 </ul>
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@@ -156,7 +157,7 @@
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+<jinja2.runtime.BlockReference object at 0x7f5a271bd1b0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html b/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html
new file mode 100644
index 0000000..67aba74
--- /dev/null
+++ b/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html
@@ -0,0 +1,675 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
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+  <title>sdynpy.modal.sdynpy_modal_test.ModalTest &mdash; SDynPy 0.14.0 documentation</title>
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+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a><ul class="current">
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html">sdynpy.modal.sdynpy_modal_test.read_modal_fit_data</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modal_test.ModalTest</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest"><code class="docutils literal notranslate"><span class="pre">ModalTest</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.__init__"><code class="docutils literal notranslate"><span class="pre">ModalTest.__init__()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs"><code class="docutils literal notranslate"><span class="pre">ModalTest.compute_resynthesized_frfs()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities"><code class="docutils literal notranslate"><span class="pre">ModalTest.compute_spectral_quantities()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI"><code class="docutils literal notranslate"><span class="pre">ModalTest.compute_spectral_quantities_SignalProcessingGUI()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex"><code class="docutils literal notranslate"><span class="pre">ModalTest.create_documentation_latex()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx"><code class="docutils literal notranslate"><span class="pre">ModalTest.create_documentation_pptx()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word"><code class="docutils literal notranslate"><span class="pre">ModalTest.create_documentation_word()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters"><code class="docutils literal notranslate"><span class="pre">ModalTest.define_spectral_processing_parameters()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments"><code class="docutils literal notranslate"><span class="pre">ModalTest.edit_mode_comments()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy"><code class="docutils literal notranslate"><span class="pre">ModalTest.fit_modes_PolyPy()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC"><code class="docutils literal notranslate"><span class="pre">ModalTest.fit_modes_SMAC()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_cmif()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_coherence_image()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_deflection_shapes()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_drive_point_frf_coherence()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_drive_point_frfs()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_figures_for_documentation()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_mac()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_mmif()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_modeshape()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_nmif()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_psmif()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_qmif()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_reciprocal_frfs()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra"><code class="docutils literal notranslate"><span class="pre">ModalTest.plot_reference_autospectra()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.response_indices"><code class="docutils literal notranslate"><span class="pre">ModalTest.response_indices</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy"><code class="docutils literal notranslate"><span class="pre">ModalTest.retrieve_modes_PolyPy()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC"><code class="docutils literal notranslate"><span class="pre">ModalTest.retrieve_modes_SMAC()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI"><code class="docutils literal notranslate"><span class="pre">ModalTest.retrieve_spectral_quantities_SignalProcessingGUI()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_autopower_spectra()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_channel_table()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_coherence()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_fit_modes()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_frfs()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_geometry()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_resynthesized_frfs()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_rigid_body_shapes()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_time_histories()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_units"><code class="docutils literal notranslate"><span class="pre">ModalTest.set_units()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_signal_processing_gui.html">sdynpy.modal.sdynpy_signal_processing_gui</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_smac.html">sdynpy.modal.sdynpy_smac</a></li>
+</ul>
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+             
+  <section id="sdynpy-modal-sdynpy-modal-test-modaltest">
+<h1>sdynpy.modal.sdynpy_modal_test.ModalTest<a class="headerlink" href="#sdynpy-modal-sdynpy-modal-test-modaltest" title="Permalink to this heading"></a></h1>
+<dl class="py class">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">ModalTest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_histories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autopower_spectra</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frfs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coherence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_frfs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rigid_body_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">time_histories</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autopower_spectra</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frfs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coherence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_frfs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rigid_body_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">channel_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.__init__" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<p class="rubric">Methods</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.__init__" title="sdynpy.modal.sdynpy_modal_test.ModalTest.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>([geometry, time_histories, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs" title="sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_resynthesized_frfs</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities" title="sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_spectral_quantities</span></code></a>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI" title="sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_spectral_quantities_SignalProcessingGUI</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex" title="sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_documentation_latex</span></code></a>([mif_plots, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx" title="sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_documentation_pptx</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word" title="sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_documentation_word</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters" title="sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters"><code class="xref py py-obj docutils literal notranslate"><span class="pre">define_spectral_processing_parameters</span></code></a>(...)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments" title="sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">edit_mode_comments</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy" title="sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_modes_PolyPy</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC" title="sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_modes_SMAC</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_cmif</span></code></a>([measured, resynthesized, ...])</p></td>
+<td><p>Plots the complex mode indicator function</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_coherence_image</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_deflection_shapes</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_drive_point_frf_coherence</span></code></a>([...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_drive_point_frfs</span></code></a>([part, plot_kwargs, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_figures_for_documentation</span></code></a>([...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_mac</span></code></a>(*matrix_plot_args, **matrix_plot_kwargs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_mmif</span></code></a>([measured, resynthesized, ...])</p></td>
+<td><p>Plots the multi mode indicator function</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_modeshape</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_nmif</span></code></a>([measured, resynthesized, ...])</p></td>
+<td><p>Plots the normal mode indicator function</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_psmif</span></code></a>([measured, resynthesized, ...])</p></td>
+<td><p>Plots the first singular value of the complex mode indicator function</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_qmif</span></code></a>([measured, resynthesized, ...])</p></td>
+<td><p>Plots the complex mode indicator function computed from the imaginary part of the FRFs</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_reciprocal_frfs</span></code></a>([plot_kwargs, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra" title="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_reference_autospectra</span></code></a>([plot_kwargs, ...])</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy" title="sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modes_PolyPy</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC" title="sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC"><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_modes_SMAC</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI" title="sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">retrieve_spectral_quantities_SignalProcessingGUI</span></code></a>()</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_autopower_spectra</span></code></a>(autopower_spectra)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_channel_table</span></code></a>(channel_table)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_coherence</span></code></a>(coherence)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_fit_modes</span></code></a>(fit_modes)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_frfs</span></code></a>(frfs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_geometry</span></code></a>(geometry)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_resynthesized_frfs</span></code></a>(resynthesized_frfs)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_rigid_body_shapes</span></code></a>(rigid_body_shapes)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_time_histories</span></code></a>(time_histories)</p></td>
+<td><p></p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_units" title="sdynpy.modal.sdynpy_modal_test.ModalTest.set_units"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_units</span></code></a>(response_unit, reference_unit)</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Attributes</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.response_indices" title="sdynpy.modal.sdynpy_modal_test.ModalTest.response_indices"><code class="xref py py-obj docutils literal notranslate"><span class="pre">response_indices</span></code></a></p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs">
+<span class="sig-name descname"><span class="pre">compute_resynthesized_frfs</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.compute_resynthesized_frfs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities">
+<span class="sig-name descname"><span class="pre">compute_spectral_quantities</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples_per_frame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frf_estimator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.compute_spectral_quantities"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI">
+<span class="sig-name descname"><span class="pre">compute_spectral_quantities_SignalProcessingGUI</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.compute_spectral_quantities_SignalProcessingGUI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex">
+<span class="sig-name descname"><span class="pre">create_documentation_latex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mif_plots</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['cmif']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">latex_root</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure_folder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'figures'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">document_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'include.tex'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">appendix_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'appendix.tex'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'{:0.1f}'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'{:0.2f}\\%'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment1_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Description'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment2_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment3_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment4_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment5_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.create_documentation_latex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx">
+<span class="sig-name descname"><span class="pre">create_documentation_pptx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.create_documentation_pptx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word">
+<span class="sig-name descname"><span class="pre">create_documentation_word</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.create_documentation_word"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters">
+<span class="sig-name descname"><span class="pre">define_spectral_processing_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples_per_frame</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_averages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_time</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trigger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trigger_channel_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trigger_slope</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trigger_level</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pretrigger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frf_estimator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.define_spectral_processing_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments">
+<span class="sig-name descname"><span class="pre">edit_mode_comments</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.edit_mode_comments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy">
+<span class="sig-name descname"><span class="pre">fit_modes_PolyPy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.fit_modes_PolyPy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC">
+<span class="sig-name descname"><span class="pre">fit_modes_SMAC</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.fit_modes_SMAC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif">
+<span class="sig-name descname"><span class="pre">plot_cmif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measured</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_cmif"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif" title="Permalink to this definition"></a></dt>
+<dd><p>Plots the complex mode indicator function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>measured</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots the measured MIF. The default is True.</p></li>
+<li><p><strong>resynthesized</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots resynthesized MIF. The default is False.</p></li>
+<li><p><strong>mark_modes</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots a vertical line at the frequency of each mode. The
+default is False.</p></li>
+<li><p><strong>measured_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the measured
+data is plotted. The default is {}.</p></li>
+<li><p><strong>resynthesized_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the
+resynthesized data is plotted. The default is {}.</p></li>
+<li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the figure
+and axes are created.  This is passed to the plt.subplots function.
+The default is {}.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if a required data has not been computed or assigned yet.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>fig</strong> (<em>matplotlib.figure.Figure</em>) – A reference to the figure on which the plot is plotted.</p></li>
+<li><p><strong>ax</strong> (<em>matplotlib.axes.Axes</em>) – A reference to the axes on which the plot is plotted.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image">
+<span class="sig-name descname"><span class="pre">plot_coherence_image</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_coherence_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes">
+<span class="sig-name descname"><span class="pre">plot_deflection_shapes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_deflection_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence">
+<span class="sig-name descname"><span class="pre">plot_drive_point_frf_coherence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_drive_point_frf_coherence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs">
+<span class="sig-name descname"><span class="pre">plot_drive_point_frfs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">part</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'imag'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_drive_point_frfs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation">
+<span class="sig-name descname"><span class="pre">plot_figures_for_documentation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_geometry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">geometry_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_coordinate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinate_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_rigid_body_checks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rigid_body_checks_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_reference_autospectra</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_autospectra_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_drive_point_frfs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drive_point_frfs_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_reciprocal_frfs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reciprocal_frfs_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_frf_coherence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frf_coherence_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_coherence_image</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coherence_image_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_cmif</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cmif_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_qmif</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qmif_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_nmif</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmif_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_mmif</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmif_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_modeshapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modeshape_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">plot_mac</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mac_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_figures_for_documentation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac">
+<span class="sig-name descname"><span class="pre">plot_mac</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">matrix_plot_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">matrix_plot_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_mac"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif">
+<span class="sig-name descname"><span class="pre">plot_mmif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measured</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_mmif"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif" title="Permalink to this definition"></a></dt>
+<dd><p>Plots the multi mode indicator function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>measured</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots the measured MIF. The default is True.</p></li>
+<li><p><strong>resynthesized</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots resynthesized MIF. The default is False.</p></li>
+<li><p><strong>mark_modes</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots a vertical line at the frequency of each mode. The
+default is False.</p></li>
+<li><p><strong>measured_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the measured
+data is plotted. The default is {}.</p></li>
+<li><p><strong>resynthesized_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the
+resynthesized data is plotted. The default is {}.</p></li>
+<li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the figure
+and axes are created.  This is passed to the plt.subplots function.
+The default is {}.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if a required data has not been computed or assigned yet.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>fig</strong> (<em>matplotlib.figure.Figure</em>) – A reference to the figure on which the plot is plotted.</p></li>
+<li><p><strong>ax</strong> (<em>matplotlib.axes.Axes</em>) – A reference to the axes on which the plot is plotted.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape">
+<span class="sig-name descname"><span class="pre">plot_modeshape</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_modeshape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif">
+<span class="sig-name descname"><span class="pre">plot_nmif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measured</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_nmif"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif" title="Permalink to this definition"></a></dt>
+<dd><p>Plots the normal mode indicator function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>measured</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots the measured MIF. The default is True.</p></li>
+<li><p><strong>resynthesized</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots resynthesized MIF. The default is False.</p></li>
+<li><p><strong>mark_modes</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots a vertical line at the frequency of each mode. The
+default is False.</p></li>
+<li><p><strong>measured_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the measured
+data is plotted. The default is {}.</p></li>
+<li><p><strong>resynthesized_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the
+resynthesized data is plotted. The default is {}.</p></li>
+<li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the figure
+and axes are created.  This is passed to the plt.subplots function.
+The default is {}.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if a required data has not been computed or assigned yet.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>fig</strong> (<em>matplotlib.figure.Figure</em>) – A reference to the figure on which the plot is plotted.</p></li>
+<li><p><strong>ax</strong> (<em>matplotlib.axes.Axes</em>) – A reference to the axes on which the plot is plotted.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif">
+<span class="sig-name descname"><span class="pre">plot_psmif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measured</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_psmif"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif" title="Permalink to this definition"></a></dt>
+<dd><p>Plots the first singular value of the complex mode indicator function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>measured</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots the measured MIF. The default is True.</p></li>
+<li><p><strong>resynthesized</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots resynthesized MIF. The default is False.</p></li>
+<li><p><strong>mark_modes</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots a vertical line at the frequency of each mode. The
+default is False.</p></li>
+<li><p><strong>measured_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the measured
+data is plotted. The default is {}.</p></li>
+<li><p><strong>resynthesized_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the
+resynthesized data is plotted. The default is {}.</p></li>
+<li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the figure
+and axes are created.  This is passed to the plt.subplots function.
+The default is {}.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if a required data has not been computed or assigned yet.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>fig</strong> (<em>matplotlib.figure.Figure</em>) – A reference to the figure on which the plot is plotted.</p></li>
+<li><p><strong>ax</strong> (<em>matplotlib.axes.Axes</em>) – A reference to the axes on which the plot is plotted.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif">
+<span class="sig-name descname"><span class="pre">plot_qmif</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measured</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mark_modes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">measured_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resynthesized_plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_qmif"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif" title="Permalink to this definition"></a></dt>
+<dd><p>Plots the complex mode indicator function computed from the imaginary
+part of the FRFs</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>measured</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots the measured MIF. The default is True.</p></li>
+<li><p><strong>resynthesized</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots resynthesized MIF. The default is False.</p></li>
+<li><p><strong>mark_modes</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, plots a vertical line at the frequency of each mode. The
+default is False.</p></li>
+<li><p><strong>measured_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the measured
+data is plotted. The default is {}.</p></li>
+<li><p><strong>resynthesized_plot_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the
+resynthesized data is plotted. The default is {}.</p></li>
+<li><p><strong>subplots_kwargs</strong> (<em>dict</em><em>, </em><em>optional</em>) – Dictionary containing keyword arguments to specify how the figure
+and axes are created.  This is passed to the plt.subplots function.
+The default is {}.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – Raised if a required data has not been computed or assigned yet.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p><strong>fig</strong> (<em>matplotlib.figure.Figure</em>) – A reference to the figure on which the plot is plotted.</p></li>
+<li><p><strong>ax</strong> (<em>matplotlib.axes.Axes</em>) – A reference to the axes on which the plot is plotted.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs">
+<span class="sig-name descname"><span class="pre">plot_reciprocal_frfs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_reciprocal_frfs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra">
+<span class="sig-name descname"><span class="pre">plot_reference_autospectra</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plot_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">subplots_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.plot_reference_autospectra"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py property">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.response_indices">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">response_indices</span></span><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.response_indices" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy">
+<span class="sig-name descname"><span class="pre">retrieve_modes_PolyPy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.retrieve_modes_PolyPy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC">
+<span class="sig-name descname"><span class="pre">retrieve_modes_SMAC</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.retrieve_modes_SMAC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI">
+<span class="sig-name descname"><span class="pre">retrieve_spectral_quantities_SignalProcessingGUI</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.retrieve_spectral_quantities_SignalProcessingGUI"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra">
+<span class="sig-name descname"><span class="pre">set_autopower_spectra</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">autopower_spectra</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_autopower_spectra"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table">
+<span class="sig-name descname"><span class="pre">set_channel_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">channel_table</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_channel_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence">
+<span class="sig-name descname"><span class="pre">set_coherence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coherence</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_coherence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes">
+<span class="sig-name descname"><span class="pre">set_fit_modes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fit_modes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_fit_modes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs">
+<span class="sig-name descname"><span class="pre">set_frfs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frfs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_frfs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry">
+<span class="sig-name descname"><span class="pre">set_geometry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">geometry</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_geometry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs">
+<span class="sig-name descname"><span class="pre">set_resynthesized_frfs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">resynthesized_frfs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_resynthesized_frfs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes">
+<span class="sig-name descname"><span class="pre">set_rigid_body_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rigid_body_shapes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_rigid_body_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories">
+<span class="sig-name descname"><span class="pre">set_time_histories</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">time_histories</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_time_histories"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.ModalTest.set_units">
+<span class="sig-name descname"><span class="pre">set_units</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">response_unit</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_unit</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#ModalTest.set_units"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.ModalTest.set_units" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
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+
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+
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modal_test.html b/_autosummary/sdynpy.modal.sdynpy_modal_test.html
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+<li class="toctree-l1"><a class="reference internal" href="../examples.html">SDynpy Examples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../modal_tutorials.html">Modal Tutorials</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../modules.html">SDynPy Programming Interface</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="sdynpy.html">sdynpy</a><ul class="current">
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+<li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modal_test</a><ul>
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.ModalTest.html">sdynpy.modal.sdynpy_modal_test.ModalTest</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_polypy.html">sdynpy.modal.sdynpy_polypy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_signal_processing_gui.html">sdynpy.modal.sdynpy_signal_processing_gui</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_smac.html">sdynpy.modal.sdynpy_smac</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
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+            <a href="../_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.rst.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="module-sdynpy.modal.sdynpy_modal_test">
+<span id="sdynpy-modal-sdynpy-modal-test"></span><h1>sdynpy.modal.sdynpy_modal_test<a class="headerlink" href="#module-sdynpy.modal.sdynpy_modal_test" title="Permalink to this heading"></a></h1>
+<p>Class defining the typical data for a modal test used to automatically create
+reports and plots</p>
+<p>Copyright 2022 National Technology &amp; Engineering Solutions of Sandia,
+LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.
+Government retains certain rights in this software.</p>
+<p>This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.</p>
+<p>This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.</p>
+<p>You should have received a copy of the GNU General Public License
+along with this program.  If not, see &lt;<a class="reference external" href="https://www.gnu.org/licenses/">https://www.gnu.org/licenses/</a>&gt;.</p>
+<p class="rubric">Functions</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html#sdynpy.modal.sdynpy_modal_test.read_modal_fit_data" title="sdynpy.modal.sdynpy_modal_test.read_modal_fit_data"><code class="xref py py-obj docutils literal notranslate"><span class="pre">read_modal_fit_data</span></code></a>(modal_fit_data)</p></td>
+<td><p>Reads Modal Fit Data from PolyPy_GUI which contains modes and FRF data</p></td>
+</tr>
+</tbody>
+</table>
+<p class="rubric">Classes</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest" title="sdynpy.modal.sdynpy_modal_test.ModalTest"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ModalTest</span></code></a>([geometry, time_histories, ...])</p></td>
+<td><p></p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.modal.sdynpy_ccmif.PropertiesDialog.html" class="btn btn-neutral float-left" title="sdynpy.modal.sdynpy_ccmif.PropertiesDialog" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html b/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html
new file mode 100644
index 0000000..94eb00a
--- /dev/null
+++ b/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html
@@ -0,0 +1,178 @@
+<!DOCTYPE html>
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+<li class="toctree-l1"><a class="reference internal" href="../usage.html">Usage</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../examples.html">SDynpy Examples</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../modal_tutorials.html">Modal Tutorials</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../modules.html">SDynPy Programming Interface</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="sdynpy.html">sdynpy</a><ul class="current">
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.core.html">sdynpy.core</a></li>
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+<li class="toctree-l3"><a class="reference internal" href="sdynpy.doc.html">sdynpy.doc</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fem.html">sdynpy.fem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a><ul class="current">
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modal_test.read_modal_fit_data</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modal_test.read_modal_fit_data"><code class="docutils literal notranslate"><span class="pre">read_modal_fit_data()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.ModalTest.html">sdynpy.modal.sdynpy_modal_test.ModalTest</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_polypy.html">sdynpy.modal.sdynpy_polypy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_signal_processing_gui.html">sdynpy.modal.sdynpy_signal_processing_gui</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_smac.html">sdynpy.modal.sdynpy_smac</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+</ul>
+
+        </div>
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+
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+          <a href="../index.html">SDynPy</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+      <li><a href="../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../modules.html">SDynPy Programming Interface</a></li>
+          <li class="breadcrumb-item"><a href="sdynpy.html">sdynpy</a></li>
+          <li class="breadcrumb-item"><a href="sdynpy.modal.html">sdynpy.modal</a></li>
+          <li class="breadcrumb-item"><a href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
+      <li class="breadcrumb-item active">sdynpy.modal.sdynpy_modal_test.read_modal_fit_data</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-modal-sdynpy-modal-test-read-modal-fit-data">
+<h1>sdynpy.modal.sdynpy_modal_test.read_modal_fit_data<a class="headerlink" href="#sdynpy-modal-sdynpy-modal-test-read-modal-fit-data" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modal_test.read_modal_fit_data">
+<span class="sig-name descname"><span class="pre">read_modal_fit_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modal_fit_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modal_test.html#read_modal_fit_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modal_test.read_modal_fit_data" title="Permalink to this definition"></a></dt>
+<dd><p>Reads Modal Fit Data from PolyPy_GUI which contains modes and FRF data</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>modal_fit_data</strong> (<em>str</em><em> or </em><em>NpzFile</em>) – Filename to or data loaded from a Modal Fit Data .npz file that is
+saved from SDynPy’s mode fitters.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>shapes</strong> (<em>ShapeArray</em>) – The modes fit to the structure</p></li>
+<li><p><strong>experimental_frfs</strong> (<em>TransferFunctionArray</em>) – The FRFs to which the modes were fit</p></li>
+<li><p><strong>resynthesized_frfs</strong> (<em>TransferFunctionArray</em>) – FRFs resynthesized from the fit modes</p></li>
+<li><p><strong>residual_frfs</strong> (<em>TransferFunctionArray</em>) – FRF contribution from the residual terms in the modal fit.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+    </div>
+
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+  <div role="contentinfo">
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+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
+  </div>
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.ShapeSelection.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.ShapeSelection.html
index d148f63..0f6f0f9 100644
--- a/_autosummary/sdynpy.modal.sdynpy_modeshape.ShapeSelection.html
+++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.ShapeSelection.html
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-  <title>sdynpy.modal.sdynpy_modeshape.ShapeSelection &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_modeshape.ShapeSelection &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
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@@ -29,7 +29,7 @@
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     <link rel="next" title="sdynpy.modal.sdynpy_polypy" href="sdynpy.modal.sdynpy_polypy.html" />
-    <link rel="prev" title="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html" /> 
+    <link rel="prev" title="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html" /> 
 </head>
 
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@@ -67,10 +67,14 @@
 <li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
 <li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a><ul class="current">
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes.html">sdynpy.modal.sdynpy_modeshape.compute_shapes</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html">sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a></li>
 <li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modeshape.ShapeSelection</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.ShapeSelection"><code class="docutils literal notranslate"><span class="pre">ShapeSelection</span></code></a><ul>
 <li class="toctree-l7"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.ShapeSelection.__init__"><code class="docutils literal notranslate"><span class="pre">ShapeSelection.__init__()</span></code></a></li>
@@ -177,14 +181,14 @@ <h1>sdynpy.modal.sdynpy_modeshape.ShapeSelection<a class="headerlink" href="#sdy
            </div>
           </div>
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+        <a href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html" class="btn btn-neutral float-left" title="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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     </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee619930>
+<jinja2.runtime.BlockReference object at 0x7f5a278d8850>
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_residues.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_residues.html
index 23e328a..f4b58a8 100644
--- a/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_residues.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_modeshape.compute_residues &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_modeshape.compute_residues &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -67,6 +67,7 @@
 <li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
 <li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a><ul class="current">
 <li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modeshape.compute_residues</a><ul>
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.compute_residues"><code class="docutils literal notranslate"><span class="pre">compute_residues()</span></code></a></li>
@@ -74,6 +75,9 @@
 </li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html">sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html">sdynpy.modal.sdynpy_modeshape.ShapeSelection</a></li>
 </ul>
 </li>
@@ -181,7 +185,7 @@ <h1>sdynpy.modal.sdynpy_modeshape.compute_residues<a class="headerlink" href="#s
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ef3eeda0>
+<jinja2.runtime.BlockReference object at 0x7f5a2715c5e0>
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   </div>
diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes.html
index 58b10e8..4191ab1 100644
--- a/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes.html
+++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_modeshape.compute_shapes &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_modeshape.compute_shapes &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -67,6 +67,7 @@
 <li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
 <li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_residues.html">sdynpy.modal.sdynpy_modeshape.compute_residues</a></li>
 <li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modeshape.compute_shapes</a><ul>
@@ -74,6 +75,9 @@
 </ul>
 </li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html">sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a></li>
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 </li>
@@ -138,7 +142,7 @@ <h1>sdynpy.modal.sdynpy_modeshape.compute_shapes<a class="headerlink" href="#sdy
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eee33640>
+<jinja2.runtime.BlockReference object at 0x7f5a26e41840>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html
index a4142b3..697b1df 100644
--- a/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html
+++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html
@@ -4,7 +4,7 @@
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-  <title>sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -28,7 +28,7 @@
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-    <link rel="next" title="sdynpy.modal.sdynpy_modeshape.ShapeSelection" href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html" />
+    <link rel="next" title="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html" />
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 </head>
 
@@ -67,6 +67,7 @@
 <li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_residues.html">sdynpy.modal.sdynpy_modeshape.compute_residues</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes.html">sdynpy.modal.sdynpy_modeshape.compute_shapes</a></li>
@@ -74,6 +75,9 @@
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference"><code class="docutils literal notranslate"><span class="pre">compute_shapes_multireference()</span></code></a></li>
 </ul>
 </li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html">sdynpy.modal.sdynpy_modeshape.ShapeSelection</a></li>
 </ul>
 </li>
@@ -122,7 +126,7 @@
 <h1>sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference<a class="headerlink" href="#sdynpy-modal-sdynpy-modeshape-compute-shapes-multireference" title="Permalink to this heading"></a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference">
-<span class="sig-name descname"><span class="pre">compute_shapes_multireference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experimental_frf</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray" title="sdynpy.core.sdynpy_data.TransferFunctionArray"><span class="pre">TransferFunctionArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">natural_frequencies</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping_ratios</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">participation_factors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">real_modes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">residuals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_frequency</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_frequency</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">displacement_derivative</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_lines_at_resonance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_lines_for_residuals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modeshape.html#compute_shapes_multireference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">compute_shapes_multireference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experimental_frf</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray" title="sdynpy.core.sdynpy_data.TransferFunctionArray"><span class="pre">TransferFunctionArray</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">natural_frequencies</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">damping_ratios</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">participation_factors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">real_modes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_residuals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper_residuals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_frequency</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_frequency</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">displacement_derivative</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_lines_at_resonance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequency_lines_for_residuals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modeshape.html#compute_shapes_multireference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference" title="Permalink to this definition"></a></dt>
 <dd><p>Computes mode shapes from multireference datasets.</p>
 <p>Uses the modal participation factor as a constraint on the mode shapes to
 solve for the shapes in one pass, rather than solving for residues and
@@ -136,7 +140,8 @@ <h1>sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference<a class="headerl
 <li><p><strong>participation_factors</strong> (<em>np.ndarray</em>) – Mode participation factors from which the shapes can be computed.
 Should have shape (n_modes x n_inputs)</p></li>
 <li><p><strong>real_modes</strong> (<em>bool</em><em>, </em><em>optional</em>) – Specifies whether to solve for real modes or complex modes (default).</p></li>
-<li><p><strong>residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – Use residuals in the FRF fit. The default is True.</p></li>
+<li><p><strong>lower_residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – Use lower residuals in the FRF fit. The default is True.</p></li>
+<li><p><strong>upper_residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – Use upper residuals in the FRF fit. The default is True.</p></li>
 <li><p><strong>min_frequency</strong> (<em>float</em><em>, </em><em>optional</em>) – Minimum frequency to use in the shape fit. The default is the lowest
 frequency in the experimental FRF.</p></li>
 <li><p><strong>max_frequency</strong> (<em>float</em><em>, </em><em>optional</em>) – Maximum frequency to use in the shape fit. The default is the highest
@@ -162,6 +167,8 @@ <h1>sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference<a class="headerl
 <li><p><strong>output_shape</strong> (<em>ShapeArray</em>) – ShapeArray containing the mode shapes of the system</p></li>
 <li><p><strong>frfs_resynthesized</strong> (<em>TransferFunctionArray</em>) – FRFs resynthesized from the fit shapes and residuals</p></li>
 <li><p><strong>residual_frfs</strong> (<em>TransferFunctionArray</em>) – FRFs resynthesized only from the residuals used in the calculation</p></li>
+<li><p><strong>kernel_frfs</strong> (<em>TransferFunctionArray</em>) – FRFs synthesized by the kernel solution without taking into account
+construction of mode shapes and reciprocity with participation factors</p></li>
 </ul>
 </p>
 </dd>
@@ -175,13 +182,13 @@ <h1>sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference<a class="headerl
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html
new file mode 100644
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+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.generate_kernel_complex"><code class="docutils literal notranslate"><span class="pre">generate_kernel_complex()</span></code></a></li>
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+  <section id="sdynpy-modal-sdynpy-modeshape-generate-kernel-complex">
+<h1>sdynpy.modal.sdynpy_modeshape.generate_kernel_complex<a class="headerlink" href="#sdynpy-modal-sdynpy-modeshape-generate-kernel-complex" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex">
+<span class="sig-name descname"><span class="pre">generate_kernel_complex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">omegas</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">participation_factors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">displacement_derivative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modeshape.html#generate_kernel_complex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modeshape.generate_kernel_complex" title="Permalink to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omegas</strong> (<em>np.ndarray</em>) – The angular frequencies (in radians/s) at which the kernel matrix will
+be computed.  This should be a 1D array with length num_freqs.</p></li>
+<li><p><strong>poles</strong> (<em>np.ndarray</em>) – An array of poles corresponding to the modes of the structure.  This
+should be a 1D array with length num_modes.</p></li>
+<li><p><strong>participation_factors</strong> (<em>np.ndarray</em>) – A 2D array of participation factors corresponding to the reference
+degrees of freedom.  This should have shape (num_modes, num_inputs).</p></li>
+<li><p><strong>lower_residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, construct the kernel matrix such that lower residuals will be
+computed in the least-squares operation.  The default is False.</p></li>
+<li><p><strong>upper_residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, construct the kernel matrix such that upper residuals will be
+computed in the least-squares operation.  The default is False.</p></li>
+<li><p><strong>displacement_derivative</strong> (<em>int</em><em>, </em><em>optional</em>) – The derivative of displacement used to construct the frequency response
+functions.  Should be 0 for a receptance (displacement/force) frf, 1
+for a mobility (velocity/force) frf, or 2 for an accelerance
+(acceleration/force) frf.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>kernel_matrix</strong> – A 3D matrix that represents the kernel that can be inverted to solve
+for mode shapes.  The size of the output array will be num_inputs x
+num_freq*2 x num_modes*2.  The top partition of the num_freq dimension
+corresponds to the real part of the frf, and the
+bottom portion corresponds to the imaginary part of the frf.  The top
+partition of the num_modes dimension corresponds to the real part of
+the mode shape matrix, and the bottom partition corresponds to the
+imaginary part.  If residuals are included, there will be an extra two
+entries along the num_modes dimension corresponding to real and
+imaginary parts of the residual matrix for each of the residuals
+included.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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+<li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a><ul class="current">
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_residues.html">sdynpy.modal.sdynpy_modeshape.compute_residues</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes.html">sdynpy.modal.sdynpy_modeshape.compute_shapes</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html">sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.generate_kernel_real"><code class="docutils literal notranslate"><span class="pre">generate_kernel_real()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html">sdynpy.modal.sdynpy_modeshape.ShapeSelection</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_polypy.html">sdynpy.modal.sdynpy_polypy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_signal_processing_gui.html">sdynpy.modal.sdynpy_signal_processing_gui</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_smac.html">sdynpy.modal.sdynpy_smac</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+</ul>
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+      <li class="breadcrumb-item active">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-modal-sdynpy-modeshape-generate-kernel-real">
+<h1>sdynpy.modal.sdynpy_modeshape.generate_kernel_real<a class="headerlink" href="#sdynpy-modal-sdynpy-modeshape-generate-kernel-real" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modeshape.generate_kernel_real">
+<span class="sig-name descname"><span class="pre">generate_kernel_real</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">omegas</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">participation_factors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper_residuals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">displacement_derivative</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modeshape.html#generate_kernel_real"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modeshape.generate_kernel_real" title="Permalink to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omegas</strong> (<em>np.ndarray</em>) – The angular frequencies (in radians/s) at which the kernel matrix will
+be computed.  This should be a 1D array with length num_freqs.</p></li>
+<li><p><strong>poles</strong> (<em>np.ndarray</em>) – An array of poles corresponding to the modes of the structure.  This
+should be a 1D array with length num_modes.</p></li>
+<li><p><strong>participation_factors</strong> (<em>np.ndarray</em>) – A 2D array of participation factors corresponding to the reference
+degrees of freedom.  This should have shape (num_modes, num_inputs).</p></li>
+<li><p><strong>lower_residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, construct the kernel matrix such that lower residuals will be
+computed in the least-squares operation.  The default is False.</p></li>
+<li><p><strong>upper_residuals</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, construct the kernel matrix such that upper residuals will be
+computed in the least-squares operation.  The default is False.</p></li>
+<li><p><strong>displacement_derivative</strong> (<em>int</em><em>, </em><em>optional</em>) – The derivative of displacement used to construct the frequency response
+functions.  Should be 0 for a receptance (displacement/force) frf, 1
+for a mobility (velocity/force) frf, or 2 for an accelerance
+(acceleration/force) frf.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><strong>kernel_matrix</strong> – A 3D matrix that represents the kernel that can be inverted to solve
+for mode shapes.  The size of the output array will be num_inputs x
+num_freq*2 x num_modes.  The top partition of the num_freq dimension
+corresponds to the real part of the frf, and the
+bottom portion corresponds to the imaginary part of the frf.
+If residuals are included, there will be an extra two
+entries along the num_modes dimension corresponding to real and
+imaginary parts of the residual matrix for each of the residuals
+included.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html" class="btn btn-neutral float-left" title="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.html
index 69f156f..0ab1956 100644
--- a/_autosummary/sdynpy.modal.sdynpy_modeshape.html
+++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.html
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   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_modeshape &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_modeshape &mdash; SDynPy 0.14.0 documentation</title>
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+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li class="toctree-l4 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modeshape</a><ul>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes.html">sdynpy.modal.sdynpy_modeshape.compute_shapes</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html">sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html">sdynpy.modal.sdynpy_modeshape.ShapeSelection</a></li>
 </ul>
 </li>
@@ -146,6 +150,25 @@
 <tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html#sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference" title="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_shapes_multireference</span></code></a>(...[, ...])</p></td>
 <td><p>Computes mode shapes from multireference datasets.</p></td>
 </tr>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html#sdynpy.modal.sdynpy_modeshape.generate_kernel_complex" title="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_kernel_complex</span></code></a>(omegas, poles, ...)</p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">param omegas</dt>
+<dd class="field-odd"><p>The angular frequencies (in radians/s) at which the kernel matrix will</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html#sdynpy.modal.sdynpy_modeshape.generate_kernel_real" title="sdynpy.modal.sdynpy_modeshape.generate_kernel_real"><code class="xref py py-obj docutils literal notranslate"><span class="pre">generate_kernel_real</span></code></a>(omegas, poles, ...[, ...])</p></td>
+<td><p><dl class="field-list simple">
+<dt class="field-odd">param omegas</dt>
+<dd class="field-odd"><p>The angular frequencies (in radians/s) at which the kernel matrix will</p>
+</dd>
+</dl>
+</p></td>
+</tr>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html#sdynpy.modal.sdynpy_modeshape.stack_and_lstsq" title="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stack_and_lstsq</span></code></a>(frf_matrix, kernel_matrix[, ...])</p></td>
+<td><p>Stacks the frf and kernel matrices appropriately, then solves the least-squares problem</p></td>
+</tr>
 </tbody>
 </table>
 <p class="rubric">Classes</p>
@@ -166,14 +189,14 @@
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           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="sdynpy.modal.sdynpy_ccmif.PropertiesDialog.html" class="btn btn-neutral float-left" title="sdynpy.modal.sdynpy_ccmif.PropertiesDialog" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.modal.sdynpy_modal_test.ModalTest.html" class="btn btn-neutral float-left" title="sdynpy.modal.sdynpy_modal_test.ModalTest" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="sdynpy.modal.sdynpy_modeshape.compute_residues.html" class="btn btn-neutral float-right" title="sdynpy.modal.sdynpy_modeshape.compute_residues" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eee31b70>
+<jinja2.runtime.BlockReference object at 0x7f5a270ee7a0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html b/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html
new file mode 100644
index 0000000..308918c
--- /dev/null
+++ b/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html
@@ -0,0 +1,203 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+<li class="toctree-l3"><a class="reference internal" href="sdynpy.doc.html">sdynpy.doc</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fem.html">sdynpy.fem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
+<li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
+<li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a><ul class="current">
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_residues.html">sdynpy.modal.sdynpy_modeshape.compute_residues</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes.html">sdynpy.modal.sdynpy_modeshape.compute_shapes</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html">sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_complex</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html">sdynpy.modal.sdynpy_modeshape.generate_kernel_real</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.modal.sdynpy_modeshape.stack_and_lstsq"><code class="docutils literal notranslate"><span class="pre">stack_and_lstsq()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html">sdynpy.modal.sdynpy_modeshape.ShapeSelection</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_polypy.html">sdynpy.modal.sdynpy_polypy</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_signal_processing_gui.html">sdynpy.modal.sdynpy_signal_processing_gui</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_smac.html">sdynpy.modal.sdynpy_smac</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="sdynpy.signal_processing.html">sdynpy.signal_processing</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+</ul>
+
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+      <li class="breadcrumb-item active">sdynpy.modal.sdynpy_modeshape.stack_and_lstsq</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-modal-sdynpy-modeshape-stack-and-lstsq">
+<h1>sdynpy.modal.sdynpy_modeshape.stack_and_lstsq<a class="headerlink" href="#sdynpy-modal-sdynpy-modeshape-stack-and-lstsq" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.modal.sdynpy_modeshape.stack_and_lstsq">
+<span class="sig-name descname"><span class="pre">stack_and_lstsq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frf_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_matrix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_reconstruction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reconstruction_partitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reconstruction_kernel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/modal/sdynpy_modeshape.html#stack_and_lstsq"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.modal.sdynpy_modeshape.stack_and_lstsq" title="Permalink to this definition"></a></dt>
+<dd><p>Stacks the frf and kernel matrices appropriately,
+then solves the least-squares problem</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>frf_matrix</strong> (<em>np.ndarray</em>) – A num_outputs x num_inputs x num_frequencies array representing the
+frequency response function matrix.</p></li>
+<li><p><strong>kernel_matrix</strong> (<em>np.ndarray</em>) – A num_inputs x 2*num_freq x num_coefficients kernel matrix from generated
+from generate_kernel_complex or generate_kernel_real functions.</p></li>
+<li><p><strong>return_reconstruction</strong> (<em>bool</em>) – If True, a reconstruction of the frequency response function matrix
+using the kernel and the computed shape coefficients.  Default is False.</p></li>
+<li><p><strong>reconstruction_paritions</strong> (<em>list</em>) – If specified, a list of reconstructions will be returned.  For each
+entry in this list, a frf matrix will be reconstructed using the entry
+as indices into the coefficient matrix’s rows and the kernel matrix’s
+columns.  This allows, for example, reconstruction of the frf matrix
+using only the residual terms or only the shape terms.  If not
+specified when <cite>return_reconstruction</cite> is True, only one frf matrix will
+be returned using all shape coefficients.</p></li>
+<li><p><strong>reconstruction_kernel</strong> (<em>np.ndarray</em>) – A separate kernel matrix used for reconstruction.  If not specified, the
+kernel matrix used to solve the least-squares problem will be used for
+the reconstruction.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>coefficients</strong> (<em>np.ndarray</em>) – A num_coefficients x num_outputs array of solution values to the
+least-squares problem.</p></li>
+<li><p><strong>reconstruction</strong> (<em>np.ndarray or list of np.ndarray</em>) – A frf matrix reconstructed from the kernel and the shape coefficients.
+If multiple <cite>reconstruction_partitions</cite> are specified in a list, then
+this return value will also be a list of reconstructions corresponding
+to those partitions.  This will only be returned if
+<cite>return_reconstruction</cite> is True.</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html" class="btn btn-neutral float-left" title="sdynpy.modal.sdynpy_modeshape.generate_kernel_real" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="sdynpy.modal.sdynpy_modeshape.ShapeSelection.html" class="btn btn-neutral float-right" title="sdynpy.modal.sdynpy_modeshape.ShapeSelection" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
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+
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diff --git a/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.html b/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.html
index 782f426..dfb8ca8 100644
--- a/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.html
+++ b/_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_polypy.PolyPy &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_polypy.PolyPy &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -67,6 +67,7 @@
 <li class="toctree-l3"><a class="reference internal" href="sdynpy.fileio.html">sdynpy.fileio</a></li>
 <li class="toctree-l3 current"><a class="reference internal" href="sdynpy.modal.html">sdynpy.modal</a><ul class="current">
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.modal.sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a></li>
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.modal.sdynpy_polypy.html">sdynpy.modal.sdynpy_polypy</a><ul class="current">
 <li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.modal.sdynpy_polypy.PolyPy</a><ul>
@@ -155,7 +156,7 @@ <h1>sdynpy.modal.sdynpy_polypy.PolyPy<a class="headerlink" href="#sdynpy-modal-s
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_polypy.PolyPy.compute_poles" title="sdynpy.modal.sdynpy_polypy.PolyPy.compute_poles"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_poles</span></code></a>(polynomial_orders[, ...])</p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_polypy.PolyPy.compute_shapes" title="sdynpy.modal.sdynpy_polypy.PolyPy.compute_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_shapes</span></code></a>(poles[, residuals, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_polypy.PolyPy.compute_shapes" title="sdynpy.modal.sdynpy_polypy.PolyPy.compute_shapes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_shapes</span></code></a>(poles[, low_residuals, ...])</p></td>
 <td><p></p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#sdynpy.modal.sdynpy_polypy.PolyPy.plot_stability" title="sdynpy.modal.sdynpy_polypy.PolyPy.plot_stability"><code class="xref py py-obj docutils literal notranslate"><span class="pre">plot_stability</span></code></a>([no_converge_marger, ...])</p></td>
@@ -201,7 +202,7 @@ <h1>sdynpy.modal.sdynpy_polypy.PolyPy<a class="headerlink" href="#sdynpy-modal-s
 
 <dl class="py method">
 <dt class="sig sig-object py" id="sdynpy.modal.sdynpy_polypy.PolyPy.compute_shapes">
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+<jinja2.runtime.BlockReference object at 0x7f5a273c2560>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html
index caf7930..0cdaddb 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd &mdash; SDynPy 0.14.0 documentation</title>
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@@ -83,6 +83,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html">sdynpy.signal_processing.sdynpy_cpsd.dB_pow</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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@@ -97,6 +98,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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 </ul>
@@ -140,7 +142,7 @@
 <h1>sdynpy.signal_processing.sdynpy_cpsd.cpsd<a class="headerlink" href="#sdynpy-signal-processing-sdynpy-cpsd-cpsd" title="Permalink to this heading"></a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sdynpy.signal_processing.sdynpy_cpsd.cpsd">
-<span class="sig-name descname"><span class="pre">cpsd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">signals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_frame</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averages_to_keep</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_asds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_cpsd.html#cpsd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_cpsd.cpsd" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">cpsd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">signals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ndarray</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_frame</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averages_to_keep</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_asds</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_signals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_cpsd.html#cpsd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_cpsd.cpsd" title="Permalink to this definition"></a></dt>
 <dd><p>Compute cpsd from signals</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -150,10 +152,15 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.cpsd<a class="headerlink" href="#sdynpy
 <li><p><strong>sample_rate</strong> (<em>int</em>) – Sample rate of the signal.</p></li>
 <li><p><strong>samples_per_frame</strong> (<em>int</em>) – Number of samples per frame in the CPSD</p></li>
 <li><p><strong>overlap</strong> (<em>float</em>) – Overlap as a fraction of the frame (e.g. 0.5 not 50).</p></li>
-<li><p><strong>window</strong> (<em>str</em>) – Name of a window function in scipy.signal.windows.</p></li>
+<li><p><strong>window</strong> (<em>str</em>) – Name of a window function in scipy.signal.windows, or a NumPy ndarray
+with the window coefficients</p></li>
 <li><p><strong>averages_to_keep</strong> (<em>int</em><em>, </em><em>optional</em>) – Optional number of averages to use. The default is None.</p></li>
 <li><p><strong>only_asds</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, only compute autospectral densities, otherwise compute the
 full CPSD matrix</p></li>
+<li><p><strong>reference_signals</strong> (<em>np.ndarray</em>) – If specified, these signals will be used for the columns of the CPSD
+matrix.  If not specified, the signals passed to the <cite>signals</cite> argument
+will be used for both the rows and columns of the CPSD matrix.  Cannot
+be used simultaneously with the <cite>only_asds</cite> argument being set to True.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -180,7 +187,7 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.cpsd<a class="headerlink" href="#sdynpy
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra.html
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra &mdash; SDynPy 0.14.0 documentation</title>
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html">sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.rms.html">sdynpy.signal_processing.sdynpy_cpsd.rms</a></li>
@@ -97,6 +98,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
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@@ -156,7 +158,7 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.cpsd_autospectra<a class="headerlink" h
   <hr/>
 
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   </div>
diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html
index 5c24032..5172a92 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence.html
@@ -4,7 +4,7 @@
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -83,6 +83,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html">sdynpy.signal_processing.sdynpy_cpsd.dB_pow</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.db2scale.html">sdynpy.signal_processing.sdynpy_cpsd.db2scale</a></li>
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html">sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.rms.html">sdynpy.signal_processing.sdynpy_cpsd.rms</a></li>
@@ -97,6 +98,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -156,7 +158,7 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.cpsd_coherence<a class="headerlink" hre
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs.html
index 14261f7..7f886f8 100644
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -83,6 +83,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html">sdynpy.signal_processing.sdynpy_cpsd.dB_pow</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.db2scale.html">sdynpy.signal_processing.sdynpy_cpsd.db2scale</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html">sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.rms.html">sdynpy.signal_processing.sdynpy_cpsd.rms</a></li>
@@ -97,6 +98,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
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@@ -156,7 +158,7 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.cpsd_from_coh_phs<a class="headerlink"
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.html
index cd213c4..05bb663 100644
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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+<td><p>Get N-th octave band frequencies</p></td>
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+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.rms.html#sdynpy.signal_processing.sdynpy_cpsd.rms" title="sdynpy.signal_processing.sdynpy_cpsd.rms"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rms</span></code></a>(x[, axis])</p></td>
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 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html#sdynpy.signal_processing.sdynpy_cpsd.rms_csd" title="sdynpy.signal_processing.sdynpy_cpsd.rms_csd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rms_csd</span></code></a>(csd, df)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html#sdynpy.signal_processing.sdynpy_cpsd.rms_csd" title="sdynpy.signal_processing.sdynpy_cpsd.rms_csd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rms_csd</span></code></a>(csd, df)</p></td>
 <td><p>Computes RMS of a CPSD matrix</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html#sdynpy.signal_processing.sdynpy_cpsd.shaped_psd" title="sdynpy.signal_processing.sdynpy_cpsd.shaped_psd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shaped_psd</span></code></a>(frequency_spacing, bandwidth[, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html#sdynpy.signal_processing.sdynpy_cpsd.shaped_psd" title="sdynpy.signal_processing.sdynpy_cpsd.shaped_psd"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shaped_psd</span></code></a>(frequency_spacing, bandwidth[, ...])</p></td>
 <td><p></p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.trace.html#sdynpy.signal_processing.sdynpy_cpsd.trace" title="sdynpy.signal_processing.sdynpy_cpsd.trace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trace</span></code></a>(cpsd)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.trace.html#sdynpy.signal_processing.sdynpy_cpsd.trace" title="sdynpy.signal_processing.sdynpy_cpsd.trace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trace</span></code></a>(cpsd)</p></td>
 <td><p></p></td>
 </tr>
 </tbody>
@@ -215,7 +220,7 @@
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html
index 755a8ee..e79defa 100644
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+  <title>sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase &mdash; SDynPy 0.14.0 documentation</title>
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@@ -83,6 +83,7 @@
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase"><code class="docutils literal notranslate"><span class="pre">match_coherence_phase()</span></code></a></li>
 </ul>
 </li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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@@ -97,6 +98,7 @@
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html
new file mode 100644
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+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs"><code class="docutils literal notranslate"><span class="pre">nth_octave_freqs()</span></code></a></li>
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+             
+  <section id="sdynpy-signal-processing-sdynpy-cpsd-nth-octave-freqs">
+<h1>sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs<a class="headerlink" href="#sdynpy-signal-processing-sdynpy-cpsd-nth-octave-freqs" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs">
+<span class="sig-name descname"><span class="pre">nth_octave_freqs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">freq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oct_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_cpsd.html#nth_octave_freqs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs" title="Permalink to this definition"></a></dt>
+<dd><p>Get N-th octave band frequencies</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>freq</strong> (<em>ndarray</em>) – array of frequency values, either including all freqs or only min and max</p></li>
+<li><p><strong>oct_order</strong> (<em>int</em><em>, </em><em>optional</em>) – octave type, 1/octave order. 3 represents 1/3 octave bands. default is 1</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>nominal_band_centers</strong> (<em>ndarray</em>) – rounded band center frequencies using ANSI standers. Used to report
+results, not to compute band limits</p></li>
+<li><p><strong>band_lb</strong> (<em>ndarray</em>) – lower band frequencies in Hz</p></li>
+<li><p><strong>band_ub</strong> (<em>ndarray</em>) – upper band frequencies in Hz</p></li>
+<li><p><strong>band_centers</strong> (<em>ndarray</em>) – exact computed octave center band frequencies</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>Uses equations in ANSI S1.11-2014 “Electroacoustics – Octave-band and
+Fractionaloctave-band Filters – Part 1: Specifications”</p>
+<p>Uses the min and max freq lines in the provided freq to determine the upper
+and lower bands</p>
+<p>Uses 1000 Hz as the reference frequency (as per the ANSI standard)</p>
+<p>Computes the 1/Nth band center frequencies, using different equations
+depending on the octave order. Orders with odd numbers (1, 1/3, etc.)
+use ANSI eqn 2. Order with even numbers (1/6, 1/12, etc.) use eqn 3.
+This causes any sub-bands to fit entirely within the full octave band</p>
+<p>Note: even-numbered sub-bands will not share center frequencies with
+the full octave band, but will share the upper and lower limits.
+ANSI S1.11 Annex A gives instructions for rounding to make the nominal
+band center freqs. If left-most digit is 1-4, round to 3 significant
+digits. If left-most digit is 5-9, round to 2 significant digits. For
+example, 41.567 rounds to 41.6. 8785.2 rounds to 8800.</p>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html
index 5e615ac..c933dc8 100644
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.plot_asds &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.plot_asds &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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-    <link rel="prev" title="sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase" href="sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html" /> 
+    <link rel="prev" title="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html" /> 
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 <body class="wy-body-for-nav"> 
@@ -80,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html">sdynpy.signal_processing.sdynpy_cpsd.dB_pow</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.db2scale.html">sdynpy.signal_processing.sdynpy_cpsd.db2scale</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html">sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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 </ul>
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -149,14 +151,14 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.plot_asds<a class="headerlink" href="#s
            </div>
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+        <a href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html" class="btn btn-neutral float-left" title="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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     </div>
 
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error &mdash; SDynPy 0.14.0 documentation</title>
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@@ -80,6 +80,7 @@
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html">sdynpy.signal_processing.sdynpy_cpsd.plot_asds</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.rms &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.rms &mdash; SDynPy 0.14.0 documentation</title>
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@@ -80,6 +80,7 @@
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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--- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms_csd.html
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-  <title>sdynpy.signal_processing.sdynpy_cpsd.rms_csd &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.rms_csd &mdash; SDynPy 0.14.0 documentation</title>
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@@ -80,6 +80,7 @@
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
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@@ -80,6 +80,7 @@
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
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@@ -156,7 +158,7 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.shaped_psd<a class="headerlink" href="#
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec92ff40>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html
index 6c7c576..a2a5a79 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_cpsd.trace &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd.trace &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -80,6 +80,7 @@
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html">sdynpy.signal_processing.sdynpy_cpsd.dB_pow</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.db2scale.html">sdynpy.signal_processing.sdynpy_cpsd.db2scale</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase.html">sdynpy.signal_processing.sdynpy_cpsd.match_coherence_phase</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html">sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs</a></li>
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_cpsd.plot_asds.html">sdynpy.signal_processing.sdynpy_cpsd.plot_asds</a></li>
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@@ -97,6 +98,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -156,7 +158,7 @@ <h1>sdynpy.signal_processing.sdynpy_cpsd.trace<a class="headerlink" href="#sdynp
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html
index 33e8679..fff29ad 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.delay_signal.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf.delay_signal &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf.delay_signal &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -88,6 +88,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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 </ul>
@@ -167,7 +168,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.delay_signal<a class="headerlink" href="
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee56eef0>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html
index 48ab1eb..03f28da 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf.fft2frf &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf.fft2frf &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -88,6 +88,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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 </ul>
@@ -131,7 +132,7 @@
 <h1>sdynpy.signal_processing.sdynpy_frf.fft2frf<a class="headerlink" href="#sdynpy-signal-processing-sdynpy-frf-fft2frf" title="Permalink to this heading"></a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sdynpy.signal_processing.sdynpy_frf.fft2frf">
-<span class="sig-name descname"><span class="pre">fft2frf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">references</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">responses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'H1'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_frf.html#fft2frf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_frf.fft2frf" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">fft2frf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">references</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">responses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freqs_in</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_frf.html#fft2frf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_frf.fft2frf" title="Permalink to this definition"></a></dt>
 <dd><p>Creates an FRF matrix given ffts of responses and references</p>
 <p>This function creates a nf x no x ni FRF matrix from the ffts
 provided.</p>
@@ -142,16 +143,23 @@ <h1>sdynpy.signal_processing.sdynpy_frf.fft2frf<a class="headerlink" href="#sdyn
 the number of frequencies in the fft.</p></li>
 <li><p><strong>responses</strong> (<em>ndarray</em>) – A no x nf or nf array where no is the number of responses and nf is the
 number of time frequencies in the fft.</p></li>
-<li><p><strong>method</strong> (<em>str in</em><em> [</em><em>'H1'</em><em>,</em><em>'H2'</em><em>,</em><em>'Hv'</em><em>,</em><em>'Hs'</em><em>]</em>) – The method for creating the frequency response function.</p></li>
+<li><p><strong>method</strong> (<em>str in</em><em> [</em><em>'H1'</em><em>,</em><em>'H2'</em><em>,</em><em>'Hv'</em><em>,</em><em>'Hs'</em><em>,</em><em>'LRM'</em><em>]</em>) – The method for creating the frequency response function.</p></li>
+<li><p><strong>freqs_in</strong> (<em>ndarray</em>) – Only used if method == ‘LRM’. A nf or nf x 1 array of frquency values.</p></li>
+<li><p><strong>**lrm_kwargs</strong> – Additional keyword arguments specify parameters if method == ‘LRM’.
+Possible arguments are: f_out (ndarray), bandwidth (float), transient
+(boolean), modelset (iterable of (3,1) ndarray), max_parallel (int),
+export_ratio (float). See sdynpy_lrm.frf_local_model.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>H</strong> – A nf x no x ni array where nf is the number of frequency lines, no is
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>H</strong> (<em>ndarray</em>) – A nf x no x ni array where nf is the number of frequency lines, no is
 the number of outputs, and ni is the number of inputs.  The output
-frequency lines will correspond to the frequency lines in the ffts.</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>ndarray</p>
+frequency lines will correspond to the frequency lines in the ffts.</p></li>
+<li><p><strong>freqs_out</strong> (<em>None or ndarray</em>) – None unless method == ‘LRM’. See sdynpy_lrm.frf_local_model.</p></li>
+<li><p><strong>model_data</strong> (<em>None dict</em>) – None unless method == ‘LRM’. See sdynpy_lrm.frf_local_model.</p></li>
+</ul>
+</p>
 </dd>
 </dl>
 <p class="rubric">Notes</p>
@@ -172,7 +180,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.fft2frf<a class="headerlink" href="#sdyn
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee9f6bc0>
+<jinja2.runtime.BlockReference object at 0x7f5a26488a60>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.html
index bd56498..2658085 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -85,6 +85,7 @@
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_geometry_fitting.html">sdynpy.signal_processing.sdynpy_geometry_fitting</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -149,7 +150,7 @@
 <tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_frf.delay_signal.html#sdynpy.signal_processing.sdynpy_frf.delay_signal" title="sdynpy.signal_processing.sdynpy_frf.delay_signal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">delay_signal</span></code></a>(times, signal, dt)</p></td>
 <td><p>Delay a time signal by the specified amount</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_frf.fft2frf.html#sdynpy.signal_processing.sdynpy_frf.fft2frf" title="sdynpy.signal_processing.sdynpy_frf.fft2frf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fft2frf</span></code></a>(references, responses[, method])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_frf.fft2frf.html#sdynpy.signal_processing.sdynpy_frf.fft2frf" title="sdynpy.signal_processing.sdynpy_frf.fft2frf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fft2frf</span></code></a>(references, responses[, method, ...])</p></td>
 <td><p>Creates an FRF matrix given ffts of responses and references</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_frf.modes2frf.html#sdynpy.signal_processing.sdynpy_frf.modes2frf" title="sdynpy.signal_processing.sdynpy_frf.modes2frf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">modes2frf</span></code></a>(frequencies, natural_frequencies, ...)</p></td>
@@ -179,7 +180,7 @@
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee640130>
+<jinja2.runtime.BlockReference object at 0x7f5a2698ad40>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html
index e2e4543..55b67d2 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.modes2frf.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf.modes2frf &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf.modes2frf &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -88,6 +88,7 @@
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_geometry_fitting.html">sdynpy.signal_processing.sdynpy_geometry_fitting</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -191,7 +192,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.modes2frf<a class="headerlink" href="#sd
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecc04340>
+<jinja2.runtime.BlockReference object at 0x7f5a268690c0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html
index acecc28..6086998 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf.plot &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf.plot &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -88,6 +88,7 @@
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_geometry_fitting.html">sdynpy.signal_processing.sdynpy_geometry_fitting</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -147,7 +148,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.plot<a class="headerlink" href="#sdynpy-
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee56f670>
+<jinja2.runtime.BlockReference object at 0x7f5a267fda20>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html
index a10a0cc..869e508 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html
@@ -4,7 +4,7 @@
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-  <title>sdynpy.signal_processing.sdynpy_frf.sysmat2frf &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf.sysmat2frf &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -88,6 +88,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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@@ -189,7 +190,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.sysmat2frf<a class="headerlink" href="#s
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html
index 460ad0e..23008db 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf.timedata2frf.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf.timedata2frf &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf.timedata2frf &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -88,6 +88,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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 </ul>
@@ -131,7 +132,7 @@
 <h1>sdynpy.signal_processing.sdynpy_frf.timedata2frf<a class="headerlink" href="#sdynpy-signal-processing-sdynpy-frf-timedata2frf" title="Permalink to this heading"></a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="sdynpy.signal_processing.sdynpy_frf.timedata2frf">
-<span class="sig-name descname"><span class="pre">timedata2frf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">references</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">responses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap=0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method='H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window=array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_fft=&lt;function</span> <span class="pre">&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_fft=&lt;function</span> <span class="pre">&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_fft_array=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_fft_array=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_frf.html#timedata2frf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_frf.timedata2frf" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">timedata2frf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">references</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">responses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dt=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples_per_average=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap=0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method='H1'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window=array([1.])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_fft=&lt;function</span> <span class="pre">&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_fft=&lt;function</span> <span class="pre">&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">response_fft_array=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reference_fft_array=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_model_data=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**lrm_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_frf.html#timedata2frf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_frf.timedata2frf" title="Permalink to this definition"></a></dt>
 <dd><p>Creates an FRF matrix given time histories of responses and references</p>
 <p>This function creates a nf x no x ni FRF matrix from the time histories
 provided.</p>
@@ -150,7 +151,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.timedata2frf<a class="headerlink" href="
 performed</p></li>
 <li><p><strong>overlap</strong> (<em>float</em>) – The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).
 If not specified, no overlap is used.</p></li>
-<li><p><strong>method</strong> (<em>str in</em><em> [</em><em>'H1'</em><em>,</em><em>'H2'</em><em>,</em><em>'Hv'</em><em>,</em><em>'Hs'</em><em>]</em>) – The method for creating the frequency response function. ‘H1’ is
+<li><p><strong>method</strong> (<em>str in</em><em> [</em><em>'H1'</em><em>,</em><em>'H2'</em><em>,</em><em>'Hv'</em><em>,</em><em>'Hs'</em><em>,</em><em>'LRM'</em><em>]</em>) – The method for creating the frequency response function. ‘H1’ is
 default if not specified.</p></li>
 <li><p><strong>window</strong> (<em>ndarray</em><em> or </em><em>str</em>) – A 1D ndarray with length samples_per_average that specifies the
 coefficients of the window.  No window is applied if not specified.
@@ -160,6 +161,13 @@ <h1>sdynpy.signal_processing.sdynpy_frf.timedata2frf<a class="headerlink" href="
 size number_of_responses, n_averages, samples_per_average</p></li>
 <li><p><strong>reference_fft_array</strong> (<em>np.ndarray</em>) – Array to store the data into before taking the FFT.  Should be
 size number_of_references, n_averages, samples_per_average</p></li>
+<li><p><strong>return_model_data</strong> (<em>boolean</em>) – Wheter to return selected model orders used in the ‘LRM’ method.
+default is False</p></li>
+<li><p><strong>**lrm_kwargs</strong> – Additional keyword arguments specify parameters if method == ‘LRM’.
+Possible arguments are: f_out (ndarray), bandwidth (float), transient
+(boolean), modelset (iterable of (3,1) ndarray), export_ratio (float),
+max_parallel (int), print_time (bool). See sdynpy_lrm.frf_local_model.
+samples_per_average, overlap, and window are void if method==’LRM’.</p></li>
 </ul>
 </dd>
 <dt class="field-even">Returns</dt>
@@ -167,6 +175,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf.timedata2frf<a class="headerlink" href="
 <li><p><strong>frequencies</strong> (<em>ndarray</em>) – A nf array of the frequency values associated with H</p></li>
 <li><p><strong>H</strong> (<em>ndarray</em>) – A nf x no x ni array where nf is the number of frequency lines, no is
 the number of outputs, and ni is the number of inputs.</p></li>
+<li><p><strong>model_data</strong> (<em>dict</em>) – Contains None if method != ‘LRM’. See sdynpy_lrm.frf_local_model.</p></li>
 </ul>
 </p>
 </dd>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html
index 8825ae4..e58eae6 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html
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@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -86,6 +86,7 @@
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_geometry_fitting.html">sdynpy.signal_processing.sdynpy_geometry_fitting</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html
index 34580c8..c79e578 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html
@@ -4,7 +4,7 @@
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-  <title>sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -86,6 +86,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -239,7 +240,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse<a class="headerlink"
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   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ecd32920>
+<jinja2.runtime.BlockReference object at 0x7f5a2a3614b0>
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   </div>
diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html
index 0de8341..ce221f9 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
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-  <title>sdynpy.signal_processing.sdynpy_frf_inverse &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf_inverse &mdash; SDynPy 0.14.0 documentation</title>
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@@ -83,6 +83,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html
index 03b1b8a..0fdced2 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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@@ -86,6 +86,7 @@
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -192,7 +193,7 @@ <h1>sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov<a class="header
   <hr/>
 
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html
index 697a247..6022b10 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -148,7 +150,7 @@ <h1>sdynpy.signal_processing.sdynpy_generator.random<a class="headerlink" href="
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ed179030>
+<jinja2.runtime.BlockReference object at 0x7f5a26689db0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html b/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html
index 1ad9671..896e470 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_generator.sine &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_generator.sine &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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 </head>
 
@@ -84,11 +84,13 @@
 <li class="toctree-l6"><a class="reference internal" href="#sdynpy.signal_processing.sdynpy_generator.sine"><code class="docutils literal notranslate"><span class="pre">sine()</span></code></a></li>
 </ul>
 </li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_generator.sine_sweep.html">sdynpy.signal_processing.sdynpy_generator.sine_sweep</a></li>
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 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
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new file mode 100644
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+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_generator.random.html">sdynpy.signal_processing.sdynpy_generator.random</a></li>
+<li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_generator.sine.html">sdynpy.signal_processing.sdynpy_generator.sine</a></li>
+<li class="toctree-l5 current"><a class="current reference internal" href="#">sdynpy.signal_processing.sdynpy_generator.sine_sweep</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="#sdynpy.signal_processing.sdynpy_generator.sine_sweep"><code class="docutils literal notranslate"><span class="pre">sine_sweep()</span></code></a></li>
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+           <div itemprop="articleBody">
+             
+  <section id="sdynpy-signal-processing-sdynpy-generator-sine-sweep">
+<h1>sdynpy.signal_processing.sdynpy_generator.sine_sweep<a class="headerlink" href="#sdynpy-signal-processing-sdynpy-generator-sine-sweep" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.signal_processing.sdynpy_generator.sine_sweep">
+<span class="sig-name descname"><span class="pre">sine_sweep</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frequencies</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sweep_rates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sweep_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amplitudes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phases</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_generator.html#sine_sweep"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_generator.sine_sweep" title="Permalink to this definition"></a></dt>
+<dd><p>Generates a sweeping sine wave with linear or logarithmic sweep rate</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dt</strong> (<em>float</em>) – The time step of the output signal</p></li>
+<li><p><strong>frequencies</strong> (<em>iterable</em>) – A list of frequency breakpoints for the sweep.  Can be ascending or
+decending or both.  Frequencies are specified in Hz, not rad/s.</p></li>
+<li><p><strong>sweep_rates</strong> (<em>iterable</em>) – A list of sweep rates between the breakpoints.  This array should have
+one fewer element than the <cite>frequencies</cite> array.  The ith element of this
+array specifies the sweep rate between <cite>frequencies[i]</cite> and
+<cite>frequencies[i+1]</cite>. For a linear sweep,
+the rate is in Hz/s.  For a logarithmic sweep, the rate is in octave/s.</p></li>
+<li><p><strong>sweep_types</strong> (<em>iterable</em><em> or </em><em>str</em>) – The type of sweep to perform between each frequency breakpoint.  Can be
+‘lin’ or ‘log’.  If a string is specified, it will be used for all
+breakpoints.  Otherwise it should be an array containing strings with
+one fewer element than that of the <cite>frequencies</cite> array.</p></li>
+<li><p><strong>amplitudes</strong> (<em>iterable</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Amplitude of the sine wave at each of the frequency breakpoints.  Can
+be specified as a single floating point value, or as an array with a
+value specified for each breakpoint. The default is 1.</p></li>
+<li><p><strong>phases</strong> (<em>iterable</em><em> or </em><em>float</em><em>, </em><em>optional</em>) – Phases of the sine wave at each of the frequency breakpoints.  Can
+be specified as a single floating point value, or as an array with a
+value specified for each breakpoint. Be aware that modifying the phase
+between breakpoints will effectively change the frequency of the signal,
+because the phase will change over time.  The default is 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>ValueError</strong> – If the sweep rate and start and end frequency would result in a negative
+    sweep time, for example if the start frequency is above the end frequency
+    and a positive sweep rate is specified.</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p><strong>ordinate</strong> – A numpy array consisting of the generated sine sweep signal.  The length
+of the signal will be determined by the frequency breakpoints and sweep
+rates.</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html
index 655c6b2..3e2e1c8 100644
--- a/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html
+++ b/_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle &mdash; SDynPy 0.14.0 documentation</title>
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+  <section id="sdynpy-signal-processing-sdynpy-lrm-frf-local-model">
+<h1>sdynpy.signal_processing.sdynpy_lrm.frf_local_model<a class="headerlink" href="#sdynpy-signal-processing-sdynpy-lrm-frf-local-model" title="Permalink to this heading"></a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="sdynpy.signal_processing.sdynpy_lrm.frf_local_model">
+<span class="sig-name descname"><span class="pre">frf_local_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">references</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">responses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abscissa</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f_out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modelset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[[1,</span> <span class="pre">1,</span> <span class="pre">0],</span> <span class="pre">[1,</span> <span class="pre">1,</span> <span class="pre">1],</span> <span class="pre">[1,</span> <span class="pre">1,</span> <span class="pre">2]]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_parallel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">print_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/sdynpy/signal_processing/sdynpy_lrm.html#frf_local_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sdynpy.signal_processing.sdynpy_lrm.frf_local_model" title="Permalink to this definition"></a></dt>
+<dd><p>Local modeling FRF estimator for MIMO systems.
+This function can be called directly or using
+frf.timedata2frf(…,method=’LRM’) or frf.fft2frf(…,method=’LRM’).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>references</strong> (<em>ndarray</em><em> (</em><em>N_in</em><em>, </em><em>N_freqs</em><em>) or </em><em>(</em><em>N_in</em><em>, </em><em>1</em><em>, </em><em>N_freqs</em><em>)</em>) – REFERENCES FREQUENCY-DOMAIN DATA.</p></li>
+<li><p><strong>responses</strong> (<em>ndarray</em><em>, </em><em>shape</em><em> (</em><em>N_out</em><em>, </em><em>N_freqs</em><em>) or </em><em>(</em><em>N_out</em><em>, </em><em>1</em><em>, </em><em>N_freqs</em><em>)</em>) – RESPONSES FREQUENCY-DOMAIN DATA.</p></li>
+<li><p><strong>abcissa</strong> (<em>ndarray</em><em> (</em><em>N_freqs</em><em>,</em><em>)</em>) – INPUT FREQUENCY VECTOR.</p></li>
+<li><p><strong>f_out</strong> (<em>ndarray</em><em> (</em><em>N_freqs_out</em><em>,</em><em>)</em><em>, </em><em>optional</em>) – Output frequency vector. Finer resolution increases computational cost,
+but only to a finite limit. Compuatational cost will not increase beyond
+f_out = f_in, but large interpolation may result in a large export_ratio.
+The default is f_in</p></li>
+<li><p><strong>bandwidth</strong> (<em>float</em><em>, </em><em>optional</em>) – Local model estimation bandwidth in units of f_in. Larger values yield
+better noise filtering but risk underfitting and take longer to run.</p></li>
+<li><p><strong>transient</strong> (<em>boolean</em><em>, </em><em>optional</em>) – whetheter to include transient estimation in local models. Recommend False
+for impact testing or burst random, True otherwise. The default is True.</p></li>
+<li><p><strong>modelset</strong> (<em>iterable</em><em> of </em><em>numpy</em><em> (</em><em>3</em><em>,</em><em>) </em><em>arrays</em><em>, </em><em>optional</em>) – Arrays contain [num. order, trans. order, denom. order].
+Recommend increasing numerator order for nonlinear structures
+and complicated FRFs. The default is [[1,1,0],[1,1,1],[1,1,2]].</p></li>
+<li><p><strong>export_ratio</strong> (<em>f;pat</em><em>, </em><em>optional</em>) – (centered) proportion of local bands that can be stored and exported.
+Larger values risk capturing volatile end behavior but yield faster
+computation times. The default is 0.1.</p></li>
+<li><p><strong>max_parallel</strong> (<em>int</em><em>, </em><em>optional</em>) – for very large systems with high frequency resolution and large bandwidths,
+system RAM can be a constraint. The default number of parallel processes
+is the number of CPU threads. If this causes freezing, max_parallel can
+be set to reduce the allowed number of parallel threads. Also, smaller
+pools may initialize faster. The default is 512.</p></li>
+<li><p><strong>print_time</strong> (<em>boolean</em><em>, </em><em>optional</em>) – print remaining time if more than 10 seconds</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>f_out</strong> (<em>ndarray (N_freqs_out,)</em>) – estimated FRM frequency vector</p></li>
+<li><p><strong>H</strong> (<em>ndarray (N_freqs_out, N_out, N_in)</em>) – frequency response matrix</p></li>
+<li><p><strong>model_data</strong> (<em>dict</em>) – candidate model info and selected model at each frequency in f_out</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
+<p>To improve noise and transient removal, recommend increasing bandwidth</p>
+<p>To reduce computation time, recommend decreasing f_out resolution,
+increasing export_ratio, or decreasing bandwidth. For very large systems,
+if freezing occurs, reduce max_parallel.</p>
+<p>For more information, see “A practitioner’s guide to local FRF estimation”,
+K. Coletti. This function uses the MISO parameterization</p>
+</dd></dl>
+
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+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="module-sdynpy.signal_processing.sdynpy_lrm">
+<span id="sdynpy-signal-processing-sdynpy-lrm"></span><h1>sdynpy.signal_processing.sdynpy_lrm<a class="headerlink" href="#module-sdynpy.signal_processing.sdynpy_lrm" title="Permalink to this heading"></a></h1>
+<p class="rubric">Functions</p>
+<table class="autosummary longtable docutils align-default">
+<colgroup>
+<col style="width: 10%" />
+<col style="width: 90%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.frf_local_model.html#sdynpy.signal_processing.sdynpy_lrm.frf_local_model" title="sdynpy.signal_processing.sdynpy_lrm.frf_local_model"><code class="xref py py-obj docutils literal notranslate"><span class="pre">frf_local_model</span></code></a>(references, responses, abscissa)</p></td>
+<td><p>Local modeling FRF estimator for MIMO systems.</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="sdynpy.signal_processing.sdynpy_integration.modal_parameters_to_MCK.html" class="btn btn-neutral float-left" title="sdynpy.signal_processing.sdynpy_integration.modal_parameters_to_MCK" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/_autosummary/sdynpy.signal_processing.sdynpy_rotation.R.html b/_autosummary/sdynpy.signal_processing.sdynpy_rotation.R.html
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-  <title>sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_compensation &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_compensation &mdash; SDynPy 0.14.0 documentation</title>
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@@ -77,6 +77,7 @@
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_geometry_fitting.html">sdynpy.signal_processing.sdynpy_geometry_fitting</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li class="toctree-l4"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li class="toctree-l4 current"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a><ul class="current">
 <li class="toctree-l5"><a class="reference internal" href="sdynpy.signal_processing.sdynpy_srs.loginterp.html">sdynpy.signal_processing.sdynpy_srs.loginterp</a></li>
@@ -179,7 +180,7 @@ <h1>sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_co
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+<jinja2.runtime.BlockReference object at 0x7f5a26ae50f0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
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diff --git a/_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png b/_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png
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diff --git a/_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png b/_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png
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diff --git a/_images/sdynpy_demo_polymax_resynthesize.png b/_images/sdynpy_demo_polypy_resynthesize.png
similarity index 100%
rename from _images/sdynpy_demo_polymax_resynthesize.png
rename to _images/sdynpy_demo_polypy_resynthesize.png
diff --git a/_images/sdynpy_demo_polymax_setup.png b/_images/sdynpy_demo_polypy_setup.png
similarity index 100%
rename from _images/sdynpy_demo_polymax_setup.png
rename to _images/sdynpy_demo_polypy_setup.png
diff --git a/_images/sdynpy_demo_polymax_stability_plot.png b/_images/sdynpy_demo_polypy_stability_plot.png
similarity index 100%
rename from _images/sdynpy_demo_polymax_stability_plot.png
rename to _images/sdynpy_demo_polypy_stability_plot.png
diff --git a/_modules/index.html b/_modules/index.html
index 140a024..2ad0885 100644
--- a/_modules/index.html
+++ b/_modules/index.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Overview: module code &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Overview: module code &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -113,6 +113,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_82.html">sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82</a></li>
 <li><a href="sdynpy/fileio/sdynpy_vic.html">sdynpy.fileio.sdynpy_vic</a></li>
 <li><a href="sdynpy/modal/sdynpy_ccmif.html">sdynpy.modal.sdynpy_ccmif</a></li>
+<li><a href="sdynpy/modal/sdynpy_modal_test.html">sdynpy.modal.sdynpy_modal_test</a></li>
 <li><a href="sdynpy/modal/sdynpy_modeshape.html">sdynpy.modal.sdynpy_modeshape</a></li>
 <li><a href="sdynpy/modal/sdynpy_polypy.html">sdynpy.modal.sdynpy_polypy</a></li>
 <li><a href="sdynpy/modal/sdynpy_signal_processing_gui.html">sdynpy.modal.sdynpy_signal_processing_gui</a></li>
@@ -127,6 +128,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="sdynpy/signal_processing/sdynpy_geometry_fitting.html">sdynpy.signal_processing.sdynpy_geometry_fitting</a></li>
 <li><a href="sdynpy/signal_processing/sdynpy_harmonic.html">sdynpy.signal_processing.sdynpy_harmonic</a></li>
 <li><a href="sdynpy/signal_processing/sdynpy_integration.html">sdynpy.signal_processing.sdynpy_integration</a></li>
+<li><a href="sdynpy/signal_processing/sdynpy_lrm.html">sdynpy.signal_processing.sdynpy_lrm</a></li>
 <li><a href="sdynpy/signal_processing/sdynpy_rotation.html">sdynpy.signal_processing.sdynpy_rotation</a></li>
 <li><a href="sdynpy/signal_processing/sdynpy_srs.html">sdynpy.signal_processing.sdynpy_srs</a></li>
 </ul>
@@ -138,7 +140,7 @@ <h1>All modules for which code is available</h1>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc4ac0>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb5c90>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_array.html b/_modules/sdynpy/core/sdynpy_array.html
index 540e78a..e53838d 100644
--- a/_modules/sdynpy/core/sdynpy_array.html
+++ b/_modules/sdynpy/core/sdynpy_array.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_array &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_array &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -333,7 +333,7 @@ <h1>Source code for sdynpy.core.sdynpy_array</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        filename : str</span>
-<span class="sd">            Filename specifying the file to load.  If the filename has </span>
+<span class="sd">            Filename specifying the file to load.  If the filename has</span>
 <span class="sd">            extension .unv or .uff, it will be loaded as a universal file.</span>
 <span class="sd">            Otherwise, it will be loaded as a NumPy file.</span>
 
@@ -404,7 +404,7 @@ <h1>Source code for sdynpy.core.sdynpy_array</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebc4ec20>
+<jinja2.runtime.BlockReference object at 0x7f5a26fab7c0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_coordinate.html b/_modules/sdynpy/core/sdynpy_coordinate.html
index 0c8d57b..613a985 100644
--- a/_modules/sdynpy/core/sdynpy_coordinate.html
+++ b/_modules/sdynpy/core/sdynpy_coordinate.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_coordinate &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_coordinate &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -114,6 +114,7 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">.sdynpy_array</span> <span class="kn">import</span> <span class="n">SdynpyArray</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
 
 <span class="c1"># This maps direction integers to their string counterparts</span>
 <span class="n">_string_map</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span> <span class="s1">&#39;X+&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span> <span class="s1">&#39;Y+&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span> <span class="s1">&#39;Z+&#39;</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span> <span class="s1">&#39;RX+&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span> <span class="s1">&#39;RY+&#39;</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span> <span class="s1">&#39;RZ+&#39;</span><span class="p">,</span>
@@ -157,8 +158,16 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
 <span class="sd">        &#39;X-&#39; = -1, &#39;Y-&#39; = -2, &#39;Z-&#39; = -3</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">node</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">coordinate</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">))</span>
-    <span class="n">direction</span> <span class="o">=</span> <span class="n">_direction_map</span><span class="p">[</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">coordinate</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">)]</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">node</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">coordinate</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">))</span>
+    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Node ID </span><span class="si">{:}</span><span class="s1"> is not Valid.  Defaulting to 0.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">coordinate</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">)))</span>
+        <span class="n">node</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">direction</span> <span class="o">=</span> <span class="n">_direction_map</span><span class="p">[</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">coordinate</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">)]</span>
+    <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Direction </span><span class="si">{:}</span><span class="s1"> is not Valid.  Defaulting to no direction.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">coordinate</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">)))</span>
+        <span class="n">direction</span> <span class="o">=</span> <span class="mi">0</span>
     <span class="k">return</span> <span class="n">node</span><span class="p">,</span> <span class="n">direction</span></div>
 
 
@@ -224,7 +233,7 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
 <div class="viewcode-block" id="CoordinateArray.direction_string_array"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html#sdynpy.core.sdynpy_coordinate.CoordinateArray.direction_string_array">[docs]</a>    <span class="k">def</span> <span class="nf">direction_string_array</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns a string array representation of the direction</span>
-<span class="sd">        </span>
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        np.ndarray</span>
@@ -281,7 +290,7 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
 
 <div class="viewcode-block" id="CoordinateArray.local_direction"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html#sdynpy.core.sdynpy_coordinate.CoordinateArray.local_direction">[docs]</a>    <span class="k">def</span> <span class="nf">local_direction</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns a local direction array </span>
+<span class="sd">        Returns a local direction array</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -306,6 +315,24 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
                 <span class="n">output</span><span class="p">[</span><span class="n">key</span> <span class="o">+</span> <span class="p">(</span><span class="n">index</span><span class="p">,)]</span> <span class="o">=</span> <span class="n">signs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">output</span></div>
 
+<div class="viewcode-block" id="CoordinateArray.offset_node_ids"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html#sdynpy.core.sdynpy_coordinate.CoordinateArray.offset_node_ids">[docs]</a>    <span class="k">def</span> <span class="nf">offset_node_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">offset_value</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns a copy of the CoordinateArray with the node IDs offset</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        offset_value : int</span>
+<span class="sd">            The value to offset the node IDs by.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        CoordinateArray</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">output</span><span class="o">.</span><span class="n">node</span> <span class="o">+=</span> <span class="n">offset_value</span>
+        <span class="k">return</span> <span class="n">output</span></div>
+
 <div class="viewcode-block" id="CoordinateArray.from_matlab_cellstr"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html#sdynpy.core.sdynpy_coordinate.CoordinateArray.from_matlab_cellstr">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_matlab_cellstr</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">cellstr_data</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -400,14 +427,14 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
 <span class="sd">    coordinate_array : CoordinateArray</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">node</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;node&#39;</span><span class="p">]</span>
             <span class="n">direction</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;direction&#39;</span><span class="p">]</span>
         <span class="k">except</span> <span class="p">(</span><span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                 <span class="s1">&#39;structured_array must be numpy.ndarray with dtype names &quot;node&quot; and &quot;direction&quot;&#39;</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="ow">not</span> <span class="n">string_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">elif</span> <span class="n">string_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">string_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">string_array</span><span class="p">)</span>
         <span class="n">node</span><span class="p">,</span> <span class="n">direction</span> <span class="o">=</span> <span class="n">parse_coordinate_string_array</span><span class="p">(</span><span class="n">string_array</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
@@ -490,7 +517,7 @@ <h1>Source code for sdynpy.core.sdynpy_coordinate</h1><div class="highlight"><pr
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc4520>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb58d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_data.html b/_modules/sdynpy/core/sdynpy_data.html
index f3560a5..e8ac02f 100644
--- a/_modules/sdynpy/core/sdynpy_data.html
+++ b/_modules/sdynpy/core/sdynpy_data.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_data &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_data &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -111,6 +111,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">along with this program.  If not, see &lt;https://www.gnu.org/licenses/&gt;.</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
+<span class="kn">import</span> <span class="nn">itertools</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">from</span> <span class="nn">.sdynpy_array</span> <span class="kn">import</span> <span class="n">SdynpyArray</span>
 <span class="kn">from</span> <span class="nn">.sdynpy_coordinate</span> <span class="kn">import</span> <span class="n">outer_product</span><span class="p">,</span> <span class="n">CoordinateArray</span><span class="p">,</span> <span class="n">coordinate_array</span>
@@ -120,7 +121,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_cpsd</span> <span class="kn">import</span> <span class="p">(</span><span class="n">cpsd</span> <span class="k">as</span> <span class="n">sp_cpsd</span><span class="p">,</span>
                                              <span class="n">cpsd_coherence</span> <span class="k">as</span> <span class="n">sp_coherence</span><span class="p">,</span>
                                              <span class="n">cpsd_to_time_history</span><span class="p">,</span>
-                                             <span class="n">cpsd_from_coh_phs</span><span class="p">,</span> 
+                                             <span class="n">cpsd_from_coh_phs</span><span class="p">,</span>
                                              <span class="n">db2scale</span><span class="p">)</span>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_srs</span> <span class="kn">import</span> <span class="p">(</span><span class="n">srs</span> <span class="k">as</span> <span class="n">sp_srs</span><span class="p">,</span>
                                             <span class="n">octspace</span><span class="p">,</span>
@@ -129,13 +130,14 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                                             <span class="n">sum_decayed_sines_displacement_velocity</span><span class="p">)</span>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_rotation</span> <span class="kn">import</span> <span class="n">lstsq_rigid_transform</span>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_generator</span> <span class="kn">import</span> <span class="p">(</span>
-    <span class="n">pseudorandom</span><span class="p">,</span><span class="n">sine</span><span class="p">,</span><span class="n">ramp_envelope</span><span class="p">,</span> <span class="n">chirp</span><span class="p">,</span><span class="n">pulse</span><span class="p">)</span>
+    <span class="n">pseudorandom</span><span class="p">,</span> <span class="n">sine</span><span class="p">,</span> <span class="n">ramp_envelope</span><span class="p">,</span> <span class="n">chirp</span><span class="p">,</span> <span class="n">pulse</span><span class="p">,</span> <span class="n">sine_sweep</span><span class="p">)</span>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_frf_inverse</span> <span class="kn">import</span> <span class="p">(</span><span class="n">frf_inverse</span><span class="p">,</span>
                                                     <span class="n">compute_tikhonov_modified_singular_values</span><span class="p">)</span>
 
 <span class="kn">from</span> <span class="nn">..fem.sdynpy_exodus</span> <span class="kn">import</span> <span class="n">Exodus</span>
 <span class="kn">from</span> <span class="nn">scipy.linalg</span> <span class="kn">import</span> <span class="n">eigh</span>
 <span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
 <span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
@@ -143,31 +145,32 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">matplotlib.figure</span> <span class="kn">import</span> <span class="n">Figure</span>
 <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <span class="n">Rectangle</span>
 <span class="kn">from</span> <span class="nn">matplotlib.gridspec</span> <span class="kn">import</span> <span class="n">GridSpec</span>
+<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">MaxNLocator</span>
 <span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">copy</span><span class="p">,</span> <span class="n">deepcopy</span>
 <span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
 <span class="kn">from</span> <span class="nn">qtpy</span> <span class="kn">import</span> <span class="n">QtWidgets</span><span class="p">,</span> <span class="n">uic</span><span class="p">,</span> <span class="n">QtGui</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtGui</span> <span class="kn">import</span> <span class="n">QIcon</span><span class="p">,</span> <span class="n">QFont</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtCore</span> <span class="kn">import</span> <span class="n">Qt</span><span class="p">,</span> <span class="n">QCoreApplication</span><span class="p">,</span> <span class="n">QRect</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtWidgets</span> <span class="kn">import</span> <span class="p">(</span><span class="n">QToolTip</span><span class="p">,</span> <span class="n">QLabel</span><span class="p">,</span> <span class="n">QPushButton</span><span class="p">,</span> <span class="n">QApplication</span><span class="p">,</span>
-                             <span class="n">QGroupBox</span><span class="p">,</span> <span class="n">QWidget</span><span class="p">,</span> <span class="n">QMessageBox</span><span class="p">,</span> <span class="n">QHBoxLayout</span><span class="p">,</span>
-                             <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="p">,</span> <span class="n">QMainWindow</span><span class="p">,</span>
-                             <span class="n">QFileDialog</span><span class="p">,</span> <span class="n">QErrorMessage</span><span class="p">,</span> <span class="n">QListWidget</span><span class="p">,</span> <span class="n">QLineEdit</span><span class="p">,</span>
-                             <span class="n">QDockWidget</span><span class="p">,</span> <span class="n">QGridLayout</span><span class="p">,</span> <span class="n">QButtonGroup</span><span class="p">,</span> <span class="n">QDialog</span><span class="p">,</span>
-                             <span class="n">QCheckBox</span><span class="p">,</span> <span class="n">QRadioButton</span><span class="p">,</span> <span class="n">QMenuBar</span><span class="p">,</span> <span class="n">QMenu</span><span class="p">)</span>
+                            <span class="n">QGroupBox</span><span class="p">,</span> <span class="n">QWidget</span><span class="p">,</span> <span class="n">QMessageBox</span><span class="p">,</span> <span class="n">QHBoxLayout</span><span class="p">,</span>
+                            <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="p">,</span> <span class="n">QMainWindow</span><span class="p">,</span>
+                            <span class="n">QFileDialog</span><span class="p">,</span> <span class="n">QErrorMessage</span><span class="p">,</span> <span class="n">QListWidget</span><span class="p">,</span> <span class="n">QLineEdit</span><span class="p">,</span>
+                            <span class="n">QDockWidget</span><span class="p">,</span> <span class="n">QGridLayout</span><span class="p">,</span> <span class="n">QButtonGroup</span><span class="p">,</span> <span class="n">QDialog</span><span class="p">,</span>
+                            <span class="n">QCheckBox</span><span class="p">,</span> <span class="n">QRadioButton</span><span class="p">,</span> <span class="n">QMenuBar</span><span class="p">,</span> <span class="n">QMenu</span><span class="p">)</span>
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">qtpy.QtGui</span> <span class="kn">import</span> <span class="n">QAction</span>
 <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">qtpy.QtWidgets</span> <span class="kn">import</span> <span class="n">QAction</span>
 <span class="kn">import</span> <span class="nn">pyqtgraph</span>
-<span class="n">pyqtgraph</span><span class="o">.</span><span class="n">setConfigOption</span><span class="p">(</span><span class="s1">&#39;background&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
-<span class="n">pyqtgraph</span><span class="o">.</span><span class="n">setConfigOption</span><span class="p">(</span><span class="s1">&#39;foreground&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">scipy.signal</span> <span class="k">as</span> <span class="nn">sig</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">import</span> <span class="nn">scipy.fft</span> <span class="k">as</span> <span class="nn">scipyfft</span>
-<span class="kn">from</span> <span class="nn">scipy.signal.windows</span> <span class="kn">import</span> <span class="n">exponential</span><span class="p">,</span><span class="n">get_window</span>
+<span class="kn">from</span> <span class="nn">scipy.signal.windows</span> <span class="kn">import</span> <span class="n">exponential</span><span class="p">,</span> <span class="n">get_window</span>
 <span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">oaconvolve</span><span class="p">,</span> <span class="n">convolve</span>
 <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+<span class="n">pyqtgraph</span><span class="o">.</span><span class="n">setConfigOption</span><span class="p">(</span><span class="s1">&#39;background&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
+<span class="n">pyqtgraph</span><span class="o">.</span><span class="n">setConfigOption</span><span class="p">(</span><span class="s1">&#39;foreground&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
 
 
 <div class="viewcode-block" id="SpecificDataType"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html#sdynpy.core.sdynpy_data.SpecificDataType">[docs]</a><span class="k">class</span> <span class="nc">SpecificDataType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
@@ -324,25 +327,38 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="n">_imat_function_type_inverse_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">val</span><span class="p">:</span> <span class="n">key</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">_imat_function_type_map</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
 
+
+<span class="k">def</span> <span class="nf">_flat_frequency_shape</span><span class="p">(</span><span class="n">freq</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">1</span>
+
+
 <div class="viewcode-block" id="AbscissaIndexExtractor"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html#sdynpy.core.sdynpy_data.AbscissaIndexExtractor">[docs]</a><span class="k">class</span> <span class="nc">AbscissaIndexExtractor</span><span class="p">:</span>
-<div class="viewcode-block" id="AbscissaIndexExtractor.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html#sdynpy.core.sdynpy_data.AbscissaIndexExtractor.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">parent</span><span class="p">):</span>
+<div class="viewcode-block" id="AbscissaIndexExtractor.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.AbscissaIndexExtractor.html#sdynpy.core.sdynpy_data.AbscissaIndexExtractor.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">parent</span> <span class="o">=</span> <span class="n">parent</span></div>
-        
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">key</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extract_elements</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-        
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">key</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extract_elements</span><span class="p">(</span><span class="n">key</span><span class="p">)</span></div>
-        
+
+
 <div class="viewcode-block" id="AbscissaValueExtractor"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html#sdynpy.core.sdynpy_data.AbscissaValueExtractor">[docs]</a><span class="k">class</span> <span class="nc">AbscissaValueExtractor</span><span class="p">:</span>
-<div class="viewcode-block" id="AbscissaValueExtractor.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html#sdynpy.core.sdynpy_data.AbscissaValueExtractor.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">parent</span><span class="p">):</span>
+<div class="viewcode-block" id="AbscissaValueExtractor.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html#sdynpy.core.sdynpy_data.AbscissaValueExtractor.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">parent</span> <span class="o">=</span> <span class="n">parent</span></div>
-        
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">key</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">key</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span></div>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">key</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span></div>
+
+
+<span class="k">def</span> <span class="nf">_update_annotations_to_axes_bottom</span><span class="p">(</span><span class="n">axes</span><span class="p">):</span>
+    <span class="n">annotations</span> <span class="o">=</span> <span class="p">[</span><span class="n">child</span> <span class="k">for</span> <span class="n">child</span> <span class="ow">in</span> <span class="n">axes</span><span class="o">.</span><span class="n">get_children</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">child</span><span class="p">,</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">text</span><span class="o">.</span><span class="n">Annotation</span><span class="p">)]</span>
+    <span class="k">for</span> <span class="n">annotation</span> <span class="ow">in</span> <span class="n">annotations</span><span class="p">:</span>
+        <span class="n">new_position</span> <span class="o">=</span> <span class="p">(</span><span class="n">annotation</span><span class="o">.</span><span class="n">xy</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">axes</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">annotation</span><span class="o">.</span><span class="n">xy</span> <span class="o">=</span> <span class="n">new_position</span>
 
 <div class="viewcode-block" id="NDDataArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray">[docs]</a><span class="k">class</span> <span class="nc">NDDataArray</span><span class="p">(</span><span class="n">SdynpyArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Generic N-Dimensional data structure</span>
@@ -359,8 +375,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="c1"># ndarray constructor, but return an object of our type.</span>
         <span class="c1"># It also triggers a call to __array_finalize__</span>
         <span class="n">data_dtype</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="p">(</span><span class="s1">&#39;abscissa&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">nelements</span><span class="p">),</span>
-            <span class="p">(</span><span class="s1">&#39;ordinate&#39;</span><span class="p">,</span> <span class="n">ordinate_dtype</span><span class="p">,</span> <span class="n">nelements</span><span class="p">),</span>
+            <span class="p">(</span><span class="s1">&#39;abscissa&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">nelements</span><span class="p">,)),</span>
+            <span class="p">(</span><span class="s1">&#39;ordinate&#39;</span><span class="p">,</span> <span class="n">ordinate_dtype</span><span class="p">,</span> <span class="p">(</span><span class="n">nelements</span><span class="p">,)),</span>
             <span class="p">(</span><span class="s1">&#39;comment1&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;comment2&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;comment3&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
@@ -426,7 +442,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        AbscissaIndexExtractor that can be indexed to extract specific elements</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">AbscissaIndexExtractor</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-    
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">idx_by_ab</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -439,14 +455,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;The spacing of the abscissa in the function.  Returns ValueError if</span>
 <span class="sd">        abscissa are not evenly spaced.&quot;&quot;&quot;</span>
         <span class="c1"># Look at the spacing between abscissa</span>
-        <span class="n">spacing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">spacing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">mean_spacing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">spacing</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">spacing</span><span class="p">,</span><span class="n">mean_spacing</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">spacing</span><span class="p">,</span> <span class="n">mean_spacing</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> do not have evenly spaced abscissa&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">mean_spacing</span>
 
 <div class="viewcode-block" id="NDDataArray.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">one_axis</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{},</span>
-             <span class="n">plot_kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{}):</span>
+             <span class="n">plot_kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
+             <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_type</span> <span class="o">=</span> <span class="s1">&#39;vline&#39;</span><span class="p">,</span>
+             <span class="n">abscissa_marker_plot_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the data array</span>
 
@@ -454,7 +472,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        one_axis : bool, optional</span>
 <span class="sd">            Set to True to plot all data on one axis.  Set to False to plot</span>
-<span class="sd">            data on multiple subplots.  one_axis can also be set to a </span>
+<span class="sd">            data on multiple subplots.  one_axis can also be set to a</span>
 <span class="sd">            matplotlib axis to plot data on an existing axis.  The default is</span>
 <span class="sd">            True.</span>
 <span class="sd">        subplots_kwargs : dict, optional</span>
@@ -462,6 +480,20 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            figure and axes. The default is {}.</span>
 <span class="sd">        plot_kwargs : dict, optional</span>
 <span class="sd">            Keywords passed to the matplotlib plot function. The default is {}.</span>
+<span class="sd">        abscissa_markers : ndarray, optional</span>
+<span class="sd">            Array containing abscissa values to mark on the plot to denote</span>
+<span class="sd">            significant events.</span>
+<span class="sd">        abscissa_marker_labels : str or ndarray</span>
+<span class="sd">            Array of strings to label the abscissa_markers with, or</span>
+<span class="sd">            alternatively a format string that accepts index and abscissa</span>
+<span class="sd">            inputs (e.g. &#39;{index:}: {abscissa:0.2f}&#39;).  By default no label</span>
+<span class="sd">            will be applied.</span>
+<span class="sd">        abscissa_marker_type : str</span>
+<span class="sd">            The type of marker to use.  This can either be the string &#39;vline&#39;</span>
+<span class="sd">            or a valid matplotlib symbol specifier (e.g. &#39;o&#39;, &#39;x&#39;, &#39;.&#39;).</span>
+<span class="sd">        abscissa_marker_plot_kwargs : dict</span>
+<span class="sd">            Additional keyword arguments used when plotting the abscissa label</span>
+<span class="sd">            markers.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -469,28 +501,133 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">             On which the data were plotted</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">abscissa_marker_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">abscissa_markers</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">abscissa_marker_labels</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">index</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">abscissa</span> <span class="o">=</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">)]</span>
+        
         <span class="k">if</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
             <span class="n">ncols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span>
             <span class="n">nrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">/</span> <span class="n">ncols</span><span class="p">))</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">function</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">())):</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">function</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()]))</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">ax</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:]:</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">axis</span> <span class="o">=</span> <span class="n">one_axis</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">axis</span></div>
 
-<div class="viewcode-block" id="NDDataArray.reshape_to_matrix"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">reshape_to_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<div class="viewcode-block" id="NDDataArray.plot_image"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.plot_image">[docs]</a>    <span class="k">def</span> <span class="nf">plot_image</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">reduction_function</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">colorbar_scale</span> <span class="o">=</span> <span class="s1">&#39;linear&#39;</span><span class="p">,</span>
+                   <span class="n">colorbar_min</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">colorbar_max</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="n">image_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="k">if</span> <span class="n">colorbar_scale</span> <span class="o">==</span> <span class="s1">&#39;log&#39;</span><span class="p">:</span>
+            <span class="n">image_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">image_data</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">colorbar_min</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">colorbar_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">colorbar_min</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">colorbar_max</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">colorbar_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">colorbar_max</span><span class="p">)</span>
+        <span class="k">def</span> <span class="nf">dof_formatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">return</span> <span class="s1">&#39;&#39;</span>
+            <span class="k">elif</span> <span class="n">x</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+                <span class="k">return</span> <span class="s1">&#39;&#39;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">return</span> <span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)]</span><span class="o">.</span><span class="n">coordinate</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">layout</span><span class="o">=</span><span class="s1">&#39;constrained&#39;</span><span class="p">)</span>
+        <span class="n">im_data</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image_data</span><span class="p">,</span><span class="n">vmin</span><span class="o">=</span><span class="n">colorbar_min</span><span class="p">,</span><span class="n">vmax</span><span class="o">=</span><span class="n">colorbar_max</span><span class="p">,</span><span class="n">aspect</span><span class="o">=</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span>
+                            <span class="n">interpolation</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">,</span><span class="n">extent</span><span class="o">=</span><span class="p">[</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span>
+                                <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span><span class="o">-</span><span class="mf">0.5</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">min_n_ticks</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">dof_formatter</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">colorbar_scale</span> <span class="o">==</span> <span class="s1">&#39;log&#39;</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
+                <span class="n">im_data</span><span class="p">,</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
+                <span class="nb">format</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">pos</span><span class="p">:</span> <span class="s1">&#39;$10^</span><span class="si">{:}</span><span class="s1">$&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span>
+                <span class="n">im_data</span><span class="p">,</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ax</span></div>
+
+<div class="viewcode-block" id="NDDataArray.reshape_to_matrix"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">reshape_to_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_if_missing</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Reshapes a data array to a matrix with response coordinates along the</span>
 <span class="sd">        rows and reference coordinates along the columns</span>
 
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        error_if_missing : bool</span>
+<span class="sd">            If True, an error will be thrown if there are missing data objects</span>
+<span class="sd">            when trying to make a matrix of functions (i.e. if a response</span>
+<span class="sd">            degree of freedom is missing from one reference).  If False,</span>
+<span class="sd">            response coordinates will simply be discarded if they do not exist</span>
+<span class="sd">            for all references.  Default is True.</span>
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        output_array : Data Aarray</span>
@@ -502,6 +639,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">reference_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">)</span>
         <span class="n">output_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span>
             <span class="p">(</span><span class="n">response_coords</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="n">reference_coords</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">error_if_missing</span><span class="p">:</span>
+            <span class="n">keep_response_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">response_coords</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span> <span class="n">response_coord</span> <span class="ow">in</span> <span class="n">response_coords</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
             <span class="k">for</span> <span class="n">col_index</span><span class="p">,</span> <span class="n">reference_coord</span> <span class="ow">in</span> <span class="n">reference_coords</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
                 <span class="n">current_function</span> <span class="o">=</span> <span class="n">flattened_functions</span><span class="p">[</span>
@@ -509,12 +648,18 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                     <span class="o">&amp;</span>
                     <span class="p">(</span><span class="n">flattened_functions</span><span class="o">.</span><span class="n">reference_coordinate</span> <span class="o">==</span> <span class="n">reference_coord</span><span class="p">)]</span>
                 <span class="k">if</span> <span class="n">current_function</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No function exists with reference coordinate </span><span class="si">{:}</span><span class="s1"> and response coordinate </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                        <span class="nb">str</span><span class="p">(</span><span class="n">reference_coord</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">response_coord</span><span class="p">)))</span>
+                    <span class="k">if</span> <span class="n">error_if_missing</span><span class="p">:</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No function exists with reference coordinate </span><span class="si">{:}</span><span class="s1"> and response coordinate </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                            <span class="nb">str</span><span class="p">(</span><span class="n">reference_coord</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">response_coord</span><span class="p">)))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">keep_response_indices</span><span class="p">[</span><span class="n">row_index</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+                        <span class="k">continue</span>
                 <span class="k">if</span> <span class="n">current_function</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Multiple functions exist (</span><span class="si">{:}</span><span class="s1">) with reference coordinate </span><span class="si">{:}</span><span class="s1"> and response coordinate </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                         <span class="n">current_function</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">reference_coord</span><span class="p">),</span> <span class="nb">str</span><span class="p">(</span><span class="n">response_coord</span><span class="p">)))</span>
                 <span class="n">output_array</span><span class="p">[</span><span class="n">row_index</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">col_index</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">current_function</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">error_if_missing</span><span class="p">:</span>
+            <span class="n">output_array</span> <span class="o">=</span> <span class="n">output_array</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">,:]</span>
         <span class="k">return</span> <span class="n">output_array</span></div>
 
 <div class="viewcode-block" id="NDDataArray.extract_elements"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.extract_elements">[docs]</a>    <span class="k">def</span> <span class="nf">extract_elements</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
@@ -523,7 +668,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        indices : </span>
+<span class="sd">        indices :</span>
 <span class="sd">            Any type of indices into a np.ndarray to select the elements to keep</span>
 
 <span class="sd">        Returns</span>
@@ -562,7 +707,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="NDDataArray.join"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.join">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span><span class="n">data_arrays</span><span class="p">,</span><span class="n">increment_abscissa</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_arrays</span><span class="p">,</span> <span class="n">increment_abscissa</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Joins several data arrays together by concatenating their ordinates</span>
 
@@ -585,13 +730,13 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">all_coordinate</span><span class="p">[:</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">all_coordinate</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Signals do not have equivalent coordinates&#39;</span><span class="p">)</span>
         <span class="n">coordinate</span> <span class="o">=</span> <span class="n">data_arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">coordinate</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">array</span><span class="o">.</span><span class="n">ordinate</span> <span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">data_arrays</span><span class="p">],</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">array</span><span class="o">.</span><span class="n">ordinate</span> <span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">data_arrays</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">increment_abscissa</span><span class="p">:</span>
             <span class="n">delta_abscissa</span> <span class="o">=</span> <span class="n">data_arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa_spacing</span>
             <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">*</span><span class="n">delta_abscissa</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">array</span><span class="p">[</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">data_arrays</span><span class="p">],</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">func_type</span><span class="p">,</span><span class="n">abscissa</span><span class="p">,</span><span class="n">ordinate</span><span class="p">,</span><span class="n">coordinate</span><span class="p">)</span></div>
+            <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">array</span><span class="p">[</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="k">for</span> <span class="n">array</span> <span class="ow">in</span> <span class="n">data_arrays</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">func_type</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span> <span class="n">coordinate</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="NDDataArray.downsample"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.downsample">[docs]</a>    <span class="k">def</span> <span class="nf">downsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">factor</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -656,7 +801,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_dimension</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">original_geometry</span> <span class="o">=</span> <span class="n">original_geometry</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">node_id_map</span><span class="o">.</span><span class="n">from_ids</span><span class="p">)</span>
                 <span class="n">original_geometry</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">node_id_map</span><span class="p">(</span><span class="n">original_geometry</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
                 <span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()[</span><span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">node</span><span class="p">,</span> <span class="n">node_id_map</span><span class="o">.</span><span class="n">from_ids</span><span class="p">)]</span>
@@ -676,7 +821,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;2D Data not Implemented Yet&#39;</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="NDDataArray.to_shape_array"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.to_shape_array">[docs]</a>    <span class="k">def</span> <span class="nf">to_shape_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">abscissa_values</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="NDDataArray.to_shape_array"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.to_shape_array">[docs]</a>    <span class="k">def</span> <span class="nf">to_shape_array</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">abscissa_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Converts an NDDataArray to a ShapeArray</span>
 
@@ -706,20 +851,20 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">abscissa_values</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">abscissa_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">flat_self</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">abscissa_values</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">abscissa_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">flat_self</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">abscissa_values</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="c1"># Check if there are repeated responses</span>
         <span class="n">coordinates</span> <span class="o">=</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">response_coordinate</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Data has duplicate response coordinates.  Please ensure that there is only one of each response coordinate in the data.&#39;</span><span class="p">)</span>
         <span class="c1"># Extract the shape matrix</span>
-        <span class="n">shape_matrix</span> <span class="o">=</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[:,</span><span class="n">abscissa_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+        <span class="n">shape_matrix</span> <span class="o">=</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[:,</span> <span class="n">abscissa_indices</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
         <span class="c1"># Create the new shape</span>
         <span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">shape_array</span>
-        <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">coordinates</span><span class="p">,</span><span class="n">shape_matrix</span><span class="p">,</span><span class="n">flat_self</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="n">abscissa_indices</span><span class="p">])</span></div>
-        
-<div class="viewcode-block" id="NDDataArray.zero_pad"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.zero_pad">[docs]</a>    <span class="k">def</span> <span class="nf">zero_pad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">num_samples</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">update_abscissa</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                 <span class="n">left</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">use_next_fast_len</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">coordinates</span><span class="p">,</span> <span class="n">shape_matrix</span><span class="p">,</span> <span class="n">flat_self</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="n">abscissa_indices</span><span class="p">])</span></div>
+
+<div class="viewcode-block" id="NDDataArray.zero_pad"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.zero_pad">[docs]</a>    <span class="k">def</span> <span class="nf">zero_pad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">update_abscissa</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                 <span class="n">left</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                 <span class="n">use_next_fast_len</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Add zeros to the beginning or end of a signal</span>
 
@@ -729,9 +874,9 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Number of zeros to add to the function.  If not specified, no zeros</span>
 <span class="sd">            are added unless `use_next_fast_len` is `True`</span>
 <span class="sd">        update_abscissa : bool, optional</span>
-<span class="sd">            If True, modify the abscissa to keep the same abscissa spacing.  </span>
+<span class="sd">            If True, modify the abscissa to keep the same abscissa spacing.</span>
 <span class="sd">            The function must have equally spaced abscissa for this to work.</span>
-<span class="sd">            If False, the added abscissa will have a value of zero.  </span>
+<span class="sd">            If False, the added abscissa will have a value of zero.</span>
 <span class="sd">            The default is True.</span>
 <span class="sd">        left : bool, optional</span>
 <span class="sd">            Add zeros to the left side (beginning) of the function. The default</span>
@@ -770,17 +915,19 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">right_samples</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="n">added_zeros_left</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">left_samples</span><span class="p">,))</span>
         <span class="n">added_zeros_right</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">right_samples</span><span class="p">,))</span>
-            
-        <span class="n">new_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">added_zeros_left</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="n">added_zeros_right</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        
+
+        <span class="n">new_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">added_zeros_left</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">added_zeros_right</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
         <span class="k">if</span> <span class="n">update_abscissa</span><span class="p">:</span>
-            <span class="n">new_abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">new_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">-</span><span class="n">added_zeros_left</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">new_abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="o">*</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">new_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">-</span><span class="n">added_zeros_left</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">new_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">added_zeros_left</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">added_zeros_right</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">function_type</span><span class="p">,</span><span class="n">new_abscissa</span><span class="p">,</span><span class="n">new_ordinate</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
-    
-<div class="viewcode-block" id="NDDataArray.interpolate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.interpolate">[docs]</a>    <span class="k">def</span> <span class="nf">interpolate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interpolated_abscissa</span><span class="p">,</span> <span class="n">kind</span> <span class="o">=</span> <span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+            <span class="n">new_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">added_zeros_left</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">added_zeros_right</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">function_type</span><span class="p">,</span> <span class="n">new_abscissa</span><span class="p">,</span> <span class="n">new_ordinate</span><span class="p">,</span>
+                          <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span>
+                          <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="NDDataArray.interpolate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.interpolate">[docs]</a>    <span class="k">def</span> <span class="nf">interpolate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interpolated_abscissa</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Interpolates the NDDataArray using SciPy&#39;s interp1d.</span>
 
@@ -793,7 +940,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Specifies the kind of interpolation as a string or as an integer</span>
 <span class="sd">            specifying the order of the spline interpolator to use. The string</span>
 <span class="sd">            has to be one of &#39;linear&#39;, &#39;nearest&#39;, &#39;nearest-up&#39;, &#39;zero&#39;,</span>
-<span class="sd">            &#39;slinear&#39;, &#39;quadratic&#39;, &#39;cubic&#39;, &#39;previous&#39;, or &#39;next&#39;. </span>
+<span class="sd">            &#39;slinear&#39;, &#39;quadratic&#39;, &#39;cubic&#39;, &#39;previous&#39;, or &#39;next&#39;.</span>
 <span class="sd">            &#39;zero&#39;, &#39;slinear&#39;, &#39;quadratic&#39; and &#39;cubic&#39; refer to a spline</span>
 <span class="sd">            interpolation of zeroth, first, second or third order; &#39;previous&#39;</span>
 <span class="sd">            and &#39;next&#39; simply return the previous or next value of the point;</span>
@@ -805,13 +952,13 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
-<span class="sd">        NDDataArray : </span>
+<span class="sd">        NDDataArray :</span>
 <span class="sd">            Array with interpolated arguments</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Flatten the abscissa</span>
-        <span class="n">interpolated_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">interpolated_abscissa</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">interpolated_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">interpolated_abscissa</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
         <span class="c1"># Create the output class</span>
-        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">interpolated_abscissa</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+        <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">interpolated_abscissa</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
         <span class="n">output</span><span class="o">.</span><span class="n">coordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span>
         <span class="n">output</span><span class="o">.</span><span class="n">comment1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span>
         <span class="n">output</span><span class="o">.</span><span class="n">comment2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span>
@@ -826,10 +973,10 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">interpolated_ordinate</span> <span class="o">=</span> <span class="n">interp</span><span class="p">(</span><span class="n">interpolated_abscissa</span><span class="p">)</span>
             <span class="n">output</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">interpolated_ordinate</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">function</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
+            <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">function</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
                 <span class="n">x</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">abscissa</span>
                 <span class="n">y</span> <span class="o">=</span> <span class="n">function</span><span class="o">.</span><span class="n">ordinate</span>
-                <span class="n">interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="n">kind</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
                 <span class="n">interpolated_ordinate</span> <span class="o">=</span> <span class="n">interp</span><span class="p">(</span><span class="n">interpolated_abscissa</span><span class="p">)</span>
                 <span class="n">output</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">interpolated_ordinate</span>
         <span class="k">return</span> <span class="n">output</span></div>
@@ -864,7 +1011,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Coordinate </span><span class="si">{:}</span><span class="s1"> not found in data array&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key</span><span class="p">[</span><span class="n">index</span><span class="p">])))</span>
             <span class="n">return_shape</span> <span class="o">=</span> <span class="n">flat_self</span><span class="p">[</span><span class="n">index_array</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_type</span> <span class="ow">in</span> <span class="p">[</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">]:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">function_type</span> <span class="ow">in</span> <span class="p">[</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">]:</span>
                 <span class="n">ordinate_multiplication_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">ordinate_multiplication_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">(</span>
@@ -873,13 +1020,13 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">ordinate_multiplication_array</span> <span class="o">=</span> <span class="n">ordinate_multiplication_array</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
             <span class="c1"># Remove zeros and replace with 1s because we don&#39;t flip signs if</span>
             <span class="c1"># there is no direction associated with the coordinate</span>
-            <span class="n">ordinate_multiplication_array</span><span class="p">[</span><span class="n">ordinate_multiplication_array</span><span class="o">==</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="n">ordinate_multiplication_array</span><span class="p">[</span><span class="n">ordinate_multiplication_array</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
             <span class="n">return_shape</span><span class="o">.</span><span class="n">coordinate</span> <span class="o">=</span> <span class="n">key</span>
             <span class="n">return_shape</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">*=</span> <span class="n">ordinate_multiplication_array</span>
             <span class="k">return</span> <span class="n">return_shape</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">output</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;coordinate&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;coordinate&#39;</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">CoordinateArray</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">output</span>
@@ -1038,9 +1185,9 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">reduction</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">*</span><span class="n">min_args</span><span class="p">,</span><span class="o">**</span><span class="n">min_kwargs</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">*</span><span class="n">min_args</span><span class="p">,</span> <span class="o">**</span><span class="n">min_kwargs</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">reduction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span><span class="o">*</span><span class="n">min_args</span><span class="p">,</span><span class="o">**</span><span class="n">min_kwargs</span><span class="p">)</span></div>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">reduction</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span> <span class="o">*</span><span class="n">min_args</span><span class="p">,</span> <span class="o">**</span><span class="n">min_kwargs</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="NDDataArray.max"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.max">[docs]</a>    <span class="k">def</span> <span class="nf">max</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">*</span><span class="n">max_args</span><span class="p">,</span> <span class="o">**</span><span class="n">max_kwargs</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1424,8 +1571,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">output_struct</span><span class="p">[</span><span class="s1">&#39;IRIGTime&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">IRIGTime</span><span class="p">,</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">output_struct</span></div>
-    
-<div class="viewcode-block" id="NDDataArray.save"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.save">[docs]</a>    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">filename</span><span class="p">):</span>
+
+<div class="viewcode-block" id="NDDataArray.save"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.save">[docs]</a>    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Save the array to a numpy file</span>
 
@@ -1436,18 +1583,18 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            .npz if not specified in the filename</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">),</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">),</span>
                  <span class="n">function_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">function_type</span><span class="o">.</span><span class="n">value</span><span class="p">)</span></div>
-        
+
 <div class="viewcode-block" id="NDDataArray.load"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.load">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span><span class="n">filename</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Load in the specified file into a SDynPy array object</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        filename : str</span>
-<span class="sd">            Filename specifying the file to load.  If the filename has </span>
+<span class="sd">            Filename specifying the file to load.  If the filename has</span>
 <span class="sd">            extension .unv or .uff, it will be loaded as a universal file.</span>
 <span class="sd">            Otherwise, it will be loaded as a NumPy file.</span>
 
@@ -1597,7 +1744,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">pass</span>
         <span class="n">data_dict</span><span class="p">[</span><span class="s1">&#39;FunctionType&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">_imat_function_type_inverse_map</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">function_type</span><span class="p">]</span>
         <span class="k">for</span> <span class="n">argument</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">arguments</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">datetime</span><span class="p">):</span>
                     <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%d</span><span class="s1">-%b-%y    %H:%M:%S&#39;</span><span class="p">)</span>
                 <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">SpecificDataType</span><span class="p">):</span>
@@ -1644,12 +1791,15 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Data read from unv</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">fn_datasets</span> <span class="o">=</span> <span class="n">unv_data_dict</span><span class="p">[</span><span class="mi">58</span><span class="p">]</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">fn_datasets</span> <span class="o">=</span> <span class="n">unv_data_dict</span><span class="p">[</span><span class="mi">58</span><span class="p">]</span>
+        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">NDDataArray</span><span class="p">((</span><span class="mi">0</span><span class="p">,),</span><span class="n">nelements</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="n">data_dimension</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">fn_types</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="o">.</span><span class="n">function_type</span> <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">fn_datasets</span><span class="p">]</span>
         <span class="n">function_type_dict</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="k">for</span> <span class="n">fn_dataset</span><span class="p">,</span> <span class="n">fn_type</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">fn_datasets</span><span class="p">,</span> <span class="n">fn_types</span><span class="p">):</span>
             <span class="n">fn_type_enum</span> <span class="o">=</span> <span class="n">FunctionTypes</span><span class="p">(</span><span class="n">fn_type</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">fn_type_enum</span> <span class="ow">in</span> <span class="n">function_type_dict</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">fn_type_enum</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">function_type_dict</span><span class="p">:</span>
                 <span class="n">function_type_dict</span><span class="p">[</span><span class="n">fn_type_enum</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
             <span class="n">function_type_dict</span><span class="p">[</span><span class="n">fn_type_enum</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fn_dataset</span><span class="p">)</span>
         <span class="n">return_functions</span> <span class="o">=</span> <span class="p">[]</span>
@@ -1685,7 +1835,89 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">return_functions</span> <span class="o">=</span> <span class="n">return_functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">return_functions</span></div>
 
-    <span class="n">from_uff</span> <span class="o">=</span> <span class="n">from_unv</span></div>
+    <span class="n">from_uff</span> <span class="o">=</span> <span class="n">from_unv</span>
+    
+<div class="viewcode-block" id="NDDataArray.get_reciprocal_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data">[docs]</a>    <span class="k">def</span> <span class="nf">get_reciprocal_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">return_indices</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Gets reciprocal pairs of data from an NDDataArray.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        return_indices : bool, optional</span>
+<span class="sd">            If True, it will return a set of indices into the original array</span>
+<span class="sd">            that extract the reciprocal functions. If False, then the</span>
+<span class="sd">            reciprocal functions are returned directly.  The default is False.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            If the data does not have reference and response coordinates, the</span>
+<span class="sd">            method will raise a ValueError.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        np.ndarray or NDDataArray subclass</span>
+<span class="sd">            If return_indices is True, this will return the indices into the</span>
+<span class="sd">            original array that extract the reciprocal data.  If return_indices</span>
+<span class="sd">            is False, this will return the reciprocal NDDataArrays directly.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Check if there are references and responses</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">[</span><span class="s1">&#39;coordinate&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot compute reciprocal data of functions with only one coordinate&#39;</span><span class="p">)</span>
+        <span class="c1"># Get all of the degrees of freedom that are both in references and</span>
+        <span class="c1"># responses</span>
+        <span class="n">references</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">))</span>
+        <span class="n">responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">))</span>
+        <span class="c1"># Get common references and responses</span>
+        <span class="n">common_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intersect1d</span><span class="p">(</span><span class="n">references</span><span class="p">,</span><span class="n">responses</span><span class="p">)</span>
+        <span class="c1"># Get pairs of those degrees of freedom</span>
+        <span class="n">dof_combos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">combo</span> <span class="k">for</span> <span class="n">combo</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">common_dofs</span><span class="p">,</span><span class="mi">2</span><span class="p">)])</span>
+        <span class="c1"># Now we need to select the reciprocal degrees of freedom</span>
+        <span class="n">reciprocal_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">dof_combos</span><span class="p">,</span><span class="n">dof_combos</span><span class="p">[</span><span class="o">...</span><span class="p">,::</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">CoordinateArray</span><span class="p">)</span>
+        <span class="n">reciprocal_slice</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="bp">Ellipsis</span><span class="p">]</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">ndim</span><span class="o">*</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">+</span><span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)])</span>
+        <span class="n">equal_logical</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span> <span class="o">==</span> <span class="n">reciprocal_dofs</span><span class="p">[</span><span class="n">reciprocal_slice</span><span class="p">],</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">equal_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">equal_logical</span><span class="p">)</span>
+        <span class="n">equal_indices</span> <span class="o">=</span>  <span class="nb">tuple</span><span class="p">([</span><span class="n">inds</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">inds</span> <span class="ow">in</span> <span class="n">equal_indices</span><span class="p">[</span><span class="mi">2</span><span class="p">:]])</span>
+        <span class="k">if</span> <span class="n">return_indices</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">equal_indices</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="p">[</span><span class="n">equal_indices</span><span class="p">]</span></div>
+        
+<div class="viewcode-block" id="NDDataArray.get_drive_points"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.get_drive_points">[docs]</a>    <span class="k">def</span> <span class="nf">get_drive_points</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">return_indices</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns data arrays where the reference is equal to the response</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        return_indices : bool, optional</span>
+<span class="sd">            If True, it will return a set of indices into the original array</span>
+<span class="sd">            that extract the drive point functions. If False, then the</span>
+<span class="sd">            drive point functions are returned directly.  The default is False.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            If the data does not have reference and response coordinates, the</span>
+<span class="sd">            method will raise a ValueError.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        np.ndarray or NDDataArray subclass</span>
+<span class="sd">            If return_indices is True, this will return the indices into the</span>
+<span class="sd">            original array that extract the drive point data.  If return_indices</span>
+<span class="sd">            is False, this will return the drive point NDDataArrays directly.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Check if there are references and responses</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">[</span><span class="s1">&#39;coordinate&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot compute drive point data of functions with only one coordinate&#39;</span><span class="p">)</span>
+        <span class="n">equal_logical</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">)</span> <span class="o">==</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">return_indices</span><span class="p">:</span>
+            <span class="n">equal_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">equal_logical</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">equal_indices</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="p">[</span><span class="n">equal_logical</span><span class="p">]</span></div></div>
 
 <div class="viewcode-block" id="TimeHistoryArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray">[docs]</a><span class="k">class</span> <span class="nc">TimeHistoryArray</span><span class="p">(</span><span class="n">NDDataArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data array used to store time history data&quot;&quot;&quot;</span>
@@ -1732,7 +1964,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">exo</span><span class="p">,</span> <span class="n">Exodus</span><span class="p">):</span>
             <span class="n">node_ids</span> <span class="o">=</span> <span class="n">exo</span><span class="o">.</span><span class="n">get_node_num_map</span><span class="p">()</span>
             <span class="n">variables</span> <span class="o">=</span> <span class="p">[(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">x_disp</span><span class="p">,</span> <span class="n">y_disp</span><span class="p">,</span>
-                                                           <span class="n">z_disp</span><span class="p">,</span> <span class="n">x_rot</span><span class="p">,</span> <span class="n">y_rot</span><span class="p">,</span> <span class="n">z_rot</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">]</span>
+                                                           <span class="n">z_disp</span><span class="p">,</span> <span class="n">x_rot</span><span class="p">,</span> <span class="n">y_rot</span><span class="p">,</span> <span class="n">z_rot</span><span class="p">])</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
             <span class="n">abscissa</span> <span class="o">=</span> <span class="n">exo</span><span class="o">.</span><span class="n">get_times</span><span class="p">()</span>
             <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">,</span>
                                <span class="n">exo</span><span class="o">.</span><span class="n">get_node_variable_values</span><span class="p">(</span><span class="n">variable</span><span class="p">,</span> <span class="n">timesteps</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
@@ -1796,16 +2028,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">diffs</span><span class="p">,</span> <span class="n">diffs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rtol</span><span class="p">,</span> <span class="n">atol</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Abscissa must have identical spacing to perform the FFT&#39;</span><span class="p">)</span>
         <span class="n">ordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">samples_per_frame</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">samples_per_frame</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">frame_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">size</span> <span class="o">//</span>
                                                                      <span class="n">samples_per_frame</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">samples_per_frame</span>
             <span class="n">ordinate</span> <span class="o">=</span> <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frame_indices</span><span class="p">]</span>
         <span class="n">dt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">diffs</span><span class="p">)</span>
         <span class="n">n</span> <span class="o">=</span> <span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="c1">#frequencies = np.fft.rfftfreq(n, dt)</span>
+        <span class="c1"># frequencies = np.fft.rfftfreq(n, dt)</span>
         <span class="n">frequencies</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-        <span class="c1">#ordinate = np.fft.rfft(ordinate, axis=-1)</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span> 
+        <span class="c1"># ordinate = np.fft.rfft(ordinate, axis=-1)</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span>
                                  <span class="o">**</span><span class="n">scipy_rfft_kwargs</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">samples_per_frame</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">2</span><span class="p">)</span>
@@ -1864,10 +2096,10 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                                 <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">coords</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="k">if</span> <span class="n">only_asds</span>
                                 <span class="k">else</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coords</span><span class="p">,</span> <span class="n">coords</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">cpsd_array</span></div>
-    
-<div class="viewcode-block" id="TimeHistoryArray.srs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.srs">[docs]</a>    <span class="k">def</span> <span class="nf">srs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-            <span class="n">damping</span> <span class="o">=</span> <span class="mf">0.03</span><span class="p">,</span> <span class="n">num_points</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">points_per_octave</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span>
-            <span class="n">srs_type</span> <span class="o">=</span> <span class="s1">&#39;MMAA&#39;</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TimeHistoryArray.srs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.srs">[docs]</a>    <span class="k">def</span> <span class="nf">srs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">damping</span><span class="o">=</span><span class="mf">0.03</span><span class="p">,</span> <span class="n">num_points</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">points_per_octave</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span>
+            <span class="n">srs_type</span><span class="o">=</span><span class="s1">&#39;MMAA&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute a shock response spectrum (SRS) from the time history</span>
 
@@ -1886,9 +2118,9 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Fraction of critical damping to use in the SRS calculation (e.g. you</span>
 <span class="sd">            should specify 0.03 to represent 3%, not 3). The default is 0.03.</span>
 <span class="sd">        num_points : int, optional</span>
-<span class="sd">            Number of frequency lines to compute from `min_frequency` to </span>
-<span class="sd">            `max_frequency`, log spaced between these two values.  If </span>
-<span class="sd">            `min_frequency` and `max_frequency` are specified, then either </span>
+<span class="sd">            Number of frequency lines to compute from `min_frequency` to</span>
+<span class="sd">            `max_frequency`, log spaced between these two values.  If</span>
+<span class="sd">            `min_frequency` and `max_frequency` are specified, then either</span>
 <span class="sd">            `num_points` or `points_per_octave` must be specified.  If</span>
 <span class="sd">            `frequencies` is specified, this argument is ignored.</span>
 <span class="sd">        points_per_octave : float, optional</span>
@@ -1908,7 +2140,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            `&#39;all&#39;`, then an extra dimension of 9 will be added to the front of</span>
 <span class="sd">            the array, and the indices in that dimension will be different SRS</span>
 <span class="sd">            types.</span>
-<span class="sd">            </span>
+
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
 <span class="sd">        The `srs_type` argument takes a 4 character string that specifies how</span>
@@ -1918,30 +2150,31 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">srs_type_val</span> <span class="o">=</span> <span class="n">ShockResponseSpectrumArray</span><span class="o">.</span><span class="n">_srs_type_map</span><span class="p">[</span><span class="n">srs_type</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
         <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid `srs_type` specified, should be one of </span><span class="si">{:}</span><span class="s1"> (case insensitive)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ShockResponseSpectrumArray</span><span class="o">.</span><span class="n">_srs_type_map</span><span class="p">]))</span>
-            
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid `srs_type` specified, should be one of </span><span class="si">{:}</span><span class="s1"> (case insensitive)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ShockResponseSpectrumArray</span><span class="o">.</span><span class="n">_srs_type_map</span><span class="p">]))</span>
+
         <span class="k">if</span> <span class="n">frequencies</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`min_frequency` and `max_frequency` must be provided if `frequencies` is not&#39;</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">num_points</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">frequencies</span> <span class="o">=</span> <span class="n">octspace</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">,</span><span class="n">points_per_octave</span><span class="p">)</span>
+                <span class="n">frequencies</span> <span class="o">=</span> <span class="n">octspace</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">,</span> <span class="n">points_per_octave</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
-                <span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">max_frequency</span><span class="p">),</span><span class="n">num_points</span><span class="p">)</span>
-        
-        <span class="n">srss</span><span class="p">,</span><span class="n">f</span> <span class="o">=</span> <span class="n">sp_srs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">damping</span><span class="p">,</span> <span class="n">srs_type_val</span><span class="p">)</span>
+                <span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">max_frequency</span><span class="p">),</span> <span class="n">num_points</span><span class="p">)</span>
+
+        <span class="n">srss</span><span class="p">,</span> <span class="n">f</span> <span class="o">=</span> <span class="n">sp_srs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">damping</span><span class="p">,</span> <span class="n">srs_type_val</span><span class="p">)</span>
         <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">srs_type_val</span><span class="p">)</span> <span class="o">==</span> <span class="mi">10</span><span class="p">:</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">srss</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        
+            <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">srss</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
         <span class="c1"># Now construct the output object</span>
-        <span class="n">srs</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SHOCK_RESPONSE_SPECTRUM</span><span class="p">,</span><span class="n">frequencies</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
-                       <span class="n">srss</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
-                       <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
-                       <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
+        <span class="n">srs</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SHOCK_RESPONSE_SPECTRUM</span><span class="p">,</span> <span class="n">frequencies</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                         <span class="n">srss</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                         <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span>
+                         <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
         <span class="k">return</span> <span class="n">srs</span></div>
-        
-<div class="viewcode-block" id="TimeHistoryArray.split_into_frames"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames">[docs]</a>    <span class="k">def</span> <span class="nf">split_into_frames</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples_per_frame</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">frame_length</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">overlap</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">overlap_samples</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">window</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">check_cola</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">allow_fractional_frames</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TimeHistoryArray.split_into_frames"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.split_into_frames">[docs]</a>    <span class="k">def</span> <span class="nf">split_into_frames</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">frame_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">overlap</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">overlap_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">check_cola</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_fractional_frames</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Splits a time history into measurement frames with a given overlap and</span>
 <span class="sd">        window function applied.</span>
@@ -2009,42 +2242,43 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`samples_per_frame` must be an even number&#39;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">overlap_samples</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">overlap_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="o">*</span><span class="n">overlap</span><span class="p">))</span>
-        
+
         <span class="c1"># If partial frames, then we will zero pad to make it the right length</span>
         <span class="k">if</span> <span class="n">allow_fractional_frames</span><span class="p">:</span>
-            <span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">num_frames</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span><span class="o">-</span><span class="n">overlap_samples</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">samples_per_frame</span> <span class="o">-</span> <span class="n">overlap_samples</span><span class="p">)))</span>
-        <span class="n">frame_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_frames</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="o">-</span><span class="n">overlap_samples</span><span class="p">)</span>
+        <span class="n">frame_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_frames</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="o">-</span><span class="n">overlap_samples</span><span class="p">)</span>
         <span class="c1"># See if we need to truncate empty frames</span>
         <span class="k">if</span> <span class="n">allow_fractional_frames</span><span class="p">:</span>
             <span class="c1"># Get rid of the first frame which is all zeros</span>
             <span class="n">frame_indices</span> <span class="o">=</span> <span class="n">frame_indices</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
             <span class="c1"># See if we need to get rid of the last frame, which could be all</span>
             <span class="c1"># zeros</span>
-            <span class="k">if</span> <span class="n">frame_indices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">frame_indices</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span><span class="p">:</span>
                 <span class="n">frame_indices</span> <span class="o">=</span> <span class="n">frame_indices</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        
+
         <span class="c1"># Put the &quot;frame&quot; axis at the front of the new array so all other parameters</span>
         <span class="c1"># can broadcast out across measurement frames</span>
-        <span class="n">new_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frame_indices</span><span class="p">],</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">new_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frame_indices</span><span class="p">],</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        
+        <span class="n">new_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frame_indices</span><span class="p">],</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">new_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frame_indices</span><span class="p">],</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
         <span class="c1"># Now apply the window</span>
         <span class="k">if</span> <span class="n">window</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">window</span> <span class="o">=</span> <span class="s1">&#39;boxcar&#39;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window</span><span class="p">,</span><span class="nb">tuple</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
             <span class="n">window</span> <span class="o">=</span> <span class="n">get_window</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="p">)</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">new_ordinate</span> <span class="o">*=</span> <span class="n">window</span>
         <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Could Not Multiply Window Function (shape </span><span class="si">{:}</span><span class="s1">) by Ordinate (shape </span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">window</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">new_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Could Not Multiply Window Function (shape </span><span class="si">{:}</span><span class="s1">) by Ordinate (shape </span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">window</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">new_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
         <span class="k">if</span> <span class="n">check_cola</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">sig</span><span class="o">.</span><span class="n">check_COLA</span><span class="p">(</span><span class="n">window</span><span class="p">,</span><span class="n">samples_per_frame</span><span class="p">,</span><span class="n">overlap_samples</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;COLA Check Failed&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">sig</span><span class="o">.</span><span class="n">check_COLA</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="p">,</span> <span class="n">overlap_samples</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;COLA Check Failed: specified window and overlap do not result in a constant overlap-add condition, see scipy.check_COLA for more information&#39;</span><span class="p">)</span>
                 
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span><span class="n">new_abscissa</span><span class="p">,</span><span class="n">new_ordinate</span><span class="p">,</span>
-                          <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
-                          <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">new_abscissa</span><span class="p">,</span> <span class="n">new_ordinate</span><span class="p">,</span>
+                          <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
+                          <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
+    
 
 <div class="viewcode-block" id="TimeHistoryArray.mimo_forward"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.mimo_forward">[docs]</a>    <span class="k">def</span> <span class="nf">mimo_forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transfer_function</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -2053,8 +2287,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        transfer_function : TransferFunctionArray or ImpulseResponseFunctionArray</span>
-<span class="sd">            This is the FRFs that will be used in the forward problem. A matrix of IRFs </span>
-<span class="sd">            is prefered, but FRFs can also be used, although the FRFs will be immediately </span>
+<span class="sd">            This is the FRFs that will be used in the forward problem. A matrix of IRFs</span>
+<span class="sd">            is prefered, but FRFs can also be used, although the FRFs will be immediately</span>
 <span class="sd">            converted to IRFs.</span>
 
 <span class="sd">        Raises</span>
@@ -2063,13 +2297,13 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            If the sampling rates for the data and IRFs/FRFs don&#39;t match.</span>
 <span class="sd">        ValueError</span>
 <span class="sd">            If the references in the IRFs/FRFs don&#39;t match the supplied input</span>
-<span class="sd">            data.  </span>
+<span class="sd">            data.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        TimeHistoryArray</span>
 <span class="sd">            Response time histories</span>
-<span class="sd">            </span>
+
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Converting FRFs to IRFs, if required</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transfer_function</span><span class="p">,</span> <span class="n">TransferFunctionArray</span><span class="p">):</span>
@@ -2081,7 +2315,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
         <span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]]</span>
         <span class="n">irfs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="n">num_references</span><span class="p">,</span><span class="n">number_responses</span><span class="p">,</span><span class="n">model_order</span> <span class="o">=</span> <span class="n">irfs</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">num_references</span><span class="p">,</span> <span class="n">number_responses</span><span class="p">,</span> <span class="n">model_order</span> <span class="o">=</span> <span class="n">irfs</span><span class="o">.</span><span class="n">shape</span>
         <span class="n">signal_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span>
 
         <span class="c1"># Checking to see if the sampling rates are the same for both data sets</span>
@@ -2089,25 +2323,25 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The transfer function sampling rate does not match the time data.&#39;</span><span class="p">)</span>
 
         <span class="c1"># Setting up and doing the convolution</span>
-        <span class="n">convolved_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_responses</span><span class="p">,</span><span class="n">signal_length</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">reference_irfs</span><span class="p">,</span><span class="n">inputs</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">irfs</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">):</span>
-            <span class="n">convolved_response</span> <span class="o">+=</span> <span class="n">oaconvolve</span><span class="p">(</span><span class="n">reference_irfs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:])[:,:</span><span class="n">signal_length</span><span class="p">]</span>
+        <span class="n">convolved_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_responses</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">reference_irfs</span><span class="p">,</span> <span class="n">inputs</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">irfs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">):</span>
+            <span class="n">convolved_response</span> <span class="o">+=</span> <span class="n">oaconvolve</span><span class="p">(</span><span class="n">reference_irfs</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:])[:,</span> <span class="p">:</span><span class="n">signal_length</span><span class="p">]</span>
 
         <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">convolved_response</span><span class="p">,</span> <span class="n">response_dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span></div>
 
-<div class="viewcode-block" id="TimeHistoryArray.mimo_inverse"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.mimo_inverse">[docs]</a>    <span class="k">def</span> <span class="nf">mimo_inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transfer_function</span><span class="p">,</span> 
-                     <span class="n">time_method</span> <span class="o">=</span> <span class="s1">&#39;single_frame&#39;</span><span class="p">,</span> 
-                     <span class="n">cola_frame_length</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">cola_window</span> <span class="o">=</span> <span class="s1">&#39;hann&#39;</span><span class="p">,</span>
-                     <span class="n">cola_overlap</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">zero_pad_length</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">inverse_method</span> <span class="o">=</span> <span class="s1">&#39;standard&#39;</span><span class="p">,</span>
-                     <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">num_retained_values</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="TimeHistoryArray.mimo_inverse"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.mimo_inverse">[docs]</a>    <span class="k">def</span> <span class="nf">mimo_inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transfer_function</span><span class="p">,</span>
+                     <span class="n">time_method</span><span class="o">=</span><span class="s1">&#39;single_frame&#39;</span><span class="p">,</span>
+                     <span class="n">cola_frame_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">cola_window</span><span class="o">=</span><span class="s1">&#39;hann&#39;</span><span class="p">,</span>
+                     <span class="n">cola_overlap</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">zero_pad_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">inverse_method</span><span class="o">=</span><span class="s1">&#39;standard&#39;</span><span class="p">,</span>
+                     <span class="n">response_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">reference_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">regularization_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">cond_num_threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">num_retained_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Performs the inverse source estimation for time domain (transient) problems</span>
 <span class="sd">        using Fourier deconvolution. The response nodes used in the inverse source</span>
@@ -2116,20 +2350,20 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        transer_function : TransferFunctionArray or ImpulseResponseFunctionArray</span>
-<span class="sd">            This is the FRFs that will be used in the inverse source estimation </span>
+<span class="sd">            This is the FRFs that will be used in the inverse source estimation</span>
 <span class="sd">        time_method : str, optional</span>
-<span class="sd">            The method to used to handle the time data for the inverse source </span>
+<span class="sd">            The method to used to handle the time data for the inverse source</span>
 <span class="sd">            estimation. The available options are:</span>
 <span class="sd">                - single_frame - this method performs the Fourier deconvolution</span>
-<span class="sd">                via an FFT on a single frame that encompases the entire time </span>
-<span class="sd">                signal. </span>
-<span class="sd">                - COLA - this method performs the Fourier deconvolution via a </span>
-<span class="sd">                series of FFTs on relatively small frames of the time signal </span>
-<span class="sd">                using a &quot;constant overlap and add&quot; method. This method may be </span>
-<span class="sd">                faster than the single_frame method.</span>
+<span class="sd">                  via an FFT on a single frame that encompases the entire time</span>
+<span class="sd">                  signal.</span>
+<span class="sd">                - COLA - this method performs the Fourier deconvolution via a</span>
+<span class="sd">                  series of FFTs on relatively small frames of the time signal</span>
+<span class="sd">                  using a &quot;constant overlap and add&quot; method. This method may be</span>
+<span class="sd">                  faster than the single_frame method.</span>
 <span class="sd">        cola_frame_length : float, optional</span>
 <span class="sd">            The frame length (in samples) if the COLA method is being used. The</span>
-<span class="sd">            default frame length is Fs/df from the transfer function. </span>
+<span class="sd">            default frame length is Fs/df from the transfer function.</span>
 <span class="sd">        cola_window : str, optional</span>
 <span class="sd">            The desired window for the COLA procedure, must exist in the scipy</span>
 <span class="sd">            window library. The default is a hann window.</span>
@@ -2138,11 +2372,11 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            COLA procedure.  If not specified, a default value of half the</span>
 <span class="sd">            cola_frame_length is used.</span>
 <span class="sd">        zero_pad_length : int, optional</span>
-<span class="sd">            The number of zeros used to pre and post pad the response data, to </span>
-<span class="sd">            avoid convolution wrap-around error. The default is to use the </span>
+<span class="sd">            The number of zeros used to pre and post pad the response data, to</span>
+<span class="sd">            avoid convolution wrap-around error. The default is to use the</span>
 <span class="sd">            &quot;determine_zero_pad_length&quot; function to determine the zero_pad_length.</span>
 <span class="sd">        inverse_method : str, optional</span>
-<span class="sd">            The method to be used for the FRF matrix inversions. The available </span>
+<span class="sd">            The method to be used for the FRF matrix inversions. The available</span>
 <span class="sd">            methods are:</span>
 <span class="sd">                - standard - basic pseudo-inverse via numpy.linalg.pinv with the</span>
 <span class="sd">                  default rcond parameter, this is the default method</span>
@@ -2150,7 +2384,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">                  condition number threshold</span>
 <span class="sd">                - tikhonov - pseudo-inverse using the Tikhonov regularization method</span>
 <span class="sd">                - truncation - pseudo-inverse where a fixed number of singular values</span>
-<span class="sd">                  are retained for the inverse </span>
+<span class="sd">                  are retained for the inverse</span>
 <span class="sd">        response_weighting_matrix : sdpy.Matrix or np.ndarray, optional</span>
 <span class="sd">            Not currently implemented</span>
 <span class="sd">        reference_weighting_matrix : sdpy.Matrix or np.ndarray, optional</span>
@@ -2159,18 +2393,18 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Matrix used to weight input degrees of freedom via Tikhonov regularization.</span>
 <span class="sd">        regularization_parameter : float or np.ndarray, optional</span>
 <span class="sd">            Scaling parameter used on the regularization weighting matrix when the tikhonov</span>
-<span class="sd">            method is chosen. A vector of regularization parameters can be provided so the </span>
-<span class="sd">            regularization is different at each frequency line. The vector must match the </span>
+<span class="sd">            method is chosen. A vector of regularization parameters can be provided so the</span>
+<span class="sd">            regularization is different at each frequency line. The vector must match the</span>
 <span class="sd">            length of the FRF abscissa in this case (either be size [num_lines,] or [num_lines, 1]).</span>
 <span class="sd">        cond_num_threshold : float or np.ndarray, optional</span>
-<span class="sd">            Condition number used for SVD truncation when the threshold method is chosen. </span>
-<span class="sd">            A vector of condition numbers can be provided so it varies as a function of </span>
+<span class="sd">            Condition number used for SVD truncation when the threshold method is chosen.</span>
+<span class="sd">            A vector of condition numbers can be provided so it varies as a function of</span>
 <span class="sd">            frequency. The vector must match the length of the FRF abscissa in this case</span>
 <span class="sd">            (either be size [num_lines,] or [num_lines, 1]).</span>
 <span class="sd">        num_retained_values : float or np.ndarray, optional</span>
-<span class="sd">            Number of singular values to retain in the pseudo-inverse when the truncation </span>
-<span class="sd">            method is chosen. A vector of can be provided so the number of retained values </span>
-<span class="sd">            can change as a function of frequency. The vector must match the length of the </span>
+<span class="sd">            Number of singular values to retain in the pseudo-inverse when the truncation</span>
+<span class="sd">            method is chosen. A vector of can be provided so the number of retained values</span>
+<span class="sd">            can change as a function of frequency. The vector must match the length of the</span>
 <span class="sd">            FRF abscissa in this case (either be size [num_lines,] or [num_lines, 1]).</span>
 
 <span class="sd">        Raises</span>
@@ -2183,7 +2417,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            If the sampling rates for the data and FRFs don&#39;t match.</span>
 <span class="sd">        ValueError</span>
 <span class="sd">            If the number of responses in the FRFs don&#39;t match the supplied response</span>
-<span class="sd">            data. </span>
+<span class="sd">            data.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -2193,38 +2427,38 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
 <span class="sd">        This function computes the time domain inputs required to match the target time traces</span>
-<span class="sd">        using Fourier deconvolution, which is essentially a frequency domain problem. The general </span>
-<span class="sd">        method is to compute the frequency spectrum of the target time traces, then solve the </span>
-<span class="sd">        inverse problem in the time domain using the supplied FRFs (H^+ * X). The inverse of the </span>
-<span class="sd">        FRF matrix is found using the same methods as the mimo_inverse function for the </span>
+<span class="sd">        using Fourier deconvolution, which is essentially a frequency domain problem. The general</span>
+<span class="sd">        method is to compute the frequency spectrum of the target time traces, then solve the</span>
+<span class="sd">        inverse problem in the time domain using the supplied FRFs (H^+ * X). The inverse of the</span>
+<span class="sd">        FRF matrix is found using the same methods as the mimo_inverse function for the</span>
 <span class="sd">        PowerSpectralDensityArray class. The input spectrum is then converted back to the time</span>
 <span class="sd">        domain via a inverse fourier transform.</span>
-<span class="sd">        </span>
+
 <span class="sd">        References</span>
 <span class="sd">        ----------</span>
 <span class="sd">        .. [1] Wikipedia, &quot;Moore-Penrose inverse&quot;.</span>
 <span class="sd">               https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse</span>
 <span class="sd">        .. [2] A.N. Tithe, D.J. Thompson, The quantification of structure-borne transmission pathsby inverse methods. Part 2: Use of regularization techniques,</span>
-<span class="sd">               Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X, </span>
+<span class="sd">               Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X,</span>
 <span class="sd">               https://doi.org/10.1016/S0022-460X(02)01203-8.</span>
 <span class="sd">        .. [3] Wikipedia, &quot;Ridge regression&quot;.</span>
 <span class="sd">               https://en.wikipedia.org/wiki/Ridge_regression</span>
 <span class="sd">        .. [4] Wikipedia, &quot;Overlap-add Method&quot;.</span>
 <span class="sd">               https://en.wikipedia.org/wiki/Overlap-add_method</span>
-<span class="sd">        &quot;&quot;&quot;</span>        
+<span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Converting IRFs to FRFs, if required</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">transfer_function</span><span class="p">,</span> <span class="n">ImpulseResponseFunctionArray</span><span class="p">):</span>
             <span class="n">transfer_function</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span>
-            
+
         <span class="c1"># Initial orginization of the data</span>
         <span class="n">indexed_transfer_function</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
         <span class="n">irf</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="o">.</span><span class="n">ifft</span><span class="p">()</span>
         <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span>
-        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">reference_coordinate</span>
+        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reference_coordinate</span>
 
         <span class="n">indexed_response_data</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]]</span>
 
-        <span class="n">number_responses</span><span class="p">,</span><span class="n">number_references</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">number_responses</span><span class="p">,</span> <span class="n">number_references</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="o">.</span><span class="n">shape</span>
         <span class="n">model_order</span> <span class="o">=</span> <span class="n">irf</span><span class="o">.</span><span class="n">num_elements</span>
 
         <span class="c1"># Checking to see if the sampling rates are the same for both data sets</span>
@@ -2232,19 +2466,19 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">fs_frf</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">irf</span><span class="o">.</span><span class="n">abscissa_spacing</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">fs_frf</span><span class="p">,</span> <span class="n">fs_inputs</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The transfer function sampling rate does not match the time data.&#39;</span><span class="p">)</span>
-            
+
         <span class="c1"># Preparing the response data and FRFs for the source estimation</span>
         <span class="k">if</span> <span class="n">time_method</span> <span class="o">==</span> <span class="s1">&#39;single_frame&#39;</span><span class="p">:</span>
             <span class="c1"># Zero pad for convolution wrap-around</span>
             <span class="k">if</span> <span class="n">zero_pad_length</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">padded_response</span> <span class="o">=</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">model_order</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                                                <span class="n">use_next_fast_len</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+                <span class="n">padded_response</span> <span class="o">=</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">model_order</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                                                 <span class="n">use_next_fast_len</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
-                <span class="n">padded_response</span> <span class="o">=</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span><span class="n">zero_pad_length</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+                <span class="n">padded_response</span> <span class="o">=</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span><span class="n">zero_pad_length</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="n">actual_zero_pad</span> <span class="o">=</span> <span class="n">padded_response</span><span class="o">.</span><span class="n">num_elements</span> <span class="o">-</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">num_elements</span>
             <span class="c1"># Now make the FRFs the same size</span>
             <span class="n">modified_frfs</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="o">.</span><span class="n">interpolate_by_zero_pad</span><span class="p">(</span><span class="n">padded_response</span><span class="o">.</span><span class="n">num_elements</span><span class="p">)</span>
-            <span class="n">padded_frequency_domain_data</span> <span class="o">=</span> <span class="n">padded_response</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span>
+            <span class="n">padded_frequency_domain_data</span> <span class="o">=</span> <span class="n">padded_response</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">time_method</span> <span class="o">==</span> <span class="s1">&#39;cola&#39;</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">cola_frame_length</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">cola_frame_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">fs_frf</span><span class="o">/</span><span class="n">indexed_transfer_function</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">)</span>
@@ -2252,56 +2486,56 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="n">cola_overlap</span> <span class="o">=</span> <span class="n">cola_frame_length</span><span class="o">//</span><span class="mi">2</span>
             <span class="c1"># Split into measurement frames</span>
             <span class="n">segmented_data</span> <span class="o">=</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">split_into_frames</span><span class="p">(</span>
-                <span class="n">samples_per_frame</span> <span class="o">=</span> <span class="n">cola_frame_length</span><span class="p">,</span> 
-                <span class="n">overlap_samples</span> <span class="o">=</span> <span class="n">cola_overlap</span><span class="p">,</span>
-                <span class="n">window</span> <span class="o">=</span> <span class="n">cola_window</span><span class="p">,</span>
-                <span class="n">check_cola</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                <span class="n">allow_fractional_frames</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+                <span class="n">samples_per_frame</span><span class="o">=</span><span class="n">cola_frame_length</span><span class="p">,</span>
+                <span class="n">overlap_samples</span><span class="o">=</span><span class="n">cola_overlap</span><span class="p">,</span>
+                <span class="n">window</span><span class="o">=</span><span class="n">cola_window</span><span class="p">,</span>
+                <span class="n">check_cola</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                <span class="n">allow_fractional_frames</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="c1"># Zero pad</span>
             <span class="k">if</span> <span class="n">zero_pad_length</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">zero_padded_data</span> <span class="o">=</span> <span class="n">segmented_data</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span>
-                    <span class="mi">2</span><span class="o">*</span><span class="n">model_order</span><span class="p">,</span><span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">use_next_fast_len</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+                    <span class="mi">2</span><span class="o">*</span><span class="n">model_order</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">use_next_fast_len</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">zero_padded_data</span> <span class="o">=</span> <span class="n">segmented_data</span><span class="o">.</span><span class="n">zero_pad</span><span class="p">(</span>
-                    <span class="n">zero_pad_length</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+                    <span class="n">zero_pad_length</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="n">actual_zero_pad</span> <span class="o">=</span> <span class="n">zero_padded_data</span><span class="o">.</span><span class="n">num_elements</span> <span class="o">-</span> <span class="n">segmented_data</span><span class="o">.</span><span class="n">num_elements</span>
             <span class="n">modified_frfs</span> <span class="o">=</span> <span class="n">indexed_transfer_function</span><span class="o">.</span><span class="n">interpolate_by_zero_pad</span><span class="p">(</span><span class="n">zero_padded_data</span><span class="o">.</span><span class="n">num_elements</span><span class="p">)</span>
-            <span class="n">padded_frequency_domain_data</span> <span class="o">=</span> <span class="n">zero_padded_data</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span>
+            <span class="n">padded_frequency_domain_data</span> <span class="o">=</span> <span class="n">zero_padded_data</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">NameError</span><span class="p">(</span><span class="s1">&#39;The selected time method is not available&#39;</span><span class="p">)</span>
-            
+
         <span class="c1"># Need to interpolate the conditioning parameters to match the length of the padded</span>
         <span class="c1"># FRFs</span>
         <span class="k">if</span> <span class="n">cond_num_threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">cond_num_threshold</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+            <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">cond_num_threshold</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">cond_num_threshold</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                 <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span>
-                    <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                    <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
                     <span class="n">cond_num_threshold</span><span class="p">,</span>
                     <span class="s1">&#39;linear&#39;</span><span class="p">,</span>
                     <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                    <span class="n">fill_value</span><span class="o">=</span><span class="p">(</span><span class="n">cond_num_threshold</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">cond_num_threshold</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
-                    <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
+                    <span class="n">fill_value</span><span class="o">=</span><span class="p">(</span><span class="n">cond_num_threshold</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cond_num_threshold</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+                    <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">num_retained_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">num_retained_values</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
+            <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">num_retained_values</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                 <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span>
-                    <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
-                    <span class="n">regularization_parameter</span><span class="p">,</span>
+                    <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                    <span class="n">num_retained_values</span><span class="p">,</span>
                     <span class="s1">&#39;previous&#39;</span><span class="p">,</span>
                     <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                    <span class="n">fill_value</span><span class="o">=</span><span class="p">(</span><span class="n">num_retained_values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">num_retained_values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
-                    <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
+                    <span class="n">fill_value</span><span class="o">=</span><span class="p">(</span><span class="n">num_retained_values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">num_retained_values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+                    <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">regularization_parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+            <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
                 <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span>
-                    <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                    <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
                     <span class="n">regularization_parameter</span><span class="p">,</span>
                     <span class="s1">&#39;linear&#39;</span><span class="p">,</span>
                     <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                    <span class="n">fill_value</span><span class="o">=</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">regularization_parameter</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
-                    <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
+                    <span class="n">fill_value</span><span class="o">=</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">regularization_parameter</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+                    <span class="n">assume_sorted</span><span class="o">=</span><span class="kc">True</span><span class="p">)(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
 
         <span class="c1"># Set up weighting matrices</span>
         <span class="k">if</span> <span class="n">response_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -2309,68 +2543,68 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">reference_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Reference weighting has not been implemented yet&#39;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">regularization_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">matrix</span>
+            <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">matrix</span>
 
         <span class="c1"># Now solve the inverse problem</span>
-        <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">frf_inverse</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">modified_frfs</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                               <span class="n">method</span> <span class="o">=</span> <span class="n">inverse_method</span><span class="p">,</span>
-                               <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span><span class="p">,</span>
-                               <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="n">reference_weighting_matrix</span><span class="p">,</span>
-                               <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">,</span>
-                               <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">regularization_parameter</span><span class="p">,</span>
-                               <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="n">cond_num_threshold</span><span class="p">,</span>
-                               <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">num_retained_values</span><span class="p">)</span>
+        <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">frf_inverse</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">modified_frfs</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                               <span class="n">method</span><span class="o">=</span><span class="n">inverse_method</span><span class="p">,</span>
+                               <span class="n">response_weighting_matrix</span><span class="o">=</span><span class="n">response_weighting_matrix</span><span class="p">,</span>
+                               <span class="n">reference_weighting_matrix</span><span class="o">=</span><span class="n">reference_weighting_matrix</span><span class="p">,</span>
+                               <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="n">regularization_weighting_matrix</span><span class="p">,</span>
+                               <span class="n">regularization_parameter</span><span class="o">=</span><span class="n">regularization_parameter</span><span class="p">,</span>
+                               <span class="n">cond_num_threshold</span><span class="o">=</span><span class="n">cond_num_threshold</span><span class="p">,</span>
+                               <span class="n">num_retained_values</span><span class="o">=</span><span class="n">num_retained_values</span><span class="p">)</span>
         <span class="n">method_statement_start</span> <span class="o">=</span> <span class="s1">&#39;The FRFs are being inverted using the &#39;</span>
         <span class="n">method_statement_end</span> <span class="o">=</span> <span class="s1">&#39; method&#39;</span>
         <span class="nb">print</span><span class="p">(</span><span class="n">method_statement_start</span><span class="o">+</span><span class="n">inverse_method</span><span class="o">+</span><span class="n">method_statement_end</span><span class="p">)</span>
 
         <span class="c1"># Get the first modified frequency line above the original starting point of the FRF</span>
-        <span class="n">inverse_start_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="o">&gt;=</span> <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">inverse_start_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">modified_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="o">&gt;=</span> <span class="n">indexed_transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 
         <span class="c1"># Doing the source estimation</span>
-        <span class="n">padded_frequency_domain_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">padded_frequency_domain_data</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">padded_frequency_domain_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">padded_frequency_domain_data</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">forces_frequency_domain</span> <span class="o">=</span> <span class="n">frf_pinv</span><span class="nd">@padded_frequency_domain_data</span>
         <span class="n">forces_frequency_domain</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">inverse_start_index</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">forces_time_domain_with_padding</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">forces_frequency_domain</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">norm</span> <span class="o">=</span> <span class="s1">&#39;backward&#39;</span><span class="p">)</span>
+        <span class="n">forces_time_domain_with_padding</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">forces_frequency_domain</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">)</span>
         <span class="c1"># Compute the zero padding used</span>
         <span class="n">pre_pad_length</span> <span class="o">=</span> <span class="n">actual_zero_pad</span><span class="o">//</span><span class="mi">2</span>
         <span class="n">post_pad_length</span> <span class="o">=</span> <span class="n">actual_zero_pad</span> <span class="o">-</span> <span class="n">pre_pad_length</span>
         <span class="k">if</span> <span class="n">time_method</span> <span class="o">==</span> <span class="s1">&#39;single_frame&#39;</span><span class="p">:</span>
             <span class="n">forces_time_domain</span> <span class="o">=</span> <span class="n">forces_time_domain_with_padding</span><span class="p">[</span><span class="n">pre_pad_length</span><span class="p">:</span><span class="o">-</span><span class="n">post_pad_length</span><span class="p">,</span> <span class="p">:]</span>
-            <span class="n">return_val</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">indexed_response_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">forces_time_domain</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> 
-                              <span class="n">reference_dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+            <span class="n">return_val</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">indexed_response_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">forces_time_domain</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                                    <span class="n">reference_dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
         <span class="k">elif</span> <span class="n">time_method</span> <span class="o">==</span> <span class="s1">&#39;cola&#39;</span><span class="p">:</span>
             <span class="n">forces_time_domain_with_padding</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span>
                 <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
-                <span class="n">zero_padded_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="mi">1</span><span class="p">,:],</span>
-                <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">forces_time_domain_with_padding</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">),</span>
-                <span class="n">reference_dofs</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
-            
+                <span class="n">zero_padded_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="p">:],</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">forces_time_domain_with_padding</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">),</span>
+                <span class="n">reference_dofs</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+
             <span class="c1"># Assemble the COLA</span>
             <span class="n">forces_time_domain_with_padding</span> <span class="o">=</span> <span class="n">TimeHistoryArray</span><span class="o">.</span><span class="n">overlap_and_add</span><span class="p">(</span>
                 <span class="n">forces_time_domain_with_padding</span><span class="p">,</span>
-                <span class="n">overlap_samples</span> <span class="o">=</span> <span class="n">actual_zero_pad</span> <span class="o">+</span> <span class="n">cola_overlap</span><span class="p">)</span>
+                <span class="n">overlap_samples</span><span class="o">=</span><span class="n">actual_zero_pad</span> <span class="o">+</span> <span class="n">cola_overlap</span><span class="p">)</span>
             <span class="n">start_index</span> <span class="o">=</span> <span class="n">cola_frame_length</span> <span class="o">-</span> <span class="n">cola_overlap</span> <span class="o">+</span> <span class="n">pre_pad_length</span>
             <span class="n">end_index</span> <span class="o">=</span> <span class="n">start_index</span> <span class="o">+</span> <span class="n">indexed_response_data</span><span class="o">.</span><span class="n">num_elements</span>
             <span class="n">return_val</span> <span class="o">=</span> <span class="n">forces_time_domain_with_padding</span><span class="o">.</span><span class="n">idx_by_el</span><span class="p">[</span><span class="n">start_index</span><span class="p">:</span><span class="n">end_index</span><span class="p">]</span>
-            
+
             <span class="c1"># Compute COLA weighting</span>
             <span class="n">window_fn</span> <span class="o">=</span> <span class="n">get_window</span><span class="p">(</span><span class="n">cola_window</span><span class="p">,</span> <span class="n">cola_frame_length</span><span class="p">)</span>
             <span class="n">step</span> <span class="o">=</span> <span class="n">cola_frame_length</span> <span class="o">-</span> <span class="n">cola_overlap</span>
             <span class="n">weighting</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">window_fn</span><span class="p">[</span><span class="n">ii</span><span class="o">*</span><span class="n">step</span><span class="p">:(</span><span class="n">ii</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">step</span><span class="p">]</span> <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cola_frame_length</span><span class="o">//</span><span class="n">step</span><span class="p">)))</span>
             <span class="n">return_val</span> <span class="o">=</span> <span class="n">return_val</span> <span class="o">/</span> <span class="n">weighting</span>
-        
+
         <span class="k">return</span> <span class="n">return_val</span></div>
 
 <div class="viewcode-block" id="TimeHistoryArray.rms"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.rms">[docs]</a>    <span class="k">def</span> <span class="nf">rms</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="TimeHistoryArray.to_rattlesnake_specification"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.to_rattlesnake_specification">[docs]</a>    <span class="k">def</span> <span class="nf">to_rattlesnake_specification</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">filename</span><span class="p">,</span><span class="n">coordinate_order</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">min_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">max_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="TimeHistoryArray.to_rattlesnake_specification"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.to_rattlesnake_specification">[docs]</a>    <span class="k">def</span> <span class="nf">to_rattlesnake_specification</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">coordinate_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">min_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">max_time</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="k">if</span> <span class="n">coordinate_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">coordinate_order</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="n">coordinate_order</span> <span class="o">=</span> <span class="n">coordinate_order</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+                <span class="n">coordinate_order</span> <span class="o">=</span> <span class="n">coordinate_order</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
             <span class="n">reshaped_data</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">coordinate_order</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">reshaped_data</span> <span class="o">=</span> <span class="bp">self</span>
@@ -2379,22 +2613,22 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="n">min_time</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
             <span class="k">if</span> <span class="n">max_time</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">max_time</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-            <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_time</span><span class="p">,</span><span class="n">max_time</span><span class="p">)</span>
+            <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_time</span><span class="p">,</span> <span class="n">max_time</span><span class="p">)</span>
         <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span>
-                 <span class="n">t</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="o">-</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
-                 <span class="n">signal</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span></div>
+                 <span class="n">t</span><span class="o">=</span><span class="n">reshaped_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span> <span class="o">-</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                 <span class="n">signal</span><span class="o">=</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="TimeHistoryArray.find_signal_shift"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.find_signal_shift">[docs]</a>    <span class="k">def</span> <span class="nf">find_signal_shift</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">other_signal</span><span class="p">,</span> 
-                          <span class="n">compute_subsample_shift</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                          <span class="n">good_line_threshold</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">):</span>
+<div class="viewcode-block" id="TimeHistoryArray.find_signal_shift"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.find_signal_shift">[docs]</a>    <span class="k">def</span> <span class="nf">find_signal_shift</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other_signal</span><span class="p">,</span>
+                          <span class="n">compute_subsample_shift</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                          <span class="n">good_line_threshold</span><span class="o">=</span><span class="mf">0.01</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes the shift between two sets of time signals</span>
-<span class="sd">        </span>
+
 <span class="sd">        This is the amount that `other_signal` leads `self`.  If the time shift</span>
 <span class="sd">        is positive, it means that features in `other_signal` occur earlier in</span>
 <span class="sd">        time compared to `self`.  If the time shift is negative, it means that</span>
 <span class="sd">        features in `other_signal` occur later in time compared to `self`.</span>
-<span class="sd">        </span>
+
 <span class="sd">        To align two signals, you can take the time shift from this function and</span>
 <span class="sd">        pass it into the `shift_signal` method of `other_signal`.</span>
 
@@ -2427,39 +2661,39 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">other_ordinate</span> <span class="o">=</span> <span class="n">other_signal</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span>
 
         <span class="n">correlation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">this_ordinate</span><span class="o">*</span><span class="n">other_ordinate</span><span class="o">.</span><span class="n">conj</span><span class="p">())</span>
-        <span class="n">time_shift_indices</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">correlation</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
+        <span class="n">time_shift_indices</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">correlation</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
 
         <span class="c1"># Roll the arrays to get them to align</span>
         <span class="n">shifted_signal</span> <span class="o">=</span> <span class="n">other_signal</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">shifted_signal</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">other_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="n">time_shift_indices</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">shifted_signal</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">other_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">time_shift_indices</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
 
-        <span class="n">dt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">dt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
         <span class="n">time_shift</span> <span class="o">=</span> <span class="n">dt</span><span class="o">*</span><span class="n">time_shift_indices</span>
 
         <span class="k">if</span> <span class="n">compute_subsample_shift</span><span class="p">:</span>
             <span class="c1"># Now compute the subsample shift</span>
             <span class="n">shifted_ordinate</span> <span class="o">=</span> <span class="n">shifted_signal</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span>
-    
+
             <span class="c1"># Only compute at frequency lines where there&#39;s signal</span>
-            <span class="n">good_lines</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">shifted_ordinate</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">shifted_ordinate</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span><span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">good_line_threshold</span>
-            <span class="n">good_lines</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
-    
+            <span class="n">good_lines</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">shifted_ordinate</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">shifted_ordinate</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">good_line_threshold</span>
+            <span class="n">good_lines</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span>
+
             <span class="n">phase_difference</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">this_ordinate</span><span class="o">/</span><span class="n">shifted_ordinate</span><span class="p">)</span>
-    
+
             <span class="n">phase_slope</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">phase_difference</span><span class="p">[</span><span class="n">good_lines</span><span class="p">]</span><span class="o">/</span><span class="n">this_fft</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="n">good_lines</span><span class="p">])</span>
-    
+
             <span class="n">time_shift</span> <span class="o">-=</span> <span class="n">phase_slope</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-        
+
         <span class="c1"># Wrap so it&#39;s negative if that&#39;s the smaller distance</span>
         <span class="k">if</span> <span class="n">time_shift</span> <span class="o">&gt;</span> <span class="n">dt</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span><span class="o">/</span><span class="mi">2</span><span class="p">:</span>
             <span class="n">time_shift</span> <span class="o">-=</span> <span class="n">dt</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">num_elements</span>
-        
+
         <span class="k">return</span> <span class="n">time_shift</span></div>
-    
-<div class="viewcode-block" id="TimeHistoryArray.shift_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.shift_signal">[docs]</a>    <span class="k">def</span> <span class="nf">shift_signal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">time_shift</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TimeHistoryArray.shift_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.shift_signal">[docs]</a>    <span class="k">def</span> <span class="nf">shift_signal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time_shift</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Shift a signal in time by a specified amount.</span>
-<span class="sd">        </span>
+
 <span class="sd">        Utilizes the FFT shift theorem to move a signal in time.</span>
 
 <span class="sd">        Parameters</span>
@@ -2481,7 +2715,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">signal_fft</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">phase_shift_slope</span><span class="o">*</span><span class="n">signal_fft</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span>
 
         <span class="n">shifted_signal</span> <span class="o">=</span> <span class="n">signal_fft</span><span class="o">.</span><span class="n">ifft</span><span class="p">()</span>
-        
+
         <span class="k">return</span> <span class="n">shifted_signal</span></div>
 
 <div class="viewcode-block" id="TimeHistoryArray.overlap_and_add"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.overlap_and_add">[docs]</a>    <span class="nd">@staticmethod</span>
@@ -2495,18 +2729,18 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            A set of TimeHistoryArrays to overlap and add together.  If a single</span>
 <span class="sd">            TimeHistoryArray is specified, then the first dimension will be used</span>
 <span class="sd">            to split the signal into segments.  All TimeHistoryArrays must have</span>
-<span class="sd">            the same shape and metadata, but need not have the same number of </span>
+<span class="sd">            the same shape and metadata, but need not have the same number of</span>
 <span class="sd">            elements.</span>
 <span class="sd">        overlap_samples : int</span>
 <span class="sd">            Number of samples to overlap the segments as they are added together</span>
-<span class="sd">            </span>
+
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        TimeHistoryArray</span>
 <span class="sd">            A TimeHistoryArray consisting of the signals overlapped and added</span>
 <span class="sd">            together.</span>
-<span class="sd">            </span>
+
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
 <span class="sd">        All metadata is taken from the first signal.  No checks are performed to</span>
@@ -2522,17 +2756,17 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="c1"># Go through each frame and add it to the function</span>
         <span class="n">starting_index</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="k">for</span> <span class="n">signal</span> <span class="ow">in</span> <span class="n">functions_to_overlap</span><span class="p">:</span>
-            <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">starting_index</span><span class="p">:</span><span class="n">starting_index</span><span class="o">+</span><span class="n">signal</span><span class="o">.</span><span class="n">num_elements</span><span class="p">]</span> <span class="o">+=</span> <span class="n">signal</span><span class="o">.</span><span class="n">ordinate</span>
+            <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">starting_index</span><span class="p">:</span><span class="n">starting_index</span><span class="o">+</span><span class="n">signal</span><span class="o">.</span><span class="n">num_elements</span><span class="p">]</span> <span class="o">+=</span> <span class="n">signal</span><span class="o">.</span><span class="n">ordinate</span>
             <span class="n">starting_index</span> <span class="o">+=</span> <span class="n">signal</span><span class="o">.</span><span class="n">num_elements</span> <span class="o">-</span> <span class="n">overlap_samples</span>
         <span class="c1"># Now set up the rest of the metadata</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">functions_to_overlap</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_samples</span><span class="p">)</span> <span class="o">+</span> <span class="n">functions_to_overlap</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span><span class="n">abscissa</span><span class="p">,</span><span class="n">ordinate</span><span class="p">,</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">,</span> <span class="n">ordinate</span><span class="p">,</span>
                           <span class="n">functions_to_overlap</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="TimeHistoryArray.remove_rigid_body_motion"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.remove_rigid_body_motion">[docs]</a>    <span class="k">def</span> <span class="nf">remove_rigid_body_motion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">geometry</span><span class="p">):</span>
+<div class="viewcode-block" id="TimeHistoryArray.remove_rigid_body_motion"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.remove_rigid_body_motion">[docs]</a>    <span class="k">def</span> <span class="nf">remove_rigid_body_motion</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">geometry</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Removes rigid body displacements from time data.</span>
-<span class="sd">        </span>
+
 <span class="sd">        This function assumes the current TimeHistoryArray is a displacement</span>
 <span class="sd">        signal and adds it to the geometry to create node positions over time,</span>
 <span class="sd">        then it fits a rigid coordinate transformation to each time step and</span>
@@ -2550,36 +2784,36 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">nodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
-        <span class="n">dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">nodes</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
-        <span class="n">sorted_self</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]]</span>
+        <span class="n">dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">nodes</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+        <span class="n">sorted_self</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]]</span>
         <span class="n">displacements</span> <span class="o">=</span> <span class="n">sorted_self</span><span class="o">.</span><span class="n">ordinate</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">sorted_self</span><span class="o">.</span><span class="n">abscissa</span>
-        <span class="n">starting_positions</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">starting_positions</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">positions_over_time</span> <span class="o">=</span> <span class="n">displacements</span> <span class="o">+</span> <span class="n">starting_positions</span>
         <span class="c1"># Rearrange indices to match the rigid transformation code</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">positions_over_time</span><span class="p">,[</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">starting_positions</span><span class="p">,[</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">R</span><span class="p">,</span><span class="n">t</span> <span class="o">=</span> <span class="n">lstsq_rigid_transform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">positions_over_time</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">starting_positions</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+        <span class="n">R</span><span class="p">,</span> <span class="n">t</span> <span class="o">=</span> <span class="n">lstsq_rigid_transform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
         <span class="n">y_rigid</span> <span class="o">=</span> <span class="n">R</span><span class="nd">@x</span><span class="o">+</span><span class="n">t</span>
         <span class="n">nonrigid_displacements</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span>
             <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
             <span class="n">abscissa</span><span class="p">,</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_rigid</span><span class="p">,[</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]),</span>
-            <span class="n">dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y_rigid</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+            <span class="n">dofs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
             <span class="n">sorted_self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span>
             <span class="n">sorted_self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span>
             <span class="n">sorted_self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
             <span class="n">sorted_self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span>
             <span class="n">sorted_self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">nonrigid_displacements</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span></div>
-    
-<div class="viewcode-block" id="TimeHistoryArray.stft"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.stft">[docs]</a>    <span class="k">def</span> <span class="nf">stft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples_per_frame</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">frame_length</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-             <span class="n">overlap</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">overlap_samples</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">window</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-             <span class="n">check_cola</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">allow_fractional_frames</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-             <span class="n">norm</span> <span class="o">=</span> <span class="s1">&#39;backward&#39;</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TimeHistoryArray.stft"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.stft">[docs]</a>    <span class="k">def</span> <span class="nf">stft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">frame_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+             <span class="n">overlap</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">overlap_samples</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+             <span class="n">check_cola</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">allow_fractional_frames</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+             <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes a Short-Time Fourier Transform (STFT)</span>
-<span class="sd">        </span>
+
 <span class="sd">        The time history is split up into frames with specified length and</span>
 <span class="sd">        computes the spectra for each frame.</span>
 
@@ -2632,19 +2866,19 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">split_frames</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">split_into_frames</span><span class="p">(</span>
             <span class="n">samples_per_frame</span><span class="p">,</span> <span class="n">frame_length</span><span class="p">,</span>
-             <span class="n">overlap</span><span class="p">,</span> <span class="n">overlap_samples</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span>
-             <span class="n">check_cola</span><span class="p">,</span> <span class="n">allow_fractional_frames</span><span class="p">)</span>
-        <span class="n">frame_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">split_frames</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">overlap</span><span class="p">,</span> <span class="n">overlap_samples</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span>
+            <span class="n">check_cola</span><span class="p">,</span> <span class="n">allow_fractional_frames</span><span class="p">)</span>
+        <span class="n">frame_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">split_frames</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">stft</span> <span class="o">=</span> <span class="n">split_frames</span><span class="o">.</span><span class="n">fft</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">frame_abscissa</span><span class="p">,</span><span class="n">stft</span></div>
+        <span class="k">return</span> <span class="n">frame_abscissa</span><span class="p">,</span> <span class="n">stft</span></div>
 
 <div class="viewcode-block" id="TimeHistoryArray.pseudorandom_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.pseudorandom_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">pseudorandom_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                            <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                            <span class="n">signal_rms</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frames</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frequency_shape</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                            <span class="n">different_realizations</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-                            <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                            <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                            <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">signal_rms</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frames</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frequency_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">different_realizations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                            <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generates a pseudorandom signal at the specified coordinates</span>
 
@@ -2691,7 +2925,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Comment used to describe the data in the data array. The default is &#39;&#39;.</span>
 <span class="sd">        comment5 : np.ndarray, optional</span>
 <span class="sd">            Comment used to describe the data in the data array. The default is &#39;&#39;.</span>
-<span class="sd">            </span>
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        TimeHistoryArray :</span>
@@ -2704,48 +2938,47 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">f_nyquist</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">dt</span><span class="p">)</span>
         <span class="n">fft_lines</span> <span class="o">=</span> <span class="n">f_nyquist</span><span class="o">/</span><span class="n">df</span>
         <span class="k">if</span> <span class="n">frequency_shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">frequency_shape</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">freq</span><span class="p">:</span> <span class="mi">1</span>
+            <span class="n">frequency_shape</span> <span class="o">=</span> <span class="n">_flat_frequency_shape</span>
         <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">type</span> <span class="ow">is</span> <span class="n">np</span><span class="o">.</span><span class="n">str_</span><span class="p">:</span>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">,))</span>
         <span class="k">if</span> <span class="n">different_realizations</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndindex</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
-                <span class="n">ordinate</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">pseudorandom</span><span class="p">(</span><span class="n">fft_lines</span><span class="p">,</span><span class="n">f_nyquist</span><span class="p">,</span>
-                                               <span class="n">min_freq</span> <span class="o">=</span> <span class="n">min_frequency</span><span class="p">,</span>
-                                               <span class="n">max_freq</span> <span class="o">=</span> <span class="n">max_frequency</span><span class="p">,</span>
-                                               <span class="n">averages</span> <span class="o">=</span> <span class="n">frames</span><span class="p">,</span>
-                                               <span class="n">shape_function</span> <span class="o">=</span> <span class="n">frequency_shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">ordinate</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">pseudorandom</span><span class="p">(</span><span class="n">fft_lines</span><span class="p">,</span> <span class="n">f_nyquist</span><span class="p">,</span>
+                                               <span class="n">min_freq</span><span class="o">=</span><span class="n">min_frequency</span><span class="p">,</span>
+                                               <span class="n">max_freq</span><span class="o">=</span><span class="n">max_frequency</span><span class="p">,</span>
+                                               <span class="n">averages</span><span class="o">=</span><span class="n">frames</span><span class="p">,</span>
+                                               <span class="n">shape_function</span><span class="o">=</span><span class="n">frequency_shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">pseudorandom</span><span class="p">(</span><span class="n">fft_lines</span><span class="p">,</span><span class="n">f_nyquist</span><span class="p">,</span>
-                                         <span class="n">min_freq</span> <span class="o">=</span> <span class="n">min_frequency</span><span class="p">,</span>
-                                         <span class="n">max_freq</span> <span class="o">=</span> <span class="n">max_frequency</span><span class="p">,</span>
-                                         <span class="n">averages</span> <span class="o">=</span> <span class="n">frames</span><span class="p">,</span>
-                                         <span class="n">shape_function</span> <span class="o">=</span> <span class="n">frequency_shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">pseudorandom</span><span class="p">(</span><span class="n">fft_lines</span><span class="p">,</span> <span class="n">f_nyquist</span><span class="p">,</span>
+                                         <span class="n">min_freq</span><span class="o">=</span><span class="n">min_frequency</span><span class="p">,</span>
+                                         <span class="n">max_freq</span><span class="o">=</span><span class="n">max_frequency</span><span class="p">,</span>
+                                         <span class="n">averages</span><span class="o">=</span><span class="n">frames</span><span class="p">,</span>
+                                         <span class="n">shape_function</span><span class="o">=</span><span class="n">frequency_shape</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
         <span class="c1"># Apply the RMS</span>
-        <span class="n">current_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">signal_rms</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">/</span><span class="n">current_rms</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">current_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">signal_rms</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">/</span><span class="n">current_rms</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="c1"># Now create the object</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">time_history</span></div>
-        
-    
+
 <div class="viewcode-block" id="TimeHistoryArray.random_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.random_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">random_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                      <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">signal_rms</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frames</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frequency_shape</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                      <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                      <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">signal_rms</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frames</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frequency_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                      <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generates a random signal with the specified parameters</span>
-<span class="sd">        </span>
+
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        dt : float</span>
@@ -2795,38 +3028,38 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="k">if</span> <span class="p">(</span><span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">frequency_shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
             <span class="k">if</span> <span class="n">frequency_shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">frequency_shape</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">freq</span><span class="p">:</span> <span class="mi">1</span>
+                <span class="n">frequency_shape</span> <span class="o">=</span> <span class="n">_flat_frequency_shape</span>
             <span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-            <span class="n">fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">index</span><span class="p">,</span><span class="n">frequency</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">frequencies</span><span class="p">):</span>
+            <span class="n">fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">frequency</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">frequencies</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">frequency</span> <span class="o">&lt;</span> <span class="n">min_frequency</span><span class="p">:</span>
-                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
                 <span class="k">elif</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">frequency</span> <span class="o">&gt;</span> <span class="n">max_frequency</span><span class="p">:</span>
-                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
                 <span class="k">else</span><span class="p">:</span>
-                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">index</span><span class="p">]</span> <span class="o">*=</span> <span class="n">frequency_shape</span><span class="p">(</span><span class="n">frequency</span><span class="p">)</span>
-            <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">fft</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">index</span><span class="p">]</span> <span class="o">*=</span> <span class="n">frequency_shape</span><span class="p">(</span><span class="n">frequency</span><span class="p">)</span>
+            <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">fft</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="c1"># Now set RMS</span>
-        <span class="n">current_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">signal_rms</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">/</span><span class="n">current_rms</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">current_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">signal_rms</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">/</span><span class="n">current_rms</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="c1"># Now create the object</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">time_history</span></div>
-    
+
 <div class="viewcode-block" id="TimeHistoryArray.sine_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">sine_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                    <span class="n">frequency</span><span class="p">,</span> <span class="n">amplitude</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">phase</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
-                    <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                    <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                    <span class="n">frequency</span><span class="p">,</span> <span class="n">amplitude</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">phase</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                    <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                    <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a sinusoidal signal with the specified parameters</span>
 
@@ -2874,29 +3107,29 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,))</span>
-        <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">sine</span><span class="p">(</span><span class="n">frequency</span><span class="p">,</span><span class="n">dt</span><span class="p">,</span><span class="n">signal_length</span><span class="p">,</span><span class="n">amplitude</span><span class="p">,</span><span class="n">phase</span><span class="p">)</span>
+        <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">sine</span><span class="p">(</span><span class="n">frequency</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">amplitude</span><span class="p">,</span> <span class="n">phase</span><span class="p">)</span>
         <span class="c1"># Now create the object</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="p">)</span>
         <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">time_history</span></div>
-    
+
 <div class="viewcode-block" id="TimeHistoryArray.burst_random_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.burst_random_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">burst_random_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                            <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                            <span class="n">signal_rms</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frames</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frequency_shape</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                            <span class="n">on_fraction</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">delay_fraction</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span> 
-                            <span class="n">ramp_fraction</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span>
-                            <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                            <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                            <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">signal_rms</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frames</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frequency_shape</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">on_fraction</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">delay_fraction</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span>
+                            <span class="n">ramp_fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+                            <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                            <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generates a burst random signal with the specified parameters</span>
-<span class="sd">        </span>
+
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        dt : float</span>
@@ -2958,24 +3191,24 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="k">if</span> <span class="p">(</span><span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">frequency_shape</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
             <span class="k">if</span> <span class="n">frequency_shape</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">frequency_shape</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">freq</span><span class="p">:</span> <span class="mi">1</span>
+                <span class="n">frequency_shape</span> <span class="o">=</span> <span class="n">_flat_frequency_shape</span>
             <span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
-            <span class="n">fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">index</span><span class="p">,</span><span class="n">frequency</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">frequencies</span><span class="p">):</span>
+            <span class="n">fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">frequency</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">frequencies</span><span class="p">):</span>
                 <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">frequency</span> <span class="o">&lt;</span> <span class="n">min_frequency</span><span class="p">:</span>
-                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
                 <span class="k">elif</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">frequency</span> <span class="o">&gt;</span> <span class="n">max_frequency</span><span class="p">:</span>
-                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
                 <span class="k">else</span><span class="p">:</span>
-                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">index</span><span class="p">]</span> <span class="o">*=</span> <span class="n">frequency_shape</span><span class="p">(</span><span class="n">frequency</span><span class="p">)</span>
-            <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">fft</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="n">fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">index</span><span class="p">]</span> <span class="o">*=</span> <span class="n">frequency_shape</span><span class="p">(</span><span class="n">frequency</span><span class="p">)</span>
+            <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">fft</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="c1"># Now set RMS</span>
-        <span class="n">current_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
-        <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">signal_rms</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">/</span><span class="n">current_rms</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">current_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">ordinate</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">signal_rms</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">/</span><span class="n">current_rms</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="c1"># Apply the window</span>
         <span class="n">delay_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">delay_fraction</span> <span class="o">*</span> <span class="n">signal_length</span><span class="p">)</span>
         <span class="n">ramp_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">ramp_fraction</span> <span class="o">*</span> <span class="n">signal_length</span><span class="p">)</span>
@@ -2983,7 +3216,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">burst_window</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,))</span>
         <span class="n">burst_window</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">delay_samples</span><span class="p">:</span><span class="n">delay_samples</span> <span class="o">+</span>
                      <span class="n">on_samples</span><span class="p">]</span> <span class="o">=</span> <span class="n">ramp_envelope</span><span class="p">(</span><span class="n">on_samples</span><span class="p">,</span> <span class="n">ramp_samples</span><span class="p">)</span>
-        <span class="n">burst_window</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">burst_window</span><span class="p">,</span><span class="n">frames</span><span class="p">)</span>
+        <span class="n">burst_window</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">burst_window</span><span class="p">,</span> <span class="n">frames</span><span class="p">)</span>
         <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">burst_window</span>
         <span class="c1"># Now create the object</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
@@ -2991,16 +3224,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">time_history</span></div>
-    
+
 <div class="viewcode-block" id="TimeHistoryArray.chirp_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.chirp_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">chirp_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                     <span class="n">start_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">end_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">frames</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">amplitude_function</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">force_integer_cycles</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-                     <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                     <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                     <span class="n">start_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">end_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">frames</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">amplitude_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">force_integer_cycles</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                     <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                     <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a chirp (sine sweep) signal with the specified parameters</span>
 
@@ -3032,7 +3265,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        force_integer_cycles : bool, optional</span>
 <span class="sd">            If True, it will force an integer number of cycles, which will</span>
 <span class="sd">            adjust the maximum frequency of the signal.  This will ensure the</span>
-<span class="sd">            signal is continuous if repeated.  If False, the </span>
+<span class="sd">            signal is continuous if repeated.  If False, the</span>
 <span class="sd">        comment1 : np.ndarray, optional</span>
 <span class="sd">            Comment used to describe the data in the data array. The default is &#39;&#39;.</span>
 <span class="sd">        comment2 : np.ndarray, optional</span>
@@ -3054,7 +3287,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="c1"># Create the chirp</span>
         <span class="n">signal_length_in_time</span> <span class="o">=</span> <span class="n">dt</span><span class="o">*</span><span class="n">signal_length</span>
         <span class="n">df</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">signal_length_in_time</span>
@@ -3063,8 +3296,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">end_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">end_frequency</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="o">/</span><span class="mi">2</span><span class="o">-</span><span class="n">df</span>
         <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,))</span>
-        <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">chirp</span><span class="p">(</span><span class="n">start_frequency</span><span class="p">,</span><span class="n">end_frequency</span><span class="p">,</span><span class="n">signal_length_in_time</span><span class="p">,</span>
-                              <span class="n">dt</span><span class="p">,</span><span class="n">force_integer_cycles</span><span class="p">)</span>
+        <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">chirp</span><span class="p">(</span><span class="n">start_frequency</span><span class="p">,</span> <span class="n">end_frequency</span><span class="p">,</span> <span class="n">signal_length_in_time</span><span class="p">,</span>
+                              <span class="n">dt</span><span class="p">,</span> <span class="n">force_integer_cycles</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">amplitude_function</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">force_integer_cycles</span><span class="p">:</span>
                 <span class="n">n_cycles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">end_frequency</span> <span class="o">*</span> <span class="n">signal_length_in_time</span><span class="p">)</span>
@@ -3074,22 +3307,22 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">amplitude_over_time</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">amplitude_function</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">frequency_over_time</span><span class="p">])</span>
             <span class="n">ordinate</span> <span class="o">*=</span> <span class="n">amplitude_over_time</span>
         <span class="c1"># Create the measurement frames</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span><span class="n">frames</span><span class="p">)</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">frames</span><span class="p">)</span>
         <span class="c1"># Now create the object</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">time_history</span></div>
-    
+
 <div class="viewcode-block" id="TimeHistoryArray.pulse_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.pulse_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">pulse_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                     <span class="n">pulse_width</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">pulse_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">pulse_peak</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                     <span class="n">sine_exponent</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frames</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                     <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                     <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                     <span class="n">pulse_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pulse_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pulse_peak</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                     <span class="n">sine_exponent</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">frames</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                     <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                     <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a pulse using a cosine function raised to a specified exponent</span>
 
@@ -3137,31 +3370,31 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,))</span>
         <span class="k">if</span> <span class="n">pulse_time</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">pulse_time</span> <span class="o">=</span> <span class="n">dt</span><span class="o">*</span><span class="mi">5</span>
         <span class="k">if</span> <span class="n">pulse_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">pulse_width</span> <span class="o">=</span> <span class="n">dt</span><span class="o">*</span><span class="mi">5</span>
-        <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">pulse</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,</span><span class="n">pulse_time</span><span class="p">,</span><span class="n">pulse_width</span><span class="p">,</span><span class="n">pulse_peak</span><span class="p">,</span>
-                              <span class="n">dt</span><span class="p">,</span><span class="n">sine_exponent</span><span class="p">)</span>
+        <span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">pulse</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,</span> <span class="n">pulse_time</span><span class="p">,</span> <span class="n">pulse_width</span><span class="p">,</span> <span class="n">pulse_peak</span><span class="p">,</span>
+                              <span class="n">dt</span><span class="p">,</span> <span class="n">sine_exponent</span><span class="p">)</span>
         <span class="c1"># Create the measurement frames</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span><span class="n">frames</span><span class="p">)</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">frames</span><span class="p">)</span>
         <span class="c1"># Now create the object</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">time_history</span></div>
-    
+
 <div class="viewcode-block" id="TimeHistoryArray.haversine_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.haversine_signal">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">haversine_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">signal_length</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
-                     <span class="n">pulse_width</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">pulse_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">pulse_peak</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                     <span class="n">frames</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-                     <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-                     <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">):</span>
+                         <span class="n">pulse_width</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pulse_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pulse_peak</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                         <span class="n">frames</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                         <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                         <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a haversine pulse with the specified parameters</span>
 
@@ -3207,7 +3440,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="c1"># Get coordinate size</span>
         <span class="k">if</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">abscissa_frame</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="p">)</span> <span class="o">*</span> <span class="n">dt</span>
         <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="p">(</span><span class="n">signal_length</span><span class="p">,))</span>
         <span class="k">if</span> <span class="n">pulse_time</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -3220,19 +3453,48 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">argument</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="n">period</span> <span class="o">*</span> <span class="p">(</span><span class="n">abscissa_frame</span> <span class="o">-</span> <span class="n">time</span><span class="p">)</span>
             <span class="n">ordinate</span> <span class="o">+=</span> <span class="n">peak</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">argument</span><span class="p">))</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">argument</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">))</span>
         <span class="c1"># Create the measurement frames</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span><span class="n">frames</span><span class="p">)</span>
+        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">frames</span><span class="p">)</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">signal_length</span><span class="o">*</span><span class="n">frames</span><span class="p">)</span>
         <span class="c1"># Now create the object</span>
         <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                   <span class="n">abscissa</span><span class="p">,</span>
                                   <span class="n">ordinate</span><span class="p">,</span>
                                   <span class="n">coordinates</span><span class="p">,</span>
-                                  <span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment4</span><span class="p">,</span><span class="n">comment5</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">time_history</span></div></div>
-    
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">time_history</span></div>
+
+<div class="viewcode-block" id="TimeHistoryArray.sine_sweep_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal">[docs]</a>    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">sine_sweep_signal</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span>
+                         <span class="n">frequency_breakpoints</span><span class="p">,</span> <span class="n">sweep_types</span><span class="p">,</span> <span class="n">sweep_rates</span><span class="p">,</span>
+                         <span class="n">amplitudes</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">phases</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+                         <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span>
+                         <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
+        <span class="n">frequency_breakpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">frequency_breakpoints</span><span class="p">)</span>
+        <span class="n">broadcast_to_shape</span> <span class="o">=</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span> <span class="o">+</span> <span class="p">(</span><span class="n">frequency_breakpoints</span><span class="o">.</span><span class="n">size</span><span class="p">,)</span>
+        <span class="n">full_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">amplitudes</span><span class="p">,</span><span class="n">broadcast_to_shape</span><span class="p">)</span>
+        <span class="n">full_phases</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">phases</span><span class="p">,</span><span class="n">broadcast_to_shape</span><span class="p">)</span>
+        <span class="n">full_sweep_types</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">sweep_types</span><span class="p">,</span> <span class="n">frequency_breakpoints</span><span class="o">.</span><span class="n">size</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">full_sweep_rates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">sweep_rates</span><span class="p">,</span> <span class="n">frequency_breakpoints</span><span class="o">.</span><span class="n">size</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">output_signals</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">coordinate</span> <span class="ow">in</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
+            <span class="n">output_signals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">sine_sweep</span><span class="p">(</span>
+                    <span class="n">dt</span><span class="p">,</span> <span class="n">frequency_breakpoints</span><span class="p">,</span> <span class="n">full_sweep_rates</span><span class="p">,</span> <span class="n">full_sweep_types</span><span class="p">,</span>
+                    <span class="n">full_amplitudes</span><span class="p">[</span><span class="n">key</span><span class="p">],</span><span class="n">full_phases</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
+        <span class="n">output_signals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">output_signals</span><span class="p">)</span>
+        <span class="n">abscissa</span> <span class="o">=</span> <span class="n">dt</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">output_signals</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="c1"># Create a time history array</span>
+        <span class="n">time_history</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
+                                  <span class="n">abscissa</span><span class="p">,</span><span class="n">output_signals</span><span class="p">,</span><span class="n">coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
+                                  <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment4</span><span class="p">,</span> <span class="n">comment5</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">time_history</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span></div></div>
+        
+        
+
 <span class="c1"># def time_history_array(abscissa,ordinate,coordinate,comment1=&#39;&#39;,comment2=&#39;&#39;,comment3=&#39;&#39;,comment4=&#39;&#39;,comment5=&#39;&#39;):</span>
 <span class="c1">#     pass</span>
 
+
 <div class="viewcode-block" id="SpectrumArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html#sdynpy.core.sdynpy_data.SpectrumArray">[docs]</a><span class="k">class</span> <span class="nc">SpectrumArray</span><span class="p">(</span><span class="n">NDDataArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data array used to store linear spectra (for example scaled FFT results)&quot;&quot;&quot;</span>
     <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nelements</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@@ -3290,7 +3552,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="SpectrumArray.interpolate_by_zero_pad"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html#sdynpy.core.sdynpy_data.SpectrumArray.interpolate_by_zero_pad">[docs]</a>    <span class="k">def</span> <span class="nf">interpolate_by_zero_pad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time_response_padded_length</span><span class="p">,</span>
-                                <span class="n">return_time_response</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+                                <span class="n">return_time_response</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Interpolates a spectrum by zero padding or truncating its</span>
 <span class="sd">        time response</span>
@@ -3308,7 +3570,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        -------</span>
 <span class="sd">        SpectrumArray or TimeHistoryArray:</span>
 <span class="sd">            Spectrum array with appropriately spaced abscissa</span>
-<span class="sd">            </span>
+
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
 <span class="sd">        This function will automatically set the last frequency line of the</span>
@@ -3326,18 +3588,21 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">time_response</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">spectrum</span> <span class="o">=</span> <span class="n">time_response</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span>
-            <span class="n">spectrum</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">spectrum</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
             <span class="k">return</span> <span class="n">spectrum</span></div>
 
-<div class="viewcode-block" id="SpectrumArray.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html#sdynpy.core.sdynpy_data.SpectrumArray.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">one_axis</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{}):</span>
+<div class="viewcode-block" id="SpectrumArray.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html#sdynpy.core.sdynpy_data.SpectrumArray.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">one_axis</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{},</span>
+             <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
+             <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_type</span> <span class="o">=</span> <span class="s1">&#39;vline&#39;</span><span class="p">,</span>
+             <span class="n">abscissa_marker_plot_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Plot the transfer functions</span>
+<span class="sd">        Plot the spectra</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        one_axis : bool, optional</span>
 <span class="sd">            Set to True to plot all data on one axis.  Set to False to plot</span>
-<span class="sd">            data on multiple subplots.  one_axis can also be set to a </span>
+<span class="sd">            data on multiple subplots.  one_axis can also be set to a</span>
 <span class="sd">            matplotlib axis to plot data on an existing axis.  The default is</span>
 <span class="sd">            True.</span>
 <span class="sd">        subplots_kwargs : dict, optional</span>
@@ -3345,6 +3610,20 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            figure and axes. The default is {}.</span>
 <span class="sd">        plot_kwargs : dict, optional</span>
 <span class="sd">            Keywords passed to the matplotlib plot function. The default is {}.</span>
+<span class="sd">        abscissa_markers : ndarray, optional</span>
+<span class="sd">            Array containing abscissa values to mark on the plot to denote</span>
+<span class="sd">            significant events.</span>
+<span class="sd">        abscissa_marker_labels : str or ndarray</span>
+<span class="sd">            Array of strings to label the abscissa_markers with, or</span>
+<span class="sd">            alternatively a format string that accepts index and abscissa</span>
+<span class="sd">            inputs (e.g. &#39;{index:}: {abscissa:0.2f}&#39;).  By default no label</span>
+<span class="sd">            will be applied.</span>
+<span class="sd">        abscissa_marker_type : str</span>
+<span class="sd">            The type of marker to use.  This can either be the string &#39;vline&#39;</span>
+<span class="sd">            or a valid matplotlib symbol specifier (e.g. &#39;o&#39;, &#39;x&#39;, &#39;.&#39;).</span>
+<span class="sd">        abscissa_marker_plot_kwargs : dict</span>
+<span class="sd">            Additional keyword arguments used when plotting the abscissa label</span>
+<span class="sd">            markers.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -3352,41 +3631,120 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">             On which the data were plotted</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">abscissa_marker_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">abscissa_markers</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">abscissa_marker_labels</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">index</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">abscissa</span> <span class="o">=</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">)]</span>
+                
         <span class="k">if</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
+            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
             <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase&#39;</span><span class="p">)</span>
             <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Amplitude&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
             <span class="n">ncols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span>
             <span class="n">nrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">/</span> <span class="n">ncols</span><span class="p">))</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">function</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">())):</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">function</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()]))</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">ax</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:]:</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">axis</span> <span class="o">=</span> <span class="n">one_axis</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">axis</span></div>
-    
-<div class="viewcode-block" id="SpectrumArray.plot_spectrogram"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html#sdynpy.core.sdynpy_data.SpectrumArray.plot_spectrogram">[docs]</a>    <span class="k">def</span> <span class="nf">plot_spectrogram</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">abscissa</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                         <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
-                         <span class="n">pcolormesh_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;shading&#39;</span><span class="p">:</span><span class="s1">&#39;auto&#39;</span><span class="p">},</span>
-                         <span class="n">log_scale</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+
+<div class="viewcode-block" id="SpectrumArray.plot_spectrogram"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.SpectrumArray.html#sdynpy.core.sdynpy_data.SpectrumArray.plot_spectrogram">[docs]</a>    <span class="k">def</span> <span class="nf">plot_spectrogram</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">abscissa</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                         <span class="n">subplots_kwargs</span><span class="o">=</span><span class="p">{},</span>
+                         <span class="n">pcolormesh_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;shading&#39;</span><span class="p">:</span> <span class="s1">&#39;auto&#39;</span><span class="p">},</span>
+                         <span class="n">log_scale</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Plots a spectrogram </span>
+<span class="sd">        Plots a spectrogram</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        abscissa : np.ndarray</span>
-<span class="sd">            Optional argument to specify as the abscissa values.  If not </span>
+<span class="sd">            Optional argument to specify as the abscissa values.  If not</span>
 <span class="sd">            specified, this will be the index of the flattened SpectrumArray.</span>
 <span class="sd">        axis : matplotlib.axis, optional</span>
 <span class="sd">            An optional argument that specifies the axis to plot the spectrogram</span>
@@ -3414,10 +3772,10 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">abscissa</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">fig</span><span class="p">,</span><span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
-        <span class="n">axis</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="n">y_coords</span><span class="p">,</span><span class="n">data</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">pcolormesh_kwargs</span><span class="p">)</span>
+            <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">y_coords</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">pcolormesh_kwargs</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">axis</span></div></div>
-        
+
 
 <span class="c1"># def spectrum_array(abscissa,ordinate,coordinate,comment1=&#39;&#39;,comment2=&#39;&#39;,comment3=&#39;&#39;,comment4=&#39;&#39;,comment5=&#39;&#39;):</span>
 <span class="c1">#     pass</span>
@@ -3437,6 +3795,78 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span>
 
+<div class="viewcode-block" id="PowerSpectralDensityArray.from_time_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data">[docs]</a>    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">from_time_data</span><span class="p">(</span><span class="n">response_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span><span class="p">,</span>
+                       <span class="n">samples_per_average</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">overlap</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
+                       <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,)),</span> 
+                       <span class="n">reference_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">only_asds</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Computes a PSD matrix from reference and response time histories</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        response_data : TimeHistoryArray</span>
+<span class="sd">            Time data to be used as responses</span>
+<span class="sd">        samples_per_average : int, optional</span>
+<span class="sd">            Number of samples used to split up the signals into averages.  The</span>
+<span class="sd">            default is None, meaning the data is treated as a single measurement</span>
+<span class="sd">            frame.</span>
+<span class="sd">        overlap : float, optional</span>
+<span class="sd">            The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).</span>
+<span class="sd">            The default is 0.0, meaning no overlap is used.</span>
+<span class="sd">        window : np.ndarray or str, optional</span>
+<span class="sd">            A 1D ndarray with length samples_per_average that specifies the</span>
+<span class="sd">            coefficients of the window.  A Hann window is applied if not specified.</span>
+<span class="sd">            If a string is specified, then the window will be obtained from scipy.</span>
+<span class="sd">        reference_data : TimeHistoryArray</span>
+<span class="sd">            Time data to be used as reference.  If not specified, the response</span>
+<span class="sd">            data will be used as references, resulting in a square CPSD matrix.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if reference and response functions do not have consistent</span>
+<span class="sd">            abscissa</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        PowerSpectralDensityArray</span>
+<span class="sd">            A PSD array computed from the specified reference and</span>
+<span class="sd">            response signals.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">reference_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">reference_data</span> <span class="o">=</span> <span class="n">response_data</span>
+            <span class="n">ref_ord</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="n">ref_data</span> <span class="o">=</span> <span class="n">reference_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">only_asds</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`only_asds` cannot be true when reference data is &#39;</span>
+                             <span class="s1">&#39;specified&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ref_data</span> <span class="o">=</span> <span class="n">reference_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+            <span class="n">ref_ord</span> <span class="o">=</span> <span class="n">ref_data</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="n">res_data</span> <span class="o">=</span> <span class="n">response_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">res_ord</span> <span class="o">=</span> <span class="n">res_data</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="k">if</span> <span class="p">((</span><span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ref_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                           <span class="n">res_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">))</span>
+            <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ref_data</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span><span class="n">res_data</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Reference and Response Data should have identical abscissa!&#39;</span><span class="p">)</span>
+        <span class="n">dt</span> <span class="o">=</span> <span class="n">res_data</span><span class="o">.</span><span class="n">abscissa_spacing</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">cpsd</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">res_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                           <span class="n">window</span><span class="p">,</span> <span class="n">reference_signals</span> <span class="o">=</span> <span class="n">ref_ord</span><span class="p">,</span><span class="n">only_asds</span> <span class="o">=</span> <span class="n">only_asds</span><span class="p">)</span>
+        <span class="n">freq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">cpsd</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">df</span>
+        <span class="c1"># Now construct the transfer function array</span>
+        <span class="k">if</span> <span class="n">only_asds</span><span class="p">:</span>
+            <span class="n">coordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">res_data</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span>
+                                         <span class="n">ref_data</span><span class="o">.</span><span class="n">coordinate</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">coordinate</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">res_data</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+                                       <span class="n">ref_data</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
+                          <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">cpsd</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">coordinate</span><span class="p">)</span></div>
+
 <div class="viewcode-block" id="PowerSpectralDensityArray.generate_time_history"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.generate_time_history">[docs]</a>    <span class="k">def</span> <span class="nf">generate_time_history</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time_length</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_oversample</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generates a time history from a CPSD matrix</span>
@@ -3526,32 +3956,31 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Check consistent abscissa</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Transfer Function Abscissa do not match CPSD&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All CPSD abscissa must be identical&#39;</span><span class="p">)</span>
         <span class="c1"># First do bookkeeping, we want to get the coordinates of the response</span>
         <span class="c1"># of the FRF corresponding to the specification matrix</span>
         <span class="n">transfer_function</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
-        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span>
-        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">reference_coordinate</span>
-        <span class="n">cpsd_dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">)</span>
-        <span class="n">output_dofs</span><span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span><span class="n">response_dofs</span><span class="p">)</span>
-        <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="p">[</span><span class="n">cpsd_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">output_matrix</span> <span class="o">=</span> <span class="n">frf_matrix</span> <span class="o">@</span> <span class="n">cpsd_matrix</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf_matrix</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span>
+        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reference_coordinate</span>
+        <span class="n">cpsd_dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">)</span>
+        <span class="n">output_dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span> <span class="n">response_dofs</span><span class="p">)</span>
+        <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="p">[</span><span class="n">cpsd_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">output_matrix</span> <span class="o">=</span> <span class="n">frf_matrix</span> <span class="o">@</span> <span class="n">cpsd_matrix</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf_matrix</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
                           <span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">output_matrix</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">output_dofs</span><span class="p">)</span></div>
-    
+
 <div class="viewcode-block" id="PowerSpectralDensityArray.mimo_inverse"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.mimo_inverse">[docs]</a>    <span class="k">def</span> <span class="nf">mimo_inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transfer_function</span><span class="p">,</span>
-                     <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;standard&#39;</span><span class="p">,</span>
-                     <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">num_retained_values</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span>
+                     <span class="n">method</span><span class="o">=</span><span class="s1">&#39;standard&#39;</span><span class="p">,</span>
+                     <span class="n">response_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">reference_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">regularization_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">cond_num_threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">num_retained_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes input estimation for MIMO random vibration problems</span>
 
@@ -3561,7 +3990,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            System transfer functions used to estimate the input from the given</span>
 <span class="sd">            response matrix</span>
 <span class="sd">        method : str, optional</span>
-<span class="sd">            The method to be used for the FRF matrix inversions. The available </span>
+<span class="sd">            The method to be used for the FRF matrix inversions. The available</span>
 <span class="sd">            methods are:</span>
 <span class="sd">                - standard - basic pseudo-inverse via numpy.linalg.pinv with the</span>
 <span class="sd">                  default rcond parameter, this is the default method</span>
@@ -3569,43 +3998,43 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">                  condition number threshold</span>
 <span class="sd">                - tikhonov - pseudo-inverse using the Tikhonov regularization method</span>
 <span class="sd">                - truncation - pseudo-inverse where a fixed number of singular values</span>
-<span class="sd">                  are retained for the inverse </span>
+<span class="sd">                  are retained for the inverse</span>
 <span class="sd">        response_weighting_matrix : sdpy.Matrix, optional</span>
-<span class="sd">            Diagonal matrix used to weight response degrees of freedom (to solve the </span>
-<span class="sd">            problem as a weight least squares) by multiplying the rows of the FRF </span>
-<span class="sd">            matrix by a scalar weights. This matrix can also be a 3D matrix such that </span>
-<span class="sd">            the the weights are different for each frequency line. The matrix should </span>
-<span class="sd">            be sized [number of lines, number of references, number of references], </span>
-<span class="sd">            where the number of lines either be one (the same weights at all frequencies) </span>
+<span class="sd">            Diagonal matrix used to weight response degrees of freedom (to solve the</span>
+<span class="sd">            problem as a weight least squares) by multiplying the rows of the FRF</span>
+<span class="sd">            matrix by a scalar weights. This matrix can also be a 3D matrix such that</span>
+<span class="sd">            the the weights are different for each frequency line. The matrix should</span>
+<span class="sd">            be sized [number of lines, number of references, number of references],</span>
+<span class="sd">            where the number of lines either be one (the same weights at all frequencies)</span>
 <span class="sd">            or the length of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">        reference_weighting_matrix : sdpy.Matrix, optional</span>
-<span class="sd">            Diagonal matrix used to weight reference degrees of freedom (generally for </span>
-<span class="sd">            normalization) by multiplying the columns of the FRF matrix by a scalar weights. </span>
+<span class="sd">            Diagonal matrix used to weight reference degrees of freedom (generally for</span>
+<span class="sd">            normalization) by multiplying the columns of the FRF matrix by a scalar weights.</span>
 <span class="sd">            This matrix can also be a 3D matrix such that the the weights are different</span>
-<span class="sd">            for each frequency line. The matrix should be sized </span>
-<span class="sd">            [number of lines, number of references, number of references], where the number </span>
+<span class="sd">            for each frequency line. The matrix should be sized</span>
+<span class="sd">            [number of lines, number of references, number of references], where the number</span>
 <span class="sd">            of lines either be one (the same weights at all frequencies) or the length</span>
 <span class="sd">            of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">        regularization_weighting_matrix : sdpy.Matrix, optional</span>
-<span class="sd">            Matrix used to weight input degrees of freedom via Tikhonov regularization. </span>
+<span class="sd">            Matrix used to weight input degrees of freedom via Tikhonov regularization.</span>
 <span class="sd">            This matrix can also be a 3D matrix such that the the weights are different</span>
-<span class="sd">            for each frequency line. The matrix should be sized </span>
-<span class="sd">            [number of lines, number of references, number of references], where the number </span>
+<span class="sd">            for each frequency line. The matrix should be sized</span>
+<span class="sd">            [number of lines, number of references, number of references], where the number</span>
 <span class="sd">            of lines either be one (the same weights at all frequencies) or the length</span>
 <span class="sd">            of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">        regularization_parameter : float or np.ndarray, optional</span>
 <span class="sd">            Scaling parameter used on the regularization weighting matrix when the tikhonov</span>
-<span class="sd">            method is chosen. A vector of regularization parameters can be provided so the </span>
-<span class="sd">            regularization is different at each frequency line. The vector must match the </span>
+<span class="sd">            method is chosen. A vector of regularization parameters can be provided so the</span>
+<span class="sd">            regularization is different at each frequency line. The vector must match the</span>
 <span class="sd">            length of the abscissa in this case (either be size [num_lines,] or [num_lines, 1]).</span>
 <span class="sd">        cond_num_threshold : float or np.ndarray, optional</span>
-<span class="sd">            Condition number used for SVD truncation when the threshold method is chosen. </span>
-<span class="sd">            A vector of condition numbers can be provided so it varies as a function of </span>
+<span class="sd">            Condition number used for SVD truncation when the threshold method is chosen.</span>
+<span class="sd">            A vector of condition numbers can be provided so it varies as a function of</span>
 <span class="sd">            frequency. The vector must match the length of the abscissa in this case.</span>
 <span class="sd">        num_retained_values : float or np.ndarray, optional</span>
-<span class="sd">            Number of singular values to retain in the pseudo-inverse when the truncation </span>
-<span class="sd">            method is chosen. A vector of can be provided so the number of retained values </span>
-<span class="sd">            can change as a function of frequency. The vector must match the length of the </span>
+<span class="sd">            Number of singular values to retain in the pseudo-inverse when the truncation</span>
+<span class="sd">            method is chosen. A vector of can be provided so the number of retained values</span>
+<span class="sd">            can change as a function of frequency. The vector must match the length of the</span>
 <span class="sd">            abscissa in this case.</span>
 
 <span class="sd">        Raises</span>
@@ -3617,10 +4046,10 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        -------</span>
 <span class="sd">        PowerSpectralDensityArray</span>
 <span class="sd">            Input CPSD matrix</span>
-<span class="sd">            </span>
+
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
-<span class="sd">        This function solves the MIMO problem Gxx = Hxv@Gvv@Hxv^* using the pseudoinverse.  </span>
+<span class="sd">        This function solves the MIMO problem Gxx = Hxv@Gvv@Hxv^* using the pseudoinverse.</span>
 <span class="sd">        Gvv = Hxv^+@Gxx@Hxv^+^*, where Gvv is the source.</span>
 
 <span class="sd">        References</span>
@@ -3628,54 +4057,54 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        .. [1] Wikipedia, &quot;Moore-Penrose inverse&quot;.</span>
 <span class="sd">               https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse</span>
 <span class="sd">        .. [2] A.N. Tithe, D.J. Thompson, The quantification of structure-borne transmission pathsby inverse methods. Part 2: Use of regularization techniques,</span>
-<span class="sd">               Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X, </span>
+<span class="sd">               Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X,</span>
 <span class="sd">               https://doi.org/10.1016/S0022-460X(02)01203-8.</span>
 <span class="sd">        .. [3] Wikipedia, &quot;Ridge regression&quot;.</span>
 <span class="sd">               https://en.wikipedia.org/wiki/Ridge_regression</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Check consistent abscissa</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Transfer Function Abscissa do not match CPSD&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All CPSD abscissa must be identical&#39;</span><span class="p">)</span>
         <span class="c1"># First do bookkeeping, we want to get the coordinates of the response</span>
         <span class="c1"># of the FRF corresponding to the specification matrix</span>
         <span class="n">transfer_function</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
-        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span>
-        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">reference_coordinate</span>
-        <span class="n">cpsd_dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span><span class="n">response_dofs</span><span class="p">)</span>
-        <span class="n">output_dofs</span><span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">)</span>
-        <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="p">[</span><span class="n">cpsd_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span>
+        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">transfer_function</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reference_coordinate</span>
+        <span class="n">cpsd_dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span> <span class="n">response_dofs</span><span class="p">)</span>
+        <span class="n">output_dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">)</span>
+        <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">transfer_function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="p">[</span><span class="n">cpsd_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
         <span class="c1"># Perform the generalized inversion</span>
         <span class="k">if</span> <span class="n">response_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">response_weighting_matrix</span><span class="p">,</span><span class="n">Matrix</span><span class="p">):</span>
-                <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">,</span><span class="n">response_dofs</span><span class="p">]</span>
-            <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span> <span class="o">@</span> <span class="n">cpsd_matrix</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">response_weighting_matrix</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">response_weighting_matrix</span><span class="p">,</span> <span class="n">Matrix</span><span class="p">):</span>
+                <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">,</span> <span class="n">response_dofs</span><span class="p">]</span>
+            <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span> <span class="o">@</span> <span class="n">cpsd_matrix</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">response_weighting_matrix</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">reference_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_weighting_matrix</span><span class="p">,</span><span class="n">Matrix</span><span class="p">):</span>
-                <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="n">reference_weighting_matrix</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">]</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">regularization_weighting_matrix</span><span class="p">,</span><span class="n">Matrix</span><span class="p">):</span>
-            <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">]</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">reference_weighting_matrix</span><span class="p">,</span> <span class="n">Matrix</span><span class="p">):</span>
+                <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="n">reference_weighting_matrix</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">]</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">regularization_weighting_matrix</span><span class="p">,</span> <span class="n">Matrix</span><span class="p">):</span>
+            <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">[</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">]</span>
         <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">frf_inverse</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
-                               <span class="n">method</span> <span class="o">=</span> <span class="n">method</span><span class="p">,</span>
-                               <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span><span class="p">,</span>
-                               <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="n">reference_weighting_matrix</span><span class="p">,</span>
-                               <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">,</span>
-                               <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">regularization_parameter</span><span class="p">,</span>
-                               <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="n">cond_num_threshold</span><span class="p">,</span>
-                               <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">num_retained_values</span><span class="p">)</span>
+                               <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
+                               <span class="n">response_weighting_matrix</span><span class="o">=</span><span class="n">response_weighting_matrix</span><span class="p">,</span>
+                               <span class="n">reference_weighting_matrix</span><span class="o">=</span><span class="n">reference_weighting_matrix</span><span class="p">,</span>
+                               <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="n">regularization_weighting_matrix</span><span class="p">,</span>
+                               <span class="n">regularization_parameter</span><span class="o">=</span><span class="n">regularization_parameter</span><span class="p">,</span>
+                               <span class="n">cond_num_threshold</span><span class="o">=</span><span class="n">cond_num_threshold</span><span class="p">,</span>
+                               <span class="n">num_retained_values</span><span class="o">=</span><span class="n">num_retained_values</span><span class="p">)</span>
         <span class="n">method_statement_start</span> <span class="o">=</span> <span class="s1">&#39;The inputs are being computed using the &#39;</span>
         <span class="n">method_statement_end</span> <span class="o">=</span> <span class="s1">&#39; method&#39;</span>
         <span class="nb">print</span><span class="p">(</span><span class="n">method_statement_start</span><span class="o">+</span><span class="n">method</span><span class="o">+</span><span class="n">method_statement_end</span><span class="p">)</span>
-        <span class="n">output_matrix</span> <span class="o">=</span> <span class="n">frf_pinv</span> <span class="o">@</span> <span class="n">cpsd_matrix</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf_pinv</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">output_matrix</span> <span class="o">=</span> <span class="n">frf_pinv</span> <span class="o">@</span> <span class="n">cpsd_matrix</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf_pinv</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">reference_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">output_matrix</span> <span class="o">=</span> <span class="n">reference_weighting_matrix</span><span class="nd">@output_matrix@reference_weighting_matrix</span>
         <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
                           <span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">output_matrix</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">output_dofs</span><span class="p">)</span></div>
-    
-<div class="viewcode-block" id="PowerSpectralDensityArray.error_summary"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary">[docs]</a>    <span class="k">def</span> <span class="nf">error_summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">figure_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{},</span><span class="o">**</span><span class="n">cpsd_matrices</span><span class="p">):</span>
+
+<div class="viewcode-block" id="PowerSpectralDensityArray.error_summary"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary">[docs]</a>    <span class="k">def</span> <span class="nf">error_summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figure_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">cpsd_matrices</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plots an error summary compared to the current array</span>
 
@@ -3704,19 +4133,20 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">def</span> <span class="nf">rms</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">))</span>
+
         <span class="k">def</span> <span class="nf">dB_pow</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
         <span class="n">frequencies</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span>
-        <span class="k">for</span> <span class="n">legend</span><span class="p">,</span><span class="n">cpsd</span> <span class="ow">in</span> <span class="n">cpsd_matrices</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span><span class="n">cpsd</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">legend</span><span class="p">,</span> <span class="n">cpsd</span> <span class="ow">in</span> <span class="n">cpsd_matrices</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">cpsd</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Compared CPSD abscissa do not match&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All CPSD abscissa must be identical&#39;</span><span class="p">)</span>
         <span class="c1"># Get ASDs</span>
         <span class="n">responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">))</span>
-        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">responses</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">responses</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
         <span class="n">channel_names</span> <span class="o">=</span> <span class="n">responses</span><span class="o">.</span><span class="n">string_array</span><span class="p">()</span>
         <span class="n">spec_asd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="bp">self</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span>
-        <span class="n">data_asd</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span><span class="n">data</span> <span class="ow">in</span> <span class="n">cpsd_matrices</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="n">data_asd</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">cpsd_matrices</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
         <span class="n">num_channels</span> <span class="o">=</span> <span class="n">spec_asd</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">ncols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">num_channels</span><span class="p">)))</span>
         <span class="n">nrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">num_channels</span> <span class="o">/</span> <span class="n">ncols</span><span class="p">))</span>
@@ -3768,7 +4198,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;dB Error&#39;</span><span class="p">)</span>
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cpsd_matrices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">prop_cycle</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;axes.prop_cycle&#39;</span><span class="p">]</span>
-            <span class="n">colors</span> <span class="o">=</span> <span class="n">prop_cycle</span><span class="o">.</span><span class="n">by_key</span><span class="p">()[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span>
+            <span class="n">colors</span> <span class="o">=</span> <span class="n">prop_cycle</span><span class="o">.</span><span class="n">by_key</span><span class="p">()[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">*</span> <span class="mi">100</span>
             <span class="n">db_error_sum_asd</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span> <span class="n">rms</span><span class="p">(</span><span class="n">dB_pow</span><span class="p">(</span><span class="n">sum_asd</span><span class="p">)</span> <span class="o">-</span> <span class="n">dB_pow</span><span class="p">(</span><span class="n">spec_sum_asd</span><span class="p">))</span>
                                 <span class="k">for</span> <span class="n">legend</span><span class="p">,</span> <span class="n">sum_asd</span> <span class="ow">in</span> <span class="n">data_sum_asd</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
             <span class="n">db_error_rms</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span> <span class="n">rms</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">db_error</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
@@ -3793,10 +4223,10 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;RMS dB Error&#39;</span><span class="p">)</span>
         <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">return_data</span></div>
-    
-<div class="viewcode-block" id="PowerSpectralDensityArray.svd"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.svd">[docs]</a>    <span class="k">def</span> <span class="nf">svd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full_matrices</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">as_matrix</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+
+<div class="viewcode-block" id="PowerSpectralDensityArray.svd"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.svd">[docs]</a>    <span class="k">def</span> <span class="nf">svd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">as_matrix</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Compute the SVD of the provided FRF matrix</span>
+<span class="sd">        Compute the SVD of the provided CPSD matrix</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -3811,7 +4241,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        u : ndarray</span>
-<span class="sd">            Left hand singular vectors, sized [..., num_responses, num_responses]. </span>
+<span class="sd">            Left hand singular vectors, sized [..., num_responses, num_responses].</span>
 <span class="sd">            Only returned when compute_uv is True.</span>
 <span class="sd">        s : ndarray</span>
 <span class="sd">            Singular values, sized [..., num_references]</span>
@@ -3821,22 +4251,22 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">cpsd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
         <span class="n">cpsdOrd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">cpsd</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">compute_uv</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">compute_uv</span><span class="p">:</span>
             <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">cpsdOrd</span><span class="p">,</span> <span class="n">full_matrices</span><span class="p">,</span> <span class="n">compute_uv</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">as_matrix</span><span class="p">:</span>
-                <span class="n">u</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">u</span><span class="p">,</span><span class="n">cpsd</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span>
-                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
-                <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
-                <span class="n">vh</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">vh</span><span class="p">,</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">vh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                            <span class="n">cpsd</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">,</span>
-                           <span class="p">)</span>
+                <span class="n">u</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">cpsd</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span>
+                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                <span class="n">vh</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">vh</span><span class="p">,</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">vh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                            <span class="n">cpsd</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">,</span>
+                            <span class="p">)</span>
             <span class="k">return</span> <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span>
-        <span class="k">elif</span> <span class="n">compute_uv</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+        <span class="k">else</span><span class="p">:</span>
             <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">cpsdOrd</span><span class="p">,</span> <span class="n">full_matrices</span><span class="p">,</span> <span class="n">compute_uv</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">as_matrix</span><span class="p">:</span>
-                 <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                            <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
             <span class="k">return</span> <span class="n">s</span></div>
 
 <div class="viewcode-block" id="PowerSpectralDensityArray.get_asd"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.get_asd">[docs]</a>    <span class="k">def</span> <span class="nf">get_asd</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -3849,7 +4279,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            PowerSpectralDensityArrays where the response is equal to the reference</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]))</span>
         <span class="k">return</span> <span class="bp">self</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span></div>
 
 <div class="viewcode-block" id="PowerSpectralDensityArray.rms"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.rms">[docs]</a>    <span class="k">def</span> <span class="nf">rms</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -3864,23 +4294,23 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">asd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_asd</span><span class="p">()</span>
         <span class="n">abscissa_spacing</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_spacing</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">asd</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">abscissa_spacing</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">asd</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">abscissa_spacing</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="PowerSpectralDensityArray.plot_asds"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_asds">[docs]</a>    <span class="k">def</span> <span class="nf">plot_asds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figure_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">linewidth</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
+<div class="viewcode-block" id="PowerSpectralDensityArray.plot_asds"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_asds">[docs]</a>    <span class="k">def</span> <span class="nf">plot_asds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figure_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
         <span class="n">asds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_asd</span><span class="p">()</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">rms</span> <span class="o">=</span> <span class="n">asds</span><span class="o">.</span><span class="n">rms</span><span class="p">()</span>
         <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
             <span class="n">rms</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">asds</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">one_axis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">subplots_kwargs</span><span class="o">=</span><span class="n">figure_kwargs</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="n">linewidth</span><span class="p">})</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">a</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">asds</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">one_axis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="o">=</span><span class="n">figure_kwargs</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span> <span class="n">linewidth</span><span class="p">})</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">asd</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span><span class="n">asds</span><span class="p">)):</span>
             <span class="k">if</span> <span class="n">rms</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">a</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">get_ylabel</span><span class="p">()</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">RMS: </span><span class="si">{:0.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rms</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
             <span class="n">a</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">ax</span></div>
 
 <div class="viewcode-block" id="PowerSpectralDensityArray.compare_asds"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.compare_asds">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">compare_asds</span><span class="p">(</span><span class="n">figure_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span><span class="o">**</span><span class="n">cpsd_matrices</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">compare_asds</span><span class="p">(</span><span class="n">figure_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">cpsd_matrices</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the diagonals of the CPSD matrix, as well as the level</span>
 
@@ -3905,8 +4335,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        None.</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">asds</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span><span class="n">cpsd</span><span class="o">.</span><span class="n">get_asd</span><span class="p">()</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span><span class="n">cpsd</span> <span class="ow">in</span> <span class="n">cpsd_matrices</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">legend</span><span class="p">,</span><span class="n">asd</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+        <span class="n">asds</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span> <span class="n">cpsd</span><span class="o">.</span><span class="n">get_asd</span><span class="p">()</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span> <span class="n">cpsd</span> <span class="ow">in</span> <span class="n">cpsd_matrices</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">legend</span><span class="p">,</span> <span class="n">asd</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
             <span class="n">this_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">asd</span><span class="o">.</span><span class="n">coordinate</span><span class="p">))</span>
             <span class="n">this_abscissa</span> <span class="o">=</span> <span class="n">asd</span><span class="o">.</span><span class="n">abscissa</span>
             <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
@@ -3914,7 +4344,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">this_dofs</span> <span class="o">==</span> <span class="n">dofs</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;CPSDs must have identical dofs&#39;</span><span class="p">)</span>
         <span class="c1"># Sort the dofs correctly</span>
-        <span class="n">asds</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span><span class="n">asd</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">dofs</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="mi">2</span><span class="p">)]</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span><span class="n">asd</span> <span class="ow">in</span> <span class="n">asds</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
+        <span class="n">asds</span> <span class="o">=</span> <span class="p">{</span><span class="n">legend</span><span class="p">:</span> <span class="n">asd</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">dofs</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="mi">2</span><span class="p">)]</span> <span class="k">for</span> <span class="n">legend</span><span class="p">,</span> <span class="n">asd</span> <span class="ow">in</span> <span class="n">asds</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
         <span class="n">num_channels</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dofs</span><span class="p">)</span>
         <span class="n">ncols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">num_channels</span><span class="p">)))</span>
         <span class="n">nrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">num_channels</span> <span class="o">/</span> <span class="n">ncols</span><span class="p">))</span>
@@ -3946,16 +4376,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">grid_spec</span><span class="p">[</span><span class="n">total_rows</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">:</span><span class="n">ncols</span><span class="p">])</span>
         <span class="n">legend_handles</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">legend_strings</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">legend</span><span class="p">,</span><span class="n">asd</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
-            <span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">fn</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">asd</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">legend</span><span class="p">,</span> <span class="n">asd</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+            <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">fn</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">asd</span><span class="p">):</span>
                 <span class="n">rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fn</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">real</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">fn</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)))</span>
                 <span class="n">x</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">asds</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">j</span>
                 <span class="n">a</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">rms</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
                 <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                     <span class="n">legend_handles</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
-                    <span class="n">legend_strings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">legend</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
+                    <span class="n">legend_strings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">legend</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="p">))</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">&#39; </span><span class="si">{:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rms</span><span class="p">),</span>
-                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">,</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
+                        <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
         <span class="c1"># Set XTicks</span>
         <span class="n">xticks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">asds</span><span class="p">)))</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dofs</span><span class="p">))</span><span class="o">*</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">asds</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">xticklabels</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">dof</span><span class="p">)</span> <span class="k">for</span> <span class="n">dof</span> <span class="ow">in</span> <span class="n">dofs</span><span class="p">]</span>
@@ -3963,16 +4393,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">xticklabels</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;RMS Levels&#39;</span><span class="p">)</span>
         <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-        <span class="n">l</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">legend_handles</span><span class="p">,</span><span class="n">legend_strings</span><span class="p">,</span><span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">legend</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">legend_handles</span><span class="p">,</span> <span class="n">legend_strings</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
         <span class="n">fig</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span>
-        <span class="n">legend_width</span> <span class="o">=</span> <span class="n">l</span><span class="o">.</span><span class="n">get_window_extent</span><span class="p">()</span><span class="o">.</span><span class="n">width</span>
+        <span class="n">legend_width</span> <span class="o">=</span> <span class="n">legend</span><span class="o">.</span><span class="n">get_window_extent</span><span class="p">()</span><span class="o">.</span><span class="n">width</span>
         <span class="n">figure_width</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">bbox</span><span class="o">.</span><span class="n">width</span>
         <span class="n">figure_fraction</span> <span class="o">=</span> <span class="n">legend_width</span><span class="o">/</span><span class="n">figure_width</span>
         <span class="n">ax_position</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span>
         <span class="n">ax_position</span><span class="o">.</span><span class="n">x1</span> <span class="o">-=</span> <span class="n">figure_fraction</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_position</span><span class="p">(</span><span class="n">ax_position</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="PowerSpectralDensityArray.plot_singular_values"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_singular_values">[docs]</a>    <span class="k">def</span> <span class="nf">plot_singular_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rcond</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">min_freqency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="PowerSpectralDensityArray.plot_singular_values"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_singular_values">[docs]</a>    <span class="k">def</span> <span class="nf">plot_singular_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_freqency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the singular values of an FRF matrix with a visualization of the rcond tolerance</span>
 
@@ -3980,7 +4410,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        rcond : value of float, optional</span>
 <span class="sd">            Cutoff for small singular values. Implemented such that the cutoff is rcond*</span>
-<span class="sd">            largest_singular_value (the same as np.linalg.pinv). This is to visualize the </span>
+<span class="sd">            largest_singular_value (the same as np.linalg.pinv). This is to visualize the</span>
 <span class="sd">            effect of rcond and is used for display purposes only.</span>
 <span class="sd">        min_frequency : float, optional</span>
 <span class="sd">            Minimum frequency to plot</span>
@@ -3989,21 +4419,21 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="n">s_cpsd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">compute_uv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_matrix</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="n">s_cpsd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">compute_uv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_matrix</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">s_cpsd</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">rcond</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">rcond</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">cutoff</span> <span class="o">=</span> <span class="n">s_cpsd</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">rcond</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Singular Values&#39;</span><span class="p">)</span>
-        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Singular Values of CPSD Matrix&#39;</span><span class="p">)</span>    
-        <span class="k">if</span> <span class="n">min_freqency</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">left</span> <span class="o">=</span> <span class="n">min_freqency</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">max_frequency</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">right</span> <span class="o">=</span> <span class="n">max_frequency</span><span class="p">)</span></div>
-        
+        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Singular Values of CPSD Matrix&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">min_freqency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="n">min_freqency</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="n">max_frequency</span><span class="p">)</span></div>
+
 <div class="viewcode-block" id="PowerSpectralDensityArray.coherence"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.coherence">[docs]</a>    <span class="k">def</span> <span class="nf">coherence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes the coherence of a PSD matrix</span>
@@ -4020,16 +4450,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">reshaped_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
-        <span class="n">abscissa</span> <span class="o">=</span> <span class="n">reshaped_array</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">abscissa</span><span class="p">):</span>
+        <span class="n">abscissa</span> <span class="o">=</span> <span class="n">reshaped_array</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;All functions must have identical abscissa&#39;</span><span class="p">)</span>
-        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">coherence_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">sp_coherence</span><span class="p">(</span><span class="n">cpsd_matrix</span><span class="p">),</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">coherence_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">sp_coherence</span><span class="p">(</span><span class="n">cpsd_matrix</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">coherence_array</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span>
-            <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span><span class="n">abscissa</span> <span class="o">=</span> <span class="n">reshaped_array</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
-            <span class="n">ordinate</span> <span class="o">=</span> <span class="n">coherence_matrix</span><span class="p">,</span> <span class="n">coordinate</span> <span class="o">=</span> <span class="n">reshaped_array</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span>
+            <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span> <span class="n">abscissa</span><span class="o">=</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+            <span class="n">ordinate</span><span class="o">=</span><span class="n">coherence_matrix</span><span class="p">,</span> <span class="n">coordinate</span><span class="o">=</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">coherence_array</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span></div>
-    
+
 <div class="viewcode-block" id="PowerSpectralDensityArray.angle"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.angle">[docs]</a>    <span class="k">def</span> <span class="nf">angle</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes the angle of a PSD matrix</span>
@@ -4041,10 +4471,10 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            frequency line</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">GENERAL</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">GENERAL</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span></div>
-    
-<div class="viewcode-block" id="PowerSpectralDensityArray.set_coherence_phase"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.set_coherence_phase">[docs]</a>    <span class="k">def</span> <span class="nf">set_coherence_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">coherence_array</span><span class="p">,</span><span class="n">angle_array</span><span class="p">):</span>
+
+<div class="viewcode-block" id="PowerSpectralDensityArray.set_coherence_phase"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.set_coherence_phase">[docs]</a>    <span class="k">def</span> <span class="nf">set_coherence_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coherence_array</span><span class="p">,</span> <span class="n">angle_array</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Sets the coherence and phase of a PSD matrix while maintaining the ASDs</span>
 
@@ -4062,22 +4492,22 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">asds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_asd</span><span class="p">()</span>
-        <span class="n">dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span><span class="n">asds</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">)</span>
+        <span class="n">dofs</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span> <span class="n">asds</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">)</span>
         <span class="n">reshaped_coherence</span> <span class="o">=</span> <span class="n">coherence_array</span><span class="p">[</span><span class="n">dofs</span><span class="p">]</span>
         <span class="n">reshaped_angle</span> <span class="o">=</span> <span class="n">angle_array</span><span class="p">[</span><span class="n">dofs</span><span class="p">]</span>
-        <span class="n">asd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">coherence_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_coherence</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">phase_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_angle</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">cpsd_from_coh_phs</span><span class="p">(</span><span class="n">asd_matrix</span><span class="p">,</span><span class="n">coherence_matrix</span><span class="p">,</span><span class="n">phase_matrix</span><span class="p">)</span>
+        <span class="n">asd_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">asds</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">coherence_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_coherence</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">phase_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_angle</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">cpsd_matrix</span> <span class="o">=</span> <span class="n">cpsd_from_coh_phs</span><span class="p">(</span><span class="n">asd_matrix</span><span class="p">,</span> <span class="n">coherence_matrix</span><span class="p">,</span> <span class="n">phase_matrix</span><span class="p">)</span>
         <span class="n">output</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                            <span class="n">asds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">cpsd_matrix</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                            <span class="n">asds</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">cpsd_matrix</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
                             <span class="n">dofs</span><span class="p">)[</span><span class="n">dofs</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">output</span></div>
 
 <div class="viewcode-block" id="PowerSpectralDensityArray.eye"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.eye">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">eye</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span><span class="n">frequencies</span><span class="p">,</span><span class="n">coordinates</span><span class="p">,</span><span class="n">rms</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">full_matrix</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-            <span class="n">breakpoint_frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">breakpoint_levels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="n">breakpoint_interpolation</span> <span class="o">=</span> <span class="s1">&#39;lin&#39;</span><span class="p">,</span>
+    <span class="k">def</span> <span class="nf">eye</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">,</span> <span class="n">rms</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">full_matrix</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">breakpoint_frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">breakpoint_levels</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">breakpoint_interpolation</span><span class="o">=</span><span class="s1">&#39;lin&#39;</span><span class="p">,</span>
             <span class="n">min_frequency</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes a diagonal CPSD matrix</span>
@@ -4129,41 +4559,41 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="n">cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">breakpoint_frequencies</span><span class="p">,</span> <span class="n">breakpoint_levels</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid Interpolation, should be &quot;lin&quot; or &quot;log&quot;&#39;</span><span class="p">)</span>
-        
+
         <span class="c1"># Truncate to the minimum frequency</span>
         <span class="n">cpsd</span><span class="p">[</span><span class="n">frequencies</span> <span class="o">&lt;</span> <span class="n">min_frequency</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">cpsd</span><span class="p">[</span><span class="n">frequencies</span> <span class="o">&gt;</span> <span class="n">max_frequency</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
 
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">rms</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">rms</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">frequency_spacing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">frequencies</span><span class="p">))</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">frequency_spacing</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">frequencies</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">frequency_spacing</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">frequencies</span><span class="p">)):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;In order to specify RMS, the spacing of frequencies must be constant&#39;</span><span class="p">)</span>
             <span class="n">cpsd_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">cpsd</span><span class="p">)</span> <span class="o">*</span> <span class="n">frequency_spacing</span><span class="p">)</span>
             <span class="n">cpsd</span> <span class="o">*=</span> <span class="p">(</span><span class="n">rms</span> <span class="o">/</span> <span class="n">cpsd_rms</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-            
+
         <span class="n">num_channels</span> <span class="o">=</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">size</span>
         <span class="k">if</span> <span class="n">full_matrix</span><span class="p">:</span>
-            <span class="n">full_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_channels</span><span class="p">,</span><span class="n">num_channels</span><span class="p">,</span><span class="n">frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
-            <span class="n">full_cpsd</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_channels</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_channels</span><span class="p">),:]</span> <span class="o">=</span> <span class="n">cpsd</span>
-            <span class="n">cpsd_coordinates</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="p">,</span><span class="n">coordinates</span><span class="p">)</span>
+            <span class="n">full_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">num_channels</span><span class="p">,</span> <span class="n">num_channels</span><span class="p">,</span> <span class="n">frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
+            <span class="n">full_cpsd</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_channels</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_channels</span><span class="p">),</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">cpsd</span>
+            <span class="n">cpsd_coordinates</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">full_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">cpsd</span><span class="p">,(</span><span class="n">num_channels</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
-            <span class="n">cpsd_coordinates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">coordinates</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
-        
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span><span class="n">frequencies</span><span class="p">,</span>
-                          <span class="n">full_cpsd</span><span class="p">,</span><span class="n">cpsd_coordinates</span><span class="p">)</span></div>
-        
-<div class="viewcode-block" id="PowerSpectralDensityArray.plot_magnitude_coherence_phase"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_magnitude_coherence_phase">[docs]</a>    <span class="k">def</span> <span class="nf">plot_magnitude_coherence_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">compare_data</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span><span class="n">plot_axes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                       <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">logx</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="n">full_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">cpsd</span><span class="p">,</span> <span class="p">(</span><span class="n">num_channels</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+            <span class="n">cpsd_coordinates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">coordinates</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span> <span class="n">frequencies</span><span class="p">,</span>
+                          <span class="n">full_cpsd</span><span class="p">,</span> <span class="n">cpsd_coordinates</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="PowerSpectralDensityArray.plot_magnitude_coherence_phase"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_magnitude_coherence_phase">[docs]</a>    <span class="k">def</span> <span class="nf">plot_magnitude_coherence_phase</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">compare_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">plot_axes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                       <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">logx</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                                        <span class="n">logy</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                                       <span class="n">magnitude_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
-                                       <span class="n">coherence_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
-                                       <span class="n">angle_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
-                                       <span class="n">figure_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+                                       <span class="n">magnitude_plot_kwargs</span><span class="o">=</span><span class="p">{},</span>
+                                       <span class="n">coherence_plot_kwargs</span><span class="o">=</span><span class="p">{},</span>
+                                       <span class="n">angle_plot_kwargs</span><span class="o">=</span><span class="p">{},</span>
+                                       <span class="n">figure_kwargs</span><span class="o">=</span><span class="p">{}):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plots the magnitude, coherence, and phase of a CPSD matrix.</span>
-<span class="sd">        </span>
+
 <span class="sd">        Coherence is plotted on the upper triangle, phase on the lower triangle,</span>
 <span class="sd">        and magnitude on the diagonal.</span>
 
@@ -4209,76 +4639,76 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">compare_coherence</span> <span class="o">=</span> <span class="n">reshaped_compare_data</span><span class="o">.</span><span class="n">coherence</span><span class="p">()</span>
             <span class="n">phase_compare</span> <span class="o">=</span> <span class="n">reshaped_compare_data</span><span class="o">.</span><span class="n">angle</span><span class="p">()</span>
         <span class="n">ax</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="n">gs</span> <span class="o">=</span> <span class="n">GridSpec</span><span class="p">(</span><span class="o">*</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">fig</span><span class="p">,</span>
+        <span class="n">gs</span> <span class="o">=</span> <span class="n">GridSpec</span><span class="p">(</span><span class="o">*</span><span class="n">reshaped_array</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span>
                       <span class="n">wspace</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="n">plot_axes</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span>
-                      <span class="n">hspace</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">plot_axes</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">),</span><span class="n">function</span> <span class="ow">in</span> <span class="n">reshaped_array</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
+                      <span class="n">hspace</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="n">plot_axes</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">),</span> <span class="n">function</span> <span class="ow">in</span> <span class="n">reshaped_array</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
             <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">j</span><span class="p">:</span>
                 <span class="k">if</span> <span class="p">((</span><span class="ow">not</span> <span class="n">sharex</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">sharey</span><span class="p">))</span> <span class="ow">or</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">])</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">])</span>
                 <span class="k">elif</span> <span class="p">(</span><span class="ow">not</span> <span class="n">sharex</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharey</span> <span class="ow">and</span> <span class="p">(</span><span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],</span><span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span> <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
                 <span class="k">elif</span> <span class="n">sharex</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">sharey</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],</span><span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
                 <span class="k">else</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],</span><span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span>
-                                              <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span>
-                             <span class="s1">&#39;r&#39;</span><span class="p">,</span><span class="o">**</span><span class="n">magnitude_plot_kwargs</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                                               <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span>
+                              <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">magnitude_plot_kwargs</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">compare_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">reshaped_compare_data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
-                                 <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">reshaped_compare_data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span>
-                                 <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">.5</span><span class="p">,</span><span class="mf">.5</span><span class="p">],</span><span class="o">**</span><span class="n">magnitude_plot_kwargs</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">reshaped_compare_data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                                  <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">reshaped_compare_data</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">),</span>
+                                  <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">.5</span><span class="p">,</span> <span class="mf">.5</span><span class="p">],</span> <span class="o">**</span><span class="n">magnitude_plot_kwargs</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">logy</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="n">j</span><span class="p">:</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span>
-                    <span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],</span>
-                    <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharex</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
-                    <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharey</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">phase</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
-                              <span class="s1">&#39;g&#39;</span><span class="p">,</span><span class="o">**</span><span class="n">angle_plot_kwargs</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span>
+                    <span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span>
+                    <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharex</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">i</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharey</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">phase</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
+                              <span class="s1">&#39;g&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">angle_plot_kwargs</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">compare_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">phase_compare</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
-                                 <span class="n">phase_compare</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
-                                 <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="o">**</span><span class="n">angle_plot_kwargs</span><span class="p">)</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">phase_compare</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                                  <span class="n">phase_compare</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
+                                  <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="o">**</span><span class="n">angle_plot_kwargs</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="n">j</span><span class="p">:</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span>
-                    <span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">],</span>
-                    <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharex</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
-                    <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharey</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">coherence</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
-                              <span class="s1">&#39;b&#39;</span><span class="p">,</span><span class="o">**</span><span class="n">coherence_plot_kwargs</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span>
+                    <span class="n">gs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">],</span>
+                    <span class="n">sharex</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharex</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="n">sharey</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="n">j</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sharey</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">coherence</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
+                              <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">coherence_plot_kwargs</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">compare_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">compare_coherence</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
-                                 <span class="n">compare_coherence</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
-                                 <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">1.0</span><span class="p">],</span><span class="o">**</span><span class="n">coherence_plot_kwargs</span><span class="p">)</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">compare_coherence</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                                  <span class="n">compare_coherence</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span>
+                                  <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="o">**</span><span class="n">coherence_plot_kwargs</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">logx</span><span class="p">:</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">j</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">))</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">))</span>
             <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="n">reshaped_array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">))</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">))</span>
             <span class="k">if</span> <span class="ow">not</span> <span class="n">plot_axes</span><span class="p">:</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([])</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([])</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">)</span>
-                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span><span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">)</span></div>
-                
-<div class="viewcode-block" id="PowerSpectralDensityArray.to_rattlesnake_specification"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.to_rattlesnake_specification">[docs]</a>    <span class="k">def</span> <span class="nf">to_rattlesnake_specification</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">filename</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">coordinate_order</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">upper_warning_db</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">lower_warning_db</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">upper_abort_db</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">lower_abort_db</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">upper_warning_psd</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">lower_warning_psd</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">upper_abort_psd</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                                     <span class="n">lower_abort_psd</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([])</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">([])</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">direction</span><span class="o">=</span><span class="s1">&#39;in&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="PowerSpectralDensityArray.to_rattlesnake_specification"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.to_rattlesnake_specification">[docs]</a>    <span class="k">def</span> <span class="nf">to_rattlesnake_specification</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">coordinate_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">upper_warning_db</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">lower_warning_db</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">upper_abort_db</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">lower_abort_db</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">upper_warning_psd</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">lower_warning_psd</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">upper_abort_psd</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                     <span class="n">lower_abort_psd</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="k">if</span> <span class="n">coordinate_order</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">coordinate_array</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinate_order</span><span class="p">)</span>
             <span class="n">reshaped_data</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">coordinate_array</span><span class="p">]</span>
@@ -4287,7 +4717,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;CPSD Matrix must be 2D to transform to rattlesnake specification&#39;</span><span class="p">)</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;CPSD Matrix must be square&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Row and column coordinates of the CPSD matrix are not ordered identically&#39;</span><span class="p">)</span>
             <span class="n">reshaped_data</span> <span class="o">=</span> <span class="bp">self</span>
             <span class="n">coordinate_array</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">coordinate</span>
@@ -4296,54 +4726,113 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="n">min_frequency</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span>
             <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">max_frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span>
-            <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">)</span>
+            <span class="n">reshaped_data</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">)</span>
         <span class="n">out_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
-            <span class="n">f</span> <span class="o">=</span> <span class="n">reshaped_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
-            <span class="n">cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+            <span class="n">f</span><span class="o">=</span><span class="n">reshaped_data</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+            <span class="n">cpsd</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
         <span class="k">if</span> <span class="n">upper_warning_db</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span><span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">upper_warning_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span> <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">upper_warning_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">lower_warning_db</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span><span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">lower_warning_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span> <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">lower_warning_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">upper_abort_db</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span><span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">upper_abort_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span> <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">upper_abort_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">lower_abort_db</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span><span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">lower_abort_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ijj-&gt;ij&#39;</span><span class="p">,</span> <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;cpsd&#39;</span><span class="p">]</span><span class="o">*</span><span class="n">db2scale</span><span class="p">(</span><span class="n">lower_abort_db</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">upper_warning_psd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">signal</span> <span class="o">=</span> <span class="n">upper_warning_psd</span>
-            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span><span class="n">coordinate_array</span><span class="p">)]</span>
+            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span> <span class="n">coordinate_array</span><span class="p">)]</span>
             <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Abscissa specified by upper warning signal is not equal to the specification signal&#39;</span><span class="p">)</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">lower_warning_psd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">signal</span> <span class="o">=</span> <span class="n">lower_warning_psd</span>
-            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span><span class="n">coordinate_array</span><span class="p">)]</span>
+            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span> <span class="n">coordinate_array</span><span class="p">)]</span>
             <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Abscissa specified by lower warning signal is not equal to the specification signal&#39;</span><span class="p">)</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;warning_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">upper_abort_psd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">signal</span> <span class="o">=</span> <span class="n">upper_abort_psd</span>
-            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span><span class="n">coordinate_array</span><span class="p">)]</span>
+            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span> <span class="n">coordinate_array</span><span class="p">)]</span>
             <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Abscissa specified by upper abort signal is not equal to the specification signal&#39;</span><span class="p">)</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_upper&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">lower_abort_psd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">signal</span> <span class="o">=</span> <span class="n">lower_abort_psd</span>
-            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span><span class="n">coordinate_array</span><span class="p">)]</span>
+            <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">signal</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;iij-&gt;ij&#39;</span><span class="p">,</span> <span class="n">coordinate_array</span><span class="p">)]</span>
             <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span><span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
+                <span class="n">reshaped_signal</span> <span class="o">=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jji-&gt;ji&#39;</span><span class="p">,</span> <span class="n">reshaped_data</span><span class="o">.</span><span class="n">abscissa</span><span class="p">)</span> <span class="o">!=</span> <span class="n">reshaped_signal</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Abscissa specified by lower abort signal is not equal to the specification signal&#39;</span><span class="p">)</span>
-            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
+            <span class="n">out_dict</span><span class="p">[</span><span class="s1">&#39;abort_lower&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">reshaped_signal</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">real</span>
         <span class="k">if</span> <span class="n">filename</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span><span class="o">**</span><span class="n">out_dict</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">out_dict</span></div></div>
+            <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="o">**</span><span class="n">out_dict</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out_dict</span></div>
     
+<div class="viewcode-block" id="PowerSpectralDensityArray.bandwidth_average"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average">[docs]</a>    <span class="k">def</span> <span class="nf">bandwidth_average</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">band_lb</span><span class="p">,</span><span class="n">band_ub</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Integrates the PSD over frequency to get the power spectrum for each </span>
+<span class="sd">        frequency bin (line)</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        band_lb : ndarray</span>
+<span class="sd">            (n_bands,1) array of bandwidth lower bounds</span>
+<span class="sd">        band_ub : ndarray</span>
+<span class="sd">            (n_bands,1) array of bandwidth upper bounds</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        PowerSpectralDensityArray with abscissa given by the mean of band_lb</span>
+<span class="sd">        and band_ub</span>
+<span class="sd">        </span>
+<span class="sd">        Notes</span>
+<span class="sd">        -------</span>
+<span class="sd">        Determines which freq bins (lines) contribute to each band. Contribute</span>
+<span class="sd">        means the freq bin is at least partially within the band limits</span>
+<span class="sd">        </span>
+<span class="sd">        The portion of the bin which contributes to the band is computed based</span>
+<span class="sd">        multiplied by the fraction of the contributing frequency to get how</span>
+<span class="sd">        much bin PS adds to the band PS</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Process inputs</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span><span class="mi">2</span><span class="p">:</span>
+            <span class="n">freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span>
+            <span class="n">ein_str</span> <span class="o">=</span> <span class="s1">&#39;jk,lk-&gt;lj&#39;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,:]</span>
+            <span class="n">ein_str</span> <span class="o">=</span> <span class="s1">&#39;jk,lmk-&gt;lmj&#39;</span>
+            
+        <span class="n">band_lb</span><span class="p">,</span> <span class="n">band_ub</span> <span class="o">=</span> <span class="p">[</span><span class="n">band_lb</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span><span class="n">band_ub</span><span class="o">.</span><span class="n">flatten</span><span class="p">()]</span>
+        <span class="n">band_lb</span><span class="p">,</span><span class="n">band_ub</span> <span class="o">=</span> <span class="p">[</span> <span class="n">band_lb</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="p">,</span> <span class="n">band_ub</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="p">]</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">freq</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">freq</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">hlf_bin</span> <span class="o">=</span> <span class="n">df</span><span class="o">/</span><span class="mi">2</span>
+        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">freq</span><span class="p">)</span><span class="o">-</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mf">1e-12</span><span class="p">:</span>
+            <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Frequencies are not evenly spaced&#39;</span><span class="p">)</span>
+        
+        <span class="c1"># Determine matrix A s.t. A_jk PSD_lmk = PSDav_lmj</span>
+        <span class="n">bandwidths</span> <span class="o">=</span> <span class="n">band_ub</span><span class="o">-</span><span class="n">band_lb</span>
+        <span class="n">bin_map_lb</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">freq</span><span class="o">-</span><span class="n">hlf_bin</span><span class="p">,</span><span class="n">band_lb</span><span class="p">)</span> <span class="c1"># LB of overlap for each bin/band combo</span>
+        <span class="n">bin_map_ub</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">minimum</span><span class="p">(</span><span class="n">freq</span><span class="o">+</span><span class="n">hlf_bin</span><span class="p">,</span><span class="n">band_ub</span><span class="p">)</span> <span class="c1"># UB of overlap for each bin/band combo</span>
+        
+        <span class="n">bin_to_band</span> <span class="o">=</span> <span class="p">(</span><span class="n">bin_map_ub</span><span class="o">-</span><span class="n">bin_map_lb</span><span class="p">)</span><span class="o">/</span><span class="n">df</span>
+        <span class="n">bin_to_band</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">bin_to_band</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">bin_to_band</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+        
+        <span class="c1"># Get PSD</span>
+        <span class="n">psd_ave</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="n">ein_str</span><span class="p">,</span><span class="n">bin_to_band</span><span class="p">,</span><span class="n">df</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span>
+            
+        <span class="n">psd_ave</span> <span class="o">=</span> <span class="n">psd_ave</span><span class="o">/</span><span class="p">(</span><span class="n">band_ub</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="n">band_lb</span><span class="p">[:,</span><span class="mi">0</span><span class="p">])</span>
+        <span class="n">freqs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span> <span class="p">(</span><span class="n">band_lb</span><span class="p">,</span><span class="n">band_ub</span><span class="p">)</span> <span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span><span class="n">freqs</span><span class="p">,</span>
+                         <span class="n">psd_ave</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
+                         <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div></div>
+
 <div class="viewcode-block" id="PowerSpectrumArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.PowerSpectrumArray.html#sdynpy.core.sdynpy_data.PowerSpectrumArray">[docs]</a><span class="k">class</span> <span class="nc">PowerSpectrumArray</span><span class="p">(</span><span class="n">NDDataArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data array used to store power spectra arrays&quot;&quot;&quot;</span>
     <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nelements</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@@ -4373,7 +4862,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                        <span class="n">response_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span><span class="p">,</span>
                        <span class="n">samples_per_average</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
                        <span class="n">overlap</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;H1&#39;</span><span class="p">,</span>
-                       <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,)),</span> <span class="o">**</span><span class="n">timedata2frf_kwargs</span><span class="p">):</span>
+                       <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,)),</span> <span class="n">return_model_data</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                       <span class="o">**</span><span class="n">timedata2frf_kwargs</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Computes a transfer function from reference and response time histories</span>
 
@@ -4391,15 +4881,18 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).</span>
 <span class="sd">            The default is 0.0, meaning no overlap is used.</span>
 <span class="sd">        method : str, optional</span>
-<span class="sd">            The method for creating the frequency response function. &#39;H1&#39; is </span>
-<span class="sd">            default if not specified.</span>
+<span class="sd">            The method for creating the frequency response function. &#39;H1&#39; is</span>
+<span class="sd">            default if not specified. samples_per_average, overlap, and window</span>
+<span class="sd">            are not used if method==&#39;LRM&#39;.</span>
 <span class="sd">        window : np.ndarray or str, optional</span>
 <span class="sd">            A 1D ndarray with length samples_per_average that specifies the</span>
 <span class="sd">            coefficients of the window.  No window is applied if not specified.</span>
 <span class="sd">            If a string is specified, then the window will be obtained from scipy.</span>
 <span class="sd">        **timedata2frf_kwargs : various</span>
 <span class="sd">            Additional keyword arguments that may be passed into the</span>
-<span class="sd">            timedata2frf function in sdynpy.frf.</span>
+<span class="sd">            timedata2frf function in sdynpy.frf.  If method==&#39;LRM&#39;, see also</span>
+<span class="sd">            frf_local_model in sdynpy.lrm for more options.</span>
+
 
 <span class="sd">        Raises</span>
 <span class="sd">        ------</span>
@@ -4422,19 +4915,33 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                            <span class="n">res_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Reference and Response Data should have identical abscissa spacing!&#39;</span><span class="p">)</span>
         <span class="n">dt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">ref_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">))</span>
-        <span class="n">freq</span><span class="p">,</span> <span class="n">frf</span> <span class="o">=</span> <span class="n">timedata2frf</span><span class="p">(</span><span class="n">ref_ord</span><span class="p">,</span> <span class="n">res_ord</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
-                                 <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="o">**</span><span class="n">timedata2frf_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">return_model_data</span><span class="p">:</span>
+            <span class="n">freq</span><span class="p">,</span><span class="n">frf</span><span class="p">,</span><span class="n">model_data</span> <span class="o">=</span> <span class="n">timedata2frf</span><span class="p">(</span><span class="n">ref_ord</span><span class="p">,</span> <span class="n">res_ord</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span>
+                                            <span class="n">overlap</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">return_model_data</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                                            <span class="o">**</span><span class="n">timedata2frf_kwargs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">freq</span><span class="p">,</span><span class="n">frf</span> <span class="o">=</span> <span class="n">timedata2frf</span><span class="p">(</span><span class="n">ref_ord</span><span class="p">,</span> <span class="n">res_ord</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span>
+                                    <span class="n">overlap</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">return_model_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                    <span class="o">**</span><span class="n">timedata2frf_kwargs</span><span class="p">)</span>
         <span class="c1"># Now construct the transfer function array</span>
         <span class="n">coordinate</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">res_data</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
                                    <span class="n">ref_data</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
-                          <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">coordinate</span><span class="p">)</span></div>
+        <span class="k">if</span> <span class="n">return_model_data</span><span class="p">:</span>
+            <span class="n">model_data</span> <span class="o">=</span> <span class="p">(</span><span class="n">model_data</span><span class="p">[</span><span class="s1">&#39;model_selected&#39;</span><span class="p">]</span><span class="o">==</span><span class="nb">len</span><span class="p">(</span><span class="n">model_data</span><span class="p">[</span><span class="s1">&#39;modelset&#39;</span><span class="p">])</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+            <span class="n">model_data</span> <span class="o">=</span> <span class="s1">&#39;Highest order model selected in &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">model_data</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="si">% o</span><span class="s1">f bins.&#39;</span>
+            <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
+                              <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">coordinate</span><span class="p">,</span>
+                              <span class="n">comment1</span><span class="o">=</span><span class="n">model_data</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
+                              <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">coordinate</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="TransferFunctionArray.ifft"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.ifft">[docs]</a>    <span class="k">def</span> <span class="nf">ifft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span> <span class="o">=</span> <span class="s2">&quot;backward&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_irfft_kwargs</span><span class="p">):</span>
+
+<div class="viewcode-block" id="TransferFunctionArray.ifft"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.ifft">[docs]</a>    <span class="k">def</span> <span class="nf">ifft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;backward&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_irfft_kwargs</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Converts frequency response functions to impulse response functions via an</span>
 <span class="sd">        inverse fourier transform.</span>
-<span class="sd">        </span>
+
 <span class="sd">        Paramters</span>
 <span class="sd">        ---------</span>
 <span class="sd">        norm : str, optional</span>
@@ -4445,13 +4952,13 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Raises</span>
 <span class="sd">        ------</span>
 <span class="sd">        Warning</span>
-<span class="sd">            Raised if the transfer function array does not have evenly spaced </span>
-<span class="sd">            frequency data in the 0-maximum frequency range, but appears to have been </span>
+<span class="sd">            Raised if the transfer function array does not have evenly spaced</span>
+<span class="sd">            frequency data in the 0-maximum frequency range, but appears to have been</span>
 <span class="sd">            high pass filtered.</span>
 <span class="sd">        ValueError</span>
-<span class="sd">            Raised if the transfer function array does not have evenly spaced </span>
-<span class="sd">            frequency data in the 0-maximum frequency range and it does not appear </span>
-<span class="sd">            to have been high pass filtered. </span>
+<span class="sd">            Raised if the transfer function array does not have evenly spaced</span>
+<span class="sd">            frequency data in the 0-maximum frequency range and it does not appear</span>
+<span class="sd">            to have been high pass filtered.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -4459,9 +4966,9 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            The impulse response function array computed from the transfer function</span>
 <span class="sd">            array.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        
+
         <span class="n">frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
-        
+
         <span class="c1"># Getting some initial sampling parameters for the transform</span>
         <span class="n">number_spectral_lines</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
         <span class="n">df</span> <span class="o">=</span> <span class="n">frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -4469,71 +4976,76 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">number_lines</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">number_samples</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">start_freq</span> <span class="o">=</span> <span class="n">frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">dt</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-      
+
         <span class="c1"># Determining if the data needs to be zero padded at low frequencies</span>
         <span class="k">if</span> <span class="n">number_spectral_lines</span><span class="o">*</span><span class="mi">2</span> <span class="o">!=</span> <span class="n">number_samples</span> <span class="ow">and</span> <span class="n">start_freq</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The FRFs are missing some frequency data and it is assumed that this is due to some high pass cut-off. The data is being zero padded at low frequencies.&#39;</span><span class="p">)</span>
-            <span class="n">start_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">start_freq</span><span class="o">/</span><span class="n">df</span><span class="p">)</span> <span class="c1"># need to check that this indexing makes sense</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s1">&#39;The FRFs are missing some frequency data&#39;</span>
+                <span class="o">+</span> <span class="s1">&#39; and it is assumed that this is due to some high pass cut-off.&#39;</span>
+                <span class="o">+</span> <span class="s1">&#39; The data is being zero padded at low frequencies.&#39;</span><span class="p">)</span>
+            <span class="n">start_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">start_freq</span><span class="o">/</span><span class="n">df</span><span class="p">)</span>  <span class="c1"># need to check that this indexing makes sense</span>
         <span class="k">elif</span> <span class="n">number_spectral_lines</span><span class="o">*</span><span class="mi">2</span> <span class="o">!=</span> <span class="n">number_samples</span> <span class="ow">and</span> <span class="n">start_freq</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The FRFs are missing some frequency data and it cannot be determined what is missing. The frequency data should be in the 0-Fs/2 range.&#39;</span><span class="p">)</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s1">&#39;The FRFs are missing some frequency data and it cannot be determined what is missing.&#39;</span>
+                <span class="o">+</span> <span class="s1">&#39; The frequency data should be in the 0-Fs/2 range.&#39;</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">start_index</span> <span class="o">=</span> <span class="mi">0</span>
 
-        <span class="c1"># Organizing the FRFs for the ifft, this handles the zero padding if low frequency </span>
+        <span class="c1"># Organizing the FRFs for the ifft, this handles the zero padding if low frequency</span>
         <span class="c1"># data is missing</span>
         <span class="n">frfs_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frfs</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
         <span class="n">frf_ordinate_for_transform</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">number_lines</span><span class="p">,</span> <span class="n">frfs_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">frfs_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">complex128</span><span class="p">)</span>
-        <span class="n">frf_ordinate_for_transform</span><span class="p">[</span><span class="n">start_index</span><span class="p">:,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">frfs_ordinate</span> 
+        <span class="n">frf_ordinate_for_transform</span><span class="p">[</span><span class="n">start_index</span><span class="p">:,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">frfs_ordinate</span>
 
-        <span class="n">irfs</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">frf_ordinate_for_transform</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">n</span> <span class="o">=</span> <span class="n">number_samples</span><span class="p">,</span> <span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_irfft_kwargs</span><span class="p">)</span>
+        <span class="n">irfs</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">frf_ordinate_for_transform</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="n">number_samples</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_irfft_kwargs</span><span class="p">)</span>
 
         <span class="c1"># Building the time vectors</span>
         <span class="n">time_vector</span> <span class="o">=</span> <span class="p">(</span><span class="n">dt</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">number_samples</span><span class="p">))</span> <span class="o">+</span> <span class="n">dt</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">time_vector</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">irfs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">IMPULSE_RESPONSE_FUNCTION</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">irfs</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span></div>
-    
-<div class="viewcode-block" id="TransferFunctionArray.enforce_causality"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.enforce_causality">[docs]</a>    <span class="k">def</span> <span class="nf">enforce_causality</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;exponential_taper&#39;</span><span class="p">,</span>
-                          <span class="n">window_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                          <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">IMPULSE_RESPONSE_FUNCTION</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">irfs</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="TransferFunctionArray.enforce_causality"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.enforce_causality">[docs]</a>    <span class="k">def</span> <span class="nf">enforce_causality</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;exponential_taper&#39;</span><span class="p">,</span>
+                          <span class="n">window_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">end_of_ringdown</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Enforces causality on the frequency response function via a conversion </span>
-<span class="sd">        to a impulse response function, applying a cutoff window, then converting </span>
-<span class="sd">        back to a frequency response function. </span>
+<span class="sd">        Enforces causality on the frequency response function via a conversion</span>
+<span class="sd">        to a impulse response function, applying a cutoff window, then converting</span>
+<span class="sd">        back to a frequency response function.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        method : str</span>
-<span class="sd">            The window type that is applied to the data to enforce causality. </span>
-<span class="sd">            Note that these options are not necessarily traditional windows </span>
+<span class="sd">            The window type that is applied to the data to enforce causality.</span>
+<span class="sd">            Note that these options are not necessarily traditional windows</span>
 <span class="sd">            (used for data processing). The current options are:</span>
-<span class="sd">                - exponential_taper (default) - this applies a exponential taper </span>
-<span class="sd">                to the end of a boxcar window on the IRF. </span>
+<span class="sd">                - exponential_taper (default) - this applies a exponential taper</span>
+<span class="sd">                  to the end of a boxcar window on the IRF.</span>
 <span class="sd">                - boxcar - this applies a boxcar (uniform) window to the IRF</span>
-<span class="sd">                with the cuttoff at a specified sample. </span>
-<span class="sd">                - exponential - this applies an exponential window to the IRF </span>
-<span class="sd">                with the 40 dB down point (of the window) at a specified sample.</span>
-<span class="sd">                Care should be taken when using this window type, since it can </span>
-<span class="sd">                lead to erratic behavior.</span>
+<span class="sd">                  with the cuttoff at a specified sample.</span>
+<span class="sd">                - exponential - this applies an exponential window to the IRF</span>
+<span class="sd">                  with the 40 dB down point (of the window) at a specified sample.</span>
+<span class="sd">                  Care should be taken when using this window type, since it can</span>
+<span class="sd">                  lead to erratic behavior.</span>
 <span class="sd">        window_parameter : int, optional</span>
-<span class="sd">            This is a parameter that defines the window for the causality </span>
+<span class="sd">            This is a parameter that defines the window for the causality</span>
 <span class="sd">            enforcement. Methods exist to define this parameter automatically</span>
 <span class="sd">            if it isn&#39;t provided. The behaviors for the options are:</span>
-<span class="sd">                - boxcar - the window_paramter is the sample after which the </span>
-<span class="sd">                IRF is set to zero. It is the same as the end_of_ringdown</span>
-<span class="sd">                parameter for this window type.</span>
+<span class="sd">                - boxcar - the window_paramter is the sample after which the</span>
+<span class="sd">                  IRF is set to zero. It is the same as the end_of_ringdown</span>
+<span class="sd">                  parameter for this window type.</span>
 <span class="sd">                - exponential - the window_parameter is where the 40 dB down</span>
-<span class="sd">                point is for the window. It is the same as the end_of_ringdown</span>
-<span class="sd">                parameter for this window type. </span>
-<span class="sd">                - exponential_taper - the window_parameter is where the end point </span>
-<span class="sd">                of the window (where the amplitude is 0.001), as defined by the </span>
-<span class="sd">                number of samples after the uniform section of the window.</span>
+<span class="sd">                  point is for the window. It is the same as the end_of_ringdown</span>
+<span class="sd">                  parameter for this window type.</span>
+<span class="sd">                - exponential_taper - the window_parameter is where the end point</span>
+<span class="sd">                  of the window (where the amplitude is 0.001), as defined by the</span>
+<span class="sd">                  number of samples after the uniform section of the window.</span>
 <span class="sd">        end_of_ringdown : int, optional</span>
-<span class="sd">            This is a parameter that defines the end of the uniform section of </span>
+<span class="sd">            This is a parameter that defines the end of the uniform section of</span>
 <span class="sd">            the exponetional_taper window. It is not used for either the boxcar</span>
-<span class="sd">            or exponential window. Methods exist to define this parameter </span>
-<span class="sd">            automatically if it isn&#39;t provided. </span>
-<span class="sd">            </span>
+<span class="sd">            or exponential window. Methods exist to define this parameter</span>
+<span class="sd">            automatically if it isn&#39;t provided.</span>
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        TransferFunctionArray</span>
@@ -4541,44 +5053,44 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
-<span class="sd">        This is a wrapper around the method in the impulse response function class </span>
-<span class="sd">        and it may be wiser to use that function instead. </span>
+<span class="sd">        This is a wrapper around the method in the impulse response function class</span>
+<span class="sd">        and it may be wiser to use that function instead.</span>
 
 <span class="sd">        Although optional, it is best practice for the user to supply a parameter</span>
-<span class="sd">        for the end_of_ringdown variable if the &quot;exponential_taper&quot; method is </span>
-<span class="sd">        being used or a window_parameter if the &quot;exponential&quot; or &quot;boxcar&quot; methods </span>
-<span class="sd">        are being used. The code will attempt to find the end of the ring-down in </span>
-<span class="sd">        the IRF and use use that as the end_of_ringdown parameter for the </span>
-<span class="sd">        &quot;exponential_taper&quot; window or the window_parameter for the exponential and </span>
+<span class="sd">        for the end_of_ringdown variable if the &quot;exponential_taper&quot; method is</span>
+<span class="sd">        being used or a window_parameter if the &quot;exponential&quot; or &quot;boxcar&quot; methods</span>
+<span class="sd">        are being used. The code will attempt to find the end of the ring-down in</span>
+<span class="sd">        the IRF and use use that as the end_of_ringdown parameter for the</span>
+<span class="sd">        &quot;exponential_taper&quot; window or the window_parameter for the exponential and</span>
 <span class="sd">        boxcar windows.</span>
 
-<span class="sd">        It is not suggested that the user provide a window_paramter if the </span>
-<span class="sd">        &quot;exponential_taper&quot; method is being used, since the default is likely the </span>
+<span class="sd">        It is not suggested that the user provide a window_paramter if the</span>
+<span class="sd">        &quot;exponential_taper&quot; method is being used, since the default is likely the</span>
 <span class="sd">        most logical choice.</span>
 
 <span class="sd">        References</span>
 <span class="sd">        ----------</span>
 <span class="sd">        .. [1] Zvonkin, M. (2015). Methods for checking and enforcing physical quality of linear electrical network models</span>
-<span class="sd">               [Masters Theses, Missouri University of Science and Technology], Missouri S&amp;T Scholars&#39; Mine, https://scholarsmine.mst.edu/masters_theses/7490/ </span>
+<span class="sd">               [Masters Theses, Missouri University of Science and Technology], Missouri S&amp;T Scholars&#39; Mine, https://scholarsmine.mst.edu/masters_theses/7490/</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">irfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ifft</span><span class="p">()</span>
-        <span class="n">causal_irfs</span> <span class="o">=</span> <span class="n">irfs</span><span class="o">.</span><span class="n">enforce_causality</span><span class="p">(</span><span class="n">method</span> <span class="o">=</span> <span class="n">method</span><span class="p">,</span>
-                                             <span class="n">window_parameter</span> <span class="o">=</span> <span class="n">window_parameter</span><span class="p">,</span> 
-                                             <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="n">end_of_ringdown</span><span class="p">)</span>
+        <span class="n">causal_irfs</span> <span class="o">=</span> <span class="n">irfs</span><span class="o">.</span><span class="n">enforce_causality</span><span class="p">(</span><span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span>
+                                             <span class="n">window_parameter</span><span class="o">=</span><span class="n">window_parameter</span><span class="p">,</span>
+                                             <span class="n">end_of_ringdown</span><span class="o">=</span><span class="n">end_of_ringdown</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">causal_irfs</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span></div>
 
-<div class="viewcode-block" id="TransferFunctionArray.svd"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.svd">[docs]</a>    <span class="k">def</span> <span class="nf">svd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full_matrices</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">as_matrix</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+<div class="viewcode-block" id="TransferFunctionArray.svd"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.svd">[docs]</a>    <span class="k">def</span> <span class="nf">svd</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">as_matrix</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute the SVD of the provided FRF matrix</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        full_matrices : bool, optional</span>
-<span class="sd">            This is an optional input for np.linalg.svd, the default for this </span>
+<span class="sd">            This is an optional input for np.linalg.svd, the default for this</span>
 <span class="sd">            function is true (which differs from  the np.linalg.svd function).</span>
 <span class="sd">        compute_uv : bool, optional</span>
-<span class="sd">            This is an optional input for np.linalg.svd, the default for this </span>
+<span class="sd">            This is an optional input for np.linalg.svd, the default for this</span>
 <span class="sd">            function is true (which differs from the np.linalg.svd function).</span>
 <span class="sd">        as_matrix : bool, optional</span>
 <span class="sd">            If True, matrices are returned as a SDynPy Matrix class with named</span>
@@ -4587,7 +5099,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        u : ndarray or Matrix</span>
-<span class="sd">            Left hand singular vectors, sized [..., num_responses, num_responses]. </span>
+<span class="sd">            Left hand singular vectors, sized [..., num_responses, num_responses].</span>
 <span class="sd">            Only returned when compute_uv is True.</span>
 <span class="sd">        s : ndarray or Matrix</span>
 <span class="sd">            Singular values, sized [..., num_references]</span>
@@ -4597,22 +5109,22 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">frf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
         <span class="n">frfOrd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">compute_uv</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">compute_uv</span><span class="p">:</span>
             <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">frfOrd</span><span class="p">,</span> <span class="n">full_matrices</span><span class="p">,</span> <span class="n">compute_uv</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">as_matrix</span><span class="p">:</span>
-                <span class="n">u</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">u</span><span class="p">,</span><span class="n">frf</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span>
-                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
-                <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
-                <span class="n">vh</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">vh</span><span class="p">,</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">vh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                            <span class="n">frf</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">,</span>
-                           <span class="p">)</span>
+                <span class="n">u</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">frf</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span>
+                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                <span class="n">vh</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">vh</span><span class="p">,</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">vh</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                            <span class="n">frf</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">,</span>
+                            <span class="p">)</span>
             <span class="k">return</span> <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span>
-        <span class="k">elif</span> <span class="n">compute_uv</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+        <span class="k">else</span><span class="p">:</span>
             <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">frfOrd</span><span class="p">,</span> <span class="n">full_matrices</span><span class="p">,</span> <span class="n">compute_uv</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">as_matrix</span><span class="p">:</span>
-                 <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
-                            <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">(</span><span class="n">s</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                           <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
             <span class="k">return</span> <span class="n">s</span></div>
 
 <div class="viewcode-block" id="TransferFunctionArray.compute_mif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.compute_mif">[docs]</a>    <span class="k">def</span> <span class="nf">compute_mif</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mif_type</span><span class="p">,</span> <span class="o">*</span><span class="n">mif_args</span><span class="p">,</span> <span class="o">**</span><span class="n">mif_kwargs</span><span class="p">):</span>
@@ -4681,7 +5193,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">ordinate</span> <span class="o">=</span> <span class="n">ordinate</span><span class="o">.</span><span class="n">real</span>
         <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">v</span> <span class="o">=</span> <span class="n">vh</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">tracking</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">tracking</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">u_unshuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">u</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
             <span class="n">s_unshuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">s</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
             <span class="n">v_unshuffled</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">v</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
@@ -4805,7 +5317,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">mif_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">output_array</span></div>
 
-<div class="viewcode-block" id="TransferFunctionArray.plot_cond_num"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_cond_num">[docs]</a>    <span class="k">def</span> <span class="nf">plot_cond_num</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">number_retained_values</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="TransferFunctionArray.plot_cond_num"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_cond_num">[docs]</a>    <span class="k">def</span> <span class="nf">plot_cond_num</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plots the condition number of the FRF matrix</span>
 
@@ -4822,40 +5334,40 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="n">s_frf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_matrix</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="n">s_frf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">compute_uv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_matrix</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
         <span class="n">cond_num</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">s_frf</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cond_num</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Unmodified Condition Number&#39;</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cond_num</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Unmodified Condition Number&#39;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">number_retained_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">cutoff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">s_frf</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="n">number_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">number_retained_values</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
+            <span class="n">number_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">number_retained_values</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">number_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                 <span class="n">cutoff</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[:,</span> <span class="n">number_retained_values</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">number_retained_values</span><span class="p">)):</span>
-                    <span class="n">cutoff</span><span class="p">[</span><span class="n">ii</span><span class="p">]</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="p">[</span><span class="n">ii</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> 
-            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">s_frf</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">cutoff</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;Modified Condition Number&#39;</span><span class="p">)</span>
+                    <span class="n">cutoff</span><span class="p">[</span><span class="n">ii</span><span class="p">]</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="p">[</span><span class="n">ii</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">s_frf</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">cutoff</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Modified Condition Number&#39;</span><span class="p">)</span>
             <span class="n">axis</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Condition Number&#39;</span><span class="p">)</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Condition Number of FRF Matrix&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">min_frequency</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span> <span class="o">=</span> <span class="n">min_frequency</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="n">min_frequency</span><span class="p">)</span>
             <span class="n">cond_num</span> <span class="o">=</span> <span class="n">cond_num</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">freq</span><span class="o">-</span><span class="n">min_frequency</span><span class="p">)):]</span>
-        <span class="k">if</span> <span class="n">max_frequency</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span> <span class="o">=</span> <span class="n">max_frequency</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="n">max_frequency</span><span class="p">)</span>
             <span class="n">cond_num</span> <span class="o">=</span> <span class="n">cond_num</span><span class="p">[:</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">freq</span><span class="o">-</span><span class="n">max_frequency</span><span class="p">))]</span>
-        <span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="n">cond_num</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">*</span><span class="mf">1.01</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="n">cond_num</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">*</span><span class="mf">1.01</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span></div>
 
-<div class="viewcode-block" id="TransferFunctionArray.plot_singular_values"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_singular_values">[docs]</a>    <span class="k">def</span> <span class="nf">plot_singular_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rcond</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                             <span class="n">condition_number</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                             <span class="n">number_retained_values</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                             <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                             <span class="n">min_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                             <span class="n">max_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="TransferFunctionArray.plot_singular_values"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_singular_values">[docs]</a>    <span class="k">def</span> <span class="nf">plot_singular_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">condition_number</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">number_retained_values</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">regularization_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">min_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                             <span class="n">max_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the singular values of an FRF matrix with a visualization of the rcond tolerance</span>
 
@@ -4863,70 +5375,73 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        rcond : float or ndarray, optional</span>
 <span class="sd">            Cutoff for small singular values. Implemented such that the cutoff is rcond*</span>
-<span class="sd">            largest_singular_value (the same as np.linalg.pinv). This is to visualize the </span>
-<span class="sd">            effect of rcond and is used for display purposes only. </span>
+<span class="sd">            largest_singular_value (the same as np.linalg.pinv). This is to visualize the</span>
+<span class="sd">            effect of rcond and is used for display purposes only.</span>
 <span class="sd">        condition_number : float or ndarray, optional</span>
 <span class="sd">            Condition number threshold for small singular values. The condition number</span>
-<span class="sd">            is the reciprocal of rcond. This is to visualize the effect of condition </span>
-<span class="sd">            number threshold and is used for display purposes only. </span>
-<span class="sd">        number_retained_values : float or ndarray, optional </span>
-<span class="sd">            Cutoff for small singular values a an integer value of number of values </span>
-<span class="sd">            to retain. This is to visualize the effect of singular value truncation </span>
-<span class="sd">            and is used for display purposes only. </span>
+<span class="sd">            is the reciprocal of rcond. This is to visualize the effect of condition</span>
+<span class="sd">            number threshold and is used for display purposes only.</span>
+<span class="sd">        number_retained_values : float or ndarray, optional</span>
+<span class="sd">            Cutoff for small singular values a an integer value of number of values</span>
+<span class="sd">            to retain. This is to visualize the effect of singular value truncation</span>
+<span class="sd">            and is used for display purposes only.</span>
 <span class="sd">        regularization_parameter: float or ndarray, optional</span>
 <span class="sd">            Regularization parameter to compute the modified singular values. This is</span>
 <span class="sd">            to visualize the effect of Tikhonov regularization and is used for display</span>
-<span class="sd">            purposes only. </span>
+<span class="sd">            purposes only.</span>
 <span class="sd">        min_frequency : float, optional</span>
 <span class="sd">            Minimum frequency to plot</span>
 <span class="sd">        max_frequency : float, optional</span>
 <span class="sd">            Maximum frequency to plot</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">freq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]</span>
-        <span class="n">s_frf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">compute_uv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_matrix</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="n">s_frf</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">compute_uv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">as_matrix</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
         <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">s_frf</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">rcond</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">cutoff</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">rcond</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">condition_number</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">threshold</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">condition_number</span>
             <span class="n">cutoff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">s_frf</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">threshold</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
                 <span class="n">number_values_above_cutoff</span> <span class="o">=</span> <span class="p">(</span><span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="p">:]</span> <span class="o">&gt;</span> <span class="n">threshold</span><span class="p">[</span><span class="n">ii</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-                <span class="n">cutoff</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_values_above_cutoff</span><span class="p">:]</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_values_above_cutoff</span><span class="p">:]</span> 
+                <span class="n">cutoff</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_values_above_cutoff</span><span class="p">:]</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_values_above_cutoff</span><span class="p">:]</span>
             <span class="n">cutoff</span><span class="p">[</span><span class="n">cutoff</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">number_retained_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">cutoff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">s_frf</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
-            <span class="n">number_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">number_retained_values</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
+            <span class="n">number_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">number_retained_values</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">number_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
                 <span class="n">cutoff</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[:,</span> <span class="n">number_retained_values</span><span class="p">:]</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">cutoff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">s_frf</span><span class="p">)</span>
                 <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">number_retained_values</span><span class="p">)):</span>
-                    <span class="n">cutoff</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="p">[</span><span class="n">ii</span><span class="p">]:]</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="p">[</span><span class="n">ii</span><span class="p">]:]</span> 
+                    <span class="n">cutoff</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="p">[</span><span class="n">ii</span><span class="p">]:]</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">ii</span><span class="p">,</span> <span class="n">number_retained_values</span><span class="p">[</span><span class="n">ii</span><span class="p">]:]</span>
             <span class="n">cutoff</span><span class="p">[</span><span class="n">cutoff</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">cutoff</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dashed&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">regularization_parameter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">s_modified</span> <span class="o">=</span> <span class="n">compute_tikhonov_modified_singular_values</span><span class="p">(</span><span class="n">s_frf</span><span class="p">,</span> <span class="n">regularization_parameter</span><span class="p">)</span>
             <span class="n">figure</span><span class="o">.</span><span class="n">gca</span><span class="p">()</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">s_modified</span><span class="p">,</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">linewidth</span> <span class="o">=</span> <span class="mi">4</span><span class="p">)</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">semilogy</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">s_modified</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;dotted&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Singular Values&#39;</span><span class="p">)</span>
         <span class="n">axis</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Singular Values of FRF Matrix&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">min_frequency</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span> <span class="o">=</span> <span class="n">min_frequency</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="n">min_frequency</span><span class="p">)</span>
             <span class="n">s_frf</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">freq</span><span class="o">-</span><span class="n">min_frequency</span><span class="p">)):,</span> <span class="p">:]</span>
-        <span class="k">if</span> <span class="n">max_frequency</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span> <span class="o">=</span> <span class="n">max_frequency</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">right</span><span class="o">=</span><span class="n">max_frequency</span><span class="p">)</span>
             <span class="n">s_frf</span> <span class="o">=</span> <span class="n">s_frf</span><span class="p">[:</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">freq</span><span class="o">-</span><span class="n">max_frequency</span><span class="p">)),</span> <span class="p">:]</span>
-        <span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span> <span class="o">=</span> <span class="n">s_frf</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">*</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">top</span> <span class="o">=</span> <span class="n">s_frf</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">*</span><span class="mf">1.1</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="n">s_frf</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">*</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="n">s_frf</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">*</span><span class="mf">1.1</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span></div>
 
-<div class="viewcode-block" id="TransferFunctionArray.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">one_axis</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{}):</span>
+<div class="viewcode-block" id="TransferFunctionArray.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">one_axis</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">part</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="o">=</span><span class="p">{},</span>
+             <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
+             <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_type</span> <span class="o">=</span> <span class="s1">&#39;vline&#39;</span><span class="p">,</span>
+             <span class="n">abscissa_marker_plot_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the transfer functions</span>
 
@@ -4934,14 +5449,33 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        one_axis : bool, optional</span>
 <span class="sd">            Set to True to plot all data on one axis.  Set to False to plot</span>
-<span class="sd">            data on multiple subplots.  one_axis can also be set to a </span>
+<span class="sd">            data on multiple subplots.  one_axis can also be set to a</span>
 <span class="sd">            matplotlib axis to plot data on an existing axis.  The default is</span>
 <span class="sd">            True.</span>
+<span class="sd">        part : str, optional</span>
+<span class="sd">            The part of the FRF to plot.  This can be, &#39;real&#39;, &#39;imag&#39; or</span>
+<span class="sd">            &#39;imaginary&#39;, &#39;mag&#39; or &#39;magnitude&#39;, or &#39;phase&#39;.  If not specified,</span>
+<span class="sd">            magnitude and phase will be plotted if `one_axis` is True, and </span>
+<span class="sd">            magnitude will be plotted if `one_axis` is False.</span>
 <span class="sd">        subplots_kwargs : dict, optional</span>
 <span class="sd">            Keywords passed to the matplotlib subplots function to create the</span>
 <span class="sd">            figure and axes. The default is {}.</span>
 <span class="sd">        plot_kwargs : dict, optional</span>
 <span class="sd">            Keywords passed to the matplotlib plot function. The default is {}.</span>
+<span class="sd">        abscissa_markers : ndarray, optional</span>
+<span class="sd">            Array containing abscissa values to mark on the plot to denote</span>
+<span class="sd">            significant events.</span>
+<span class="sd">        abscissa_marker_labels : str or ndarray</span>
+<span class="sd">            Array of strings to label the abscissa_markers with, or</span>
+<span class="sd">            alternatively a format string that accepts index and abscissa</span>
+<span class="sd">            inputs (e.g. &#39;{index:}: {abscissa:0.2f}&#39;).  By default no label</span>
+<span class="sd">            will be applied.</span>
+<span class="sd">        abscissa_marker_type : str</span>
+<span class="sd">            The type of marker to use.  This can either be the string &#39;vline&#39;</span>
+<span class="sd">            or a valid matplotlib symbol specifier (e.g. &#39;o&#39;, &#39;x&#39;, &#39;.&#39;).</span>
+<span class="sd">        abscissa_marker_plot_kwargs : dict</span>
+<span class="sd">            Additional keyword arguments used when plotting the abscissa label</span>
+<span class="sd">            markers.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -4949,33 +5483,261 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">             On which the data were plotted</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">abscissa_marker_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">abscissa_markers</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">abscissa_marker_labels</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">index</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">abscissa</span> <span class="o">=</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">)]</span>
+                
+        <span class="n">part_fns</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;imag&#39;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span>
+                    <span class="s1">&#39;real&#39;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span>
+                    <span class="s1">&#39;mag&#39;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">,</span>
+                    <span class="s1">&#39;magnitude&#39;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">,</span>
+                    <span class="s1">&#39;phase&#39;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span>
+                    <span class="s1">&#39;imaginary&#39;</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">}</span>
+        <span class="n">part_labels</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;imag&#39;</span><span class="p">:</span><span class="s1">&#39;Imaginary&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;real&#39;</span><span class="p">:</span><span class="s1">&#39;Real&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;mag&#39;</span><span class="p">:</span><span class="s1">&#39;Magnitude&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;magnitude&#39;</span><span class="p">:</span><span class="s1">&#39;Magnitude&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;phase&#39;</span><span class="p">:</span><span class="s1">&#39;Phase&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;imaginary&#39;</span><span class="p">:</span><span class="s1">&#39;Imaginary&#39;</span><span class="p">}</span>
+        <span class="n">part_yscale</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;imag&#39;</span><span class="p">:</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;real&#39;</span><span class="p">:</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;mag&#39;</span><span class="p">:</span><span class="s1">&#39;log&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;magnitude&#39;</span><span class="p">:</span><span class="s1">&#39;log&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;phase&#39;</span><span class="p">:</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span>
+                       <span class="s1">&#39;imaginary&#39;</span><span class="p">:</span><span class="s1">&#39;linear&#39;</span><span class="p">}</span>
         <span class="k">if</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
-            <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase&#39;</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Amplitude&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">part</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase&#39;</span><span class="p">)</span>
+                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Amplitude&#39;</span><span class="p">)</span>
+                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency&#39;</span><span class="p">)</span>
+                
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">part_fns</span><span class="p">[</span><span class="n">part</span><span class="p">](</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="n">axis</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">part_yscale</span><span class="p">[</span><span class="n">part</span><span class="p">])</span>
+                <span class="n">axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">part_labels</span><span class="p">[</span><span class="n">part</span><span class="p">])</span>
+                <span class="n">axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
             <span class="n">ncols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span>
             <span class="n">nrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">/</span> <span class="n">ncols</span><span class="p">))</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">function</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">())):</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">function</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()]))</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">part</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">function</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">())):</span>
+                    <span class="n">lines</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">function</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()]))</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                                <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                                <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                                <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                                <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                                <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                    <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">function</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">())):</span>
+                    <span class="n">lines</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">part_fns</span><span class="p">[</span><span class="n">part</span><span class="p">](</span><span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">function</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()]))</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">part_yscale</span><span class="p">[</span><span class="n">part</span><span class="p">])</span>
+                    <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                                <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                                <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                                <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                                <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                                <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                    <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:]:</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">axis</span> <span class="o">=</span> <span class="n">one_axis</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">part</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                                <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                                <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                                <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                                <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                                <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                    <span class="n">axis</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                                <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                                <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                                <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                                <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                                <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                                <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                    <span class="n">axis</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+                    <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                                <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                                <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                                <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                                <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                                <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                                <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                    <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">part_fns</span><span class="p">[</span><span class="n">part</span><span class="p">](</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">axis</span></div>
 
+<div class="viewcode-block" id="TransferFunctionArray.plot_with_coherence"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence">[docs]</a>    <span class="k">def</span> <span class="nf">plot_with_coherence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coherence</span><span class="p">,</span> <span class="n">part</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="o">=</span><span class="p">{},</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{}):</span>
+        <span class="n">axes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">one_axis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">part</span><span class="o">=</span><span class="n">part</span><span class="p">,</span><span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="n">subplots_kwargs</span><span class="p">,</span>
+                         <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">plot_kwargs</span><span class="p">)</span>
+        <span class="c1"># Get the corresponding coherences</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">coherence</span><span class="p">,</span><span class="n">CoherenceArray</span><span class="p">):</span>
+            <span class="n">coherence</span> <span class="o">=</span> <span class="n">coherence</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">coherence</span><span class="p">,</span><span class="n">MultipleCoherenceArray</span><span class="p">):</span>
+            <span class="n">coherence</span> <span class="o">=</span> <span class="n">coherence</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="mi">1</span><span class="p">]]</span>
+        <span class="n">coh_axes</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">coh</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span><span class="n">coherence</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
+            <span class="n">coh_ax</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
+            <span class="n">coh_ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">coh</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">coh</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;axes.prop_cycle&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">by_key</span><span class="p">()[</span><span class="s1">&#39;color&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span><span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">coh_ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;linear&#39;</span><span class="p">)</span>
+            <span class="n">coh_ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">1.05</span><span class="p">])</span>
+            <span class="n">coh_axes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coh_ax</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">axes</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">coh_axes</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span></div>
+
 <div class="viewcode-block" id="TransferFunctionArray.delay_response"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.delay_response">[docs]</a>    <span class="k">def</span> <span class="nf">delay_response</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dt</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Adjusts the FRF phases as if the response had been shifted `dt` in time</span>
@@ -5002,8 +5764,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span>
 
-
-<div class="viewcode-block" id="TransferFunctionArray.interpolate_by_zero_pad"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.interpolate_by_zero_pad">[docs]</a>    <span class="k">def</span> <span class="nf">interpolate_by_zero_pad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">irf_padded_length</span><span class="p">,</span> <span class="n">return_irf</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="TransferFunctionArray.interpolate_by_zero_pad"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.interpolate_by_zero_pad">[docs]</a>    <span class="k">def</span> <span class="nf">interpolate_by_zero_pad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">irf_padded_length</span><span class="p">,</span> <span class="n">return_irf</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Interpolates a transfer function by zero padding or truncating its</span>
 <span class="sd">        impulse response</span>
@@ -5021,7 +5782,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        -------</span>
 <span class="sd">        TransferFunctionArray or ImpulseResponseFunctionArray:</span>
 <span class="sd">            Transfer function array with appropriately spaced abscissa</span>
-<span class="sd">            </span>
+
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
 <span class="sd">        This function will automatically set the last frequency line of the</span>
@@ -5038,12 +5799,12 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">irf</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">frf</span> <span class="o">=</span> <span class="n">irf</span><span class="o">.</span><span class="n">fft</span><span class="p">()</span>
-            <span class="n">frf</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">frf</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
             <span class="k">return</span> <span class="n">frf</span></div>
 
 <div class="viewcode-block" id="TransferFunctionArray.substructure_by_constraint_matrix"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.substructure_by_constraint_matrix">[docs]</a>    <span class="k">def</span> <span class="nf">substructure_by_constraint_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dofs</span><span class="p">,</span> <span class="n">constraint_matrix</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Performs frequency based substructuring using the </span>
+<span class="sd">        Performs frequency based substructuring using the</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -5146,7 +5907,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="n">obj</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nelements</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">buffer</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">order</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">obj</span>
-    
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">function_type</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -5154,11 +5915,11 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">IMPULSE_RESPONSE_FUNCTION</span>
 
-<div class="viewcode-block" id="ImpulseResponseFunctionArray.fft"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.fft">[docs]</a>    <span class="k">def</span> <span class="nf">fft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span> <span class="o">=</span> <span class="s1">&#39;backward&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_rfft_kwargs</span><span class="p">):</span>
+<div class="viewcode-block" id="ImpulseResponseFunctionArray.fft"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.fft">[docs]</a>    <span class="k">def</span> <span class="nf">fft</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;backward&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_rfft_kwargs</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Converts the impulse response function to a frequency response function </span>
+<span class="sd">        Converts the impulse response function to a frequency response function</span>
 <span class="sd">        using the fft function.</span>
-<span class="sd">        </span>
+
 <span class="sd">        Paramters</span>
 <span class="sd">        ---------</span>
 <span class="sd">        norm : str, optional</span>
@@ -5178,86 +5939,87 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
         <span class="c1"># Getting sampling parameters for the fft</span>
         <span class="n">number_samples</span> <span class="o">=</span> <span class="n">irf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">dt</span> <span class="o">=</span> <span class="n">irfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">irfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">dt</span> <span class="o">=</span> <span class="n">irfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">irfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 
         <span class="c1"># Doing the fft</span>
-        <span class="n">frf_ordinate</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">irf_ordinate</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">norm</span> <span class="o">=</span> <span class="n">norm</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_rfft_kwargs</span><span class="p">)</span>
-        <span class="n">freq_vector</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">number_samples</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span> 
+        <span class="n">frf_ordinate</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">irf_ordinate</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">norm</span><span class="p">,</span> <span class="o">**</span><span class="n">scipy_rfft_kwargs</span><span class="p">)</span>
+        <span class="n">freq_vector</span> <span class="o">=</span> <span class="n">scipyfft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">number_samples</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
 
         <span class="c1"># Broadcasting the frequency vector to the correct size</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">freq_vector</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">frf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span> 
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">abscissa</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span>
                           <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">frf_ordinate</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">irfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="ImpulseResponseFunctionArray.find_end_of_ringdown"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.find_end_of_ringdown">[docs]</a>    <span class="k">def</span> <span class="nf">find_end_of_ringdown</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Finds the end of the ringdown in a impulse response function (IRF).</span>
-<span class="sd">        </span>
-<span class="sd">        It does this by smoothing the IRF via a moving average filter, then finding </span>
-<span class="sd">        the index of the minimum of the smoothed IRF (for each response/reference </span>
-<span class="sd">        pair). The &quot;end of ringdown&quot; is defined as the median of the possible </span>
+
+<span class="sd">        It does this by smoothing the IRF via a moving average filter, then finding</span>
+<span class="sd">        the index of the minimum of the smoothed IRF (for each response/reference</span>
+<span class="sd">        pair). The &quot;end of ringdown&quot; is defined as the median of the possible</span>
 <span class="sd">        indices.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        end_of_ringdow : int</span>
-<span class="sd">            Index that represents the end of the ringdown for the supplied IRFs.  </span>
+<span class="sd">            Index that represents the end of the ringdown for the supplied IRFs.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">irf_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
 
         <span class="c1"># Start off by performing a moving average on the IRF to make</span>
-                <span class="c1"># it easier to find the end of the ring down</span>
+        <span class="c1"># it easier to find the end of the ring down</span>
         <span class="n">moving_average_kernel_length</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">irf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">5</span><span class="p">)</span>
-        <span class="n">moving_average_kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">moving_average_kernel_length</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="p">(</span><span class="n">moving_average_kernel_length</span><span class="p">,</span> <span class="n">irf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">irf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
-        <span class="n">irf_moving_average</span> <span class="o">=</span> <span class="n">convolve</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">absolute</span><span class="p">(</span><span class="n">irf_ordinate</span><span class="p">),</span> <span class="n">moving_average_kernel</span><span class="p">,</span> <span class="n">mode</span> <span class="o">=</span> <span class="s1">&#39;same&#39;</span><span class="p">)</span>
-                
+        <span class="n">moving_average_kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">moving_average_kernel_length</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
+                                                <span class="p">(</span><span class="n">moving_average_kernel_length</span><span class="p">,</span> <span class="n">irf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">irf_ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
+        <span class="n">irf_moving_average</span> <span class="o">=</span> <span class="n">convolve</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">absolute</span><span class="p">(</span><span class="n">irf_ordinate</span><span class="p">),</span> <span class="n">moving_average_kernel</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
+
         <span class="c1"># Find where the smoothed version of the IRF is at a minimum and</span>
         <span class="c1"># then using the median index (by channel) as the window_parameter</span>
-        <span class="n">min_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">irf_moving_average</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">min_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">irf_moving_average</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
         <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">min_index</span><span class="p">))</span>
 
         <span class="k">return</span> <span class="n">end_of_ringdown</span></div>
 
-<div class="viewcode-block" id="ImpulseResponseFunctionArray.enforce_causality"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.enforce_causality">[docs]</a>    <span class="k">def</span> <span class="nf">enforce_causality</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;exponential_taper&#39;</span><span class="p">,</span>
-                          <span class="n">window_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
-                          <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="ImpulseResponseFunctionArray.enforce_causality"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.enforce_causality">[docs]</a>    <span class="k">def</span> <span class="nf">enforce_causality</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;exponential_taper&#39;</span><span class="p">,</span>
+                          <span class="n">window_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">end_of_ringdown</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Enforces causality on the impulse response function via a cutoff</span>
-<span class="sd">        of some sort. </span>
+<span class="sd">        of some sort.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        method : str</span>
-<span class="sd">            The window type that is applied to the data to enforce causality. </span>
-<span class="sd">            Note that these options are not necessarily traditional windows </span>
+<span class="sd">            The window type that is applied to the data to enforce causality.</span>
+<span class="sd">            Note that these options are not necessarily traditional windows</span>
 <span class="sd">            (used for data processing). The current options are:</span>
-<span class="sd">                - exponential_taper (default) - this applies a exponential taper </span>
-<span class="sd">                to the end of a boxcar window on the IRF. </span>
+<span class="sd">                - exponential_taper (default) - this applies a exponential taper</span>
+<span class="sd">                to the end of a boxcar window on the IRF.</span>
 <span class="sd">                - boxcar - this applies a boxcar (uniform) window to the IRF</span>
-<span class="sd">                with the cuttoff at a specified sample. </span>
-<span class="sd">                - exponential - this applies an exponential window to the IRF </span>
+<span class="sd">                with the cuttoff at a specified sample.</span>
+<span class="sd">                - exponential - this applies an exponential window to the IRF</span>
 <span class="sd">                with the 40 dB down point (of the window) at a specified sample.</span>
-<span class="sd">                Care should be taken when using this window type, since it can </span>
+<span class="sd">                Care should be taken when using this window type, since it can</span>
 <span class="sd">                lead to erratic behavior.</span>
 <span class="sd">        window_parameter : int, optional</span>
-<span class="sd">            This is a parameter that defines the window for the causality </span>
+<span class="sd">            This is a parameter that defines the window for the causality</span>
 <span class="sd">            enforcement. Methods exist to define this parameter automatically</span>
 <span class="sd">            if it isn&#39;t provided. The behaviors for the options are:</span>
-<span class="sd">                - boxcar - the window_paramter is the sample after which the </span>
+<span class="sd">                - boxcar - the window_paramter is the sample after which the</span>
 <span class="sd">                IRF is set to zero. It is the same as the end_of_ringdown</span>
 <span class="sd">                parameter for this window type.</span>
 <span class="sd">                - exponential - the window_parameter is where the 40 dB down</span>
 <span class="sd">                point is for the window. It is the same as the end_of_ringdown</span>
-<span class="sd">                parameter for this window type. </span>
-<span class="sd">                - exponential_taper - the window_parameter is where the end point </span>
-<span class="sd">                of the window (where the amplitude is 0.001), as defined by the </span>
+<span class="sd">                parameter for this window type.</span>
+<span class="sd">                - exponential_taper - the window_parameter is where the end point</span>
+<span class="sd">                of the window (where the amplitude is 0.001), as defined by the</span>
 <span class="sd">                number of samples after the uniform section of the window.</span>
 <span class="sd">        end_of_ringdown : int, optional</span>
-<span class="sd">            This is a parameter that defines the end of the uniform section of </span>
+<span class="sd">            This is a parameter that defines the end of the uniform section of</span>
 <span class="sd">            the exponetional_taper window. It is not used for either the boxcar</span>
-<span class="sd">            or exponential window. Methods exist to define this parameter </span>
-<span class="sd">            automatically if it isn&#39;t provided. </span>
+<span class="sd">            or exponential window. Methods exist to define this parameter</span>
+<span class="sd">            automatically if it isn&#39;t provided.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -5267,26 +6029,26 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
 <span class="sd">        Although optional, it is best practice for the user to supply a parameter</span>
-<span class="sd">        for the end_of_ringdown variable if the &quot;exponential_taper&quot; method is </span>
-<span class="sd">        being used or a window_parameter if the &quot;exponential&quot; or &quot;boxcar&quot; methods </span>
-<span class="sd">        are being used. The code will attempt to find the end of the ring-down in </span>
-<span class="sd">        the IRF and use use that as the end_of_ringdown parameter for the </span>
-<span class="sd">        &quot;exponential_taper&quot; window or the window_parameter for the exponential and </span>
+<span class="sd">        for the end_of_ringdown variable if the &quot;exponential_taper&quot; method is</span>
+<span class="sd">        being used or a window_parameter if the &quot;exponential&quot; or &quot;boxcar&quot; methods</span>
+<span class="sd">        are being used. The code will attempt to find the end of the ring-down in</span>
+<span class="sd">        the IRF and use use that as the end_of_ringdown parameter for the</span>
+<span class="sd">        &quot;exponential_taper&quot; window or the window_parameter for the exponential and</span>
 <span class="sd">        boxcar windows.</span>
 
-<span class="sd">        It is not suggested that the user provide a window_paramter if the </span>
-<span class="sd">        &quot;exponential_taper&quot; method is being used, since the default is likely the </span>
+<span class="sd">        It is not suggested that the user provide a window_paramter if the</span>
+<span class="sd">        &quot;exponential_taper&quot; method is being used, since the default is likely the</span>
 <span class="sd">        most logical choice.</span>
 
 <span class="sd">        References</span>
 <span class="sd">        ----------</span>
 <span class="sd">        .. [1] Zvonkin, M. (2015). Methods for checking and enforcing physical quality of linear electrical network models</span>
-<span class="sd">               [Masters Theses, Missouri University of Science and Technology], Missouri S&amp;T Scholars&#39; Mine, https://scholarsmine.mst.edu/masters_theses/7490/ </span>
+<span class="sd">               [Masters Theses, Missouri University of Science and Technology], Missouri S&amp;T Scholars&#39; Mine, https://scholarsmine.mst.edu/masters_theses/7490/</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Organizing the IRFs and pulling the ordinate out</span>
         <span class="n">irfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
         <span class="n">irf_ord</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">irfs</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        
+
         <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;exponential_taper&#39;</span> <span class="ow">and</span> <span class="n">end_of_ringdown</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">find_end_of_ringdown</span><span class="p">()</span>
 
@@ -5304,11 +6066,11 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="n">window_parameter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">window_parameter</span> <span class="o">=</span> <span class="n">window_length</span>
             <span class="n">window_tau</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">window_parameter</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">0.001</span><span class="p">)</span>
-            <span class="n">window</span><span class="p">[</span><span class="n">end_of_ringdown</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">window_length</span><span class="p">)]</span> <span class="o">=</span> <span class="n">exponential</span><span class="p">(</span><span class="n">window_length</span><span class="p">,</span> <span class="n">center</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">window_tau</span><span class="p">,</span> <span class="n">sym</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+            <span class="n">window</span><span class="p">[</span><span class="n">end_of_ringdown</span><span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">window_length</span><span class="p">)]</span> <span class="o">=</span> <span class="n">exponential</span><span class="p">(</span><span class="n">window_length</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="n">window_tau</span><span class="p">,</span> <span class="n">sym</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;exponential&#39;</span><span class="p">:</span>
             <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="n">window_parameter</span>
             <span class="n">window_tau</span> <span class="o">=</span> <span class="n">window_parameter</span><span class="o">*</span><span class="mf">8.69</span><span class="o">/</span><span class="mi">20</span>
-            <span class="n">window</span> <span class="o">=</span> <span class="n">exponential</span><span class="p">(</span><span class="n">irf_ord</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">center</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">tau</span> <span class="o">=</span> <span class="n">window_tau</span><span class="p">,</span> <span class="n">sym</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+            <span class="n">window</span> <span class="o">=</span> <span class="n">exponential</span><span class="p">(</span><span class="n">irf_ord</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="n">window_tau</span><span class="p">,</span> <span class="n">sym</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;boxcar&#39;</span><span class="p">:</span>
             <span class="n">end_of_ringdown</span> <span class="o">=</span> <span class="n">window_parameter</span>
             <span class="n">window</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">irf_ord</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
@@ -5316,7 +6078,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
         <span class="c1"># Setting up the time reversal window so the non-causal portion of the IRF can be added back to the IRFs</span>
         <span class="n">time_reversal_window</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">irf_ord</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="n">window</span>
-        <span class="c1">#time_reversal_window[:window_parameter] = 0, might need this if using an exponential window</span>
+        <span class="c1"># time_reversal_window[:window_parameter] = 0, might need this if using an exponential window</span>
 
         <span class="n">method_statement_start</span> <span class="o">=</span> <span class="s1">&#39;Causality is being enforced using &#39;</span>
         <span class="n">method_statement_middle</span> <span class="o">=</span> <span class="s1">&#39; method with a end_of_ringdown of &#39;</span>
@@ -5332,6 +6094,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
         <span class="k">return</span> <span class="n">irfs_causal</span></div></div>
 
+
 <div class="viewcode-block" id="TransmissibilityArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.TransmissibilityArray.html#sdynpy.core.sdynpy_data.TransmissibilityArray">[docs]</a><span class="k">class</span> <span class="nc">TransmissibilityArray</span><span class="p">(</span><span class="n">NDDataArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data array used to store transmissibility data&quot;&quot;&quot;</span>
     <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nelements</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@@ -5361,8 +6124,76 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the function type of the data array</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span></div>
+        <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span>
+
+<div class="viewcode-block" id="CoherenceArray.from_time_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html#sdynpy.core.sdynpy_data.CoherenceArray.from_time_data">[docs]</a>    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">from_time_data</span><span class="p">(</span><span class="n">response_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span><span class="p">,</span>
+                       <span class="n">samples_per_average</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">overlap</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
+                       <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,)),</span>
+                       <span class="n">reference_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Computes coherence from reference and response time histories</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        response_data : TimeHistoryArray</span>
+<span class="sd">            Time data to be used as responses</span>
+<span class="sd">        samples_per_average : int, optional</span>
+<span class="sd">            Number of samples used to split up the signals into averages.  The</span>
+<span class="sd">            default is None, meaning the data is treated as a single measurement</span>
+<span class="sd">            frame.</span>
+<span class="sd">        overlap : float, optional</span>
+<span class="sd">            The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).</span>
+<span class="sd">            The default is 0.0, meaning no overlap is used.</span>
+<span class="sd">        window : np.ndarray or str, optional</span>
+<span class="sd">            A 1D ndarray with length samples_per_average that specifies the</span>
+<span class="sd">            coefficients of the window.  A Hann window is applied if not specified.</span>
+<span class="sd">            If a string is specified, then the window will be obtained from scipy.</span>
+<span class="sd">        reference_data : TimeHistoryArray</span>
+<span class="sd">            Time data to be used as reference.  If not specified, the response</span>
+<span class="sd">            data will be used as references, resulting in a square coherence matrix.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if reference and response functions do not have consistent</span>
+<span class="sd">            abscissa</span>
 
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        PowerSpectralDensityArray</span>
+<span class="sd">            A PSD array computed from the specified reference and</span>
+<span class="sd">            response signals.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">reference_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">reference_data</span> <span class="o">=</span> <span class="n">response_data</span>
+        <span class="n">ref_data</span> <span class="o">=</span> <span class="n">reference_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">res_data</span> <span class="o">=</span> <span class="n">response_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">ref_ord</span> <span class="o">=</span> <span class="n">ref_data</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="n">res_ord</span> <span class="o">=</span> <span class="n">res_data</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="k">if</span> <span class="p">((</span><span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ref_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                           <span class="n">res_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">))</span>
+            <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ref_data</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span><span class="n">res_data</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Reference and Response Data should have identical abscissa!&#39;</span><span class="p">)</span>
+        <span class="n">dt</span> <span class="o">=</span> <span class="n">res_data</span><span class="o">.</span><span class="n">abscissa_spacing</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">cpsd</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">res_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                           <span class="n">window</span><span class="p">,</span> <span class="n">reference_signals</span> <span class="o">=</span> <span class="n">ref_ord</span><span class="p">)</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">res_asds</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">res_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                           <span class="n">window</span><span class="p">,</span> <span class="n">only_asds</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">ref_asds</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">ref_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                               <span class="n">window</span><span class="p">,</span> <span class="n">only_asds</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="n">num</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cpsd</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
+        <span class="n">den</span> <span class="o">=</span> <span class="n">res_asds</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">ref_asds</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+        <span class="n">den</span><span class="p">[</span><span class="n">den</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># Set to 1 if denominator is zero</span>
+        <span class="n">coh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">num</span><span class="o">/</span><span class="n">den</span><span class="p">)</span>
+        <span class="n">freq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">cpsd</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">df</span>
+        <span class="c1"># Now construct the transfer function array</span>
+        <span class="n">coordinate</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">res_data</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+                                   <span class="n">ref_data</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span>
+                          <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">coh</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">coordinate</span><span class="p">)</span></div></div>
 
 <div class="viewcode-block" id="MultipleCoherenceArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html#sdynpy.core.sdynpy_data.MultipleCoherenceArray">[docs]</a><span class="k">class</span> <span class="nc">MultipleCoherenceArray</span><span class="p">(</span><span class="n">NDDataArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Data array used to store coherence data&quot;&quot;&quot;</span>
@@ -5376,7 +6207,74 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the function type of the data array</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span></div>
+        <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span>
+    
+<div class="viewcode-block" id="MultipleCoherenceArray.from_time_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html#sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data">[docs]</a>    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">from_time_data</span><span class="p">(</span><span class="n">response_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span><span class="p">,</span>
+                       <span class="n">samples_per_average</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">overlap</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
+                       <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,)),</span>
+                       <span class="n">reference_data</span><span class="p">:</span> <span class="n">TimeHistoryArray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Computes coherence from reference and response time histories</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        response_data : TimeHistoryArray</span>
+<span class="sd">            Time data to be used as responses</span>
+<span class="sd">        samples_per_average : int, optional</span>
+<span class="sd">            Number of samples used to split up the signals into averages.  The</span>
+<span class="sd">            default is None, meaning the data is treated as a single measurement</span>
+<span class="sd">            frame.</span>
+<span class="sd">        overlap : float, optional</span>
+<span class="sd">            The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).</span>
+<span class="sd">            The default is 0.0, meaning no overlap is used.</span>
+<span class="sd">        window : np.ndarray or str, optional</span>
+<span class="sd">            A 1D ndarray with length samples_per_average that specifies the</span>
+<span class="sd">            coefficients of the window.  A Hann window is applied if not specified.</span>
+<span class="sd">            If a string is specified, then the window will be obtained from scipy.</span>
+<span class="sd">        reference_data : TimeHistoryArray</span>
+<span class="sd">            Time data to be used as reference.  If not specified, the response</span>
+<span class="sd">            data will be used as references, resulting in a square coherence matrix.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if reference and response functions do not have consistent</span>
+<span class="sd">            abscissa</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        PowerSpectralDensityArray</span>
+<span class="sd">            A PSD array computed from the specified reference and</span>
+<span class="sd">            response signals.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">reference_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">reference_data</span> <span class="o">=</span> <span class="n">response_data</span>
+        <span class="n">ref_data</span> <span class="o">=</span> <span class="n">reference_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">res_data</span> <span class="o">=</span> <span class="n">response_data</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="n">ref_ord</span> <span class="o">=</span> <span class="n">ref_data</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="n">res_ord</span> <span class="o">=</span> <span class="n">res_data</span><span class="o">.</span><span class="n">ordinate</span>
+        <span class="k">if</span> <span class="p">((</span><span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ref_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                           <span class="n">res_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">))</span>
+            <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">ref_data</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">,</span><span class="n">res_data</span><span class="o">.</span><span class="n">abscissa_spacing</span><span class="p">))):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Reference and Response Data should have identical abscissa!&#39;</span><span class="p">)</span>
+        <span class="n">dt</span> <span class="o">=</span> <span class="n">res_data</span><span class="o">.</span><span class="n">abscissa_spacing</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">cross_cpsd</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">res_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                           <span class="n">window</span><span class="p">,</span> <span class="n">reference_signals</span> <span class="o">=</span> <span class="n">ref_ord</span><span class="p">)</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">res_apsd</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">res_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                              <span class="n">window</span><span class="p">,</span><span class="n">only_asds</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">df</span><span class="p">,</span> <span class="n">ref_cpsd</span> <span class="o">=</span> <span class="n">sp_cpsd</span><span class="p">(</span><span class="n">ref_ord</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">dt</span><span class="p">,</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span>
+                               <span class="n">window</span><span class="p">)</span>
+        
+        <span class="n">num</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;fij,fji-&gt;fi&#39;</span><span class="p">,</span> <span class="n">cross_cpsd</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">ref_cpsd</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">cross_cpsd</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
+        <span class="n">den</span> <span class="o">=</span> <span class="n">res_apsd</span>
+        <span class="n">den</span><span class="p">[</span><span class="n">den</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># Set to 1 if denominator is zero</span>
+        <span class="n">mcoh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">num</span><span class="o">/</span><span class="n">den</span><span class="p">)</span>
+        <span class="n">freq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">df</span>
+        <span class="k">return</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">,</span>
+                          <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">mcoh</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">res_data</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span></div></div>
 
 <span class="c1"># def coherence_array(abscissa,ordinate,coordinate,comment1=&#39;&#39;,comment2=&#39;&#39;,comment3=&#39;&#39;,comment4=&#39;&#39;,comment5=&#39;&#39;):</span>
 <span class="c1">#     pass</span>
@@ -5409,29 +6307,30 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MODE_INDICATOR_FUNCTION</span></div>
 
+
 <div class="viewcode-block" id="ShockResponseSpectrumArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray">[docs]</a><span class="k">class</span> <span class="nc">ShockResponseSpectrumArray</span><span class="p">(</span><span class="n">NDDataArray</span><span class="p">):</span>
-    
-    <span class="n">_srs_type_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ppaa&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span>
-                     <span class="s1">&#39;pnaa&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">,</span>
-                     <span class="s1">&#39;pmaa&#39;</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span>
-                     <span class="s1">&#39;rpaa&#39;</span><span class="p">:</span><span class="mi">4</span><span class="p">,</span>
-                     <span class="s1">&#39;rnaa&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span>
-                     <span class="s1">&#39;rmaa&#39;</span><span class="p">:</span><span class="mi">6</span><span class="p">,</span>
-                     <span class="s1">&#39;mpaa&#39;</span><span class="p">:</span><span class="mi">7</span><span class="p">,</span>
-                     <span class="s1">&#39;mnaa&#39;</span><span class="p">:</span><span class="mi">8</span><span class="p">,</span>
-                     <span class="s1">&#39;mmaa&#39;</span><span class="p">:</span><span class="mi">9</span><span class="p">,</span>
-                     <span class="s1">&#39;alaa&#39;</span><span class="p">:</span><span class="mi">10</span><span class="p">,</span>
-                     <span class="s1">&#39;pprd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span>
-                     <span class="s1">&#39;pnrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span>
-                     <span class="s1">&#39;pmrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span>
-                     <span class="s1">&#39;rprd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span>
-                     <span class="s1">&#39;rnrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span>
-                     <span class="s1">&#39;rmrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">6</span><span class="p">,</span>
-                     <span class="s1">&#39;mprd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">7</span><span class="p">,</span>
-                     <span class="s1">&#39;mnrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">8</span><span class="p">,</span>
-                     <span class="s1">&#39;mmrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">9</span><span class="p">,</span>
-                     <span class="s1">&#39;alrd&#39;</span><span class="p">:</span><span class="o">-</span><span class="mi">10</span><span class="p">}</span>
-<span class="w">    </span>
+
+    <span class="n">_srs_type_map</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ppaa&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+                     <span class="s1">&#39;pnaa&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+                     <span class="s1">&#39;pmaa&#39;</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span>
+                     <span class="s1">&#39;rpaa&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+                     <span class="s1">&#39;rnaa&#39;</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>
+                     <span class="s1">&#39;rmaa&#39;</span><span class="p">:</span> <span class="mi">6</span><span class="p">,</span>
+                     <span class="s1">&#39;mpaa&#39;</span><span class="p">:</span> <span class="mi">7</span><span class="p">,</span>
+                     <span class="s1">&#39;mnaa&#39;</span><span class="p">:</span> <span class="mi">8</span><span class="p">,</span>
+                     <span class="s1">&#39;mmaa&#39;</span><span class="p">:</span> <span class="mi">9</span><span class="p">,</span>
+                     <span class="s1">&#39;alaa&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">,</span>
+                     <span class="s1">&#39;pprd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
+                     <span class="s1">&#39;pnrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span>
+                     <span class="s1">&#39;pmrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">3</span><span class="p">,</span>
+                     <span class="s1">&#39;rprd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span>
+                     <span class="s1">&#39;rnrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span>
+                     <span class="s1">&#39;rmrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">6</span><span class="p">,</span>
+                     <span class="s1">&#39;mprd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">7</span><span class="p">,</span>
+                     <span class="s1">&#39;mnrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">8</span><span class="p">,</span>
+                     <span class="s1">&#39;mmrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">9</span><span class="p">,</span>
+                     <span class="s1">&#39;alrd&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">10</span><span class="p">}</span>
+
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Shock Response Spectrum (SRS)&quot;&quot;&quot;</span>
     <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nelements</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                 <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -5446,22 +6345,22 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SHOCK_RESPONSE_SPECTRUM</span>
 
 <div class="viewcode-block" id="ShockResponseSpectrumArray.sum_decayed_sines"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.sum_decayed_sines">[docs]</a>    <span class="k">def</span> <span class="nf">sum_decayed_sines</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
-                          <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_range</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_per_octave</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_decays</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_delays</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">srs_damping</span> <span class="o">=</span> <span class="mf">0.03</span><span class="p">,</span> <span class="n">srs_type</span> <span class="o">=</span> <span class="s2">&quot;MMAA&quot;</span><span class="p">,</span>
-                          <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">compensation_decay</span> <span class="o">=</span> <span class="mf">0.95</span><span class="p">,</span>
+                          <span class="n">sine_frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_per_octave</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">sine_amplitudes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_decays</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_delays</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">srs_damping</span><span class="o">=</span><span class="mf">0.03</span><span class="p">,</span> <span class="n">srs_type</span><span class="o">=</span><span class="s2">&quot;MMAA&quot;</span><span class="p">,</span>
+                          <span class="n">compensation_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
                           <span class="c1"># Paramters for the iteration</span>
-                          <span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">convergence</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span> 
-                          <span class="n">error_tolerance</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span>
-                          <span class="n">tau</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">num_time_constants</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">decay_resolution</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">scale_factor</span> <span class="o">=</span> <span class="mf">1.02</span><span class="p">,</span> 
-                          <span class="n">acceleration_factor</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
-                          <span class="n">plot_results</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">srs_frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">return_velocity</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">return_displacement</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-                          <span class="n">return_srs</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">return_sine_table</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-                          <span class="n">verbose</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+                          <span class="n">number_of_iterations</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">convergence</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
+                          <span class="n">error_tolerance</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+                          <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_time_constants</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">decay_resolution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">scale_factor</span><span class="o">=</span><span class="mf">1.02</span><span class="p">,</span>
+                          <span class="n">acceleration_factor</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+                          <span class="n">plot_results</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">srs_frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                          <span class="n">return_velocity</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_displacement</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                          <span class="n">return_srs</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">return_sine_table</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                          <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Generate a Sum of Decayed Sines signal given an SRS.</span>
-<span class="sd">        </span>
+
 <span class="sd">        Note that there are many approaches to do this, with many optional arguments</span>
 <span class="sd">        so please read the documentation carefully to understand which arguments</span>
 <span class="sd">        must be passed to the function.</span>
@@ -5474,18 +6373,18 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            The number of samples in the generated signal.</span>
 <span class="sd">        sine_frequencies : np.ndarray, optional</span>
 <span class="sd">            The frequencies of the sine tones.  If this argument is not specified</span>
-<span class="sd">            and the `sine_tone_range` argument is not specified, then the </span>
+<span class="sd">            and the `sine_tone_range` argument is not specified, then the</span>
 <span class="sd">            `sine_tone_range` will be set to the maximum and minimum abscissa</span>
 <span class="sd">            value for this `ShockResponseSpectrumArray`.</span>
 <span class="sd">        sine_tone_range : np.ndarray, optional</span>
 <span class="sd">            A length-2 array containing the minimum and maximum sine tone to</span>
 <span class="sd">            generate.  If this argument is not specified</span>
-<span class="sd">            and the `sine_frequencies` argument is not specified, then the </span>
+<span class="sd">            and the `sine_frequencies` argument is not specified, then the</span>
 <span class="sd">            `sine_tone_range` will be set to the maximum and minimum abscissa</span>
 <span class="sd">            value for this `ShockResponseSpectrumArray`.</span>
 <span class="sd">        sine_tone_per_octave : int, optional</span>
 <span class="sd">            The number of sine tones per octave. If not specified along with</span>
-<span class="sd">            `sine_tone_range`, then a default value of 4 will be used if the </span>
+<span class="sd">            `sine_tone_range`, then a default value of 4 will be used if the</span>
 <span class="sd">            `srs_damping` is &gt;= 0.05.  Otherwise, the formula of</span>
 <span class="sd">            `sine_tone_per_octave = 9 - srs_damping*100` will be used.</span>
 <span class="sd">        sine_amplitudes : np.ndarray, optional</span>
@@ -5496,7 +6395,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            An array of decay value time constants (often represented by variable</span>
 <span class="sd">            tau).  Tau is the time for the amplitude of motion to decay 63% defined</span>
 <span class="sd">            by the equation `1/(2*np.pi*freq*zeta)` where `freq` is the frequency</span>
-<span class="sd">            of the sine tone and `zeta` is the fraction of critical damping.  </span>
+<span class="sd">            of the sine tone and `zeta` is the fraction of critical damping.</span>
 <span class="sd">            If not specified, then either the `tau` or `num_time_constants`</span>
 <span class="sd">            arguments must be specified instead.</span>
 <span class="sd">        sine_delays : np.ndarray, optional</span>
@@ -5535,7 +6434,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            Allowable relative error in the SRS. The default is 0.05.</span>
 <span class="sd">        tau : float, optional</span>
 <span class="sd">            If a floating point number is passed, then this will be used for the</span>
-<span class="sd">            `sine_decay` values.  Alternatively, a dictionary can be passed with </span>
+<span class="sd">            `sine_decay` values.  Alternatively, a dictionary can be passed with</span>
 <span class="sd">            the keys containing a length-2 tuple specifying the minimum and maximum</span>
 <span class="sd">            frequency range, and the value specifying the value of `tau` within that</span>
 <span class="sd">            frequency range.  If this latter approach is used, all `sine_frequencies`</span>
@@ -5585,7 +6484,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            If True, a sine table will also be returned</span>
 <span class="sd">        verbose : True, optional</span>
 <span class="sd">            If True, additional diagnostics will be printed to the console.</span>
-<span class="sd">            </span>
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        acceleration : TimeHistoryArray</span>
@@ -5605,12 +6504,13 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            A `DecayedSineTable` object containing the frequency, amplitude,</span>
 <span class="sd">            delay, and decay parameters that are used to generate `acceleration`.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span> 
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">srs_type</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">srs_type</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
                 <span class="n">srs_type</span> <span class="o">=</span> <span class="n">ShockResponseSpectrumArray</span><span class="o">.</span><span class="n">_srs_type_map</span><span class="p">[</span><span class="n">srs_type</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
         <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid `srs_type` specified, should be one of </span><span class="si">{:}</span><span class="s1"> (case insensitive)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ShockResponseSpectrumArray</span><span class="o">.</span><span class="n">_srs_type_map</span><span class="p">]))</span>
-        
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid `srs_type` specified, should be one of </span><span class="si">{:}</span><span class="s1"> (case insensitive)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ShockResponseSpectrumArray</span><span class="o">.</span><span class="n">_srs_type_map</span><span class="p">]))</span>
+
         <span class="n">acceleration</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">block_size</span><span class="p">,))</span>
         <span class="k">if</span> <span class="n">return_displacement</span><span class="p">:</span>
             <span class="n">displacement</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">block_size</span><span class="p">,))</span>
@@ -5620,16 +6520,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">srs</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">if</span> <span class="n">return_sine_table</span><span class="p">:</span>
             <span class="n">sine_table</span> <span class="o">=</span> <span class="kc">None</span>
-        
-        <span class="k">for</span> <span class="n">index</span><span class="p">,</span><span class="n">srs_fn</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
-        
+
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">srs_fn</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
+
             <span class="k">if</span> <span class="n">sine_frequencies</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">sine_tone_range</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">this_sine_tone_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">srs_fn</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span><span class="n">srs_fn</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">max</span><span class="p">()]</span>
+                <span class="n">this_sine_tone_range</span> <span class="o">=</span> <span class="p">[</span><span class="n">srs_fn</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">srs_fn</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">max</span><span class="p">()]</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">this_sine_tone_range</span> <span class="o">=</span> <span class="n">sine_tone_range</span>
-            
-            <span class="n">srs_breakpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">srs_fn</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span><span class="n">srs_fn</span><span class="o">.</span><span class="n">ordinate</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
-            
+
+            <span class="n">srs_breakpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">srs_fn</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">srs_fn</span><span class="o">.</span><span class="n">ordinate</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+
             <span class="p">(</span><span class="n">acceleration_signal</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">,</span>
              <span class="n">all_frequencies</span><span class="p">,</span> <span class="n">all_amplitudes</span><span class="p">,</span> <span class="n">all_decays</span><span class="p">,</span> <span class="n">all_delays</span><span class="p">,</span>
              <span class="o">*</span><span class="n">plot_stuff</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp_sds</span><span class="p">(</span>
@@ -5639,9 +6539,9 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                  <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span>
                  <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">number_of_iterations</span><span class="p">,</span> <span class="n">convergence</span><span class="p">,</span>
                  <span class="n">error_tolerance</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">num_time_constants</span><span class="p">,</span> <span class="n">decay_resolution</span><span class="p">,</span>
-                 <span class="n">scale_factor</span><span class="p">,</span> <span class="n">acceleration_factor</span><span class="p">,</span> <span class="n">plot_results</span><span class="p">,</span> 
+                 <span class="n">scale_factor</span><span class="p">,</span> <span class="n">acceleration_factor</span><span class="p">,</span> <span class="n">plot_results</span><span class="p">,</span>
                  <span class="n">srs_frequencies</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
-                
+
             <span class="n">acceleration</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">acceleration_signal</span>
             <span class="k">if</span> <span class="n">return_displacement</span><span class="p">:</span>
                 <span class="n">displacement</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">displacement_signal</span>
@@ -5653,16 +6553,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                     <span class="n">this_srs_frequencies</span> <span class="o">=</span> <span class="n">all_frequencies</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">this_srs_frequencies</span> <span class="o">=</span> <span class="n">srs_frequencies</span>
-                <span class="n">this_srs</span><span class="p">,</span><span class="n">freq</span> <span class="o">=</span> <span class="n">sp_srs</span><span class="p">(</span><span class="n">acceleration_signal</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> 
-                                       <span class="n">this_srs_frequencies</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
+                <span class="n">this_srs</span><span class="p">,</span> <span class="n">freq</span> <span class="o">=</span> <span class="n">sp_srs</span><span class="p">(</span><span class="n">acceleration_signal</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span>
+                                        <span class="n">this_srs_frequencies</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">srs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">srs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">this_srs</span><span class="o">.</span><span class="n">size</span><span class="p">,))</span>
                 <span class="n">srs</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">this_srs</span>
             <span class="k">if</span> <span class="n">return_sine_table</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">sine_table</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">sine_table</span> <span class="o">=</span> <span class="n">DecayedSineTable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">all_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+                    <span class="n">sine_table</span> <span class="o">=</span> <span class="n">DecayedSineTable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">all_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
                 <span class="n">sine_table</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">decayed_sine_table</span><span class="p">(</span><span class="n">all_frequencies</span><span class="p">,</span> <span class="n">all_amplitudes</span><span class="p">,</span> <span class="n">all_decays</span><span class="p">,</span> <span class="n">all_delays</span><span class="p">,</span> <span class="n">srs_fn</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span>
-            
+
         <span class="c1"># Now convert to objects</span>
         <span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
         <span class="n">acceleration</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
@@ -5690,14 +6590,16 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="n">return_values</span> <span class="o">+=</span> <span class="p">(</span><span class="n">srs</span><span class="p">,)</span>
         <span class="k">if</span> <span class="n">return_sine_table</span><span class="p">:</span>
             <span class="n">return_values</span> <span class="o">+=</span> <span class="p">(</span><span class="n">sine_table</span><span class="p">,)</span>
-            
+
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">return_values</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">return_values</span> <span class="o">=</span> <span class="n">return_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            
+
         <span class="k">return</span> <span class="n">return_values</span></div>
-    
+
 <div class="viewcode-block" id="ShockResponseSpectrumArray.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.html#sdynpy.core.sdynpy_data.ShockResponseSpectrumArray.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">one_axis</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">subplots_kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{},</span>
-             <span class="n">plot_kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{}):</span>
+             <span class="n">plot_kwargs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
+             <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_type</span> <span class="o">=</span> <span class="s1">&#39;vline&#39;</span><span class="p">,</span>
+             <span class="n">abscissa_marker_plot_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plot the shock response spectrum</span>
 
@@ -5705,7 +6607,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        one_axis : bool, optional</span>
 <span class="sd">            Set to True to plot all data on one axis.  Set to False to plot</span>
-<span class="sd">            data on multiple subplots.  one_axis can also be set to a </span>
+<span class="sd">            data on multiple subplots.  one_axis can also be set to a</span>
 <span class="sd">            matplotlib axis to plot data on an existing axis.  The default is</span>
 <span class="sd">            True.</span>
 <span class="sd">        subplots_kwargs : dict, optional</span>
@@ -5713,6 +6615,20 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">            figure and axes. The default is {}.</span>
 <span class="sd">        plot_kwargs : dict, optional</span>
 <span class="sd">            Keywords passed to the matplotlib plot function. The default is {}.</span>
+<span class="sd">        abscissa_markers : ndarray, optional</span>
+<span class="sd">            Array containing abscissa values to mark on the plot to denote</span>
+<span class="sd">            significant events.</span>
+<span class="sd">        abscissa_marker_labels : str or ndarray</span>
+<span class="sd">            Array of strings to label the abscissa_markers with, or</span>
+<span class="sd">            alternatively a format string that accepts index and abscissa</span>
+<span class="sd">            inputs (e.g. &#39;{index:}: {abscissa:0.2f}&#39;).  By default no label</span>
+<span class="sd">            will be applied.</span>
+<span class="sd">        abscissa_marker_type : str</span>
+<span class="sd">            The type of marker to use.  This can either be the string &#39;vline&#39;</span>
+<span class="sd">            or a valid matplotlib symbol specifier (e.g. &#39;o&#39;, &#39;x&#39;, &#39;.&#39;).</span>
+<span class="sd">        abscissa_marker_plot_kwargs : dict</span>
+<span class="sd">            Additional keyword arguments used when plotting the abscissa label</span>
+<span class="sd">            markers.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -5720,25 +6636,86 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">             On which the data were plotted</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">abscissa_marker_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">for</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">abscissa_markers</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+                <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">abscissa_marker_labels</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">index</span> <span class="o">=</span> <span class="n">i</span><span class="p">,</span> <span class="n">abscissa</span> <span class="o">=</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">)]</span>
+                
         <span class="k">if</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">True</span><span class="p">:</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
             <span class="n">axis</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
             <span class="n">axis</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">one_axis</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
             <span class="n">ncols</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">)))</span>
             <span class="n">nrows</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">/</span> <span class="n">ncols</span><span class="p">))</span>
             <span class="n">figure</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">function</span><span class="p">))</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">())):</span>
-                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+                <span class="n">lines</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">function</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">function</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">function</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()]))</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                        <span class="n">ax</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                            <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                            <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                            <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                            <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                            <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                                <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axis</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">:]:</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">axis</span> <span class="o">=</span> <span class="n">one_axis</span>
-            <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">lines</span> <span class="o">=</span> <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">abscissa_markers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">abscissa_marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                    <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">}</span>
+                    <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                    <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">abscissa_marker_labels</span><span class="p">):</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">axis</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">rotation</span> <span class="o">=</span> <span class="mi">90</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span><span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span><span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span><span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
+                    <span class="n">axis</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s1">&#39;ylim_changed&#39;</span><span class="p">,</span><span class="n">_update_annotations_to_axes_bottom</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
+                        <span class="n">x</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_xdata</span><span class="p">()</span>
+                        <span class="n">y</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">get_ydata</span><span class="p">()</span>
+                        <span class="n">marker_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                        <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="n">line</span><span class="o">.</span><span class="n">get_color</span><span class="p">(),</span><span class="s1">&#39;marker&#39;</span><span class="p">:</span><span class="n">abscissa_marker_type</span><span class="p">,</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+                        <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">abscissa_marker_plot_kwargs</span><span class="p">)</span>
+                        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                        <span class="k">for</span> <span class="n">label</span><span class="p">,</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">abscissa_marker_labels</span><span class="p">,</span><span class="n">abscissa_markers</span><span class="p">,</span><span class="n">marker_y</span><span class="p">):</span>
+                            <span class="n">axis</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="n">mx</span><span class="p">,</span><span class="n">my</span><span class="p">),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s1">&#39;offset pixels&#39;</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">&#39;left&#39;</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;bottom&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">axis</span></div></div>
 
 <span class="c1"># def mode_indicator_function_array(abscissa,ordinate,coordinate,comment1=&#39;&#39;,comment2=&#39;&#39;,comment3=&#39;&#39;,comment4=&#39;&#39;,comment5=&#39;&#39;):</span>
@@ -5769,7 +6746,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                             <span class="c1"># FunctionTypes.PARTIAL_COHERENCE, : ,</span>
                             <span class="c1"># FunctionTypes.EIGENVALUE, : ,</span>
                             <span class="c1"># FunctionTypes.EIGENVECTOR, : ,</span>
-                            <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SHOCK_RESPONSE_SPECTRUM</span> <span class="p">:</span> <span class="n">ShockResponseSpectrumArray</span><span class="p">,</span>
+                            <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SHOCK_RESPONSE_SPECTRUM</span><span class="p">:</span> <span class="n">ShockResponseSpectrumArray</span><span class="p">,</span>
                             <span class="c1"># FunctionTypes.FINITE_IMPULSE_RESPONSE_FILTER, : ,</span>
                             <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">:</span> <span class="n">MultipleCoherenceArray</span><span class="p">,</span>
                             <span class="c1"># FunctionTypes.ORDER_FUNCTION, : ,</span>
@@ -5815,7 +6792,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
     <span class="n">nelem</span> <span class="o">=</span> <span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
     <span class="n">shape</span> <span class="o">=</span> <span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
     <span class="k">if</span> <span class="n">data_type</span> <span class="ow">is</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">GENERAL</span><span class="p">:</span>
-        <span class="n">obj</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span><span class="n">nelem</span><span class="p">,</span><span class="n">coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">obj</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">nelem</span><span class="p">,</span> <span class="n">coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">obj</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">nelem</span><span class="p">)</span>
     <span class="n">obj</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">ordinate</span>
@@ -5837,6 +6814,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="n">from_uff</span> <span class="o">=</span> <span class="n">from_unv</span>
 <span class="n">load</span> <span class="o">=</span> <span class="n">NDDataArray</span><span class="o">.</span><span class="n">load</span>
 
+
 <div class="viewcode-block" id="from_imat_struct"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.from_imat_struct.html#sdynpy.core.sdynpy_data.from_imat_struct">[docs]</a><span class="k">def</span> <span class="nf">from_imat_struct</span><span class="p">(</span><span class="n">imat_fn_struct</span><span class="p">,</span> <span class="n">squeeze</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Constructs a NDDataArray from an imat_fn class saved to a Matlab structure</span>
@@ -5872,7 +6850,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
     <span class="n">function_type_dict</span> <span class="o">=</span> <span class="p">{}</span>
     <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">fn_type</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">fn_types</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
         <span class="n">fn_type_enum</span> <span class="o">=</span> <span class="n">_imat_function_type_map</span><span class="p">[</span><span class="n">fn_type</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">fn_type_enum</span> <span class="ow">in</span> <span class="n">function_type_dict</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">fn_type_enum</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">function_type_dict</span><span class="p">:</span>
             <span class="n">function_type_dict</span><span class="p">[</span><span class="n">fn_type_enum</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">function_type_dict</span><span class="p">[</span><span class="n">fn_type_enum</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
     <span class="n">return_functions</span> <span class="o">=</span> <span class="p">[]</span>
@@ -5923,6 +6901,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">return_functions</span> <span class="o">=</span> <span class="n">return_functions</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">return_functions</span></div>
 
+
 <div class="viewcode-block" id="DecayedSineTable"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable">[docs]</a><span class="k">class</span> <span class="nc">DecayedSineTable</span><span class="p">(</span><span class="n">SdynpyArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Structure for storing sum-of-decayed-sines information</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -5935,8 +6914,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">data_dtype</span> <span class="o">=</span> <span class="p">[</span>
             <span class="p">(</span><span class="s1">&#39;frequency&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">num_elements</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;amplitude&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">num_elements</span><span class="p">),</span>
-            <span class="p">(</span><span class="s1">&#39;decay&#39;</span><span class="p">,</span><span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">num_elements</span><span class="p">),</span>
-            <span class="p">(</span><span class="s1">&#39;delay&#39;</span><span class="p">,</span><span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">num_elements</span><span class="p">),</span>
+            <span class="p">(</span><span class="s1">&#39;decay&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">num_elements</span><span class="p">),</span>
+            <span class="p">(</span><span class="s1">&#39;delay&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="n">num_elements</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;comment1&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;comment2&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;comment3&#39;</span><span class="p">,</span> <span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
@@ -5948,14 +6927,14 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                                                   <span class="n">data_dtype</span><span class="p">,</span> <span class="n">buffer</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span> <span class="n">order</span><span class="p">)</span>
         <span class="c1"># Finally, we must return the newly created object:</span>
         <span class="k">return</span> <span class="n">obj</span>
-    
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">key</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
         <span class="n">output</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;coordinate&#39;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;coordinate&#39;</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">CoordinateArray</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">output</span>
-        
+
 <div class="viewcode-block" id="DecayedSineTable.construct_signal"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable.construct_signal">[docs]</a>    <span class="k">def</span> <span class="nf">construct_signal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">):</span>
         <span class="n">output_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
         <span class="n">output_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">block_size</span><span class="p">,))</span>
@@ -5968,8 +6947,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                           <span class="n">output_ordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
-    
-<div class="viewcode-block" id="DecayedSineTable.construct_velocity"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable.construct_velocity">[docs]</a>    <span class="k">def</span> <span class="nf">construct_velocity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">acceleration_factor</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
+
+<div class="viewcode-block" id="DecayedSineTable.construct_velocity"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable.construct_velocity">[docs]</a>    <span class="k">def</span> <span class="nf">construct_velocity</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">acceleration_factor</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
         <span class="n">output_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
         <span class="n">output_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">block_size</span><span class="p">,))</span>
         <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">table</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
@@ -5981,8 +6960,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                           <span class="n">output_ordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
-    
-<div class="viewcode-block" id="DecayedSineTable.construct_displacement"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable.construct_displacement">[docs]</a>    <span class="k">def</span> <span class="nf">construct_displacement</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">acceleration_factor</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
+
+<div class="viewcode-block" id="DecayedSineTable.construct_displacement"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable.construct_displacement">[docs]</a>    <span class="k">def</span> <span class="nf">construct_displacement</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">acceleration_factor</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
         <span class="n">output_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
         <span class="n">output_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">block_size</span><span class="p">,))</span>
         <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">table</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
@@ -5995,6 +6974,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div></div>
 
+
 <div class="viewcode-block" id="decayed_sine_table"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.decayed_sine_table.html#sdynpy.core.sdynpy_data.decayed_sine_table">[docs]</a><span class="k">def</span> <span class="nf">decayed_sine_table</span><span class="p">(</span><span class="n">frequency</span><span class="p">,</span> <span class="n">amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">coordinate</span><span class="p">,</span> <span class="n">comment1</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment2</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment3</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment4</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Helper function to create a DecayedSineTable object.</span>
@@ -6026,7 +7006,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
-<span class="sd">    SineTable : </span>
+<span class="sd">    SineTable :</span>
 <span class="sd">        A SineTable object containing the specified information</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -6047,6 +7027,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
     <span class="n">st</span><span class="o">.</span><span class="n">comment5</span> <span class="o">=</span> <span class="n">comment5</span>
     <span class="k">return</span> <span class="n">st</span></div>
 
+
 <div class="viewcode-block" id="ComplexType"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.ComplexType.html#sdynpy.core.sdynpy_data.ComplexType">[docs]</a><span class="k">class</span> <span class="nc">ComplexType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumeration containing the various ways to plot complex data&quot;&quot;&quot;</span>
     <span class="n">REAL</span> <span class="o">=</span> <span class="mi">0</span>
@@ -6056,6 +7037,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
     <span class="n">REALIMAG</span> <span class="o">=</span> <span class="mi">4</span>
     <span class="n">MAGPHASE</span> <span class="o">=</span> <span class="mi">5</span></div>
 
+
 <div class="viewcode-block" id="GUIPlot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot">[docs]</a><span class="k">class</span> <span class="nc">GUIPlot</span><span class="p">(</span><span class="n">QMainWindow</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;An iteractive plot window allowing users to visualize data&quot;&quot;&quot;</span>
 
@@ -6067,10 +7049,6 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        arguments.  Position arguments will be labelled generically in the</span>
 <span class="sd">        legend.  Keyword arguments will be labelled with their keywords with</span>
 <span class="sd">        `_` replaced by ` `.</span>
-<span class="sd">        </span>
-<span class="sd">        Note that all datasets will be reshaped so that the coordinates match</span>
-<span class="sd">        the first dataset.  If not all coordinates exist in subsequent datasets</span>
-<span class="sd">        an error will be thrown.</span>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
@@ -6080,6 +7058,35 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="sd">        **labeled_data_to_plot : NDDataArray</span>
 <span class="sd">            Data to visualize.  Data passed by keyword argument will be</span>
 <span class="sd">            labeled with its keyword</span>
+<span class="sd">        abscissa_markers : np.ndarray</span>
+<span class="sd">            Abscissa values at which markers will be placed.  If not specified,</span>
+<span class="sd">            no markers will be added.  Markers will be added to all plotted</span>
+<span class="sd">            curves if this argument is passed.  To add markers to just a</span>
+<span class="sd">            specific plotted data, pass the argument `abscissa_markers_*` where</span>
+<span class="sd">            `*` is replaced with either the index of the data that was passed</span>
+<span class="sd">            via a positional argument, or the keyword of the data that was</span>
+<span class="sd">            passed via a keyword argument.  Must be passed as a keyword argument.</span>
+<span class="sd">        abscissa_marker_labels : iterable</span>
+<span class="sd">            Labels that will be applied to the markers.  If not specified, no</span>
+<span class="sd">            label will be applied.  If a single string is passed, it will be</span>
+<span class="sd">            passed to the `.format` method with keyword arguments `index` and</span>
+<span class="sd">            `abscissa`.  This marker label will be used for all plotted</span>
+<span class="sd">            curves if this argument is passed.  To add markers to just a</span>
+<span class="sd">            specific plotted data, pass the argument `abscissa_marker_labels_*` where</span>
+<span class="sd">            `*` is replaced with either the index of the data that was passed</span>
+<span class="sd">            via a positional argument, or the keyword of the data that was</span>
+<span class="sd">            passed via a keyword argument.  Must be passed as a keyword argument.</span>
+<span class="sd">        abscissa_marker_type : str:</span>
+<span class="sd">            The type of marker that will be applied.  Can be &#39;vline&#39; for a</span>
+<span class="sd">            vertical line across the axis, or it can be a pyqtgraph symbol specifier</span>
+<span class="sd">            (e.g. &#39;x&#39;, &#39;o&#39;, &#39;star&#39;, etc.) which will be placed on the plotted curves.</span>
+<span class="sd">            If not specified, a vertical line will be used.</span>
+<span class="sd">            This marker type will be used for all plotted</span>
+<span class="sd">            curves if this argument is passed.  To add markers to just a</span>
+<span class="sd">            specific plotted data, pass the argument `abscissa_marker_type_*` where</span>
+<span class="sd">            `*` is replaced with either the index of the data that was passed</span>
+<span class="sd">            via a positional argument, or the keyword of the data that was</span>
+<span class="sd">            passed via a keyword argument.  Must be passed as a keyword argument.</span>
 
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
@@ -6087,17 +7094,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="c1"># Parse the dataset arguments</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">labeled_data_to_plot</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;At least one dataset must be provided!&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">dataset</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="s1">&#39;Dataset </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dataset</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">dataset</span> <span class="ow">in</span> <span class="n">labeled_data_to_plot</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dataset</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_parse_arguments</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">,</span><span class="n">labeled_data_to_plot</span><span class="p">)</span>
         <span class="c1"># Now go through and reshape the data so it&#39;s all the same size</span>
         <span class="n">first_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="o">.</span><span class="n">values</span><span class="p">()][</span><span class="mi">0</span><span class="p">]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">data_original_shape</span> <span class="o">=</span> <span class="n">first_data</span><span class="o">.</span><span class="n">shape</span>
@@ -6144,34 +7141,34 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">20</span>
         <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
             <span class="c1"># Combine tab20b and tab20c</span>
-            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span><span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
-            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span><span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span><span class="n">tab_b</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span> <span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span> <span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span> <span class="n">tab_b</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">30</span>
         <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
             <span class="c1"># Combine tab20b and tab20c</span>
-            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span><span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
-            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span><span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span><span class="n">tab_b</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span> <span class="n">tab_b</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">40</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">tab10</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">10</span>
         <span class="c1"># Adjust the default plotting</span>
         <span class="k">if</span> <span class="n">function_type</span> <span class="ow">in</span> <span class="p">[</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">GENERAL</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
-                                          <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">AUTOCORRELATION</span><span class="p">,</span>
-                                          <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">CROSSCORRELATION</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MODE_INDICATOR_FUNCTION</span><span class="p">,</span>
-                                          <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">]:</span>
+                             <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">AUTOCORRELATION</span><span class="p">,</span>
+                             <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">CROSSCORRELATION</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MODE_INDICATOR_FUNCTION</span><span class="p">,</span>
+                             <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">]:</span>
             <span class="n">complex_type</span> <span class="o">=</span> <span class="n">ComplexType</span><span class="o">.</span><span class="n">REAL</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_log</span> <span class="o">=</span> <span class="kc">False</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">ordinate_log</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="k">elif</span> <span class="n">function_type</span> <span class="ow">in</span> <span class="p">[</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">AUTOSPECTRUM</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                                            <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">ENERGY_SPECTRAL_DENSITY</span><span class="p">]:</span>
+                               <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">ENERGY_SPECTRAL_DENSITY</span><span class="p">]:</span>
             <span class="n">complex_type</span> <span class="o">=</span> <span class="n">ComplexType</span><span class="o">.</span><span class="n">MAGNITUDE</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_log</span> <span class="o">=</span> <span class="kc">False</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">ordinate_log</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="k">elif</span> <span class="n">function_type</span> <span class="ow">in</span> <span class="p">[</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">CROSSSPECTRUM</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
-                                            <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TRANSMISIBILITY</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SPECTRUM</span><span class="p">]:</span>
+                               <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TRANSMISIBILITY</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="o">.</span><span class="n">SPECTRUM</span><span class="p">]:</span>
             <span class="n">complex_type</span> <span class="o">=</span> <span class="n">ComplexType</span><span class="o">.</span><span class="n">MAGPHASE</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_log</span> <span class="o">=</span> <span class="kc">False</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">ordinate_log</span> <span class="o">=</span> <span class="kc">True</span>
@@ -6214,6 +7211,60 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">setWindowTitle</span><span class="p">(</span><span class="s1">&#39;GUIPlot&#39;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
 
+    <span class="k">def</span> <span class="nf">_parse_arguments</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data_to_plot</span><span class="p">,</span><span class="n">labeled_data_to_plot</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="s1">&#39;Dataset </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dataset</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">labeled_data_to_plot</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;abscissa_markers&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
+            <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;abscissa_marker_labels&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
+            <span class="k">elif</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;abscissa_marker_type&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
+            <span class="k">elif</span> <span class="n">key</span><span class="p">[:</span><span class="mi">17</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;abscissa_markers_&#39;</span><span class="p">:</span>
+                <span class="n">marker_key</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;abscissa_markers_&#39;</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">marker_key</span><span class="p">)</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="s1">&#39;Dataset </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">marker_key</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="n">marker_key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">marker_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">marker_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">marker_key</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
+            <span class="k">elif</span> <span class="n">key</span><span class="p">[:</span><span class="mi">23</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;abscissa_marker_labels_&#39;</span><span class="p">:</span>
+                <span class="n">marker_key</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;abscissa_marker_labels_&#39;</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">marker_key</span><span class="p">)</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="s1">&#39;Dataset </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">marker_key</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="n">marker_key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">marker_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">marker_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">marker_key</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
+            <span class="k">elif</span> <span class="n">key</span><span class="p">[:</span><span class="mi">21</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;abscissa_marker_type_&#39;</span><span class="p">:</span>
+                <span class="n">marker_key</span> <span class="o">=</span> <span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;abscissa_marker_type_&#39;</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">marker_key</span><span class="p">)</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="s1">&#39;Dataset </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">marker_key</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                    <span class="n">marker_key</span> <span class="o">=</span> <span class="n">marker_key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">marker_key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">marker_key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">marker_key</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="s1">&#39; &#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dataset</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;At least one dataset must be provided!&#39;</span><span class="p">)</span>
+
 <div class="viewcode-block" id="GUIPlot.connect_callbacks"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.connect_callbacks">[docs]</a>    <span class="k">def</span> <span class="nf">connect_callbacks</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Connects the callback functions to events</span>
@@ -6234,7 +7285,8 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">actionOrdinate_Log</span><span class="o">.</span><span class="n">triggered</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update_ordinate_log</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">actionAbscissa_Log</span><span class="o">.</span><span class="n">triggered</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update_abscissa_log</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">autoupdate_checkbox</span><span class="o">.</span><span class="n">clicked</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update_checkbox</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">linewidth_selector</span><span class="o">.</span><span class="n">valueChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">)</span></div>
+        <span class="bp">self</span><span class="o">.</span><span class="n">linewidth_selector</span><span class="o">.</span><span class="n">valueChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">symbolsize_selector</span><span class="o">.</span><span class="n">valueChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="GUIPlot.update"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.update">[docs]</a>    <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -6273,34 +7325,78 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                     <span class="n">plot</span><span class="o">.</span><span class="n">addLegend</span><span class="p">()</span>
                 <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">row_indices</span><span class="p">):</span>
                     <span class="n">original_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unravel_index</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_original_shape</span><span class="p">)</span>
-                    <span class="k">for</span> <span class="n">k</span><span class="p">,(</span><span class="n">label</span><span class="p">,</span><span class="n">dataset</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
+                    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="p">(</span><span class="n">label</span><span class="p">,</span> <span class="n">dataset</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="o">.</span><span class="n">items</span><span class="p">()):</span>
                         <span class="n">data_entry</span> <span class="o">=</span> <span class="n">dataset</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
                         <span class="n">legend_entry</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                             <span class="n">original_index</span><span class="p">,</span> <span class="n">data_entry</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="n">label</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>
                         <span class="n">pen</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">mkPen</span><span class="p">(</span>
                             <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">((</span><span class="n">j</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span><span class="p">)</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span><span class="p">)],</span>
-                            <span class="n">width</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linewidth_selector</span><span class="o">.</span><span class="n">value</span><span class="p">())</span>
+                            <span class="n">width</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">linewidth_selector</span><span class="o">.</span><span class="n">value</span><span class="p">())</span>
                         <span class="n">plot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data_entry</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">complex_fn</span><span class="p">(</span><span class="n">data_entry</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span> <span class="o">*</span>
                                   <span class="p">(</span><span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="k">if</span> <span class="n">complex_fn</span> <span class="ow">is</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span> <span class="k">else</span> <span class="mi">1</span><span class="p">),</span> <span class="n">name</span><span class="o">=</span><span class="n">legend_entry</span><span class="p">,</span> <span class="n">pen</span><span class="o">=</span><span class="n">pen</span><span class="p">)</span>
+                        <span class="c1"># Now handle the markers</span>
+                        <span class="c1"># First see if there is a matching marker set</span>
+                        <span class="k">if</span> <span class="n">label</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                            <span class="n">marker_abscissa</span><span class="p">,</span> <span class="n">marker_labels</span><span class="p">,</span> <span class="n">marker_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">marker_abscissa</span><span class="p">,</span> <span class="n">marker_labels</span><span class="p">,</span> <span class="n">marker_type</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">]</span>
+                        <span class="c1"># Now handle if things are missing</span>
+                        <span class="k">if</span> <span class="n">marker_abscissa</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="kc">None</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                            <span class="n">marker_abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">marker_labels</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="kc">None</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                            <span class="n">marker_labels</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">marker_type</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="kc">None</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">:</span>
+                            <span class="n">marker_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="kc">None</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="c1"># Now finally go back to the defaults if they still are</span>
+                        <span class="c1"># not defined</span>
+                        <span class="k">if</span> <span class="n">marker_abscissa</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># If marker_abscissa is not defined, we don&#39;t plot markers</span>
+                            <span class="c1"># The default marker is a vertical line</span>
+                            <span class="k">if</span> <span class="n">marker_type</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                                <span class="n">marker_type</span> <span class="o">=</span> <span class="s1">&#39;vline&#39;</span>
+                            <span class="c1"># Parse out special values of the marker_labels</span>
+                            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">marker_labels</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+                                <span class="n">marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">marker_labels</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="n">i</span><span class="p">,</span><span class="n">abscissa</span><span class="o">=</span><span class="n">a</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">a</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">marker_abscissa</span><span class="p">)]</span>
+                            <span class="c1"># Now that we have all of the data we can plot it.</span>
+                            <span class="k">if</span> <span class="n">marker_type</span> <span class="o">==</span> <span class="s1">&#39;vline&#39;</span><span class="p">:</span>
+                                <span class="k">if</span> <span class="n">marker_labels</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                                    <span class="n">marker_labels</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">marker_abscissa</span><span class="p">)</span>
+                                <span class="k">for</span> <span class="n">value</span><span class="p">,</span><span class="n">marker_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">marker_abscissa</span><span class="p">,</span><span class="n">marker_labels</span><span class="p">):</span>
+                                    <span class="n">vlinepen</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">mkPen</span><span class="p">(</span>
+                                        <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">((</span><span class="n">j</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span><span class="p">)</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span><span class="p">)],</span>
+                                        <span class="n">width</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+                                    <span class="n">vline</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">InfiniteLine</span><span class="p">(</span>
+                                        <span class="n">value</span><span class="p">,</span><span class="n">pen</span><span class="o">=</span><span class="n">vlinepen</span><span class="p">,</span><span class="n">hoverPen</span><span class="o">=</span><span class="n">pen</span><span class="p">,</span>
+                                        <span class="n">label</span><span class="o">=</span><span class="n">marker_label</span><span class="p">,</span>
+                                        <span class="n">labelOpts</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;position&#39;</span><span class="p">:</span><span class="mf">0.0</span><span class="p">,</span><span class="s1">&#39;rotateAxis&#39;</span><span class="p">:(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span><span class="s1">&#39;anchors&#39;</span><span class="p">:[(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)],</span>
+                                                   <span class="s1">&#39;color&#39;</span><span class="p">:(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)})</span>
+                                    <span class="n">plot</span><span class="o">.</span><span class="n">addItem</span><span class="p">(</span><span class="n">vline</span><span class="p">)</span>
+                            <span class="k">else</span><span class="p">:</span>
+                                <span class="n">x</span><span class="o">=</span><span class="n">data_entry</span><span class="o">.</span><span class="n">abscissa</span>
+                                <span class="n">y</span><span class="o">=</span><span class="n">complex_fn</span><span class="p">(</span><span class="n">data_entry</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="k">if</span> <span class="n">complex_fn</span> <span class="ow">is</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span> <span class="k">else</span> <span class="mi">1</span><span class="p">)</span>
+                                <span class="n">marker_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">marker_abscissa</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+                                <span class="n">brush</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">mkBrush</span><span class="p">(</span>
+                                    <span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">((</span><span class="n">j</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span><span class="p">)</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span><span class="p">)])</span>
+                                <span class="n">plot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">marker_abscissa</span><span class="p">,</span><span class="n">y</span><span class="o">=</span><span class="n">marker_ordinate</span><span class="p">,</span><span class="n">pen</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                          <span class="n">symbol</span><span class="o">=</span><span class="n">marker_type</span><span class="p">,</span> <span class="n">symbolSize</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">symbolsize_selector</span><span class="o">.</span><span class="n">value</span><span class="p">(),</span> 
+                                          <span class="n">symbolPen</span><span class="o">=</span><span class="n">pen</span><span class="p">,</span><span class="n">symbolBrush</span><span class="o">=</span><span class="n">brush</span><span class="p">)</span>
+                                <span class="k">if</span> <span class="n">marker_labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                                    <span class="k">for</span> <span class="n">text_abscissa</span><span class="p">,</span><span class="n">text_ordinate</span><span class="p">,</span><span class="n">text_label</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">marker_abscissa</span><span class="p">,</span><span class="n">marker_ordinate</span><span class="p">,</span><span class="n">marker_labels</span><span class="p">):</span>
+                                        <span class="n">ti</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">TextItem</span><span class="p">(</span><span class="n">text_label</span><span class="p">,</span><span class="n">anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span><span class="n">color</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+                                        <span class="n">plot</span><span class="o">.</span><span class="n">addItem</span><span class="p">(</span><span class="n">ti</span><span class="p">)</span>
+                                        <span class="c1"># TODO: Remove the log10s when pyqtgraph issue 2166 is fixed</span>
+                                        <span class="c1"># https://github.com/pyqtgraph/pyqtgraph/issues/2166</span>
+                                        <span class="n">ti</span><span class="o">.</span><span class="n">setPos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">text_abscissa</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_log</span> <span class="k">else</span> <span class="n">text_abscissa</span><span class="p">,</span>
+                                                  <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">text_ordinate</span><span class="p">)</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate_log</span> <span class="k">else</span> <span class="n">text_ordinate</span><span class="p">)</span>
+                        
                 <span class="n">plot</span><span class="o">.</span><span class="n">setLogMode</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa_log</span><span class="p">,</span>
                                 <span class="kc">False</span> <span class="k">if</span> <span class="n">complex_fn</span> <span class="ow">is</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordinate_log</span><span class="p">)</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">xrange</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">xrange</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">plot</span><span class="o">.</span><span class="n">setXRange</span><span class="p">(</span><span class="o">*</span><span class="n">xrange</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
                 <span class="n">plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">plot</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="GUIPlot.update_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.update_data">[docs]</a>    <span class="k">def</span> <span class="nf">update_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">data_to_plot</span><span class="p">,</span> <span class="o">**</span><span class="n">labeled_data_to_plot</span><span class="p">):</span>
         <span class="c1"># Parse the dataset arguments</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">labeled_data_to_plot</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;At least one dataset must be provided!&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">dataset</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">dataset</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="s1">&#39;Dataset </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dataset</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">dataset</span> <span class="ow">in</span> <span class="n">labeled_data_to_plot</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">,</span><span class="s1">&#39; &#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="n">dataset</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_parse_arguments</span><span class="p">(</span><span class="n">data_to_plot</span><span class="p">,</span> <span class="n">labeled_data_to_plot</span><span class="p">)</span>
         <span class="c1"># Now go through and reshape the data so it&#39;s all the same size</span>
         <span class="n">first_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_dictionary</span><span class="o">.</span><span class="n">values</span><span class="p">()][</span><span class="mi">0</span><span class="p">]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">data_original_shape</span> <span class="o">=</span> <span class="n">first_data</span><span class="o">.</span><span class="n">shape</span>
@@ -6351,15 +7447,15 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">20</span>
         <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datasets</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
             <span class="c1"># Combine tab20b and tab20c</span>
-            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span><span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
-            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span><span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span><span class="n">tab_b</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span> <span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span> <span class="mi">15</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">15</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span> <span class="n">tab_b</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">30</span>
         <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_datsets</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
             <span class="c1"># Combine tab20b and tab20c</span>
-            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span><span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
-            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span><span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">20</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span><span class="n">tab_b</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+            <span class="n">tab_b</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20b&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+            <span class="n">tab_c</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20c&#39;</span><span class="p">,</span> <span class="mi">20</span><span class="p">)(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">ListedColormap</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tab_c</span><span class="p">,</span> <span class="n">tab_b</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span> <span class="o">=</span> <span class="mi">40</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">cm</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">tab10</span>
@@ -6451,6 +7547,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">FigureCanvas</span><span class="o">.</span><span class="n">setSizePolicy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="o">.</span><span class="n">Expanding</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="o">.</span><span class="n">Expanding</span><span class="p">)</span>
         <span class="n">FigureCanvas</span><span class="o">.</span><span class="n">updateGeometry</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div></div>
 
+
 <div class="viewcode-block" id="MPLMultiCanvas"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html#sdynpy.core.sdynpy_data.MPLMultiCanvas">[docs]</a><span class="k">class</span> <span class="nc">MPLMultiCanvas</span><span class="p">(</span><span class="n">FigureCanvas</span><span class="p">):</span>
     <span class="c1"># This is a custom widget that can be used to put plots into a GUI window</span>
 <div class="viewcode-block" id="MPLMultiCanvas.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.MPLMultiCanvas.html#sdynpy.core.sdynpy_data.MPLMultiCanvas.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parent</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">subplots</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">ignore_subplots</span><span class="o">=</span><span class="p">[]):</span>
@@ -6470,6 +7567,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="n">FigureCanvas</span><span class="o">.</span><span class="n">setSizePolicy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="o">.</span><span class="n">Expanding</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="o">.</span><span class="n">Expanding</span><span class="p">)</span>
         <span class="n">FigureCanvas</span><span class="o">.</span><span class="n">updateGeometry</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div></div>
 
+
 <div class="viewcode-block" id="CPSDPlot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot">[docs]</a><span class="k">class</span> <span class="nc">CPSDPlot</span><span class="p">(</span><span class="n">QMainWindow</span><span class="p">):</span>
 
 <div class="viewcode-block" id="CPSDPlot.DataType"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.DataType">[docs]</a>    <span class="k">class</span> <span class="nc">DataType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
@@ -6483,7 +7581,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                  <span class="n">compare_data</span><span class="p">:</span> <span class="n">PowerSpectralDensityArray</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">validate_common_abscissa</span><span class="p">(</span><span class="n">rtol</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Data must have common abscissa&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">compare_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">compare_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">compare_data</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;compare_data.ordinate must have the same size as data.ordinate (</span><span class="si">{:}</span><span class="s1"> != </span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                     <span class="n">compare_data</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
@@ -6934,7 +8032,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
         <span class="c1"># Create the figure and put it in the dock</span>
         <span class="n">functions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">plotted_function</span><span class="p">]</span>
         <span class="n">coherence_functions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coh_matrix</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">plotted_function</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">compare_functions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">plotted_function</span><span class="p">]</span>
             <span class="n">compare_coherence_functions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_coh_matrix</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">plotted_function</span><span class="p">]</span>
             <span class="n">cm_increment</span> <span class="o">=</span> <span class="mi">2</span>
@@ -6978,7 +8076,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
                 <span class="k">continue</span>
             <span class="n">function</span> <span class="o">=</span> <span class="n">functions</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
             <span class="n">coh_function</span> <span class="o">=</span> <span class="n">coherence_functions</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">compare_function</span> <span class="o">=</span> <span class="n">compare_functions</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
                 <span class="n">compare_coh_function</span> <span class="o">=</span> <span class="n">compare_coherence_functions</span><span class="p">[:,</span> <span class="n">index</span><span class="p">]</span>
             <span class="k">else</span><span class="p">:</span>
@@ -7003,7 +8101,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
             <span class="k">for</span> <span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="n">compare_fn</span><span class="p">,</span> <span class="n">color_index</span><span class="p">,</span> <span class="n">scale</span><span class="p">),</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">plots</span><span class="p">,</span> <span class="n">this_ax</span><span class="p">):</span>
                 <span class="n">color</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">(</span><span class="n">color_index</span> <span class="o">*</span> <span class="n">cm_increment</span><span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">)</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">compare_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">compare_color</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">(</span><span class="n">color_index</span> <span class="o">*</span> <span class="n">cm_increment</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
                     <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">compare_fn</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">compare_color</span><span class="p">)</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
@@ -7012,7 +8110,6 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
 <span class="c1">#        self.plot_canvas.fig.tight_layout()</span>
 
 
-
 <span class="n">frf_from_time_data</span> <span class="o">=</span> <span class="n">TransferFunctionArray</span><span class="o">.</span><span class="n">from_time_data</span>
 <span class="n">join</span> <span class="o">=</span> <span class="n">NDDataArray</span><span class="o">.</span><span class="n">join</span>
 </pre></div>
@@ -7024,7 +8121,7 @@ <h1>Source code for sdynpy.core.sdynpy_data</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebc4e0e0>
+<jinja2.runtime.BlockReference object at 0x7f5a265a3e50>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_geometry.html b/_modules/sdynpy/core/sdynpy_geometry.html
index d0d9a58..82c65e8 100644
--- a/_modules/sdynpy/core/sdynpy_geometry.html
+++ b/_modules/sdynpy/core/sdynpy_geometry.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_geometry &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_geometry &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -131,6 +131,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 <span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">Delaunay</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
 <span class="k">try</span><span class="p">:</span>
     <span class="nb">repr</span><span class="p">(</span><span class="n">pv</span><span class="o">.</span><span class="n">GPUInfo</span><span class="p">())</span>
@@ -246,7 +247,16 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                          <span class="s1">&#39;hex20&#39;</span><span class="p">:</span> <span class="mi">116</span><span class="p">,</span>
                          <span class="s1">&#39;tetra10&#39;</span><span class="p">:</span> <span class="mi">118</span><span class="p">,</span>
                          <span class="s1">&#39;hex&#39;</span><span class="p">:</span> <span class="mi">115</span><span class="p">,</span>
-                         <span class="s1">&#39;beam&#39;</span><span class="p">:</span> <span class="mi">21</span>
+                         <span class="s1">&#39;beam&#39;</span><span class="p">:</span> <span class="mi">21</span><span class="p">,</span>
+                         <span class="s1">&#39;rod&#39;</span><span class="p">:</span><span class="mi">21</span><span class="p">,</span> <span class="c1"># 21 : &#39;Linear beam&#39;,</span>
+                         <span class="s1">&#39;truss&#39;</span><span class="p">:</span><span class="mi">21</span><span class="p">,</span> <span class="c1"># 21 : &#39;Linear beam&#39;,</span>
+                         <span class="s1">&#39;tri&#39;</span><span class="p">:</span><span class="mi">41</span><span class="p">,</span> <span class="c1"># 41 : &#39;Plane Stress Linear Triangle&#39;,</span>
+                         <span class="s1">&#39;triangle&#39;</span><span class="p">:</span><span class="mi">41</span><span class="p">,</span> <span class="c1"># 41 : &#39;Plane Stress Linear Triangle&#39;,</span>
+                         <span class="s1">&#39;quad&#39;</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span> <span class="c1"># 44 : &#39;Plane Stress Linear Quadrilateral&#39;,</span>
+                         <span class="s1">&#39;quadrilateral&#39;</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span> <span class="c1"># 44 : &#39;Plane Stress Linear Quadrilateral&#39;,</span>
+                         <span class="s1">&#39;hexahedral&#39;</span><span class="p">:</span><span class="mi">115</span><span class="p">,</span>
+                         <span class="s1">&#39;tet&#39;</span><span class="p">:</span><span class="mi">111</span><span class="p">,</span>
+                         <span class="s1">&#39;tetrahedral&#39;</span><span class="p">:</span><span class="mi">111</span><span class="p">,</span>
                          <span class="c1"># Add new elements here</span>
                          <span class="p">}</span>
 
@@ -269,6 +279,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 
 <span class="n">MAX_NUMBER_REPR</span> <span class="o">=</span> <span class="mi">100</span>
 
+
 <div class="viewcode-block" id="GeometryPlotter"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.GeometryPlotter.html#sdynpy.core.sdynpy_geometry.GeometryPlotter">[docs]</a><span class="k">class</span> <span class="nc">GeometryPlotter</span><span class="p">(</span><span class="n">pvqt</span><span class="o">.</span><span class="n">BackgroundPlotter</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Class used to plot geometry</span>
 
@@ -424,7 +435,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        None.</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shape_select_toolbar</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QToolbar</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape_select_toolbar</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QToolBar</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">animation_control_toolbar</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QToolBar</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">loop_action</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">plot_abscissa_action</span> <span class="o">=</span> <span class="kc">None</span>
@@ -544,7 +555,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
             <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_mesh_deformed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_mesh_undeformed</span><span class="p">),</span>
             <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solid_mesh_deformed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">solid_mesh_undeformed</span><span class="p">)</span>
         <span class="p">):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">mesh_deformed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">mesh_deformed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">mesh_deformed</span><span class="o">.</span><span class="n">points</span> <span class="o">=</span> <span class="p">(</span><span class="n">mesh_undeformed</span><span class="o">.</span><span class="n">points</span> <span class="o">+</span>
                                         <span class="n">displacement</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">displacement_scale</span>
                                         <span class="p">)</span>
@@ -750,7 +761,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">last_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="TransientPlotter.stop_animation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.TransientPlotter.html#sdynpy.core.sdynpy_geometry.TransientPlotter.stop_animation">[docs]</a>    <span class="k">def</span> <span class="nf">stop_animation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">timer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">timer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">timer</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">timer</span> <span class="o">=</span> <span class="kc">None</span></div>
 
@@ -817,11 +828,11 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">stop_animation</span><span class="p">()</span>
         <span class="n">imgs</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">start_time</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">start_time</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">start_time</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">stop_time</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">stop_time</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">abscissa</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">stop_time</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">step</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">step</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">indices</span> <span class="o">=</span> <span class="n">indices</span><span class="p">[::</span><span class="n">step</span><span class="p">]</span>
         <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">abscissa_index</span> <span class="o">=</span> <span class="n">index</span>
@@ -842,10 +853,12 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                     <span class="s1">&#39;P&#39;</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="n">Image</span><span class="o">.</span><span class="n">ADAPTIVE</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span> <span class="k">for</span> <span class="n">img</span> <span class="ow">in</span> <span class="n">imgs</span><span class="p">]</span>
                 <span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">fp</span><span class="o">=</span><span class="n">filename</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;GIF&#39;</span><span class="p">,</span> <span class="n">append_images</span><span class="o">=</span><span class="n">imgs</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span>
                              <span class="n">save_all</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">duration</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">frame_rate</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">),</span> <span class="n">loop</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
+
     <span class="k">def</span> <span class="nf">_close</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">stop_animation</span><span class="p">()</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">TransientPlotter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_close</span><span class="p">()</span></div>
 
+
 <div class="viewcode-block" id="ShapePlotter"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter">[docs]</a><span class="k">class</span> <span class="nc">ShapePlotter</span><span class="p">(</span><span class="n">GeometryPlotter</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Class used to plot animated shapes&quot;&quot;&quot;</span>
 
@@ -874,13 +887,13 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        starting_scale : float, optional</span>
 <span class="sd">            Starting scale of the shapes on the plot. The default is 1.0.</span>
 <span class="sd">        shape_name : str, optional</span>
-<span class="sd">            Name that will be used in the plotter to describe the shape. The </span>
+<span class="sd">            Name that will be used in the plotter to describe the shape. The</span>
 <span class="sd">            default is &quot;Mode&quot;</span>
 <span class="sd">        show_damping : bool, optional</span>
 <span class="sd">            Boolean that specifies whether the damping should be displayed in</span>
 <span class="sd">            the comment.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shape_select_toolbar</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QToolbar</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape_select_toolbar</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QToolBar</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">animation_control_toolbar</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QToolBar</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">signed_amplitude_action</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">complex_action</span> <span class="o">=</span> <span class="kc">None</span>
@@ -891,7 +904,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">save_animation_action</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">display_undeformed</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="n">geometry</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span> <span class="o">=</span> <span class="n">shapes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span> <span class="o">=</span> <span class="n">shapes</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">node_displacements</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">current_shape</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">displacement_scale</span> <span class="o">=</span> <span class="mf">1.0</span>
@@ -902,6 +915,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">timer</span><span class="p">:</span> <span class="n">pvqt</span><span class="o">.</span><span class="n">plotting</span><span class="o">.</span><span class="n">QTimer</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">shape_name</span> <span class="o">=</span> <span class="n">shape_name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">show_damping</span> <span class="o">=</span> <span class="n">show_damping</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape_comment_table</span> <span class="o">=</span> <span class="kc">None</span>
 
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">background_plotter_kwargs</span><span class="p">)</span>
 
@@ -931,7 +945,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">coordinate_index</span><span class="p">,</span> <span class="n">node_id</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">(</span><span class="n">nodes</span><span class="p">):</span>
             <span class="k">if</span> <span class="n">direction</span><span class="p">[</span><span class="n">coordinate_index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">abs</span><span class="p">(</span><span class="n">direction</span><span class="p">[</span><span class="n">coordinate_index</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>
                 <span class="k">continue</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">node_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_node_index_map</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">node_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_node_index_map</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_node_index_map</span><span class="p">[</span><span class="n">node_id</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_node_index_map</span><span class="p">[</span><span class="n">node_id</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coordinate_index</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
         <span class="n">local_deformations</span> <span class="o">=</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">local_direction</span><span class="p">()</span>
@@ -986,7 +1000,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        phase : float, optional</span>
-<span class="sd">            Sets the current phase of the shape. The default is None, which </span>
+<span class="sd">            Sets the current phase of the shape. The default is None, which</span>
 <span class="sd">            computes the new phase based on the time elapsed.</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -1025,7 +1039,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_mesh_deformed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_mesh_undeformed</span><span class="p">),</span>
                 <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">solid_mesh_deformed</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">solid_mesh_undeformed</span><span class="p">)</span>
         <span class="p">):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">mesh_deformed</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">mesh_deformed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">mesh_deformed</span><span class="o">.</span><span class="n">points</span> <span class="o">=</span> <span class="p">(</span><span class="n">mesh_undeformed</span><span class="o">.</span><span class="n">points</span> <span class="o">+</span>
                                         <span class="n">deformation</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">displacement_scale</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">displacement_scale_modification</span>
                                         <span class="p">)</span>
@@ -1227,7 +1241,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="ShapePlotter.stop_animation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.stop_animation">[docs]</a>    <span class="k">def</span> <span class="nf">stop_animation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Stops the animation from playing&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">timer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">timer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">timer</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">timer</span> <span class="o">=</span> <span class="kc">None</span></div>
 
@@ -1366,8 +1380,19 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">update_shape</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">show_comment</span><span class="p">()</span></div>
 
+<div class="viewcode-block" id="ShapePlotter.reset_shape"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape">[docs]</a>    <span class="k">def</span> <span class="nf">reset_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Resets a shape if the value of the current shape is changed</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">compute_displacements</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">last_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update_shape</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">show_comment</span><span class="p">()</span></div>
+
 <div class="viewcode-block" id="ShapePlotter.select_shape"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.select_shape">[docs]</a>    <span class="k">def</span> <span class="nf">select_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">pass</span></div>
+        <span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">ShapeCommentTable</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shape_comment_table</span> <span class="o">=</span> <span class="n">ShapeCommentTable</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="ShapePlotter.show_comment"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.show_comment">[docs]</a>    <span class="k">def</span> <span class="nf">show_comment</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1390,6 +1415,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">stop_animation</span><span class="p">()</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ShapePlotter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">_close</span><span class="p">()</span></div>
 
+
 <div class="viewcode-block" id="DeflectionShapePlotter"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html#sdynpy.core.sdynpy_geometry.DeflectionShapePlotter">[docs]</a><span class="k">class</span> <span class="nc">DeflectionShapePlotter</span><span class="p">(</span><span class="n">ShapePlotter</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Class used to plot animated deflection shapes from spectra&quot;&quot;&quot;</span>
 
@@ -1517,7 +1543,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    ----------</span>
 <span class="sd">    seq : iterable</span>
 <span class="sd">        The sequence to split up</span>
-<span class="sd">    value : </span>
+<span class="sd">    value :</span>
 <span class="sd">        Value within the list to use to split up the list</span>
 
 <span class="sd">    Yields</span>
@@ -1540,7 +1566,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="w">    </span><span class="sd">&#39;&#39;&#39;Compute global coordinates from local coordinates</span>
 
 <span class="sd">    Compute the global coordinates of a node from its local coordinates in the</span>
-<span class="sd">    corresponding coordinate system.  </span>
+<span class="sd">    corresponding coordinate system.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -1550,7 +1576,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    local_coord : ndarray</span>
 <span class="sd">        An array of coordinates of shape [...,3]</span>
 
-<span class="sd">    Returns </span>
+<span class="sd">    Returns</span>
 <span class="sd">    ------</span>
 <span class="sd">    global_coord : ndarray</span>
 <span class="sd">        An array of coordinates of shape [...,3]</span>
@@ -1582,7 +1608,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Compute local coordinates from global coordinates</span>
 
 <span class="sd">    Compute the local coordinates of a node in the</span>
-<span class="sd">    corresponding coordinate system from its global coordinates.  </span>
+<span class="sd">    corresponding coordinate system from its global coordinates.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -1592,7 +1618,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    global_coord : ndarray</span>
 <span class="sd">        An array of coordinates of shape [...,3]</span>
 
-<span class="sd">    Returns </span>
+<span class="sd">    Returns</span>
 <span class="sd">    ------</span>
 <span class="sd">    local_coord : ndarray</span>
 <span class="sd">        An array of coordinates of shape [...,3]</span>
@@ -1608,7 +1634,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
     <span class="n">cylindrical_css</span> <span class="o">=</span> <span class="n">coordinate_system</span><span class="o">.</span><span class="n">cs_type</span> <span class="o">==</span> <span class="mi">1</span>
     <span class="n">cyl_coord</span> <span class="o">=</span> <span class="n">local_coordinate</span><span class="p">[</span><span class="n">cylindrical_css</span><span class="p">]</span>
     <span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
-                                        <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+                                            <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">cyl_coord</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
     <span class="n">local_coordinate</span><span class="p">[</span><span class="n">cylindrical_css</span><span class="p">]</span> <span class="o">=</span> <span class="n">cyl_coord</span>
 
     <span class="n">spherical_css</span> <span class="o">=</span> <span class="n">coordinate_system</span><span class="o">.</span><span class="n">cs_type</span> <span class="o">==</span> <span class="mi">2</span>
@@ -1655,7 +1681,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="c1"># Make sure we don&#39;t change local_deflection or point</span>
     <span class="n">local_deflection</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">local_deflection</span><span class="p">))</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">global_point</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">global_point</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">global_point</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">global_point</span><span class="p">))</span>
     <span class="c1"># Convert cylindrical and spherical to a local cartesian AT THE POINT</span>
     <span class="c1"># OF INTEREST (i.e. rotated in the coordinate system)</span>
@@ -1779,6 +1805,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
             <span class="n">output_arrays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">output_arrays</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">output_arrays</span></div></div>
 
+
 <div class="viewcode-block" id="coordinate_system_array"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.coordinate_system_array.html#sdynpy.core.sdynpy_geometry.coordinate_system_array">[docs]</a><span class="k">def</span> <span class="nf">coordinate_system_array</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">cs_type</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">matrix</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
                             <span class="n">structured_array</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1811,7 +1838,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    -------</span>
 <span class="sd">    coordinate_system_array : CoordinateSystemArray</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="nb">id</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span>
             <span class="n">name</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span>
@@ -1839,6 +1866,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 
     <span class="k">return</span> <span class="n">coord_sys_array</span></div>
 
+
 <div class="viewcode-block" id="ElementArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.ElementArray.html#sdynpy.core.sdynpy_geometry.ElementArray">[docs]</a><span class="k">class</span> <span class="nc">ElementArray</span><span class="p">(</span><span class="n">SdynpyArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Element information array</span>
 
@@ -2084,7 +2112,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">       231: &#39;Axisymetric linear rigid surface&#39;,</span>
 <span class="sd">       232: &#39;Axisymentric parabolic rigid surface&#39;</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="nb">id</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span>
             <span class="nb">type</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span>
@@ -2108,11 +2136,11 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
     <span class="n">earray</span><span class="o">.</span><span class="n">color</span> <span class="o">=</span> <span class="n">color</span>
     <span class="k">if</span> <span class="n">earray</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="c1"># This is gross, but it works!</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">earray</span><span class="p">,</span> <span class="s1">&#39;connectivity&#39;</span><span class="p">)[()]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">earray</span><span class="p">,</span> <span class="s1">&#39;connectivity&#39;</span><span class="p">)[()]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">connectivity</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">(</span><span class="nb">id</span><span class="p">):</span>
-            <span class="n">earray</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">],</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
+            <span class="n">earray</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">earray</span></div>
 
 
@@ -2318,7 +2346,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        cs_array : CoordinateSystemArray</span>
 <span class="sd">            CoordinateSystemArray containing coordinate systems for each node.</span>
 <span class="sd">        projection_function : function, optional</span>
-<span class="sd">            Function to use to project 3D coordinates to 2D coordinates for </span>
+<span class="sd">            Function to use to project 3D coordinates to 2D coordinates for</span>
 <span class="sd">            triangulation. The default is None.</span>
 <span class="sd">        return_element_array : bool, optional</span>
 <span class="sd">            Returns an ElementArray if True, otherwise it simply returns the</span>
@@ -2346,7 +2374,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="c1"># Now triangulate</span>
         <span class="n">tri</span> <span class="o">=</span> <span class="n">Delaunay</span><span class="p">(</span><span class="n">projected_positions</span><span class="p">)</span>
         <span class="n">simplices</span> <span class="o">=</span> <span class="n">tri</span><span class="o">.</span><span class="n">simplices</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">condition_threshold</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">condition_threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># Now remove small triangles via condition number</span>
             <span class="n">tri_points</span> <span class="o">=</span> <span class="n">projected_positions</span><span class="p">[</span><span class="n">tri</span><span class="o">.</span><span class="n">simplices</span><span class="p">,</span> <span class="p">:]</span>
             <span class="n">tri_points</span> <span class="o">-=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tri_points</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -2426,6 +2454,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="n">candidate_node_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">candidate_nodes</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">candidate_nodes</span><span class="p">(</span><span class="n">candidate_node_ids</span><span class="p">)</span></div></div>
 
+
 <div class="viewcode-block" id="node_array"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.node_array.html#sdynpy.core.sdynpy_geometry.node_array">[docs]</a><span class="k">def</span> <span class="nf">node_array</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">coordinate</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">color</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">def_cs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">disp_cs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                <span class="n">structured_array</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
@@ -2459,7 +2488,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    -------</span>
 <span class="sd">    node_array : NodeArray</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="nb">id</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span>
             <span class="n">coordinate</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;coordinate&#39;</span><span class="p">]</span>
@@ -2641,7 +2670,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">    traceline_array : TracelineArray</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="nb">id</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span>
             <span class="n">description</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="s1">&#39;description&#39;</span><span class="p">]</span>
@@ -2664,17 +2693,18 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
     <span class="n">tlarray</span><span class="o">.</span><span class="n">description</span> <span class="o">=</span> <span class="n">description</span>
     <span class="n">tlarray</span><span class="o">.</span><span class="n">color</span> <span class="o">=</span> <span class="n">color</span>
     <span class="k">if</span> <span class="n">tlarray</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">tlarray</span><span class="p">,</span> <span class="s1">&#39;connectivity&#39;</span><span class="p">)[()]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">tlarray</span><span class="p">,</span> <span class="s1">&#39;connectivity&#39;</span><span class="p">)[()]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">connectivity</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">(</span><span class="nb">id</span><span class="p">):</span>
-            <span class="n">tlarray</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">],</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
+            <span class="n">tlarray</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint32&#39;</span><span class="p">)</span>
 
     <span class="k">return</span> <span class="n">tlarray</span></div>
 
 
-<span class="kn">from</span> <span class="nn">..fem.sdynpy_exodus</span> <span class="kn">import</span> <span class="n">Exodus</span><span class="p">,</span> <span class="n">ExodusInMemory</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">shape_array</span>
+<span class="kn">from</span> <span class="nn">..fem.sdynpy_exodus</span> <span class="kn">import</span> <span class="n">Exodus</span><span class="p">,</span> <span class="n">ExodusInMemory</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">shape_array</span>  <span class="c1"># noqa: E402</span>
+
 
 <div class="viewcode-block" id="Geometry"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry">[docs]</a><span class="k">class</span> <span class="nc">Geometry</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Container for nodes, coordinate systems, tracelines, and elements</span>
@@ -2780,14 +2810,14 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                                         <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)])</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">traceline</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">traceline</span><span class="p">,</span> <span class="n">new_traceline</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="Geometry.add_element"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.add_element">[docs]</a>    <span class="k">def</span> <span class="nf">add_element</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elem_type</span><span class="p">,</span> <span class="n">connectivity</span><span class="p">,</span> <span class="nb">id</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<div class="viewcode-block" id="Geometry.add_element"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.add_element">[docs]</a>    <span class="k">def</span> <span class="nf">add_element</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">elem_type</span><span class="p">,</span> <span class="n">connectivity</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="nb">id</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">id</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="nb">id</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="n">new_element</span> <span class="o">=</span> <span class="n">element_array</span><span class="p">((</span><span class="nb">id</span><span class="p">,),</span><span class="n">elem_type</span><span class="p">,</span><span class="n">color</span><span class="p">,[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">element</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="p">,</span><span class="n">new_element</span><span class="p">))</span></div>
+        <span class="n">new_element</span> <span class="o">=</span> <span class="n">element_array</span><span class="p">((</span><span class="nb">id</span><span class="p">,),</span> <span class="n">elem_type</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">element</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="p">,</span> <span class="n">new_element</span><span class="p">))</span></div>
 
 <div class="viewcode-block" id="Geometry.from_unv"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.from_unv">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_unv</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">unv_dict</span><span class="p">):</span>
@@ -2839,11 +2869,175 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 
     <span class="n">from_uff</span> <span class="o">=</span> <span class="n">from_unv</span>
 
+<div class="viewcode-block" id="Geometry.write_excel_template"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.write_excel_template">[docs]</a>    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">write_excel_template</span><span class="p">(</span><span class="n">path_to_xlsx</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Writes an Excel File Template for Creating Geometry</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        path_to_xlsx : string</span>
+<span class="sd">            Path to write xlsx Excel file</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Nothing</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        See documentation for from_excel_template for instructions on filling</span>
+<span class="sd">        out the template to create a geometry.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">writer</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">ExcelWriter</span><span class="p">(</span><span class="n">path_to_xlsx</span><span class="p">,</span><span class="n">engine</span><span class="o">=</span><span class="s1">&#39;xlsxwriter&#39;</span><span class="p">)</span>
+
+        <span class="n">df_coordinate_systems</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">,</span><span class="s1">&#39;Name&#39;</span><span class="p">,</span><span class="s1">&#39;Color&#39;</span><span class="p">,</span><span class="s1">&#39;Type&#39;</span><span class="p">,</span><span class="s1">&#39;X Location&#39;</span><span class="p">,</span><span class="s1">&#39;Y Location&#39;</span><span class="p">,</span><span class="s1">&#39;Z Location&#39;</span><span class="p">,</span><span class="s1">&#39;Rotation 1 Angle&#39;</span><span class="p">,</span><span class="s1">&#39;Rotation 1 Axis&#39;</span><span class="p">,</span><span class="s1">&#39;Rotation 2 Angle&#39;</span><span class="p">,</span><span class="s1">&#39;Rotation 2 Axis&#39;</span><span class="p">,</span><span class="s1">&#39;Rotation 3 Angle&#39;</span><span class="p">,</span><span class="s1">&#39;Rotation 3 Axis&#39;</span><span class="p">])</span>
+        <span class="n">df_coordinate_systems</span><span class="o">.</span><span class="n">to_excel</span><span class="p">(</span><span class="n">excel_writer</span> <span class="o">=</span> <span class="n">writer</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Coordinate Systems&#39;</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">df_nodes</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">,</span><span class="s1">&#39;Color&#39;</span><span class="p">,</span><span class="s1">&#39;X Location&#39;</span><span class="p">,</span><span class="s1">&#39;Y Location&#39;</span><span class="p">,</span><span class="s1">&#39;Z Location&#39;</span><span class="p">,</span><span class="s1">&#39;Displacement CS&#39;</span><span class="p">,</span><span class="s1">&#39;Definition CS&#39;</span><span class="p">])</span>
+        <span class="n">df_nodes</span><span class="o">.</span><span class="n">to_excel</span><span class="p">(</span><span class="n">excel_writer</span> <span class="o">=</span> <span class="n">writer</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Nodes&#39;</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">df_elements</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">,</span><span class="s1">&#39;Color&#39;</span><span class="p">,</span><span class="s1">&#39;Type&#39;</span><span class="p">]</span><span class="o">+</span><span class="p">[</span><span class="s1">&#39;Node </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">)])</span>
+        <span class="n">df_elements</span><span class="o">.</span><span class="n">to_excel</span><span class="p">(</span><span class="n">excel_writer</span> <span class="o">=</span> <span class="n">writer</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Elements&#39;</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">df_trace_lines</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">,</span><span class="s1">&#39;Description&#39;</span><span class="p">,</span><span class="s1">&#39;Color&#39;</span><span class="p">]</span><span class="o">+</span><span class="p">[</span><span class="s1">&#39;Node </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">)])</span>
+        <span class="n">df_trace_lines</span><span class="o">.</span><span class="n">to_excel</span><span class="p">(</span><span class="n">excel_writer</span> <span class="o">=</span> <span class="n">writer</span><span class="p">,</span> <span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Trace Lines&#39;</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="Geometry.from_excel_template"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.from_excel_template">[docs]</a>    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_excel_template</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path_to_xlsx</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a geometry from Excel file template</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        path_to_xlsx : string</span>
+<span class="sd">            Path to xlsx Excel file containing geometry information</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        Geometry</span>
+<span class="sd">            Geometry object created from the Excel file</span>
+
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        To use this function, first save out an excel template file using the</span>
+<span class="sd">        `write_excel_template` function.  This will construct an excel</span>
+<span class="sd">        workbook with four worksheets on which the different portions of the</span>
+<span class="sd">        Geometry are defined.</span>
+
+<span class="sd">        On the Coordinate Systems tab, users will define the various global and</span>
+<span class="sd">        local coordinate systems in their geometry.  Each geometry requires</span>
+<span class="sd">        an ID number.   A Name can optionally be given.  The Color should be</span>
+<span class="sd">        specified as an integer corresponding to the Ideas color map.  The</span>
+<span class="sd">        Type of the coordinate system should be an integer:</span>
+<span class="sd">            0 - Cartesian</span>
+<span class="sd">            1 - Polar</span>
+<span class="sd">            2 - Spherical</span>
+<span class="sd">        The origin of the coordinate system can be specified with the X Location,</span>
+<span class="sd">        Y Location, Z Location columns.  Then rotations of the coordinate system</span>
+<span class="sd">        can be specified using rotations about axes.  Up to three axes and angles</span>
+<span class="sd">        can be specified to create arbitrary compound rotations.  The rotation</span>
+<span class="sd">        axes should be X, Y, or Z, and the rotation angles are in degrees.</span>
+
+<span class="sd">        On the Nodes tab, all tabs must be filled out.  ID numbers must be unique.</span>
+<span class="sd">        Colors should be an integer corresponding to the Ideas color map.  The</span>
+<span class="sd">        position of the node is specified using the X Location, Y Location, Z</span>
+<span class="sd">        Location columns.  Each node has a displacement coordinate system in which</span>
+<span class="sd">        its position is defined, and a a definition coordinate system in which</span>
+<span class="sd">        its displacements are defined.  These columns should consist of integers</span>
+<span class="sd">        corresponding to ID numbers from the Coordinate Systems tab.</span>
+
+<span class="sd">        On the Elements tab, elements connecting nodes are defined.  The ID</span>
+<span class="sd">        column must consist of unique integer identifiers.  The Color tab should</span>
+<span class="sd">        be specified as an integer corresponding to the Ideas color map.  The</span>
+<span class="sd">        type can be a string (hex, quad, tet, tri, beam, etc.) or an integer</span>
+<span class="sd">        consisting of a universal file format element type.  If the type column</span>
+<span class="sd">        is empty, an element type based on the number of connections given will</span>
+<span class="sd">        be used. Defult element types for connection length of 2 is &quot;Type 21 - Linear Beam&quot;,</span>
+<span class="sd">        for a connection length of 3 is &quot;Type 41 - Plane Stress Linear Triangle&quot;,</span>
+<span class="sd">        and for a connection length of 4 is &quot;Type 44 - Plane Stress Linear Quadrilateral&quot;</span>
+<span class="sd">        The columns Node 1 through Node 20 contain the nodes in each element.  Only the required</span>
+<span class="sd">        number of nodes must be filled out (e.g. a tri element would only contain</span>
+<span class="sd">        3 nodes, so only columns Node 1, Node 2, and Node 3 would be filled).</span>
+
+<span class="sd">        On the Trace Lines tab, lines connecting the nodes are defined.  The</span>
+<span class="sd">        ID column must consist of unique integer identifiers.  The Description</span>
+<span class="sd">        column contains a string description of the line.  The Color column</span>
+<span class="sd">        should consist of an integer corresponding to the Ideas color map.  The</span>
+<span class="sd">        Node 1 through Node 20 columns should contain the nodes for each line.</span>
+<span class="sd">        Only the number of nodes in the line must be filled out, so if a line</span>
+<span class="sd">        only connects 5 nodes, only Node 1 though Node 5 must be filled.</span>
+
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="c1">#%% Read Geometry Information from Excel File</span>
+        <span class="n">df_coordinate_systems</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">io</span> <span class="o">=</span> <span class="n">path_to_xlsx</span><span class="p">,</span><span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Coordinate Systems&#39;</span><span class="p">)</span>
+        <span class="n">df_nodes</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">io</span> <span class="o">=</span> <span class="n">path_to_xlsx</span><span class="p">,</span><span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Nodes&#39;</span><span class="p">)</span>
+        <span class="n">df_elements</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">io</span> <span class="o">=</span> <span class="n">path_to_xlsx</span><span class="p">,</span><span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Elements&#39;</span><span class="p">)</span>
+        <span class="n">df_trace_lines</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="n">io</span> <span class="o">=</span> <span class="n">path_to_xlsx</span><span class="p">,</span><span class="n">sheet_name</span><span class="o">=</span><span class="s1">&#39;Trace Lines&#39;</span><span class="p">)</span>
+
+        <span class="c1">#%% Add Coordinate Systems</span>
+        <span class="c1"># Start with an empty coordinate system</span>
+        <span class="n">coordinate_systems</span> <span class="o">=</span> <span class="n">coordinate_system_array</span><span class="p">([])</span>
+        <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span> <span class="n">df_coordinate_system</span> <span class="ow">in</span> <span class="n">df_coordinate_systems</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+            <span class="c1"># Create Transformation Matrix</span>
+            <span class="n">T</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">rotation</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_coordinate_system</span><span class="p">)</span><span class="o">-</span><span class="mi">7</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+                <span class="n">rotation_angle</span> <span class="o">=</span> <span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Rotation &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">rotation</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; Angle&#39;</span><span class="p">]</span>
+                <span class="n">rotation_axis</span> <span class="o">=</span> <span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Rotation &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">rotation</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; Axis&#39;</span><span class="p">]</span>
+                <span class="n">rotation_axis_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;X&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span><span class="s1">&#39;Y&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span><span class="s1">&#39;Z&#39;</span><span class="p">:</span><span class="mi">2</span><span class="p">}</span>
+                <span class="k">if</span> <span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">rotation_angle</span><span class="p">)</span> <span class="ow">and</span> <span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">rotation_axis</span><span class="p">):</span>
+                    <span class="n">T</span> <span class="o">=</span> <span class="n">R</span><span class="p">(</span><span class="n">rotation_axis_dict</span><span class="p">[</span><span class="n">rotation_axis</span><span class="p">],</span><span class="n">rotation_angle</span><span class="p">,</span><span class="n">degrees</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="nd">@T</span>
+
+            <span class="c1"># Add Coordinate System to Geometry</span>
+            <span class="n">origin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;X Location&#39;</span><span class="p">],</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Y Location&#39;</span><span class="p">],</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Z Location&#39;</span><span class="p">]))[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+            <span class="n">coordinate_systems</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">coordinate_systems</span><span class="p">,</span><span class="n">coordinate_system_array</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">],</span> <span class="n">name</span><span class="o">=</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Name&#39;</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Color&#39;</span><span class="p">],</span> <span class="n">cs_type</span><span class="o">=</span><span class="n">df_coordinate_system</span><span class="p">[</span><span class="s1">&#39;Type&#39;</span><span class="p">],</span> <span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">T</span><span class="p">,</span><span class="n">origin</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))),</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="c1">#%% Add Nodes</span>
+        <span class="n">nodes</span> <span class="o">=</span> <span class="n">node_array</span><span class="p">(</span>
+            <span class="nb">id</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">]),</span>
+            <span class="n">coordinate</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;X Location&#39;</span><span class="p">])[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;Y Location&#39;</span><span class="p">])[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;Z Location&#39;</span><span class="p">])[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]],</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+            <span class="n">color</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;Color&#39;</span><span class="p">]),</span>
+            <span class="n">def_cs</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;Definition CS&#39;</span><span class="p">]),</span>
+            <span class="n">disp_cs</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">df_nodes</span><span class="p">[</span><span class="s1">&#39;Displacement CS&#39;</span><span class="p">]))</span>
+
+        <span class="c1">#%% Add Elements</span>
+        <span class="n">elements</span> <span class="o">=</span> <span class="n">element_array</span><span class="p">([])</span>
+        <span class="n">element_connection_length_dict</span> <span class="o">=</span> <span class="p">{</span>
+                                        <span class="mi">2</span><span class="p">:</span><span class="mi">21</span><span class="p">,</span> <span class="c1"># 21 : &#39;Linear Beam&#39;</span>
+                                        <span class="mi">3</span><span class="p">:</span><span class="mi">41</span><span class="p">,</span> <span class="c1"># 41 : &#39;Plane Stress Linear Triangle&#39;</span>
+                                        <span class="mi">4</span><span class="p">:</span><span class="mi">44</span><span class="p">,</span> <span class="c1"># 44 : &#39;Plane Stress Linear Quadrilateral&#39;</span>
+                                            <span class="p">}</span>
+        <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span> <span class="n">df_element</span> <span class="ow">in</span> <span class="n">df_elements</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+            <span class="n">connectivity</span> <span class="o">=</span> <span class="n">df_element</span><span class="p">[</span><span class="mi">3</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">connectivity</span> <span class="o">=</span> <span class="n">connectivity</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)]</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;Type&#39;</span><span class="p">],</span><span class="nb">str</span><span class="p">):</span>
+                <span class="n">element_type</span> <span class="o">=</span> <span class="n">_exodus_elem_type_map</span><span class="p">[</span><span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;Type&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span><span class="p">()]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;Type&#39;</span><span class="p">],</span><span class="nb">int</span><span class="p">):</span>
+                <span class="n">element_type</span> <span class="o">=</span> <span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;Type&#39;</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">pd</span><span class="o">.</span><span class="n">isnull</span><span class="p">(</span><span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;Type&#39;</span><span class="p">]):</span>
+                <span class="n">element_type</span> <span class="o">=</span> <span class="n">element_connection_length_dict</span><span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)]</span>
+            <span class="n">new_element</span> <span class="o">=</span> <span class="n">element_array</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">],</span> <span class="nb">type</span><span class="o">=</span><span class="n">element_type</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">df_element</span><span class="p">[</span><span class="s1">&#39;Color&#39;</span><span class="p">],</span><span class="n">connectivity</span><span class="o">=</span><span class="n">connectivity</span><span class="p">)</span>
+            <span class="n">elements</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">elements</span><span class="p">,</span><span class="n">new_element</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="c1">#%% Add Tracelines</span>
+        <span class="n">tracelines</span> <span class="o">=</span> <span class="n">traceline_array</span><span class="p">([])</span>
+        <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span> <span class="n">df_trace_line</span> <span class="ow">in</span> <span class="n">df_trace_lines</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
+            <span class="n">connectivity</span><span class="o">=</span><span class="n">df_trace_line</span><span class="p">[</span><span class="mi">3</span><span class="p">:]</span><span class="o">.</span><span class="n">values</span>
+            <span class="n">connectivity</span> <span class="o">=</span> <span class="n">connectivity</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">notnull</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)]</span>
+            <span class="n">new_traceline</span> <span class="o">=</span> <span class="n">traceline_array</span><span class="p">(</span><span class="n">connectivity</span><span class="o">=</span><span class="n">connectivity</span><span class="p">,</span><span class="nb">id</span><span class="o">=</span><span class="n">df_trace_line</span><span class="p">[</span><span class="s1">&#39;ID&#39;</span><span class="p">],</span><span class="n">description</span><span class="o">=</span><span class="n">df_trace_line</span><span class="p">[</span><span class="s1">&#39;Description&#39;</span><span class="p">],</span><span class="n">color</span><span class="o">=</span><span class="n">df_trace_line</span><span class="p">[</span><span class="s1">&#39;Color&#39;</span><span class="p">])</span>
+            <span class="n">tracelines</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">tracelines</span><span class="p">,</span><span class="n">new_traceline</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">coordinate_systems</span><span class="p">,</span> <span class="n">tracelines</span><span class="p">,</span> <span class="n">elements</span><span class="p">)</span></div>
+
 <div class="viewcode-block" id="Geometry.from_exodus"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.from_exodus">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_exodus</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">exo</span><span class="p">:</span> <span class="n">Exodus</span><span class="p">,</span> <span class="n">blocks</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">local</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
                     <span class="n">preferred_local_orientation</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)),</span>
                     <span class="n">secondary_preferred_local_orientation</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)),</span>
-                    <span class="n">local_nodes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+                    <span class="n">local_nodes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Generate a geometry from exodus file data</span>
 
@@ -2857,7 +3051,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        local : bool, optional</span>
 <span class="sd">            Flag to specify whether or not to create local coordinate systems</span>
 <span class="sd">            for each node in the model.  This can be useful when creating</span>
-<span class="sd">            instrumnetation positions from a finite element model, where the </span>
+<span class="sd">            instrumnetation positions from a finite element model, where the</span>
 <span class="sd">            sensor will be oriented perpendicular to the surface it is mounted</span>
 <span class="sd">            on.  The default is False, which returns all data in a single</span>
 <span class="sd">            global coordinate system.</span>
@@ -2870,7 +3064,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            along the global Z+ axis.</span>
 <span class="sd">        secondary_preferred_local_orientation : np.ndarray, optional</span>
 <span class="sd">            A secondary preferred direction is only used if the surface normal</span>
-<span class="sd">            direction is parallel to the primary preferred_local_orientation. </span>
+<span class="sd">            direction is parallel to the primary preferred_local_orientation.</span>
 <span class="sd">            The default is np.array((1.0,0.0,0.0)), which points the local</span>
 <span class="sd">            coordinate system along the local Z+ axis.</span>
 <span class="sd">        local_nodes : np.ndarray, optional</span>
@@ -2902,7 +3096,10 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
             <span class="n">blocks</span> <span class="o">=</span> <span class="p">[</span><span class="n">block</span> <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">exo</span><span class="o">.</span><span class="n">blocks</span> <span class="k">if</span> <span class="n">block</span><span class="o">.</span><span class="n">id</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">]</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">block</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">blocks</span><span class="p">):</span>
             <span class="n">color_index</span> <span class="o">=</span> <span class="p">(</span><span class="n">i</span> <span class="o">%</span> <span class="mi">14</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
-            <span class="n">connectivity</span> <span class="o">=</span> <span class="n">node_map</span><span class="p">[</span><span class="n">block</span><span class="o">.</span><span class="n">connectivity</span><span class="p">]</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">connectivity</span> <span class="o">=</span> <span class="n">node_map</span><span class="p">[</span><span class="n">block</span><span class="o">.</span><span class="n">connectivity</span><span class="p">]</span>
+            <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+                <span class="k">continue</span>
             <span class="n">elem_type</span> <span class="o">=</span> <span class="n">block</span><span class="o">.</span><span class="n">elem_type</span>
             <span class="k">if</span> <span class="n">connectivity</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
                 <span class="c1"># Skip conmasses</span>
@@ -2918,15 +3115,15 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                               <span class="n">elem_types</span><span class="p">,</span> <span class="n">elem_color</span><span class="p">,</span> <span class="n">elem_connectivity</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">local</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">local_nodes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">node_map_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">val</span><span class="p">:</span><span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span><span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">node_map</span><span class="p">)}</span>
+                <span class="n">node_map_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">val</span><span class="p">:</span> <span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">node_map</span><span class="p">)}</span>
                 <span class="n">local_node_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">node_map_dict</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">local_nodes</span><span class="p">])</span>
-                <span class="n">local_node_index_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">node_index</span><span class="p">:</span><span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span><span class="n">node_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">local_node_indices</span><span class="p">)}</span>
+                <span class="n">local_node_index_map</span> <span class="o">=</span> <span class="p">{</span><span class="n">node_index</span><span class="p">:</span> <span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">node_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">local_node_indices</span><span class="p">)}</span>
             <span class="n">preferred_local_orientation</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">preferred_local_orientation</span><span class="p">)</span>
             <span class="n">secondary_preferred_local_orientation</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span>
                 <span class="n">secondary_preferred_local_orientation</span><span class="p">)</span>
             <span class="c1"># Go through each node, find the block that it&#39;s in, get its elements</span>
             <span class="n">normal_sum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
-                <span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="k">if</span> <span class="n">local_nodes</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">local_nodes</span><span class="p">),</span>
+                <span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">local_nodes</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">local_nodes</span><span class="p">),</span>
                  <span class="mi">3</span><span class="p">))</span>
             <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">blocks</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">block</span><span class="o">.</span><span class="n">connectivity</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
@@ -2938,14 +3135,14 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                 <span class="n">normal_vectors</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">normal_vectors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
                 <span class="n">block_nodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">block</span><span class="o">.</span><span class="n">connectivity</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">local_nodes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">block_nodes</span> <span class="o">=</span> <span class="n">local_node_indices</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="n">local_node_indices</span><span class="p">,</span><span class="n">block_nodes</span><span class="p">)]</span>
+                    <span class="n">block_nodes</span> <span class="o">=</span> <span class="n">local_node_indices</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="n">local_node_indices</span><span class="p">,</span> <span class="n">block_nodes</span><span class="p">)]</span>
                 <span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">block_nodes</span><span class="p">:</span>
                     <span class="n">node_elements</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">block</span><span class="o">.</span><span class="n">connectivity</span> <span class="o">==</span> <span class="n">index</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
                     <span class="n">node_normals</span> <span class="o">=</span> <span class="n">normal_vectors</span><span class="p">[</span><span class="n">node_elements</span><span class="p">]</span>
                     <span class="k">if</span> <span class="n">local_nodes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                         <span class="n">normal_sum</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">node_normals</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
                     <span class="k">else</span><span class="p">:</span>
-                        <span class="n">normal_sum</span><span class="p">[</span><span class="n">local_node_index_map</span><span class="p">[</span><span class="n">index</span><span class="p">]]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">node_normals</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                        <span class="n">normal_sum</span><span class="p">[</span><span class="n">local_node_index_map</span><span class="p">[</span><span class="n">index</span><span class="p">]]</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">node_normals</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
             <span class="c1"># Put small nodes to reasonable values to avoid divide by zero</span>
             <span class="n">normal_sum</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">normal_sum</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">1e-14</span><span class="p">]</span> <span class="o">=</span> <span class="n">preferred_local_orientation</span>
             <span class="n">node_rotations</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,)</span> <span class="o">+</span> <span class="n">normal_sum</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
@@ -3052,9 +3249,9 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
             <span class="n">geometry</span><span class="o">.</span><span class="n">add_traceline</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">color</span><span class="o">=</span><span class="n">this_color</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">geometry</span></div>
 
-<div class="viewcode-block" id="Geometry.compress_ids"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.compress_ids">[docs]</a>    <span class="k">def</span> <span class="nf">compress_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">compress_nodes</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">compress_elements</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                     <span class="n">compress_tracelines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">compress_cs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                     <span class="n">return_maps</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<div class="viewcode-block" id="Geometry.compress_ids"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.compress_ids">[docs]</a>    <span class="k">def</span> <span class="nf">compress_ids</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">compress_nodes</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">compress_elements</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                     <span class="n">compress_tracelines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">compress_cs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                     <span class="n">return_maps</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compresses ID numbers to make node, element, etc. contiguous</span>
 
@@ -3099,30 +3296,30 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">compress_nodes</span><span class="p">:</span>
             <span class="n">to_nodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">size</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
             <span class="n">from_nodes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="n">node_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_nodes</span><span class="p">,</span><span class="n">to_nodes</span><span class="p">)</span>
+            <span class="n">node_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_nodes</span><span class="p">,</span> <span class="n">to_nodes</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">node_map</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">if</span> <span class="n">compress_cs</span><span class="p">:</span>
             <span class="n">to_cs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate_system</span><span class="o">.</span><span class="n">size</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
             <span class="n">from_cs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_system</span><span class="o">.</span><span class="n">id</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="n">cs_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_cs</span><span class="p">,</span><span class="n">to_cs</span><span class="p">)</span>
+            <span class="n">cs_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_cs</span><span class="p">,</span> <span class="n">to_cs</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">cs_map</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">if</span> <span class="n">compress_elements</span><span class="p">:</span>
             <span class="n">to_elements</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
             <span class="n">from_elements</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_system</span><span class="o">.</span><span class="n">id</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="n">element_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_elements</span><span class="p">,</span><span class="n">to_elements</span><span class="p">)</span>
+            <span class="n">element_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_elements</span><span class="p">,</span> <span class="n">to_elements</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">element_map</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">if</span> <span class="n">compress_tracelines</span><span class="p">:</span>
             <span class="n">to_tls</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">size</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
             <span class="n">from_tls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_system</span><span class="o">.</span><span class="n">id</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="n">traceline_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_tls</span><span class="p">,</span><span class="n">to_tls</span><span class="p">)</span>
+            <span class="n">traceline_map</span> <span class="o">=</span> <span class="n">id_map</span><span class="p">(</span><span class="n">from_tls</span><span class="p">,</span> <span class="n">to_tls</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">traceline_map</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">mapped_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_ids</span><span class="p">(</span><span class="n">node_map</span><span class="p">,</span><span class="n">traceline_map</span><span class="p">,</span><span class="n">element_map</span><span class="p">,</span><span class="n">cs_map</span><span class="p">)</span>
+        <span class="n">mapped_geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">map_ids</span><span class="p">(</span><span class="n">node_map</span><span class="p">,</span> <span class="n">traceline_map</span><span class="p">,</span> <span class="n">element_map</span><span class="p">,</span> <span class="n">cs_map</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">return_maps</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">mapped_geometry</span><span class="p">,</span><span class="n">node_map</span><span class="p">,</span><span class="n">traceline_map</span><span class="p">,</span><span class="n">element_map</span><span class="p">,</span><span class="n">cs_map</span>
+            <span class="k">return</span> <span class="n">mapped_geometry</span><span class="p">,</span> <span class="n">node_map</span><span class="p">,</span> <span class="n">traceline_map</span><span class="p">,</span> <span class="n">element_map</span><span class="p">,</span> <span class="n">cs_map</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">mapped_geometry</span></div>
 
@@ -3180,18 +3377,18 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                               <span class="n">connectivity</span><span class="o">=</span><span class="p">[</span><span class="n">val</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">tl_struct</span><span class="p">[</span><span class="s1">&#39;conn&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">flatten</span><span class="p">()])</span>
         <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">css</span><span class="p">,</span> <span class="n">tls</span><span class="p">,</span> <span class="n">elems</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="Geometry.global_node_coordinate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.global_node_coordinate">[docs]</a>    <span class="k">def</span> <span class="nf">global_node_coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_ids</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="Geometry.global_node_coordinate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.global_node_coordinate">[docs]</a>    <span class="k">def</span> <span class="nf">global_node_coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Position of the Geometry&#39;s nodes in the global coordinate system</span>
-<span class="sd">        </span>
+
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        node_ids : np.ndarray</span>
 <span class="sd">            An array of node id numbers to keep in the output coordinate.  If</span>
 <span class="sd">            not specified, all nodes will be returned in the order they are in</span>
 <span class="sd">            the model.</span>
-<span class="sd">        </span>
-<span class="sd">            </span>
+
+
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        np.ndarray</span>
@@ -3207,7 +3404,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="n">cs_array</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate_system</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">def_cs</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">node</span><span class="o">.</span><span class="n">global_coordinate</span><span class="p">(</span><span class="n">cs_array</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="Geometry.node_by_global_position"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position">[docs]</a>    <span class="k">def</span> <span class="nf">node_by_global_position</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">global_position_array</span><span class="p">):</span>
+<div class="viewcode-block" id="Geometry.node_by_global_position"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position">[docs]</a>    <span class="k">def</span> <span class="nf">node_by_global_position</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">global_position_array</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Select node by closest position</span>
 
@@ -3270,7 +3467,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            geometry</span>
 <span class="sd">        color_override : iterable, optional</span>
 <span class="sd">            An iterble of integers specifying colors, which will override the</span>
-<span class="sd">            existing geometry colors.  This should have the same length as the </span>
+<span class="sd">            existing geometry colors.  This should have the same length as the</span>
 <span class="sd">            `geometries` input.  The default is None, which keeps the original</span>
 <span class="sd">            geometry colors.</span>
 <span class="sd">        return_node_id_offset : bool, optional</span>
@@ -3317,7 +3514,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
             <span class="n">element_change</span><span class="o">=</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">elem_length</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span>
             <span class="n">coordinate_system_change</span><span class="o">=</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">cs_length</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">geometry</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">geometries</span><span class="p">)]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">color_override</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">color_override</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">color_override</span><span class="p">):</span>
                 <span class="n">new_geometries</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">color</span> <span class="o">=</span> <span class="n">color</span>
                 <span class="n">new_geometries</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">color</span> <span class="o">=</span> <span class="n">color</span>
@@ -3341,20 +3538,20 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        node_size : int, optional</span>
-<span class="sd">            Size to display the nodes in pixels.  Set to 0 to not display nodes. </span>
+<span class="sd">            Size to display the nodes in pixels.  Set to 0 to not display nodes.</span>
 <span class="sd">            The default is 5.</span>
 <span class="sd">        line_width : int, optional</span>
 <span class="sd">            Width to display tracelines and element edges in pixels.  Set to 0</span>
 <span class="sd">            to not show tracelines or edges.  The default is 1.</span>
 <span class="sd">        opacity : float, optional</span>
 <span class="sd">            A float between 0 and 1 to specify the transparency of the geometry.</span>
-<span class="sd">            Set to 1 for completely opaque, and 0 for completely transparent </span>
+<span class="sd">            Set to 1 for completely opaque, and 0 for completely transparent</span>
 <span class="sd">            (invisible).  The default is 1.0, no transparency.</span>
 <span class="sd">        view_up : np.ndarray, optional</span>
 <span class="sd">            Set the &quot;up&quot; direction in the plot by passing in a size-3 numpy</span>
 <span class="sd">            array.  The default is None.</span>
 <span class="sd">        view_from : np.ndarray, optional</span>
-<span class="sd">            Specify the direction from which the geometry is viewed.  The </span>
+<span class="sd">            Specify the direction from which the geometry is viewed.  The</span>
 <span class="sd">            default is None.</span>
 <span class="sd">        plotter : BackgroundPlotter, optional</span>
 <span class="sd">            A plotter can be specified to plot the geometry in an existing</span>
@@ -3404,7 +3601,6 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="n">solid_element_connectivity</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">solid_element_colors</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">solid_element_types</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">solid_element_offsets</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">node_colors</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">line_connectivity</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">line_colors</span> <span class="o">=</span> <span class="p">[]</span>
@@ -3437,7 +3633,6 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                 <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Do not know equivalent VTK element type for </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                         <span class="n">element</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="n">_element_types</span><span class="p">[</span><span class="n">element</span><span class="o">.</span><span class="n">type</span><span class="p">]))</span>
-                <span class="n">solid_element_offsets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">solid_element_connectivity</span><span class="p">))</span>
                 <span class="n">solid_element_connectivity</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">element</span><span class="o">.</span><span class="n">connectivity</span><span class="p">))</span>
                 <span class="k">try</span><span class="p">:</span>
                     <span class="k">if</span> <span class="n">solid_element_types</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="ow">in</span> <span class="n">_vtk_connectivity_reorder</span><span class="p">:</span>
@@ -3485,8 +3680,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">solid_element_connectivity</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">solid_mesh</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">solid_mesh</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">UnstructuredGrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">solid_element_offsets</span><span class="p">),</span>
-                                             <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">solid_element_connectivity</span><span class="p">),</span>
+            <span class="n">solid_mesh</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">UnstructuredGrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">solid_element_connectivity</span><span class="p">),</span>
                                              <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">solid_element_types</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;uint8&#39;</span><span class="p">),</span>
                                              <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">global_node_positions</span><span class="p">))</span>
             <span class="n">solid_mesh</span><span class="o">.</span><span class="n">cell_data</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">solid_element_colors</span>
@@ -3499,19 +3693,19 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">node_size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">point_mesh</span> <span class="o">=</span> <span class="n">pv</span><span class="o">.</span><span class="n">PolyData</span><span class="p">(</span><span class="n">global_node_positions</span><span class="p">)</span>
             <span class="n">point_mesh</span><span class="o">.</span><span class="n">cell_data</span><span class="p">[</span><span class="s1">&#39;color&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">node_colors</span>
-            <span class="n">plotter</span><span class="o">.</span><span class="n">add_mesh</span><span class="p">(</span><span class="n">point_mesh</span><span class="p">,</span> <span class="n">scalars</span><span class="o">=</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">colormap</span><span class="p">,</span> <span class="n">clim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">15</span><span class="p">],</span>
+            <span class="n">plotter</span><span class="o">.</span><span class="n">add_mesh</span><span class="p">(</span><span class="n">point_mesh</span><span class="p">,</span> <span class="n">scalars</span><span class="o">=</span><span class="n">node_colors</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">colormap</span><span class="p">,</span> <span class="n">clim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">15</span><span class="p">],</span>
                              <span class="n">show_edges</span><span class="o">=</span><span class="n">show_edges</span><span class="p">,</span>  <span class="c1"># True if line_width &gt; 0 else False,</span>
                              <span class="n">show_scalar_bar</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">point_size</span><span class="o">=</span><span class="n">node_size</span><span class="p">,</span>
                              <span class="n">opacity</span><span class="o">=</span><span class="n">opacity</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">point_mesh</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">view_from</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">view_from</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">focus</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">camera</span><span class="o">.</span><span class="n">focal_point</span>
             <span class="n">distance</span> <span class="o">=</span> <span class="n">plotter</span><span class="o">.</span><span class="n">camera</span><span class="o">.</span><span class="n">distance</span>
             <span class="n">plotter</span><span class="o">.</span><span class="n">camera</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
                 <span class="n">focus</span><span class="p">)</span> <span class="o">+</span> <span class="n">distance</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">view_from</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">view_from</span><span class="p">)</span>
             <span class="n">plotter</span><span class="o">.</span><span class="n">camera</span><span class="o">.</span><span class="n">focal_point</span> <span class="o">=</span> <span class="n">focus</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">view_up</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">view_up</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">plotter</span><span class="o">.</span><span class="n">camera</span><span class="o">.</span><span class="n">up</span> <span class="o">=</span> <span class="n">view_up</span>
         <span class="n">plotter</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
         <span class="n">plotter</span><span class="o">.</span><span class="n">render</span><span class="p">()</span>
@@ -3532,7 +3726,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            Coordinates to draw on the geometry.  If no coordinates are specified,</span>
 <span class="sd">            all translation degrees of freedom at each node will be plotted.</span>
 <span class="sd">        arrow_scale : float, optional</span>
-<span class="sd">            Size of the arrows in proportion to the length of the diagonal of </span>
+<span class="sd">            Size of the arrows in proportion to the length of the diagonal of</span>
 <span class="sd">            the bounding box of the Geometry if `arrow_scale_type` is &#39;bbox&#39;,</span>
 <span class="sd">            otherwise the raw length of the arrow.  The default is 0.1.</span>
 <span class="sd">        arrow_scale_type : str, optional</span>
@@ -3547,10 +3741,10 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            Default is 16.</span>
 <span class="sd">        opacity : float, optional</span>
 <span class="sd">            A float between 0 and 1 to specify the transparency of the geometry.</span>
-<span class="sd">            Set to 1 for completely opaque, and 0 for completely transparent </span>
+<span class="sd">            Set to 1 for completely opaque, and 0 for completely transparent</span>
 <span class="sd">            (invisible).  The default is 1.0, no transparency.</span>
 <span class="sd">        arrow_ends_on_node : bool, optional</span>
-<span class="sd">            If True, arrow tip ends at the node, otherwise the arrow begins at </span>
+<span class="sd">            If True, arrow tip ends at the node, otherwise the arrow begins at</span>
 <span class="sd">            node.</span>
 <span class="sd">            Defualt is False</span>
 <span class="sd">        plot_kwargs : dict, optional</span>
@@ -3698,11 +3892,11 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            Any additional arguments that should be passed to the</span>
 <span class="sd">            BackgroundPlotter initializer.  The default is {&#39;editor&#39;:False}.</span>
 <span class="sd">        undeformed_opacity : float, optional</span>
-<span class="sd">            A float between 0 and 1 to specify the transparency of the undeformed </span>
+<span class="sd">            A float between 0 and 1 to specify the transparency of the undeformed</span>
 <span class="sd">            geometry.  Set to 1 for completely opaque, and 0 for completely</span>
 <span class="sd">            transparent (invisible).  The default is 0.25.</span>
 <span class="sd">        deformed_opacity : float, optional</span>
-<span class="sd">            A float between 0 and 1 to specify the transparency of the deformed </span>
+<span class="sd">            A float between 0 and 1 to specify the transparency of the deformed</span>
 <span class="sd">            geometry.  Set to 1 for completely opaque, and 0 for completely</span>
 <span class="sd">            transparent (invisible). The default is 1.0.</span>
 <span class="sd">        starting_scale : float, optional</span>
@@ -3717,7 +3911,8 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">IGNORE_PLOTS</span><span class="p">:</span>
             <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="n">ShapePlotter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">background_plotter_kwargs</span><span class="p">,</span> <span class="n">undeformed_opacity</span><span class="p">,</span> <span class="n">deformed_opacity</span><span class="o">=</span><span class="n">deformed_opacity</span><span class="p">,</span> <span class="n">starting_scale</span><span class="o">=</span><span class="n">starting_scale</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">ShapePlotter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">background_plotter_kwargs</span><span class="p">,</span>
+                            <span class="n">undeformed_opacity</span><span class="p">,</span> <span class="n">deformed_opacity</span><span class="o">=</span><span class="n">deformed_opacity</span><span class="p">,</span> <span class="n">starting_scale</span><span class="o">=</span><span class="n">starting_scale</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="Geometry.plot_deflection_shape"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.plot_deflection_shape">[docs]</a>    <span class="k">def</span> <span class="nf">plot_deflection_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deflection_shape_data</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="p">{},</span>
                               <span class="n">background_plotter_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;editor&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -3737,11 +3932,11 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            Any additional arguments that should be passed to the</span>
 <span class="sd">            BackgroundPlotter initializer.  The default is {&#39;editor&#39;:False}.</span>
 <span class="sd">        undeformed_opacity : float, optional</span>
-<span class="sd">            A float between 0 and 1 to specify the transparency of the undeformed </span>
+<span class="sd">            A float between 0 and 1 to specify the transparency of the undeformed</span>
 <span class="sd">            geometry.  Set to 1 for completely opaque, and 0 for completely</span>
 <span class="sd">            transparent (invisible).  The default is 0.25.</span>
 <span class="sd">        deformed_opacity : float, optional</span>
-<span class="sd">            A float between 0 and 1 to specify the transparency of the deformed </span>
+<span class="sd">            A float between 0 and 1 to specify the transparency of the deformed</span>
 <span class="sd">            geometry.  Set to 1 for completely opaque, and 0 for completely</span>
 <span class="sd">            transparent (invisible). The default is 1.0.</span>
 <span class="sd">        starting_scale : float, optional</span>
@@ -3756,7 +3951,8 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">IGNORE_PLOTS</span><span class="p">:</span>
             <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">return</span> <span class="n">DeflectionShapePlotter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deflection_shape_data</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">background_plotter_kwargs</span><span class="p">,</span> <span class="n">undeformed_opacity</span><span class="p">,</span> <span class="n">deformed_opacity</span><span class="o">=</span><span class="n">deformed_opacity</span><span class="p">,</span> <span class="n">starting_scale</span><span class="o">=</span><span class="n">starting_scale</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">DeflectionShapePlotter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deflection_shape_data</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">background_plotter_kwargs</span><span class="p">,</span>
+                                      <span class="n">undeformed_opacity</span><span class="p">,</span> <span class="n">deformed_opacity</span><span class="o">=</span><span class="n">deformed_opacity</span><span class="p">,</span> <span class="n">starting_scale</span><span class="o">=</span><span class="n">starting_scale</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="Geometry.plot_transient"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.plot_transient">[docs]</a>    <span class="k">def</span> <span class="nf">plot_transient</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">displacement_data</span><span class="p">,</span> <span class="n">displacement_scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
                        <span class="n">frames_per_second</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
@@ -3950,19 +4146,19 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">geom_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">geom_out</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">node_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
             <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
                 <span class="n">geom_out</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">node_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
             <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
                 <span class="n">geom_out</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">node_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">connectivity</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">coordinate_system_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">coordinate_system_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">geom_out</span><span class="o">.</span><span class="n">coordinate_system</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">coordinate_system_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate_system</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
             <span class="n">geom_out</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">disp_cs</span> <span class="o">=</span> <span class="n">coordinate_system_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">disp_cs</span><span class="p">)</span>
             <span class="n">geom_out</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">def_cs</span> <span class="o">=</span> <span class="n">coordinate_system_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">def_cs</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">traceline_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">traceline_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">geom_out</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">traceline_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">traceline</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">element_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">element_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">geom_out</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">element_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">geom_out</span></div>
 
@@ -4051,7 +4247,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">full_coordinates</span><span class="p">)):</span>
                 <span class="n">direction</span> <span class="o">=</span> <span class="n">full_coordinates</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">local_direction</span><span class="p">()</span>
-                <span class="n">coord</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">node</span><span class="p">(</span><span class="n">full_coordinates</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">)</span><span class="o">.</span><span class="n">coordinate</span>
+                <span class="n">coord</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_node_coordinate</span><span class="p">(</span><span class="n">full_coordinates</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
                 <span class="n">rotation_shape_matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">phi</span><span class="p">[</span><span class="n">i</span> <span class="o">+</span> <span class="mi">3</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">3</span><span class="p">]</span> <span class="o">*</span> \
                     <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">direction</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_directions</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">coord</span> <span class="o">-</span> <span class="n">cg</span><span class="p">))</span>
         <span class="c1"># Concatenate shapes together</span>
@@ -4123,7 +4319,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        filename : str</span>
-<span class="sd">            Filename to which the geometry will be written.  If None, a </span>
+<span class="sd">            Filename to which the geometry will be written.  If None, a</span>
 <span class="sd">            unv data dictionary will be returned instead, similar to that</span>
 <span class="sd">            obtained from the readunv function in sdynpy</span>
 <span class="sd">        write_nodes : bool, optional</span>
@@ -4133,10 +4329,10 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">            If True, write the geometry&#39;s coordinate systems to dataset 2420</span>
 <span class="sd">            in the output file. The default is True.</span>
 <span class="sd">        write_tracelines : bool, optional</span>
-<span class="sd">            If True, write the geometry&#39;s tracelines to dataset 82 in the </span>
+<span class="sd">            If True, write the geometry&#39;s tracelines to dataset 82 in the</span>
 <span class="sd">            output file. The default is True.</span>
 <span class="sd">        write_elements : TYPE, optional</span>
-<span class="sd">            If True, write the geometry&#39;s elements to dataset 2412 in the </span>
+<span class="sd">            If True, write the geometry&#39;s elements to dataset 2412 in the</span>
 <span class="sd">            output file. The default is True.</span>
 <span class="sd">        dataset_2412_kwargs : dict, optional</span>
 <span class="sd">            Allows users to specify additional element parameters not stored</span>
@@ -4177,16 +4373,16 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                 <span class="n">node_unv</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">if</span> <span class="n">write_elements</span><span class="p">:</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;physical_property_table_numbers&#39;</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s1">&#39;physical_property_table_numbers&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
                     <span class="n">dataset_2412_kwargs</span><span class="p">[</span><span class="s1">&#39;physical_property_table_numbers&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;material_property_table_numbers&#39;</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s1">&#39;material_property_table_numbers&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
                     <span class="n">dataset_2412_kwargs</span><span class="p">[</span><span class="s1">&#39;material_property_table_numbers&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;beam_orientations&#39;</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s1">&#39;beam_orientations&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
                     <span class="n">dataset_2412_kwargs</span><span class="p">[</span><span class="s1">&#39;beam_aft_cross_section_numbers&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
                         <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;beam_aft_cross_section_numbers&#39;</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s1">&#39;beam_aft_cross_section_numbers&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
                     <span class="n">dataset_2412_kwargs</span><span class="p">[</span><span class="s1">&#39;beam_orientations&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;beam_fore_cross_section_numbers&#39;</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s1">&#39;beam_fore_cross_section_numbers&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">dataset_2412_kwargs</span><span class="p">:</span>
                     <span class="n">dataset_2412_kwargs</span><span class="p">[</span><span class="s1">&#39;beam_fore_cross_section_numbers&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
                         <span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">element</span><span class="o">.</span><span class="n">size</span>
                 <span class="n">elem_unv</span> <span class="o">=</span> <span class="n">dataset_2412</span><span class="o">.</span><span class="n">Sdynpy_UFF_Dataset_2412</span><span class="p">(</span>
@@ -4214,17 +4410,17 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">unv_dict</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">cs_unv</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">cs_unv</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;    -1</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;  2420</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">cs_unv</span><span class="o">.</span><span class="n">write_string</span><span class="p">())</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;    -1</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">node_unv</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">node_unv</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;    -1</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;  2411</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">node_unv</span><span class="o">.</span><span class="n">write_string</span><span class="p">())</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;    -1</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">elem_unv</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">elem_unv</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;    -1</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;  2412</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">elem_unv</span><span class="o">.</span><span class="n">write_string</span><span class="p">())</span>
@@ -4236,6 +4432,8 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
                     <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s1">&#39;    -1</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span></div></div>
 
 
+<span class="n">from_excel_template</span> <span class="o">=</span> <span class="n">Geometry</span><span class="o">.</span><span class="n">from_excel_template</span>
+<span class="n">write_excel_template</span> <span class="o">=</span> <span class="n">Geometry</span><span class="o">.</span><span class="n">write_excel_template</span>
 <span class="n">from_imat_struct</span> <span class="o">=</span> <span class="n">Geometry</span><span class="o">.</span><span class="n">from_imat_struct</span>
 <span class="n">from_exodus</span> <span class="o">=</span> <span class="n">Geometry</span><span class="o">.</span><span class="n">from_exodus</span>
 <span class="n">from_unv</span> <span class="o">=</span> <span class="n">Geometry</span><span class="o">.</span><span class="n">from_unv</span>
@@ -4264,7 +4462,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">from_ids</span> <span class="o">=</span> <span class="n">from_ids</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">to_ids</span> <span class="o">=</span> <span class="n">to_ids</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mapper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">({</span><span class="n">f</span><span class="p">:</span> <span class="n">t</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">from_ids</span><span class="p">,</span> <span class="n">to_ids</span><span class="p">)}</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">,</span><span class="n">otypes</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;uint64&#39;</span><span class="p">])</span></div>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mapper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vectorize</span><span class="p">({</span><span class="n">f</span><span class="p">:</span> <span class="n">t</span> <span class="k">for</span> <span class="n">f</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">from_ids</span><span class="p">,</span> <span class="n">to_ids</span><span class="p">)}</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">,</span> <span class="n">otypes</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;uint64&#39;</span><span class="p">])</span></div>
 
     <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -4293,7 +4491,7 @@ <h1>Source code for sdynpy.core.sdynpy_geometry</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebb760b0>
+<jinja2.runtime.BlockReference object at 0x7f5a2704fd60>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_matrix.html b/_modules/sdynpy/core/sdynpy_matrix.html
index b1ac56b..046aa20 100644
--- a/_modules/sdynpy/core/sdynpy_matrix.html
+++ b/_modules/sdynpy/core/sdynpy_matrix.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_matrix &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_matrix &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -110,16 +110,17 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">matplotlib.ticker</span> <span class="k">as</span> <span class="nn">ticker</span>
 <span class="kn">from</span> <span class="nn">.sdynpy_array</span> <span class="kn">import</span> <span class="n">SdynpyArray</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_coordinate</span> <span class="kn">import</span> <span class="n">CoordinateArray</span><span class="p">,</span><span class="n">outer_product</span><span class="p">,</span><span class="n">coordinate_array</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_coordinate</span> <span class="kn">import</span> <span class="n">CoordinateArray</span><span class="p">,</span> <span class="n">outer_product</span>
+
 
 <div class="viewcode-block" id="Matrix"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix">[docs]</a><span class="k">class</span> <span class="nc">Matrix</span><span class="p">(</span><span class="n">SdynpyArray</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Matrix with degrees of freedom stored for better bookkeeping.</span>
 
 <span class="sd">    Use the matrix helper function to create the object.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    
+
 <div class="viewcode-block" id="Matrix.data_dtype"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.data_dtype">[docs]</a>    <span class="nd">@staticmethod</span>
-    <span class="k">def</span> <span class="nf">data_dtype</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span><span class="n">columns</span><span class="p">,</span> <span class="n">is_complex</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">data_dtype</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">is_complex</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Data type of the underlying numpy structured array for real shapes</span>
 
@@ -127,7 +128,7 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 <span class="sd">        ----------</span>
 <span class="sd">        rows : int</span>
 <span class="sd">            Number of rows in the matrix</span>
-<span class="sd">        columns : int </span>
+<span class="sd">        columns : int</span>
 <span class="sd">            Number of columns in the matrix</span>
 
 <span class="sd">        Returns</span>
@@ -138,56 +139,56 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="p">[</span>
-            <span class="p">(</span><span class="s1">&#39;matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;complex128&#39;</span> <span class="k">if</span> <span class="n">is_complex</span> <span class="k">else</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span><span class="n">columns</span><span class="p">)),</span>
+            <span class="p">(</span><span class="s1">&#39;matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;complex128&#39;</span> <span class="k">if</span> <span class="n">is_complex</span> <span class="k">else</span> <span class="s1">&#39;float64&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="p">)),</span>
             <span class="p">(</span><span class="s1">&#39;row_coordinate&#39;</span><span class="p">,</span> <span class="n">CoordinateArray</span><span class="o">.</span><span class="n">data_dtype</span><span class="p">,</span> <span class="p">(</span><span class="n">rows</span><span class="p">,)),</span>
             <span class="p">(</span><span class="s1">&#39;column_coordinate&#39;</span><span class="p">,</span> <span class="n">CoordinateArray</span><span class="o">.</span><span class="n">data_dtype</span><span class="p">,</span> <span class="p">(</span><span class="n">columns</span><span class="p">,)),</span>
         <span class="p">]</span></div>
-<span class="w">    </span>
+
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Datatype for the underlying numpy structured array&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">is_complex</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">is_complex</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
                 <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="c1"># Create the ndarray instance of our type, given the usual</span>
         <span class="c1"># ndarray input arguments.  This will call the standard</span>
         <span class="c1"># ndarray constructor, but return an object of our type.</span>
         <span class="c1"># It also triggers a call to InfoArray.__array_finalize__</span>
         <span class="n">obj</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">Matrix</span><span class="p">,</span> <span class="n">subtype</span><span class="p">)</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="n">subtype</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span>
-                                             <span class="n">Matrix</span><span class="o">.</span><span class="n">data_dtype</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span><span class="n">ncols</span><span class="p">,</span> <span class="n">is_complex</span><span class="p">),</span>
+                                             <span class="n">Matrix</span><span class="o">.</span><span class="n">data_dtype</span><span class="p">(</span><span class="n">nrows</span><span class="p">,</span> <span class="n">ncols</span><span class="p">,</span> <span class="n">is_complex</span><span class="p">),</span>
                                              <span class="n">buffer</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">strides</span><span class="p">,</span>
                                              <span class="n">order</span><span class="p">)</span>
         <span class="c1"># Finally, we must return the newly created object:</span>
         <span class="k">return</span> <span class="n">obj</span>
-    
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the full coordinate array for the matrix</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="n">outer_product</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">)</span>
-    
+        <span class="k">return</span> <span class="n">outer_product</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">)</span>
+
     <span class="nd">@coordinate</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">value</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s1">&#39;Cannot set coordinate directly.  Set row_coordinate or column_coordinate instead.&#39;</span><span class="p">)</span>
-    
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">num_coordinate_rows</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the number of coordinate rows</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-    
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">num_coordinate_columns</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the number of coordinate columns</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    
+
     <span class="k">def</span> <span class="fm">__setitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">([(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="ow">is</span> <span class="n">CoordinateArray</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">key</span><span class="p">]):</span>
             <span class="c1"># Get indices corresponding to the coordinates</span>
-            <span class="n">row_request</span><span class="p">,</span><span class="n">column_request</span> <span class="o">=</span> <span class="n">key</span>
-            <span class="k">if</span> <span class="n">row_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">row_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">slice</span> <span class="ow">and</span> <span class="n">row_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">row_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">):</span>
+            <span class="n">row_request</span><span class="p">,</span> <span class="n">column_request</span> <span class="o">=</span> <span class="n">key</span>
+            <span class="k">if</span> <span class="n">row_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">row_request</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="ow">and</span> <span class="n">row_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row_request</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">)):</span>
                 <span class="n">matrix_row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_rows</span><span class="p">)</span>
                 <span class="n">request_row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_rows</span><span class="p">)</span>
                 <span class="n">row_multiplications</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_rows</span><span class="p">)</span>
@@ -218,7 +219,7 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
                 <span class="c1"># # Invert the indices to return the dofs in the correct order as specified in keys</span>
                 <span class="c1"># inverse_row_indices = np.zeros(matrix_row_indices.shape, dtype=int)</span>
                 <span class="c1"># inverse_row_indices[matrix_row_indices] = np.arange(len(matrix_row_indices))</span>
-            <span class="k">if</span> <span class="n">column_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">column_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">slice</span> <span class="ow">and</span> <span class="n">column_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">column_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">column_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">column_request</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="ow">and</span> <span class="n">column_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">column_request</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">)):</span>
                 <span class="n">matrix_col_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_columns</span><span class="p">)</span>
                 <span class="n">request_col_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_columns</span><span class="p">)</span>
                 <span class="n">col_multiplications</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_columns</span><span class="p">)</span>
@@ -250,18 +251,18 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
             <span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
             <span class="n">value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">value</span><span class="p">,</span>
                                     <span class="n">value</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">consistent_row_coordinates</span><span class="p">),</span>
-                                     <span class="nb">len</span><span class="p">(</span><span class="n">consistent_col_coordinates</span><span class="p">)))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">matrix_row_indices</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">matrix_col_indices</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
-                <span class="n">value</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">request_row_indices</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">request_col_indices</span><span class="p">]</span>
-                <span class="o">*</span> <span class="n">row_multiplications</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">col_multiplications</span><span class="p">)</span>
+                                                        <span class="nb">len</span><span class="p">(</span><span class="n">consistent_col_coordinates</span><span class="p">)))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">matrix_row_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">matrix_col_indices</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">value</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">request_row_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">request_col_indices</span><span class="p">]</span>
+                <span class="o">*</span> <span class="n">row_multiplications</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">col_multiplications</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__setitem__</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="n">value</span><span class="p">)</span>
-    
+            <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__setitem__</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
+
     <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
         <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="ow">is</span> <span class="nb">tuple</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">key</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">([(</span><span class="nb">type</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="ow">is</span> <span class="n">CoordinateArray</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">key</span><span class="p">]):</span>
             <span class="c1"># Get indices corresponding to the coordinates</span>
-            <span class="n">row_request</span><span class="p">,</span><span class="n">column_request</span> <span class="o">=</span> <span class="n">key</span>
-            <span class="k">if</span> <span class="n">row_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">row_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">slice</span> <span class="ow">and</span> <span class="n">row_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">row_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">):</span>
+            <span class="n">row_request</span><span class="p">,</span> <span class="n">column_request</span> <span class="o">=</span> <span class="n">key</span>
+            <span class="k">if</span> <span class="n">row_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">row_request</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="ow">and</span> <span class="n">row_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">row_request</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">)):</span>
                 <span class="n">matrix_row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_rows</span><span class="p">)</span>
                 <span class="n">inverse_row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_rows</span><span class="p">)</span>
                 <span class="n">row_multiplications</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_rows</span><span class="p">)</span>
@@ -292,7 +293,7 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
                 <span class="c1"># Invert the indices to return the dofs in the correct order as specified in keys</span>
                 <span class="n">inverse_row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">request_row_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
                 <span class="n">inverse_row_indices</span><span class="p">[</span><span class="n">request_row_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">request_row_indices</span><span class="p">))</span>
-            <span class="k">if</span> <span class="n">column_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">column_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">slice</span> <span class="ow">and</span> <span class="n">column_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">type</span><span class="p">(</span><span class="n">column_request</span><span class="p">)</span> <span class="o">==</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">column_request</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">column_request</span><span class="p">,</span> <span class="nb">slice</span><span class="p">)</span> <span class="ow">and</span> <span class="n">column_request</span> <span class="o">==</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">column_request</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="bp">Ellipsis</span><span class="p">)):</span>
                 <span class="n">matrix_col_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_columns</span><span class="p">)</span>
                 <span class="n">inverse_col_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_columns</span><span class="p">)</span>
                 <span class="n">col_multiplications</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_coordinate_columns</span><span class="p">)</span>
@@ -321,21 +322,21 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
                 <span class="c1"># Invert the indices to return the dofs in the correct order as specified in keys</span>
                 <span class="n">inverse_col_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">request_col_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
                 <span class="n">inverse_col_indices</span><span class="p">[</span><span class="n">request_col_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">request_col_indices</span><span class="p">))</span>
-            <span class="n">return_value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">matrix_row_indices</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">matrix_col_indices</span><span class="p">]</span> <span class="o">*</span> <span class="n">row_multiplications</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">col_multiplications</span>
-            <span class="n">return_value</span> <span class="o">=</span> <span class="n">return_value</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">inverse_row_indices</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">inverse_col_indices</span><span class="p">]</span>
+            <span class="n">return_value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">matrix_row_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">matrix_col_indices</span><span class="p">]</span> <span class="o">*</span> <span class="n">row_multiplications</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">col_multiplications</span>
+            <span class="n">return_value</span> <span class="o">=</span> <span class="n">return_value</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">inverse_row_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">inverse_col_indices</span><span class="p">]</span>
             <span class="k">return</span> <span class="n">return_value</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">output</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;row_coordinate&#39;</span><span class="p">,</span><span class="s1">&#39;column_coordinate&#39;</span><span class="p">]:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;row_coordinate&#39;</span><span class="p">,</span> <span class="s1">&#39;column_coordinate&#39;</span><span class="p">]:</span>
                 <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">CoordinateArray</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">output</span>
-            
+
     <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;matrix = </span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">+</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">),</span>
-                          <span class="s1">&#39;row coordinates = </span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">+</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">),</span>
-                          <span class="s1">&#39;column coordinates = </span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">+</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">)])</span>
-    
+                            <span class="s1">&#39;row coordinates = </span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">+</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">),</span>
+                            <span class="s1">&#39;column coordinates = </span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">+</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">)])</span>
+
 <div class="viewcode-block" id="Matrix.argsort_coordinate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.argsort_coordinate">[docs]</a>    <span class="k">def</span> <span class="nf">argsort_coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns indices used to sort the coordinates on the rows and columns</span>
@@ -348,13 +349,13 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 <span class="sd">            Indices used to sort the column coordinates</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-        <span class="n">column_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">matrix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
+        <span class="n">row_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+        <span class="n">column_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">matrix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
             <span class="n">row_indices</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
             <span class="n">column_indices</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">row_indices</span><span class="p">[</span><span class="o">...</span><span class="p">,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">column_indices</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span></div>
-    
+        <span class="k">return</span> <span class="n">row_indices</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">column_indices</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span></div>
+
 <div class="viewcode-block" id="Matrix.sort_coordinate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.sort_coordinate">[docs]</a>    <span class="k">def</span> <span class="nf">sort_coordinate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns a copy of the Matrix with coordinate sorted</span>
@@ -367,13 +368,13 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">sorting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">argsort_coordinate</span><span class="p">()</span>
         <span class="n">return_val</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span><span class="n">matrix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
-            <span class="n">return_val</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="o">...</span><span class="p">,</span><span class="n">sorting</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">key</span><span class="p">],</span><span class="n">sorting</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">key</span><span class="p">]]</span>
-            <span class="n">return_val</span><span class="o">.</span><span class="n">row_coordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">sorting</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">key</span><span class="p">][:,</span><span class="mi">0</span><span class="p">]]</span>
-            <span class="n">return_val</span><span class="o">.</span><span class="n">column_coordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">sorting</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">,:]]</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">matrix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">():</span>
+            <span class="n">return_val</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="o">...</span><span class="p">,</span> <span class="n">sorting</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">key</span><span class="p">],</span> <span class="n">sorting</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">key</span><span class="p">]]</span>
+            <span class="n">return_val</span><span class="o">.</span><span class="n">row_coordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">sorting</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">key</span><span class="p">][:,</span> <span class="mi">0</span><span class="p">]]</span>
+            <span class="n">return_val</span><span class="o">.</span><span class="n">column_coordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">sorting</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">,</span> <span class="p">:]]</span>
         <span class="k">return</span> <span class="n">return_val</span></div>
-            
-<div class="viewcode-block" id="Matrix.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span><span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="o">**</span><span class="n">imshow_args</span><span class="p">):</span>
+
+<div class="viewcode-block" id="Matrix.plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.plot">[docs]</a>    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_colorbar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">imshow_args</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Plots the matrix with coordinates labeled</span>
 
@@ -396,7 +397,8 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot plot more than one Matrix object simultaneously&#39;</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+
         <span class="nd">@ticker</span><span class="o">.</span><span class="n">FuncFormatter</span>
         <span class="k">def</span> <span class="nf">row_formatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span>
             <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">):</span>
@@ -405,6 +407,7 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
                 <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">row_coordinate</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)])</span>
             <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
                 <span class="k">return</span> <span class="s1">&#39;&#39;</span>
+
         <span class="nd">@ticker</span><span class="o">.</span><span class="n">FuncFormatter</span>
         <span class="k">def</span> <span class="nf">col_formatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span>
             <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">):</span>
@@ -413,16 +416,16 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
                 <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">column_coordinate</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)])</span>
             <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
                 <span class="k">return</span> <span class="s1">&#39;&#39;</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">,</span><span class="o">**</span><span class="n">imshow_args</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">matrix</span><span class="p">,</span> <span class="o">**</span><span class="n">imshow_args</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">show_colorbar</span><span class="p">:</span>
             <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">col_formatter</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">row_formatter</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span></div>
-        
+
 <div class="viewcode-block" id="Matrix.eye"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.eye">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">eye</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span><span class="n">row_coordinate</span><span class="p">,</span><span class="n">column_coordinate</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">eye</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">row_coordinate</span><span class="p">,</span> <span class="n">column_coordinate</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates an identity matrix with the specified coordinates</span>
 
@@ -442,11 +445,12 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">column_coordinate</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">column_coordinate</span> <span class="o">=</span> <span class="n">row_coordinate</span>
-        <span class="k">return</span> <span class="n">matrix</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">row_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="n">column_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
-                      <span class="n">row_coordinate</span><span class="p">,</span><span class="n">column_coordinate</span><span class="p">)</span></div></div>
-    
-<div class="viewcode-block" id="matrix"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.matrix.html#sdynpy.core.sdynpy_matrix.matrix">[docs]</a><span class="k">def</span> <span class="nf">matrix</span><span class="p">(</span><span class="n">matrix</span><span class="p">,</span><span class="n">row_coordinate</span><span class="p">,</span><span class="n">column_coordinate</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-            <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">matrix</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">row_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">column_coordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+                      <span class="n">row_coordinate</span><span class="p">,</span> <span class="n">column_coordinate</span><span class="p">)</span></div></div>
+
+
+<div class="viewcode-block" id="matrix"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_matrix.matrix.html#sdynpy.core.sdynpy_matrix.matrix">[docs]</a><span class="k">def</span> <span class="nf">matrix</span><span class="p">(</span><span class="n">matrix</span><span class="p">,</span> <span class="n">row_coordinate</span><span class="p">,</span> <span class="n">column_coordinate</span><span class="p">,</span> <span class="n">buffer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+           <span class="n">strides</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create a matrix object</span>
 
@@ -466,8 +470,8 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">shape</span> <span class="o">=</span> <span class="n">matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-    <span class="n">nrow</span><span class="p">,</span><span class="n">ncol</span> <span class="o">=</span> <span class="n">matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span>
-    <span class="n">m</span> <span class="o">=</span> <span class="n">Matrix</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span><span class="n">nrow</span><span class="p">,</span><span class="n">ncol</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">matrix</span><span class="p">),</span>
+    <span class="n">nrow</span><span class="p">,</span> <span class="n">ncol</span> <span class="o">=</span> <span class="n">matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">Matrix</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">nrow</span><span class="p">,</span> <span class="n">ncol</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="n">matrix</span><span class="p">),</span>
                <span class="n">buffer</span><span class="o">=</span><span class="n">buffer</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="n">offset</span><span class="p">,</span>
                <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
     <span class="n">m</span><span class="o">.</span><span class="n">row_coordinate</span> <span class="o">=</span> <span class="n">row_coordinate</span>
@@ -483,7 +487,7 @@ <h1>Source code for sdynpy.core.sdynpy_matrix</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebb77580>
+<jinja2.runtime.BlockReference object at 0x7f5a292c4280>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_shape.html b/_modules/sdynpy/core/sdynpy_shape.html
index c1a8da2..dd244df 100644
--- a/_modules/sdynpy/core/sdynpy_shape.html
+++ b/_modules/sdynpy/core/sdynpy_shape.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_shape &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_shape &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -125,6 +125,9 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">..fem.sdynpy_dof</span> <span class="kn">import</span> <span class="n">by_condition_number</span><span class="p">,</span> <span class="n">by_effective_independence</span>
 <span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">qtpy.QtWidgets</span> <span class="kn">import</span> <span class="n">QDialog</span><span class="p">,</span> <span class="n">QTableWidget</span><span class="p">,</span> <span class="n">QDialogButtonBox</span><span class="p">,</span> <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QTableWidgetItem</span><span class="p">,</span> <span class="n">QAbstractItemView</span>
+<span class="kn">from</span> <span class="nn">qtpy.QtCore</span> <span class="kn">import</span> <span class="n">Qt</span>
+<span class="kn">import</span> <span class="nn">time</span>
 
 
 <div class="viewcode-block" id="ShapeArray"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray">[docs]</a><span class="k">class</span> <span class="nc">ShapeArray</span><span class="p">(</span><span class="n">sdynpy_array</span><span class="o">.</span><span class="n">SdynpyArray</span><span class="p">):</span>
@@ -181,7 +184,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="p">[</span>
             <span class="p">(</span><span class="s1">&#39;frequency&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">),</span>
-            <span class="p">(</span><span class="s1">&#39;damping&#39;</span><span class="p">,</span> <span class="s1">&#39;complex128&#39;</span><span class="p">),</span>
+            <span class="p">(</span><span class="s1">&#39;damping&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">),</span>
             <span class="p">(</span><span class="s1">&#39;coordinate&#39;</span><span class="p">,</span> <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">CoordinateArray</span><span class="o">.</span><span class="n">data_dtype</span><span class="p">,</span> <span class="p">(</span><span class="n">ndof</span><span class="p">,)),</span>
             <span class="p">(</span><span class="s1">&#39;shape_matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;complex128&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">ndof</span><span class="p">,)),</span>
             <span class="p">(</span><span class="s1">&#39;modal_mass&#39;</span><span class="p">,</span> <span class="s1">&#39;complex128&#39;</span><span class="p">),</span>
@@ -289,7 +292,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">return_value</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">output</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;coordinate&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;coordinate&#39;</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">output</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">CoordinateArray</span><span class="p">)</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">output</span>
@@ -375,18 +378,34 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot handle shapes besides 3Dof and 6Dof&#39;</span><span class="p">)</span>
             <span class="n">data_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">dataset</span><span class="o">.</span><span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">nodes</span><span class="p">])</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="n">this_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span>
-                <span class="n">coordinate</span><span class="o">=</span><span class="n">coordinates</span><span class="p">,</span>
-                <span class="n">shape_matrix</span><span class="o">=</span><span class="n">data_matrix</span><span class="p">,</span>
-                <span class="n">frequency</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span>
-                <span class="n">damping</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">modal_viscous_damping</span><span class="p">,</span>
-                <span class="n">modal_mass</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">modal_mass</span><span class="p">,</span>
-                <span class="n">comment1</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline1</span><span class="p">,</span>
-                <span class="n">comment2</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline2</span><span class="p">,</span>
-                <span class="n">comment3</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline3</span><span class="p">,</span>
-                <span class="n">comment4</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline4</span><span class="p">,</span>
-                <span class="n">comment5</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline5</span><span class="p">,</span>
-            <span class="p">)</span>
+            <span class="k">if</span> <span class="n">dataset</span><span class="o">.</span><span class="n">analysis_type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">this_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span>
+                    <span class="n">coordinate</span><span class="o">=</span><span class="n">coordinates</span><span class="p">,</span>
+                    <span class="n">shape_matrix</span><span class="o">=</span><span class="n">data_matrix</span><span class="p">,</span>
+                    <span class="n">frequency</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span>
+                    <span class="n">damping</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">modal_viscous_damping</span><span class="p">,</span>
+                    <span class="n">modal_mass</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">modal_mass</span><span class="p">,</span>
+                    <span class="n">comment1</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline1</span><span class="p">,</span>
+                    <span class="n">comment2</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline2</span><span class="p">,</span>
+                    <span class="n">comment3</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline3</span><span class="p">,</span>
+                    <span class="n">comment4</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline4</span><span class="p">,</span>
+                    <span class="n">comment5</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline5</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="n">dataset</span><span class="o">.</span><span class="n">analysis_type</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="n">this_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span>
+                    <span class="n">coordinate</span><span class="o">=</span><span class="n">coordinates</span><span class="p">,</span>
+                    <span class="n">shape_matrix</span><span class="o">=</span><span class="n">data_matrix</span><span class="p">,</span>
+                    <span class="n">frequency</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">eigenvalue</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">),</span>
+                    <span class="n">damping</span><span class="o">=-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">eigenvalue</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">eigenvalue</span><span class="p">),</span>
+                    <span class="n">modal_mass</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">modal_a</span><span class="p">,</span>
+                    <span class="n">comment1</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline1</span><span class="p">,</span>
+                    <span class="n">comment2</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline2</span><span class="p">,</span>
+                    <span class="n">comment3</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline3</span><span class="p">,</span>
+                    <span class="n">comment4</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline4</span><span class="p">,</span>
+                    <span class="n">comment5</span><span class="o">=</span><span class="n">dataset</span><span class="o">.</span><span class="n">idline5</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Analysis Type </span><span class="si">{:}</span><span class="s1"> for UFF Dataset 55 not implemented yet&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">analysis_type</span><span class="p">))</span>
             <span class="n">output_arrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_shape</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">combine</span><span class="p">:</span>
             <span class="n">output_arrays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">val</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">output_arrays</span><span class="p">])</span>
@@ -442,7 +461,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">            String denoting the nodal variable in the exodus file from which</span>
 <span class="sd">            the Z-direction rotation should be read. The default is `None` which</span>
 <span class="sd">            results in the Z-direction rotation not being read. Typically this</span>
-<span class="sd">            would be set to &#39;RotZ&#39; if rotational values are desired.    </span>
+<span class="sd">            would be set to &#39;RotZ&#39; if rotational values are desired.</span>
 <span class="sd">        timesteps : iterable, optional</span>
 <span class="sd">            A list of timesteps from which data should be read. The default is</span>
 <span class="sd">            `None`, which reads all timesteps.</span>
@@ -454,7 +473,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">exo</span><span class="p">,</span> <span class="n">Exodus</span><span class="p">):</span>
-            <span class="n">variables</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x_disp</span><span class="p">,</span> <span class="n">y_disp</span><span class="p">,</span> <span class="n">z_disp</span><span class="p">,</span> <span class="n">x_rot</span><span class="p">,</span> <span class="n">y_rot</span><span class="p">,</span> <span class="n">z_rot</span><span class="p">]</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">]</span>
+            <span class="n">variables</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x_disp</span><span class="p">,</span> <span class="n">y_disp</span><span class="p">,</span> <span class="n">z_disp</span><span class="p">,</span> <span class="n">x_rot</span><span class="p">,</span> <span class="n">y_rot</span><span class="p">,</span> <span class="n">z_rot</span><span class="p">]</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
             <span class="n">exo</span> <span class="o">=</span> <span class="n">exo</span><span class="o">.</span><span class="n">load_into_memory</span><span class="p">(</span><span class="n">close</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">variables</span><span class="o">=</span><span class="n">variables</span><span class="p">,</span> <span class="n">timesteps</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">blocks</span><span class="o">=</span><span class="p">[])</span>
         <span class="n">node_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
             <span class="n">exo</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">exo</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">exo</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span>
@@ -579,20 +598,20 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="n">damping_ratios</span> <span class="o">=</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">damping</span>
         <span class="n">angular_natural_frequencies</span> <span class="o">=</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
         <span class="n">angular_frequencies</span> <span class="o">=</span> <span class="n">frequencies</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-        <span class="n">response_shape_matrix</span> <span class="o">=</span> <span class="n">flat_self</span><span class="p">[</span><span class="n">responses</span><span class="p">]</span> <span class="c1"># nm x no</span>
-        <span class="n">reference_shape_matrix</span> <span class="o">=</span> <span class="n">flat_self</span><span class="p">[</span><span class="n">references</span><span class="p">]</span> <span class="c1"># nm x ni</span>
+        <span class="n">response_shape_matrix</span> <span class="o">=</span> <span class="n">flat_self</span><span class="p">[</span><span class="n">responses</span><span class="p">]</span>  <span class="c1"># nm x no</span>
+        <span class="n">reference_shape_matrix</span> <span class="o">=</span> <span class="n">flat_self</span><span class="p">[</span><span class="n">references</span><span class="p">]</span>  <span class="c1"># nm x ni</span>
         <span class="n">modal_mass</span> <span class="o">=</span> <span class="n">flat_self</span><span class="o">.</span><span class="n">modal_mass</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_complex</span><span class="p">():</span>
             <span class="n">poles</span> <span class="o">=</span> <span class="o">-</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_natural_frequencies</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">damping_ratios</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">angular_natural_frequencies</span>
-            <span class="n">denominator</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">modal_mass</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles</span><span class="p">))</span> <span class="c1"># nf x nm</span>
-            <span class="n">denominator_conj</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">modal_mass</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()))</span> <span class="c1"># nf x nm</span>
-            <span class="n">frf_ordinate</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;mo,mi,fm-&gt;oif&#39;</span><span class="p">,</span><span class="n">response_shape_matrix</span><span class="p">,</span><span class="n">reference_shape_matrix</span><span class="p">,</span><span class="n">denominator</span><span class="p">)</span> <span class="o">+</span> 
-                            <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;mo,mi,fm-&gt;oif&#39;</span><span class="p">,</span><span class="n">response_shape_matrix</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(),</span> <span class="n">reference_shape_matrix</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(),</span> <span class="n">denominator_conj</span><span class="p">))</span>
+            <span class="n">denominator</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">modal_mass</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles</span><span class="p">))</span>  <span class="c1"># nf x nm</span>
+            <span class="n">denominator_conj</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">modal_mass</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()))</span>  <span class="c1"># nf x nm</span>
+            <span class="n">frf_ordinate</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;mo,mi,fm-&gt;oif&#39;</span><span class="p">,</span> <span class="n">response_shape_matrix</span><span class="p">,</span> <span class="n">reference_shape_matrix</span><span class="p">,</span> <span class="n">denominator</span><span class="p">)</span> <span class="o">+</span>
+                            <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;mo,mi,fm-&gt;oif&#39;</span><span class="p">,</span> <span class="n">response_shape_matrix</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(),</span> <span class="n">reference_shape_matrix</span><span class="o">.</span><span class="n">conjugate</span><span class="p">(),</span> <span class="n">denominator_conj</span><span class="p">))</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">denominator</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">modal_mass</span><span class="o">*</span>
-                             <span class="p">(</span><span class="o">-</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span>
+            <span class="n">denominator</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="n">modal_mass</span> <span class="o">*</span>
+                             <span class="p">(</span><span class="o">-</span><span class="n">angular_frequencies</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span>
                               <span class="o">+</span> <span class="n">angular_natural_frequencies</span><span class="o">**</span><span class="mi">2</span>
-                              <span class="o">+</span> <span class="mi">2</span><span class="n">j</span><span class="o">*</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">angular_natural_frequencies</span><span class="p">))</span>
+                              <span class="o">+</span> <span class="mi">2</span><span class="n">j</span><span class="o">*</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">angular_natural_frequencies</span><span class="p">))</span>
 
             <span class="n">frf_ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;mo,mi,fm-&gt;oif&#39;</span><span class="p">,</span>
                                      <span class="n">response_shape_matrix</span><span class="p">,</span>
@@ -605,7 +624,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="c1"># Now package into a sdynpy array</span>
         <span class="n">output_data</span> <span class="o">=</span> <span class="n">sdynpy_data</span><span class="o">.</span><span class="n">data_array</span><span class="p">(</span><span class="n">sdynpy_data</span><span class="o">.</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
                                              <span class="n">frequencies</span><span class="p">,</span> <span class="n">frf_ordinate</span><span class="p">,</span>
-                                             <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">outer_product</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span><span class="n">references</span><span class="p">))</span>
+                                             <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">outer_product</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span> <span class="n">references</span><span class="p">))</span>
 
         <span class="k">return</span> <span class="n">output_data</span></div>
 
@@ -616,7 +635,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        nodelist_or_coordinate_array : iterable or CoordinateArray</span>
-<span class="sd">            Consists of either a list of node ids or a CoordinateArray.  The </span>
+<span class="sd">            Consists of either a list of node ids or a CoordinateArray.  The</span>
 <span class="sd">            ShapeArray will be reduced to only the degrees of freedom specified</span>
 
 <span class="sd">        Returns</span>
@@ -719,7 +738,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">expanded_shapes</span></div>
 
 <div class="viewcode-block" id="ShapeArray.transform_coordinate_system"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.transform_coordinate_system">[docs]</a>    <span class="k">def</span> <span class="nf">transform_coordinate_system</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">original_geometry</span><span class="p">,</span> <span class="n">new_geometry</span><span class="p">,</span> <span class="n">node_id_map</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rotations</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                                    <span class="n">missing_dofs_are_zero</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+                                    <span class="n">missing_dofs_are_zero</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Performs coordinate system transformations on the shape</span>
 
@@ -749,7 +768,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">            A ShapeArray that can now be plotted with the new geometry</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">original_geometry</span> <span class="o">=</span> <span class="n">original_geometry</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">node_id_map</span><span class="o">.</span><span class="n">from_ids</span><span class="p">)</span>
             <span class="n">original_geometry</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">node_id_map</span><span class="p">(</span><span class="n">original_geometry</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span><span class="p">)</span>
             <span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">node_id_map</span><span class="o">.</span><span class="n">from_ids</span><span class="p">)</span>
@@ -763,25 +782,25 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">missing_dofs_are_zero</span><span class="p">:</span>
             <span class="c1"># Find any coordinates that are not in shapes</span>
             <span class="n">shape_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">))</span>
-            <span class="n">coords_not_in_shape</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">coordinates</span><span class="p">),</span><span class="n">shape_coords</span><span class="p">)]</span>
+            <span class="n">coords_not_in_shape</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">coordinates</span><span class="p">),</span> <span class="n">shape_coords</span><span class="p">)]</span>
             <span class="c1"># Create a new shape array and set the coefficients to zero</span>
             <span class="n">append_shape_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="n">coords_not_in_shape</span><span class="o">.</span><span class="n">size</span><span class="p">,),</span>
                                            <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_matrix</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
             <span class="c1"># Create a shape</span>
-            <span class="n">append_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">coords_not_in_shape</span><span class="p">,</span><span class="n">append_shape_matrix</span><span class="p">)</span>
+            <span class="n">append_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">coords_not_in_shape</span><span class="p">,</span> <span class="n">append_shape_matrix</span><span class="p">)</span>
             <span class="c1"># Append it</span>
-            <span class="bp">self</span> <span class="o">=</span> <span class="n">ShapeArray</span><span class="o">.</span><span class="n">concatenate_dofs</span><span class="p">((</span><span class="bp">self</span><span class="p">,</span><span class="n">append_shape</span><span class="p">))</span>
+            <span class="bp">self</span> <span class="o">=</span> <span class="n">ShapeArray</span><span class="o">.</span><span class="n">concatenate_dofs</span><span class="p">((</span><span class="bp">self</span><span class="p">,</span> <span class="n">append_shape</span><span class="p">))</span>
         <span class="n">shape_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">coordinates</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="o">*</span><span class="n">coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
         <span class="n">new_shape_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;nij,nkj,...nk-&gt;...ni&#39;</span><span class="p">,</span> <span class="n">transform_to_new</span><span class="p">,</span>
                                      <span class="n">transform_from_original</span><span class="p">,</span> <span class="n">shape_matrix</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">new_shape_matrix</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">modal_mass</span><span class="p">,</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="ShapeArray.reduce_for_comparison"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.reduce_for_comparison">[docs]</a>    <span class="k">def</span> <span class="nf">reduce_for_comparison</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">comparison_shape</span><span class="p">,</span> <span class="n">node_id_map</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+<div class="viewcode-block" id="ShapeArray.reduce_for_comparison"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.reduce_for_comparison">[docs]</a>    <span class="k">def</span> <span class="nf">reduce_for_comparison</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">comparison_shape</span><span class="p">,</span> <span class="n">node_id_map</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">node_id_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">node_id_map</span><span class="o">.</span><span class="n">from_ids</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">node</span> <span class="o">=</span> <span class="n">node_id_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
-        <span class="n">common_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intersect1d</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">),</span><span class="nb">abs</span><span class="p">(</span><span class="n">comparison_shape</span><span class="o">.</span><span class="n">coordinate</span><span class="p">))</span>
+        <span class="n">common_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intersect1d</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">),</span> <span class="nb">abs</span><span class="p">(</span><span class="n">comparison_shape</span><span class="o">.</span><span class="n">coordinate</span><span class="p">))</span>
         <span class="n">reduced_self</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">common_dofs</span><span class="p">)</span>
         <span class="n">reduced_comparison</span> <span class="o">=</span> <span class="n">comparison_shape</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">common_dofs</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">reduced_self</span><span class="p">,</span> <span class="n">reduced_comparison</span></div>
@@ -817,7 +836,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">ys</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">ax</span></div>
 
-<div class="viewcode-block" id="ShapeArray.to_real"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.to_real">[docs]</a>    <span class="k">def</span> <span class="nf">to_real</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">force_angle</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<div class="viewcode-block" id="ShapeArray.to_real"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.to_real">[docs]</a>    <span class="k">def</span> <span class="nf">to_real</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">force_angle</span><span class="o">=-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">4</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates real shapes from complex shapes by collapsing the complexity</span>
 
@@ -838,14 +857,85 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_complex</span><span class="p">():</span>
             <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">matrix</span> <span class="o">=</span> <span class="n">collapse_complex_to_real</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_matrix</span><span class="p">,</span> 
-                                          <span class="n">force_angle</span> <span class="o">=</span> <span class="n">force_angle</span><span class="p">,</span>
+        <span class="n">matrix</span> <span class="o">=</span> <span class="n">collapse_complex_to_real</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_matrix</span><span class="p">,</span>
+                                          <span class="n">force_angle</span><span class="o">=</span><span class="n">force_angle</span><span class="p">,</span>
                                           <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="n">damped_natural_frequency</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">real</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">damped_natural_frequency</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">real</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="n">matrix</span> <span class="o">*=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">damped_natural_frequency</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="n">matrix</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="o">.</span><span class="n">real</span><span class="p">,</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">modal_mass</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ShapeArray.to_complex"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.to_complex">[docs]</a>    <span class="k">def</span> <span class="nf">to_complex</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates complex shapes from real shapes</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ShapeArray</span>
+<span class="sd">            Complex shapes compute from the real shape coefficients</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_complex</span><span class="p">():</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_matrix</span><span class="o">-</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_matrix</span>
+        <span class="n">damped_natural_frequency</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">real</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">matrix</span> <span class="o">/=</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">damped_natural_frequency</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="n">matrix</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="o">.</span><span class="n">real</span><span class="p">,</span>
+                           <span class="bp">self</span><span class="o">.</span><span class="n">modal_mass</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
+                           <span class="bp">self</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">comment5</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ShapeArray.normalize"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.normalize">[docs]</a>    <span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">system_or_matrix</span><span class="p">,</span> <span class="n">return_modal_matrix</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Computes A-normalized or mass-normalized shapes</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        system_or_matrix : System or np.ndarray</span>
+<span class="sd">            A System object or a mass matrix for real modes or A-matrix for</span>
+<span class="sd">            complex modes.</span>
+<span class="sd">        return_modal_matrix : bool, optional</span>
+<span class="sd">            If true, it will return the modal mass or modal-A matrix computed</span>
+<span class="sd">            from the normalized mode shapes.  The default is False.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ShapeArray</span>
+<span class="sd">            A copy of the original shape array with normalized shape coefficients</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_complex</span><span class="p">():</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">system_or_matrix</span><span class="p">,</span><span class="n">sdynpy_system</span><span class="o">.</span><span class="n">System</span><span class="p">):</span>
+                <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">system_or_matrix</span><span class="o">.</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span>                 <span class="n">Z</span><span class="p">,</span> <span class="n">system_or_matrix</span><span class="o">.</span><span class="n">M</span><span class="p">],</span>
+                              <span class="p">[</span><span class="n">system_or_matrix</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="n">system_or_matrix</span><span class="o">.</span><span class="n">C</span><span class="p">]])</span>
+                <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">system_or_matrix</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">A</span> <span class="o">=</span> <span class="n">system_or_matrix</span>
+                <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modeshape</span>
+            <span class="n">omega_r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
+            <span class="n">zeta_r</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">damping</span>
+            <span class="n">lam</span> <span class="o">=</span> <span class="o">-</span><span class="n">omega_r</span><span class="o">*</span><span class="n">zeta_r</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_r</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">lam</span><span class="o">*</span><span class="n">shapes</span><span class="p">,</span><span class="n">shapes</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">scale_factors</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E</span><span class="p">,</span><span class="n">A</span><span class="p">,</span><span class="n">E</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">return_modal_matrix</span><span class="p">:</span>
+                <span class="n">modal_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,kl-&gt;il&#39;</span><span class="p">,</span><span class="n">E</span><span class="o">*</span><span class="n">scale_factors</span><span class="p">,</span><span class="n">A</span><span class="p">,</span><span class="n">E</span><span class="o">*</span><span class="n">scale_factors</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">system_or_matrix</span><span class="p">,</span><span class="n">sdynpy_system</span><span class="o">.</span><span class="n">System</span><span class="p">):</span>
+                <span class="n">M</span> <span class="o">=</span> <span class="n">system_or_matrix</span><span class="o">.</span><span class="n">M</span>
+                <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">system_or_matrix</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">M</span> <span class="o">=</span> <span class="n">system_or_matrix</span>
+                <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">modeshape</span>
+            <span class="n">scale_factors</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">shapes</span><span class="p">,</span><span class="n">M</span><span class="p">,</span><span class="n">shapes</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">return_modal_matrix</span><span class="p">:</span>
+                <span class="n">modal_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,kl-&gt;il&#39;</span><span class="p">,</span><span class="n">shapes</span><span class="o">*</span><span class="n">scale_factors</span><span class="p">,</span><span class="n">M</span><span class="p">,</span><span class="n">shapes</span><span class="o">*</span><span class="n">scale_factors</span><span class="p">)</span>
+        <span class="n">output_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">output_shape</span> <span class="o">*=</span> <span class="n">scale_factors</span>
+        <span class="k">if</span> <span class="n">return_modal_matrix</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">output_shape</span><span class="p">,</span> <span class="n">modal_matrix</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">output_shape</span></div>
 
 <div class="viewcode-block" id="ShapeArray.write_to_unv"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.write_to_unv">[docs]</a>    <span class="k">def</span> <span class="nf">write_to_unv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">,</span> <span class="n">specific_data_type</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">load_case_number</span><span class="o">=</span><span class="mi">0</span>
                      <span class="p">):</span>
@@ -855,7 +945,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
 <span class="sd">        filename : str</span>
-<span class="sd">            Filename to which the geometry will be written.  If None, </span>
+<span class="sd">            Filename to which the geometry will be written.  If None,</span>
 <span class="sd">            unv data will be returned instead, similar to that</span>
 <span class="sd">            obtained from the readunv function in sdynpy</span>
 <span class="sd">        specific_data_type : int, optional</span>
@@ -972,7 +1062,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">            ShapeArray</span>
 <span class="sd">        color_override : iterable, optional</span>
 <span class="sd">            An iterble of integers specifying colors, which will override the</span>
-<span class="sd">            existing geometry colors.  This should have the same length as the </span>
+<span class="sd">            existing geometry colors.  This should have the same length as the</span>
 <span class="sd">            `geometries` input.  The default is None, which keeps the original</span>
 <span class="sd">            geometry colors.</span>
 
@@ -1027,9 +1117,9 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">        Returns</span>
 <span class="sd">        -------</span>
 <span class="sd">        response_array : TimeHistoryArray</span>
-<span class="sd">            Input forces assembled into a TimeHistoryArray.</span>
-<span class="sd">        reference_array : TimeHistoryArray</span>
 <span class="sd">            Responses assembled into a TimeHistoryArray.</span>
+<span class="sd">        reference_array : TimeHistoryArray</span>
+<span class="sd">            Input forces assembled into a TimeHistoryArray.</span>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_complex</span><span class="p">():</span>
@@ -1055,14 +1145,16 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="n">response</span> <span class="o">=</span> <span class="n">phi_out</span> <span class="o">@</span> <span class="n">modal_response</span>
             <span class="n">response_array</span> <span class="o">=</span> <span class="n">sdynpy_data</span><span class="o">.</span><span class="n">data_array</span><span class="p">(</span><span class="n">sdynpy_data</span><span class="o">.</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                                     <span class="n">times</span><span class="p">,</span> <span class="n">response</span><span class="p">,</span>
-                                                    <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">flat_self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="k">if</span> <span class="n">responses</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">responses</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+                                                    <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">flat_self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+                                                    <span class="k">if</span> <span class="n">responses</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">responses</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
             <span class="n">reference_array</span> <span class="o">=</span> <span class="n">sdynpy_data</span><span class="o">.</span><span class="n">data_array</span><span class="p">(</span><span class="n">sdynpy_data</span><span class="o">.</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
                                                      <span class="n">times</span><span class="p">,</span> <span class="n">forces</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">forces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
-                                                     <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">flat_self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="k">if</span> <span class="n">references</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">references</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+                                                     <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">flat_self</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+                                                     <span class="k">if</span> <span class="n">references</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">references</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
             <span class="k">return</span> <span class="n">response_array</span><span class="p">,</span> <span class="n">reference_array</span></div>
 
 <div class="viewcode-block" id="ShapeArray.optimize_degrees_of_freedom"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.optimize_degrees_of_freedom">[docs]</a>    <span class="k">def</span> <span class="nf">optimize_degrees_of_freedom</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sensors_to_keep</span><span class="p">,</span>
-                                    <span class="n">group_by_node</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;ei&#39;</span><span class="p">):</span>
+                                    <span class="n">group_by_node</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;ei&#39;</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a reduced set of shapes using optimal degrees of freedom</span>
 
@@ -1087,11 +1179,11 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="n">group_by_node</span><span class="p">:</span>
             <span class="n">nodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
             <span class="n">directions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">)</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
-            <span class="n">coordinate_array</span> <span class="o">=</span> <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">nodes</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">directions</span><span class="p">)</span>
+            <span class="n">coordinate_array</span> <span class="o">=</span> <span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">nodes</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">directions</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">coordinate_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">))</span>
         <span class="n">shape_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="p">[</span><span class="n">coordinate_array</span><span class="p">]</span>
-        <span class="n">shape_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">shape_matrix</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">shape_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">shape_matrix</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;ei&#39;</span><span class="p">:</span>
             <span class="n">indices</span> <span class="o">=</span> <span class="n">by_effective_independence</span><span class="p">(</span><span class="n">sensors_to_keep</span><span class="p">,</span> <span class="n">shape_matrix</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;cond&#39;</span><span class="p">:</span>
@@ -1205,9 +1297,122 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="k">if</span> <span class="n">table_format</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;csv&#39;</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
             <span class="k">elif</span> <span class="n">table_format</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;latex&#39;</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div></div>
+                <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">to_latex</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></div>
+            
+<div class="viewcode-block" id="ShapeArray.edit_comments"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.edit_comments">[docs]</a>    <span class="k">def</span> <span class="nf">edit_comments</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">geometry</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Opens up a table where the shape comments can be edited</span>
+<span class="sd">        </span>
+<span class="sd">        If a geometry is also passed, it will also open up a mode shape plotter</span>
+<span class="sd">        window where you can visualize the modes you are looking at.</span>
+<span class="sd">        </span>
+<span class="sd">        Edited comments will be stored back into the ShapeArray object when the</span>
+<span class="sd">        OK button is pressed.  Comments will not be stored if the Cancel button</span>
+<span class="sd">        is pressed.</span>
 
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        geometry : Geometry, optional</span>
+<span class="sd">            A geometry on which the shapes will be plotted.  If not specified,</span>
+<span class="sd">            a table will just open up.</span>
 
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ShapeCommentTable</span>
+<span class="sd">            A ShapeCommentTable displaying the modal information where comments</span>
+<span class="sd">            can be edited.</span>
+<span class="sd">            </span>
+<span class="sd">        Notes</span>
+<span class="sd">        -----</span>
+<span class="sd">        Due to how Python handles garbage collection, the table may be</span>
+<span class="sd">        immediately closed if not assigned to a variable, as Python things it</span>
+<span class="sd">        is no longer in use.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">plotter</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">plot_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ShapeCommentTable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">plotter</span><span class="p">)</span></div></div>
+        
+<div class="viewcode-block" id="ShapeCommentTable"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable">[docs]</a><span class="k">class</span> <span class="nc">ShapeCommentTable</span><span class="p">(</span><span class="n">QDialog</span><span class="p">):</span>
+<div class="viewcode-block" id="ShapeCommentTable.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">plotter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">parent</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Creates a table window that allows editing of comments on the mode</span>
+<span class="sd">        shapes.</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        shapes : ShapeArray</span>
+<span class="sd">            The shapes for which the comments need to be modified.</span>
+<span class="sd">        plotter : ShapePlotter, optional</span>
+<span class="sd">            A shape plotter that is to be linked to the table.  It should have</span>
+<span class="sd">            the same modes used for the table plotted on it.  The plotter will</span>
+<span class="sd">            automatically update the mode being displayed as different rows of</span>
+<span class="sd">            the table are selected. If not specified, there will be no mode</span>
+<span class="sd">            shape display linked to the table.</span>
+<span class="sd">        parent : QWidget, optional</span>
+<span class="sd">            Parent widget for the window. The default is No parent.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        None.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">parent</span><span class="p">)</span>
+        <span class="c1"># Add a table widget</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">setWindowTitle</span><span class="p">(</span><span class="s1">&#39;Shape Comment Editor&#39;</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">plotter</span> <span class="o">=</span> <span class="n">plotter</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span> <span class="o">=</span> <span class="n">shapes</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">button_box</span> <span class="o">=</span> <span class="n">QDialogButtonBox</span><span class="p">(</span><span class="n">QDialogButtonBox</span><span class="o">.</span><span class="n">Ok</span> <span class="o">|</span> <span class="n">QDialogButtonBox</span><span class="o">.</span><span class="n">Cancel</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">button_box</span><span class="o">.</span><span class="n">accepted</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">accept</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">button_box</span><span class="o">.</span><span class="n">rejected</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reject</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">layout</span> <span class="o">=</span> <span class="n">QVBoxLayout</span><span class="p">()</span>
+        <span class="c1"># Columns will be key, frequency, damping, comment1, comment2, ... comment5</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span> <span class="o">=</span> <span class="n">QTableWidget</span><span class="p">(</span><span class="n">shapes</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">currentItemChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update_mode</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">setHorizontalHeaderLabels</span><span class="p">([</span><span class="s1">&#39;Index&#39;</span><span class="p">,</span><span class="s1">&#39;Frequency&#39;</span><span class="p">,</span><span class="s1">&#39;Damping&#39;</span><span class="p">,</span><span class="s1">&#39;Comment1&#39;</span><span class="p">,</span><span class="s1">&#39;Comment2&#39;</span><span class="p">,</span><span class="s1">&#39;Comment3&#39;</span><span class="p">,</span><span class="s1">&#39;Comment4&#39;</span><span class="p">,</span><span class="s1">&#39;Comment5&#39;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">setSelectionMode</span><span class="p">(</span><span class="n">QAbstractItemView</span><span class="o">.</span><span class="n">SingleSelection</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">row</span><span class="p">,(</span><span class="n">shape_index</span><span class="p">,</span><span class="n">shape</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shapes</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()):</span>
+            <span class="k">for</span> <span class="n">column</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">shape_index</span><span class="p">,</span><span class="n">shape</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span>
+                                           <span class="n">shape</span><span class="o">.</span><span class="n">damping</span><span class="p">,</span>
+                                           <span class="n">shape</span><span class="o">.</span><span class="n">comment1</span><span class="p">,</span>
+                                           <span class="n">shape</span><span class="o">.</span><span class="n">comment2</span><span class="p">,</span>
+                                           <span class="n">shape</span><span class="o">.</span><span class="n">comment3</span><span class="p">,</span>
+                                           <span class="n">shape</span><span class="o">.</span><span class="n">comment4</span><span class="p">,</span>
+                                           <span class="n">shape</span><span class="o">.</span><span class="n">comment5</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="n">column</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="n">item</span> <span class="o">=</span> <span class="n">QTableWidgetItem</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+                <span class="k">elif</span> <span class="n">column</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="n">item</span> <span class="o">=</span> <span class="n">QTableWidgetItem</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">value</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">item</span> <span class="o">=</span> <span class="n">QTableWidgetItem</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">value</span><span class="p">))</span>
+                <span class="k">if</span> <span class="n">column</span> <span class="o">&lt;=</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="n">item</span><span class="o">.</span><span class="n">setFlags</span><span class="p">(</span><span class="n">item</span><span class="o">.</span><span class="n">flags</span><span class="p">()</span> <span class="o">^</span> <span class="n">Qt</span><span class="o">.</span><span class="n">ItemIsEditable</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">setItem</span><span class="p">(</span><span class="n">row</span><span class="p">,</span><span class="n">column</span><span class="p">,</span><span class="n">item</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">layout</span><span class="o">.</span><span class="n">addWidget</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">layout</span><span class="o">.</span><span class="n">addWidget</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">button_box</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">setLayout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">layout</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
+        
+<div class="viewcode-block" id="ShapeCommentTable.update_mode"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode">[docs]</a>    <span class="k">def</span> <span class="nf">update_mode</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">currentRow</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">plotter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">plotter</span><span class="o">.</span><span class="n">current_shape</span> <span class="o">=</span> <span class="n">row</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">plotter</span><span class="o">.</span><span class="n">reset_shape</span><span class="p">()</span></div>
+        
+<div class="viewcode-block" id="ShapeCommentTable.accept"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable.accept">[docs]</a>    <span class="k">def</span> <span class="nf">accept</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,(</span><span class="n">shape_index</span><span class="p">,</span><span class="n">shape</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">()):</span>
+            <span class="n">shape</span><span class="o">.</span><span class="n">comment1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">text</span><span class="p">()</span>
+            <span class="n">shape</span><span class="o">.</span><span class="n">comment2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">text</span><span class="p">()</span>
+            <span class="n">shape</span><span class="o">.</span><span class="n">comment3</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">text</span><span class="p">()</span>
+            <span class="n">shape</span><span class="o">.</span><span class="n">comment4</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">6</span><span class="p">)</span><span class="o">.</span><span class="n">text</span><span class="p">()</span>
+            <span class="n">shape</span><span class="o">.</span><span class="n">comment5</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_table</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">7</span><span class="p">)</span><span class="o">.</span><span class="n">text</span><span class="p">()</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">accept</span><span class="p">()</span></div></div>
+    
 <span class="c1">#    def string_array(self):</span>
 <span class="c1">#        return create_coordinate_string_array(self.node,self.direction)</span>
 <span class="c1">#</span>
@@ -1307,7 +1512,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="s1">&#39;comment5&#39;</span><span class="p">,</span>
     <span class="p">]</span>
     <span class="n">data</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">structured_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
             <span class="k">try</span><span class="p">:</span>
                 <span class="n">data</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">structured_array</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
@@ -1392,7 +1597,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="rigid_body_check"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.html#sdynpy.core.sdynpy_shape.rigid_body_check">[docs]</a><span class="k">def</span> <span class="nf">rigid_body_check</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">rigid_shapes</span><span class="p">,</span> <span class="n">distance</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">distance_number</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">distance_yscale</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">residuals_to_label</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
-                     <span class="n">return_shape_diagnostics</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">rigid_shape_kwargs</span><span class="p">):</span>
+                     <span class="n">return_shape_diagnostics</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">return_figures</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">rigid_shape_kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Performs rigid body checks, both looking at the complex plane and residuals</span>
 
@@ -1412,7 +1617,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">        Penalty factor applied to the complex portion of the shape when</span>
 <span class="sd">        computing distances. The default is 20.</span>
 <span class="sd">    residuals_to_label : int, optional</span>
-<span class="sd">        The top `residuals_to_label` residuals will be highlighted in the </span>
+<span class="sd">        The top `residuals_to_label` residuals will be highlighted in the</span>
 <span class="sd">        residual plots. The default is 5.</span>
 <span class="sd">    return_shape_diagnostics : True, optional</span>
 <span class="sd">        If True, additional outputs are returned to help diagnose issues. The</span>
@@ -1428,13 +1633,13 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
 <span class="sd">    suspicious_channels : CoordinateArray</span>
 <span class="sd">        A set of suspicous channels that should be investigated</span>
 <span class="sd">    analytic_rigid_shapes : ShapeArray</span>
-<span class="sd">        Rigid body shapes created from the geometry.  Only returned if </span>
+<span class="sd">        Rigid body shapes created from the geometry.  Only returned if</span>
 <span class="sd">        return_shape_diagnostics is True</span>
 <span class="sd">    residual : np.ndarray</span>
 <span class="sd">        Values of the residuals at coordinates np.unique(rigid_shapes.coordinate).</span>
 <span class="sd">        Only returned if return_shape_diagnostics is True</span>
 <span class="sd">    shape_matrix_exp : np.ndarray</span>
-<span class="sd">        The shape matrix of the supplied rigid shapes.  Only returned if </span>
+<span class="sd">        The shape matrix of the supplied rigid shapes.  Only returned if</span>
 <span class="sd">        return_shape_diagnostics is True</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -1446,6 +1651,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
     <span class="n">residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">shape_matrix_exp</span> <span class="o">-</span> <span class="n">projection</span><span class="p">)</span>
     <span class="n">suspicious_channels</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="c1"># Plot the complex plane for each shape</span>
+    <span class="n">figs</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">rigid_shapes</span><span class="p">,</span> <span class="n">shape_matrix_exp</span><span class="o">.</span><span class="n">T</span><span class="p">)):</span>
             <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">num</span><span class="o">=</span><span class="s1">&#39;Shape </span><span class="si">{:}</span><span class="s1"> Complex Plane&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
@@ -1461,11 +1667,13 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="k">for</span> <span class="n">outlier</span> <span class="ow">in</span> <span class="n">outliers</span><span class="p">:</span>
                 <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">outlier</span><span class="p">]</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">outlier</span><span class="p">]</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">coordinates</span><span class="p">[</span><span class="n">outlier</span><span class="p">]))</span>
                 <span class="n">suspicious_channels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coordinates</span><span class="p">[</span><span class="n">outlier</span><span class="p">])</span>
+            <span class="n">figs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
     <span class="c1"># Now plot the residuals</span>
     <span class="n">residual_order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">residual</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
         <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">num</span><span class="o">=</span><span class="s1">&#39;Projection Residuals&#39;</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">residual</span><span class="p">,</span> <span class="s1">&#39;x&#39;</span><span class="p">)</span>
+        <span class="n">figs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">residuals_to_label</span><span class="p">):</span>
         <span class="n">index</span> <span class="o">=</span> <span class="n">residual_order</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span> <span class="o">-</span> <span class="n">i</span><span class="p">]</span>
         <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
@@ -1475,10 +1683,15 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="c1"># ax.set_yscale(&#39;log&#39;)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Residual&#39;</span><span class="p">)</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Degree of Freedom&#39;</span><span class="p">)</span>
+    <span class="n">return_object</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">suspicious_channels</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">CoordinateArray</span><span class="p">),)</span>
     <span class="k">if</span> <span class="n">return_shape_diagnostics</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">suspicious_channels</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">CoordinateArray</span><span class="p">),</span> <span class="n">true_rigid_shapes</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="n">shape_matrix_exp</span>
+        <span class="n">return_object</span> <span class="o">=</span> <span class="n">return_object</span> <span class="o">+</span> <span class="p">(</span><span class="n">true_rigid_shapes</span><span class="p">,</span> <span class="n">residual</span><span class="p">,</span> <span class="n">shape_matrix_exp</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">return_figures</span><span class="p">:</span>
+        <span class="n">return_object</span> <span class="o">=</span> <span class="n">return_object</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">figs</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">return_object</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">return_object</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">suspicious_channels</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">sdynpy_coordinate</span><span class="o">.</span><span class="n">CoordinateArray</span><span class="p">)</span></div>
+        <span class="k">return</span> <span class="n">return_object</span></div>
 
 
 <div class="viewcode-block" id="rigid_body_fix_node_orientation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation.html#sdynpy.core.sdynpy_shape.rigid_body_fix_node_orientation">[docs]</a><span class="k">def</span> <span class="nf">rigid_body_fix_node_orientation</span><span class="p">(</span><span class="n">geometry</span><span class="p">,</span> <span class="n">rigid_shapes</span><span class="p">,</span> <span class="n">suspect_nodes</span><span class="p">,</span> <span class="n">new_cs_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">gtol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">,</span> <span class="n">xtol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
@@ -1523,7 +1736,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
     <span class="c1"># Loop through all suspect nodes</span>
     <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">suspect_nodes</span><span class="p">):</span>
         <span class="n">keep_nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">val</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">geometry</span><span class="o">.</span><span class="n">node</span><span class="o">.</span><span class="n">id</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span>
-            <span class="ow">not</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">suspect_nodes</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">val</span> <span class="o">==</span> <span class="n">node</span><span class="p">)]</span>
+            <span class="n">val</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">suspect_nodes</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">val</span> <span class="o">==</span> <span class="n">node</span><span class="p">)]</span>
         <span class="n">error_geometry</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">keep_nodes</span><span class="p">)</span>
         <span class="n">error_shapes</span> <span class="o">=</span> <span class="n">rigid_shapes</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">keep_nodes</span><span class="p">)</span>
 
@@ -1650,7 +1863,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
     <span class="n">freq_table_format</span> <span class="o">=</span> <span class="s1">&#39;{{:&gt;</span><span class="si">{:}</span><span class="s1">}}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">freq_size</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">)</span>
     <span class="n">mac_table_format</span> <span class="o">=</span> <span class="s1">&#39;{{:&gt;</span><span class="si">{:}</span><span class="s1">}}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mac_size</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">)</span>
     <span class="n">freqerr_table_format</span> <span class="o">=</span> <span class="s1">&#39;{{:&gt;</span><span class="si">{:}</span><span class="s1">}}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">freqerr_size</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">damping_strings_1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Damp 1&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">damping_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">damping</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
                                           <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">shape_1</span><span class="p">]</span>
         <span class="n">damping_strings_2</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Damp 2&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">damping_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">damping</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
@@ -1674,7 +1887,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
         <span class="n">separator_string</span> <span class="o">=</span> <span class="s1">&#39;|&#39;</span>
         <span class="n">lineend_string</span> <span class="o">=</span> <span class="s1">&#39;|</span><span class="se">\n</span><span class="s1">&#39;</span>
         <span class="n">linebegin_string</span> <span class="o">=</span> <span class="s1">&#39;|&#39;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">header_separator_string</span> <span class="o">=</span> <span class="n">lineend_string</span> <span class="o">+</span> <span class="n">linebegin_string</span> <span class="o">+</span> <span class="n">separator_string</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;-&#39;</span> <span class="o">*</span> <span class="p">(</span><span class="n">length</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">)</span> <span class="k">for</span> <span class="n">length</span> <span class="ow">in</span> <span class="p">[</span>
                 <span class="n">index_size</span><span class="p">,</span> <span class="n">freq_size</span><span class="p">,</span> <span class="n">freq_size</span><span class="p">,</span> <span class="n">freqerr_size</span><span class="p">,</span>
                 <span class="n">damp_size</span><span class="p">,</span> <span class="n">damp_size</span><span class="p">,</span> <span class="n">damperr_size</span><span class="p">,</span> <span class="n">mac_size</span><span class="p">]])</span> <span class="o">+</span> <span class="n">lineend_string</span>
@@ -1682,11 +1895,11 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
             <span class="n">header_separator_string</span> <span class="o">=</span> <span class="n">lineend_string</span> <span class="o">+</span> <span class="n">linebegin_string</span> <span class="o">+</span> <span class="n">separator_string</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;-&#39;</span> <span class="o">*</span> <span class="p">(</span><span class="n">length</span> <span class="o">+</span> <span class="n">spacing</span><span class="p">)</span> <span class="k">for</span> <span class="n">length</span> <span class="ow">in</span> <span class="p">[</span>
                 <span class="n">index_size</span><span class="p">,</span> <span class="n">freq_size</span><span class="p">,</span> <span class="n">freq_size</span><span class="p">,</span> <span class="n">freqerr_size</span><span class="p">,</span> <span class="n">mac_size</span><span class="p">]])</span> <span class="o">+</span> <span class="n">lineend_string</span>
     <span class="k">elif</span> <span class="n">table_format</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;latex&#39;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">table_begin_string</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\\</span><span class="s1">begin</span><span class="si">{tabular}{rrrrrrrr}</span><span class="se">\n</span><span class="s1">&#39;</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">table_begin_string</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\\</span><span class="s1">begin</span><span class="si">{tabular}{rrrrr}</span><span class="se">\n</span><span class="s1">&#39;</span>
-        <span class="n">table_end_string</span> <span class="o">=</span> <span class="s1">&#39;\end</span><span class="si">{tabular}</span><span class="s1">&#39;</span>
+        <span class="n">table_end_string</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;\end</span><span class="si">{tabular}</span><span class="s1">&#39;</span>
         <span class="n">separator_string</span> <span class="o">=</span> <span class="s1">&#39; &amp; &#39;</span>
         <span class="n">lineend_string</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\\\\\n</span><span class="s1">&#39;</span>
         <span class="n">linebegin_string</span> <span class="o">=</span> <span class="s1">&#39;    &#39;</span>
@@ -1694,7 +1907,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Invalid table_format.  Must be one of [&#39;text&#39;,&#39;markdown&#39;,&#39;latex&#39;].&quot;</span><span class="p">)</span>
     <span class="n">output_string</span> <span class="o">=</span> <span class="n">table_begin_string</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">damping_format</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">,</span> <span class="n">d1</span><span class="p">,</span> <span class="n">d2</span><span class="p">,</span> <span class="n">fe</span><span class="p">,</span> <span class="n">de</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
                 <span class="n">index_strings</span><span class="p">,</span> <span class="n">frequency_strings_1</span><span class="p">,</span> <span class="n">frequency_strings_2</span><span class="p">,</span>
                 <span class="n">damping_strings_1</span><span class="p">,</span> <span class="n">damping_strings_2</span><span class="p">,</span> <span class="n">freq_error_strings</span><span class="p">,</span>
@@ -1734,7 +1947,7 @@ <h1>Source code for sdynpy.core.sdynpy_shape</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc5150>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb6bc0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/core/sdynpy_system.html b/_modules/sdynpy/core/sdynpy_system.html
index 7ac6031..3d250e5 100644
--- a/_modules/sdynpy/core/sdynpy_system.html
+++ b/_modules/sdynpy/core/sdynpy_system.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.core.sdynpy_system &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.core.sdynpy_system &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -112,17 +112,18 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
 <span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.linalg</span> <span class="kn">import</span> <span class="n">eigh</span>
 <span class="kn">from</span> <span class="nn">.sdynpy_coordinate</span> <span class="kn">import</span> <span class="n">CoordinateArray</span><span class="p">,</span> <span class="n">from_nodelist</span><span class="p">,</span> <span class="n">outer_product</span><span class="p">,</span> <span class="n">coordinate_array</span>
 <span class="kn">from</span> <span class="nn">..fem.sdynpy_beam</span> <span class="kn">import</span> <span class="n">beamkm</span><span class="p">,</span> <span class="n">rect_beam_props</span>
 <span class="kn">from</span> <span class="nn">..fem.sdynpy_exodus</span> <span class="kn">import</span> <span class="n">Exodus</span><span class="p">,</span> <span class="n">ExodusInMemory</span><span class="p">,</span> <span class="n">reduce_exodus_to_surfaces</span>
 <span class="kn">from</span> <span class="nn">..signal_processing</span> <span class="kn">import</span> <span class="n">frf</span> <span class="k">as</span> <span class="n">spfrf</span>
 <span class="kn">from</span> <span class="nn">..signal_processing</span> <span class="kn">import</span> <span class="n">generator</span>
-<span class="kn">from</span> <span class="nn">scipy.linalg</span> <span class="kn">import</span> <span class="n">eigh</span><span class="p">,</span> <span class="n">block_diag</span><span class="p">,</span> <span class="n">null_space</span>
+<span class="kn">from</span> <span class="nn">scipy.linalg</span> <span class="kn">import</span> <span class="n">eigh</span><span class="p">,</span> <span class="n">block_diag</span><span class="p">,</span> <span class="n">null_space</span><span class="p">,</span> <span class="n">eig</span>
 <span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">lsim</span><span class="p">,</span> <span class="n">StateSpace</span><span class="p">,</span> <span class="n">resample</span><span class="p">,</span> <span class="n">butter</span><span class="p">,</span> <span class="n">filtfilt</span>
 <span class="kn">import</span> <span class="nn">copy</span>
 <span class="kn">import</span> <span class="nn">netCDF4</span> <span class="k">as</span> <span class="nn">nc4</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+
 
 <div class="viewcode-block" id="System"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System">[docs]</a><span class="k">class</span> <span class="nc">System</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Matrix Equations representing a Structural Dynamics System&quot;&quot;&quot;</span>
@@ -193,7 +194,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="c1"># Check symmetry</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">mass</span><span class="p">,</span> <span class="n">mass</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;mass matrix must be symmetric&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">stiffness</span><span class="p">,</span> <span class="n">stiffness</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="n">atol</span><span class="o">=</span><span class="mf">1e-6</span><span class="o">*</span><span class="n">stiffness</span><span class="o">.</span><span class="n">max</span><span class="p">()):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">stiffness</span><span class="p">,</span> <span class="n">stiffness</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-6</span><span class="o">*</span><span class="n">stiffness</span><span class="o">.</span><span class="n">max</span><span class="p">()):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;stiffness matrix must be symmetric&#39;</span><span class="p">)</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">damping</span><span class="p">,</span> <span class="n">damping</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;damping matrix must be symmetric&#39;</span><span class="p">)</span>
@@ -346,7 +347,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 
 <div class="viewcode-block" id="System.to_state_space"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.to_state_space">[docs]</a>    <span class="k">def</span> <span class="nf">to_state_space</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_displacement</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_velocity</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_acceleration</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">output_force</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                       <span class="n">response_coordinates</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span><span class="n">input_coordinates</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+                       <span class="n">response_coordinates</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">input_coordinates</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Compute the state space representation of the system</span>
 
@@ -383,7 +384,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="n">C</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">C</span>
         <span class="k">if</span> <span class="n">response_coordinates</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">phi_response</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">response_coordinates</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">response_coordinates</span> <span class="o">==</span> <span class="s1">&#39;state&#39;</span><span class="p">:</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">response_coordinates</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">response_coordinates</span> <span class="o">==</span> <span class="s1">&#39;state&#39;</span><span class="p">:</span>
             <span class="c1"># If you want the state variables back, the transformation is the</span>
             <span class="c1"># identity matrix.</span>
             <span class="n">phi_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
@@ -443,7 +444,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="n">D_state</span> <span class="o">=</span> <span class="n">D_state</span><span class="p">[</span><span class="n">output_indices</span><span class="p">]</span>
         <span class="k">return</span> <span class="n">A_state</span><span class="p">,</span> <span class="n">B_state</span><span class="p">,</span> <span class="n">C_state</span><span class="p">,</span> <span class="n">D_state</span></div>
 
-<div class="viewcode-block" id="System.time_integrate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.time_integrate">[docs]</a>    <span class="k">def</span> <span class="nf">time_integrate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forces</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">responses</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">references</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<div class="viewcode-block" id="System.time_integrate"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.time_integrate">[docs]</a>    <span class="k">def</span> <span class="nf">time_integrate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forces</span><span class="p">,</span> <span class="n">dt</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">responses</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">references</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                        <span class="n">displacement_derivative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">initial_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                        <span class="n">integration_oversample</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -492,12 +493,12 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="kn">from</span> <span class="nn">.sdynpy_data</span> <span class="kn">import</span> <span class="n">data_array</span><span class="p">,</span> <span class="n">FunctionTypes</span><span class="p">,</span> <span class="n">TimeHistoryArray</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forces</span><span class="p">,</span> <span class="n">TimeHistoryArray</span><span class="p">):</span>
             <span class="n">dt</span> <span class="o">=</span> <span class="n">forces</span><span class="o">.</span><span class="n">abscissa_spacing</span>
-            <span class="n">references</span> <span class="o">=</span> <span class="n">forces</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">references</span> <span class="o">=</span> <span class="n">forces</span><span class="o">.</span><span class="n">coordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
             <span class="n">forces</span> <span class="o">=</span> <span class="n">forces</span><span class="o">.</span><span class="n">ordinate</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">dt</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`dt` must be specified if `forces` is not a `TimeHistoryArray`&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">responses</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">responses</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
             <span class="n">responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">responses</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">references</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">references</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">references</span><span class="p">)</span>
@@ -505,7 +506,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
                                          <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span>
                                          <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span>
                                          <span class="kc">False</span><span class="p">,</span>
-                                         <span class="n">responses</span><span class="p">,</span><span class="n">references</span>
+                                         <span class="n">responses</span><span class="p">,</span> <span class="n">references</span>
                                          <span class="p">)</span>
         <span class="n">forces</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">forces</span><span class="p">)</span>
         <span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">forces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">dt</span>
@@ -517,8 +518,8 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="n">time_response</span> <span class="o">=</span> <span class="n">time_response</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
         <span class="k">if</span> <span class="n">responses</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">response_coordinate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span><span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">responses</span> <span class="o">==</span> <span class="s1">&#39;state&#39;</span><span class="p">:</span>
-            <span class="n">response_coordinate</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">),</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">responses</span> <span class="o">==</span> <span class="s1">&#39;state&#39;</span><span class="p">:</span>
+            <span class="n">response_coordinate</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">response_coordinate</span> <span class="o">=</span> <span class="n">responses</span>
         <span class="n">response_array</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
@@ -566,9 +567,9 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
 
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="n">complex_modes</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">num_modes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">num_modes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">num_modes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">maximum_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">maximum_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">maximum_frequency</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">maximum_frequency</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
                 <span class="n">maximum_frequency</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="n">maximum_frequency</span><span class="p">,</span> <span class="n">maximum_frequency</span><span class="p">]</span>  <span class="c1"># Convert to eigenvalue</span>
             <span class="n">lam</span><span class="p">,</span> <span class="n">phi</span> <span class="o">=</span> <span class="n">eigh</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="n">subset_by_index</span><span class="o">=</span><span class="n">num_modes</span><span class="p">,</span>
@@ -591,15 +592,69 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">return</span> <span class="n">System</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">freq</span><span class="o">.</span><span class="n">size</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">freq</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">freq</span><span class="p">)</span> <span class="o">*</span> <span class="n">damping</span><span class="p">),</span> <span class="n">phi</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Complex Modes are not implemented yet!&#39;</span><span class="p">)</span></div>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndof</span> <span class="o">&gt;</span> <span class="mi">1000</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The complex mode implementation currently computes all eigenvalues and eigenvectors, which may take a long time for large systems.&#39;</span><span class="p">)</span>
+            <span class="c1"># For convenience, assign a zeros matrix</span>
+            <span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span>     <span class="n">Z</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">],</span>
+                          <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">C</span><span class="p">]])</span>
+            <span class="n">B</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">M</span><span class="p">,</span>      <span class="n">Z</span><span class="p">],</span>
+                          <span class="p">[</span>      <span class="n">Z</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">]])</span>
+            <span class="n">lam</span><span class="p">,</span> <span class="n">E</span> <span class="o">=</span> <span class="n">eig</span><span class="p">(</span><span class="o">-</span><span class="n">B</span><span class="p">,</span><span class="n">A</span><span class="p">)</span>
+            <span class="c1"># Sort the eigenvalues such that they are increasing in frequency</span>
+            <span class="n">isort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam</span><span class="p">))</span>
+            <span class="n">lam</span> <span class="o">=</span> <span class="n">lam</span><span class="p">[</span><span class="n">isort</span><span class="p">]</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="n">E</span><span class="p">[:,</span><span class="n">isort</span><span class="p">]</span>
+            <span class="c1"># Eigenvalues will be in complex conjugate pairs.  Let&#39;s only keep</span>
+            <span class="c1"># the ones that are greater than zero.</span>
+            <span class="n">keep</span> <span class="o">=</span> <span class="n">lam</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mi">0</span>
+            <span class="n">lam</span> <span class="o">=</span> <span class="n">lam</span><span class="p">[</span><span class="n">keep</span><span class="p">]</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="n">E</span><span class="p">[:,</span><span class="n">keep</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span> <span class="o">!=</span> <span class="n">lam</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The complex mode implementation currently does not do well with rigid body modes (0 Hz frequencies).  Compute them from a real-modes solution then transform to complex.&#39;</span><span class="p">)</span>
+            <span class="c1"># Cull values we don&#39;t want</span>
+            <span class="k">if</span> <span class="n">num_modes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">lam</span> <span class="o">=</span> <span class="n">lam</span><span class="p">[:</span><span class="n">num_modes</span><span class="p">]</span>
+                <span class="n">E</span> <span class="o">=</span> <span class="n">E</span><span class="p">[:,:</span><span class="n">num_modes</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">maximum_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">keep</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">maximum_frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
+                <span class="n">lam</span> <span class="o">=</span> <span class="n">lam</span><span class="p">[</span><span class="n">keep</span><span class="p">]</span>
+                <span class="n">E</span> <span class="o">=</span> <span class="n">E</span><span class="p">[:,</span><span class="n">keep</span><span class="p">]</span>
+            <span class="c1"># Mass normalize the mode shapes</span>
+            <span class="n">E</span> <span class="o">=</span> <span class="n">E</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E</span><span class="p">,</span><span class="n">A</span><span class="p">,</span><span class="n">E</span><span class="p">))</span>
+            <span class="c1"># Find repeated eigenvalues where the eigenvectors are not orthogonal</span>
+            <span class="c1"># TODO: Might have to do some orthogonalization for repeated</span>
+            <span class="c1"># eigenvalues</span>
+            <span class="c1"># A_modal = np.einsum(&#39;ji,jk,kl-&gt;il&#39;,E,A,E)</span>
+            <span class="c1"># Extract just the displacement partition</span>
+            <span class="n">psi</span> <span class="o">=</span> <span class="n">E</span><span class="p">[</span><span class="n">E</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:,:]</span>
+            <span class="c1"># Add in a transformation to get to physical dofs</span>
+            <span class="n">psi_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">@</span> <span class="n">psi</span>
+            <span class="n">frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+            <span class="n">damping</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">return_shape</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">shape_array</span>
+                <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="n">psi</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">damping</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Complex Modes will in general not diagonalize the system M, C, and K, matrices.&#39;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">System</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span>
+                              <span class="n">psi</span><span class="o">.</span><span class="n">T</span><span class="nd">@self</span><span class="o">.</span><span class="n">M</span><span class="nd">@psi</span><span class="p">,</span>
+                              <span class="n">psi</span><span class="o">.</span><span class="n">T</span><span class="nd">@self</span><span class="o">.</span><span class="n">K</span><span class="nd">@psi</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                              <span class="n">psi</span><span class="o">.</span><span class="n">T</span><span class="nd">@self</span><span class="o">.</span><span class="n">C</span><span class="nd">@psi</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                              <span class="n">psi_t</span><span class="p">)</span></div>
+            
 
-<div class="viewcode-block" id="System.transformation_shapes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.transformation_shapes">[docs]</a>    <span class="k">def</span> <span class="nf">transformation_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">shape_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="System.transformation_shapes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.transformation_shapes">[docs]</a>    <span class="k">def</span> <span class="nf">transformation_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">shape_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">shape_array</span>
         <span class="k">if</span> <span class="n">shape_indices</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">shape_indices</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
-        <span class="n">shape_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">[:,</span><span class="n">shape_indices</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span><span class="n">shape_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
-                           <span class="n">frequency</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span><span class="n">damping</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
+        <span class="n">shape_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">[:,</span> <span class="n">shape_indices</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">shape_array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coordinate</span><span class="p">,</span> <span class="n">shape_matrix</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                           <span class="n">frequency</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">damping</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="System.remove_transformation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.remove_transformation">[docs]</a>    <span class="k">def</span> <span class="nf">remove_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">System</span><span class="p">(</span><span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
+                      <span class="bp">self</span><span class="o">.</span><span class="n">mass</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">stiffness</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span></div>
 
 <div class="viewcode-block" id="System.frequency_response"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.frequency_response">[docs]</a>    <span class="k">def</span> <span class="nf">frequency_response</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">responses</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">references</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                            <span class="n">displacement_derivative</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
@@ -654,7 +709,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
                          <span class="n">outer_product</span><span class="p">(</span><span class="n">output_coordinates</span><span class="p">,</span> <span class="n">input_coordinates</span><span class="p">))</span>
         <span class="k">return</span> <span class="n">frf</span></div>
 
-<div class="viewcode-block" id="System.assign_modal_damping"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.assign_modal_damping">[docs]</a>    <span class="k">def</span> <span class="nf">assign_modal_damping</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">damping_ratios</span><span class="p">):</span>
+<div class="viewcode-block" id="System.assign_modal_damping"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.assign_modal_damping">[docs]</a>    <span class="k">def</span> <span class="nf">assign_modal_damping</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">damping_ratios</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Assigns a damping matrix to the system that results in equivalent</span>
 <span class="sd">        modal damping</span>
@@ -671,7 +726,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">damping_ratios</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">damping_ratios</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">damping_ratios</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">eigensolution</span><span class="p">(</span><span class="n">num_modes</span> <span class="o">=</span> <span class="n">damping_ratios</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
+            <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">eigensolution</span><span class="p">(</span><span class="n">num_modes</span><span class="o">=</span><span class="n">damping_ratios</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">shapes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">eigensolution</span><span class="p">()</span>
         <span class="n">shapes</span><span class="o">.</span><span class="n">damping</span> <span class="o">=</span> <span class="n">damping_ratios</span>
@@ -680,7 +735,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="n">shape_pinv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">modal_system</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
         <span class="n">full_damping_matrix</span> <span class="o">=</span> <span class="n">shape_pinv</span><span class="nd">@modal_system</span><span class="o">.</span><span class="n">damping</span><span class="nd">@shape_pinv</span><span class="o">.</span><span class="n">T</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">damping</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">full_damping_matrix</span></div>
-        
+
 <div class="viewcode-block" id="System.save"><a class="viewcode-back" href="../../../_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.save">[docs]</a>    <span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filename</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Saves the system to a file</span>
@@ -923,7 +978,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="n">constraint_matrix</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">constraint_dof_0</span><span class="p">,</span> <span class="n">constraint_dof_1</span> <span class="ow">in</span> <span class="n">dof_pairs</span><span class="p">:</span>
             <span class="n">constraint</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_matrix_at_coordinates</span><span class="p">(</span><span class="n">constraint_dof_0</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">constraint_dof_1</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">constraint_dof_1</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">constraint</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_matrix_at_coordinates</span><span class="p">(</span><span class="n">constraint_dof_1</span><span class="p">)</span>
             <span class="n">constraint_matrix</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">constraint</span><span class="p">)</span>
         <span class="n">constraint_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">constraint_matrix</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@@ -945,7 +1000,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
 <span class="sd">        connection_dofs_0 : CoordinateArray</span>
 <span class="sd">            Array of coordinates to use in the constraints</span>
 <span class="sd">        connection_dofs_1 : CoordinateArray, optional</span>
-<span class="sd">            Array of coordinates to constrain to the coordinates in </span>
+<span class="sd">            Array of coordinates to constrain to the coordinates in</span>
 <span class="sd">            `connection_dofs_0`. If not specified, the `connection_dofs_0`</span>
 <span class="sd">            degrees of freedom will be constrained to ground.</span>
 <span class="sd">        rcond : float, optional</span>
@@ -966,7 +1021,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
         <span class="n">shape_matrix_0</span> <span class="o">=</span> <span class="n">constraint_shapes</span><span class="p">[</span><span class="n">connection_dofs_0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
         <span class="n">transform_matrix_0</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_matrix_at_coordinates</span><span class="p">(</span><span class="n">connection_dofs_0</span><span class="p">)</span>
         <span class="n">constraint_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">shape_matrix_0</span><span class="p">,</span> <span class="n">transform_matrix_0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">connection_dofs_1</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">connection_dofs_1</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">shape_matrix_1</span> <span class="o">=</span> <span class="n">constraint_shapes</span><span class="p">[</span><span class="n">connection_dofs_1</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
             <span class="n">transform_matrix_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_matrix_at_coordinates</span><span class="p">(</span><span class="n">connection_dofs_1</span><span class="p">)</span>
             <span class="n">constraint_matrix</span> <span class="o">-=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">shape_matrix_1</span><span class="p">,</span> <span class="n">transform_matrix_1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
@@ -992,7 +1047,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Sets the damping matrix to a proportion of the mass and stiffness matrices.</span>
 
-<span class="sd">        The damping matrix will be set to `mass_fraction*self.mass + </span>
+<span class="sd">        The damping matrix will be set to `mass_fraction*self.mass +</span>
 <span class="sd">        stiffness_fraction*self.stiffness`</span>
 
 <span class="sd">        Parameters</span>
@@ -1179,7 +1234,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="n">keep_dofs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_indices_by_coordinate</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">keep_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
-        <span class="n">discard_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">keep_dofs</span><span class="p">])</span>
+        <span class="n">discard_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">keep_dofs</span><span class="p">])</span>
         <span class="n">I_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">keep_dofs</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
         <span class="n">K_dd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stiffness</span><span class="p">[</span><span class="n">discard_dofs</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
                               <span class="n">discard_dofs</span><span class="p">]</span>
@@ -1218,7 +1273,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="n">keep_dofs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_indices_by_coordinate</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">keep_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
-        <span class="n">discard_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">keep_dofs</span><span class="p">])</span>
+        <span class="n">discard_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">keep_dofs</span><span class="p">])</span>
         <span class="n">I_a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">keep_dofs</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
         <span class="n">D</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stiffness</span> <span class="o">-</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">frequency</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">mass</span>
         <span class="n">D_dd</span> <span class="o">=</span> <span class="n">D</span><span class="p">[</span><span class="n">discard_dofs</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
@@ -1267,7 +1322,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="n">connection_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_indices_by_coordinate</span><span class="p">(</span><span class="n">connection_degrees_of_freedom</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">connection_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connection_degrees_of_freedom</span><span class="p">)</span>
-        <span class="n">other_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">connection_indices</span><span class="p">])</span>
+        <span class="n">other_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ndof</span><span class="p">)</span> <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">connection_indices</span><span class="p">])</span>
 
         <span class="c1"># Extract portions of the mass and stiffness matrices</span>
         <span class="n">K_ii</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">K</span><span class="p">[</span><span class="n">other_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">other_indices</span><span class="p">]</span>
@@ -1301,7 +1356,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">from_exodus_superelement</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">superelement_nc4</span><span class="p">,</span> <span class="n">transformation_exodus_file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                  <span class="n">x_disp</span><span class="o">=</span><span class="s1">&#39;DispX&#39;</span><span class="p">,</span> <span class="n">y_disp</span><span class="o">=</span><span class="s1">&#39;DispY&#39;</span><span class="p">,</span> <span class="n">z_disp</span><span class="o">=</span><span class="s1">&#39;DispZ&#39;</span><span class="p">,</span>
                                  <span class="n">x_rot</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">y_rot</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">z_rot</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                                 <span class="n">reduce_to_external_surfaces</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+                                 <span class="n">reduce_to_external_surfaces</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a system from a superelement from Sierra/SD</span>
 
@@ -1383,7 +1438,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="k">else</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;transformation_exodus_file must be a string or a sdpy.Exodus&#39;</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">reduce_to_external_surfaces</span><span class="p">:</span>
-                <span class="n">exo</span> <span class="o">=</span> <span class="n">reduce_exodus_to_surfaces</span><span class="p">(</span><span class="n">exo</span><span class="p">,</span><span class="n">variables_to_transform</span><span class="o">=</span><span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x_disp</span><span class="p">,</span><span class="n">y_disp</span><span class="p">,</span><span class="n">z_disp</span><span class="p">,</span><span class="n">x_rot</span><span class="p">,</span><span class="n">y_rot</span><span class="p">,</span><span class="n">z_rot</span><span class="p">]</span> <span class="k">if</span> <span class="n">var</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">])</span>
+                <span class="n">exo</span> <span class="o">=</span> <span class="n">reduce_exodus_to_surfaces</span><span class="p">(</span><span class="n">exo</span><span class="p">,</span> <span class="n">variables_to_transform</span><span class="o">=</span><span class="p">[</span><span class="n">var</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="p">[</span><span class="n">x_disp</span><span class="p">,</span> <span class="n">y_disp</span><span class="p">,</span> <span class="n">z_disp</span><span class="p">,</span> <span class="n">x_rot</span><span class="p">,</span> <span class="n">y_rot</span><span class="p">,</span> <span class="n">z_rot</span><span class="p">]</span> <span class="k">if</span> <span class="n">var</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">])</span>
             <span class="kn">from</span> <span class="nn">.sdynpy_shape</span> <span class="kn">import</span> <span class="n">ShapeArray</span>
             <span class="n">shapes</span> <span class="o">=</span> <span class="n">ShapeArray</span><span class="o">.</span><span class="n">from_exodus</span><span class="p">(</span><span class="n">exo</span><span class="p">,</span> <span class="n">x_disp</span><span class="p">,</span> <span class="n">y_disp</span><span class="p">,</span> <span class="n">z_disp</span><span class="p">,</span> <span class="n">x_rot</span><span class="p">,</span> <span class="n">y_rot</span><span class="p">,</span> <span class="n">z_rot</span><span class="p">)</span>
             <span class="n">transformation</span> <span class="o">=</span> <span class="n">shapes</span><span class="o">.</span><span class="n">shape_matrix</span><span class="o">.</span><span class="n">T</span>
@@ -1500,7 +1555,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
             <span class="n">signals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">signals</span><span class="p">,</span> <span class="p">[</span><span class="n">num_signals</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
         <span class="k">elif</span> <span class="n">excitation</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;multi-hammer&#39;</span><span class="p">:</span>
             <span class="n">signal_length</span> <span class="o">=</span> <span class="n">frame_length</span> <span class="o">*</span> <span class="n">integration_oversample</span>
-            <span class="n">pulse_width</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">/</span> <span class="n">bandwidth</span>
+            <span class="n">pulse_width</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="n">bandwidth</span> <span class="k">if</span> <span class="n">excitation_max_frequency</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">excitation_max_frequency</span><span class="p">)</span>
             <span class="n">signals</span> <span class="o">=</span> <span class="p">[]</span>
             <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_signals</span><span class="p">):</span>
                 <span class="n">signals</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>
@@ -1533,12 +1588,12 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
                     <span class="s1">&#39;Warning: Sine signal generally not recommended for multi-reference excitation&#39;</span><span class="p">)</span>
             <span class="n">frequencies</span> <span class="o">=</span> <span class="n">excitation_max_frequency</span> <span class="k">if</span> <span class="n">excitation_min_frequency</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">excitation_min_frequency</span>
             <span class="n">num_samples</span> <span class="o">=</span> <span class="n">frame_length</span> <span class="o">*</span> <span class="n">integration_oversample</span> <span class="o">*</span> <span class="n">num_averages</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">steady_state_time</span> <span class="o">*</span> <span class="n">sample_rate</span><span class="p">)</span>
-            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;frequencies&#39;</span><span class="p">:</span><span class="n">frequencies</span><span class="p">,</span>
-                      <span class="s1">&#39;dt&#39;</span><span class="p">:</span><span class="n">dt</span><span class="p">,</span>
-                      <span class="s1">&#39;num_samples&#39;</span><span class="p">:</span><span class="n">num_samples</span><span class="p">,</span>
-                      <span class="s1">&#39;amplitudes&#39;</span><span class="p">:</span><span class="n">excitation_level</span><span class="p">}</span>
+            <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;frequencies&#39;</span><span class="p">:</span> <span class="n">frequencies</span><span class="p">,</span>
+                      <span class="s1">&#39;dt&#39;</span><span class="p">:</span> <span class="n">dt</span><span class="p">,</span>
+                      <span class="s1">&#39;num_samples&#39;</span><span class="p">:</span> <span class="n">num_samples</span><span class="p">,</span>
+                      <span class="s1">&#39;amplitudes&#39;</span><span class="p">:</span> <span class="n">excitation_level</span><span class="p">}</span>
             <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">generator_kwargs</span><span class="p">)</span>
-            <span class="n">signals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">generator</span><span class="o">.</span><span class="n">sine</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">),(</span><span class="n">num_signals</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
+            <span class="n">signals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">generator</span><span class="o">.</span><span class="n">sine</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">),</span> <span class="p">(</span><span class="n">num_signals</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
         <span class="c1"># Set up the integration</span>
         <span class="n">responses</span><span class="p">,</span> <span class="n">references</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_integrate</span><span class="p">(</span>
             <span class="n">signals</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">responses</span><span class="p">,</span> <span class="n">references</span><span class="p">,</span> <span class="n">displacement_derivative</span><span class="p">)</span>
@@ -1572,7 +1627,7 @@ <h1>Source code for sdynpy.core.sdynpy_system</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebb3eef0>
+<jinja2.runtime.BlockReference object at 0x7f5a2704dea0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/demo/beam_airplane.html b/_modules/sdynpy/demo/beam_airplane.html
index 6db26db..99e09a1 100644
--- a/_modules/sdynpy/demo/beam_airplane.html
+++ b/_modules/sdynpy/demo/beam_airplane.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.demo.beam_airplane &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.demo.beam_airplane &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -259,14 +259,14 @@ <h1>Source code for sdynpy.demo.beam_airplane</h1><div class="highlight"><pre>
         <span class="p">)</span>
 
     <span class="c1"># Now let&#39;s create the full airplane</span>
-    <span class="n">systems</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_systems</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;transmission_simulator&#39;</span><span class="p">]]</span>
-    <span class="n">geometries</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_geometries</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;transmission_simulator&#39;</span><span class="p">]]</span>
+    <span class="n">systems</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_systems</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;transmission_simulator&#39;</span><span class="p">]]</span>
+    <span class="n">geometries</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_geometries</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;transmission_simulator&#39;</span><span class="p">]]</span>
 
     <span class="n">full_system</span><span class="p">,</span> <span class="n">full_geometry</span> <span class="o">=</span> <span class="n">_substructure_by_position</span><span class="p">(</span><span class="n">systems</span><span class="p">,</span> <span class="n">geometries</span><span class="p">)</span>
 
     <span class="c1"># Now let&#39;s create the stubby airplane</span>
-    <span class="n">systems</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_systems</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;wing&#39;</span><span class="p">]]</span>
-    <span class="n">geometries</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_geometries</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;wing&#39;</span><span class="p">]]</span>
+    <span class="n">systems</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_systems</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;wing&#39;</span><span class="p">]]</span>
+    <span class="n">geometries</span> <span class="o">=</span> <span class="p">[</span><span class="n">all_geometries</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;wing&#39;</span><span class="p">]]</span>
 
     <span class="n">transmission_system</span><span class="p">,</span> <span class="n">transmission_geometry</span> <span class="o">=</span> <span class="n">_substructure_by_position</span><span class="p">(</span><span class="n">systems</span><span class="p">,</span> <span class="n">geometries</span><span class="p">)</span>
 
@@ -284,7 +284,7 @@ <h1>Source code for sdynpy.demo.beam_airplane</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebdfc700>
+<jinja2.runtime.BlockReference object at 0x7f5a26fab9a0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/demo/beam_plate.html b/_modules/sdynpy/demo/beam_plate.html
index 5309c68..7b60114 100644
--- a/_modules/sdynpy/demo/beam_plate.html
+++ b/_modules/sdynpy/demo/beam_plate.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.demo.beam_plate &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.demo.beam_plate &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -189,7 +189,7 @@ <h1>Source code for sdynpy.demo.beam_plate</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebc4d3c0>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb55a0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/doc/sdynpy_latex.html b/_modules/sdynpy/doc/sdynpy_latex.html
index 45c7c80..532c2ee 100644
--- a/_modules/sdynpy/doc/sdynpy_latex.html
+++ b/_modules/sdynpy/doc/sdynpy_latex.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc.sdynpy_latex &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_latex &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -109,7 +109,11 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">pyqtgraph</span> <span class="k">as</span> <span class="nn">pqtg</span>
+<span class="kn">import</span> <span class="nn">PIL</span>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_correlation</span> <span class="kn">import</span> <span class="n">mac</span><span class="p">,</span> <span class="n">matrix_plot</span>
+<span class="kn">from</span> <span class="nn">..core.sdynpy_geometry</span> <span class="kn">import</span> <span class="n">GeometryPlotter</span><span class="p">,</span> <span class="n">ShapePlotter</span>
+<span class="kn">from</span> <span class="nn">shutil</span> <span class="kn">import</span> <span class="n">copy</span>
 
 
 <div class="viewcode-block" id="create_latex_summary"><a class="viewcode-back" href="../../../_autosummary/sdynpy.doc.sdynpy_latex.create_latex_summary.html#sdynpy.doc.sdynpy_latex.create_latex_summary">[docs]</a><span class="k">def</span> <span class="nf">create_latex_summary</span><span class="p">(</span><span class="n">figure_basename</span><span class="p">,</span> <span class="n">geometry</span><span class="p">,</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">frfs</span><span class="p">,</span>
@@ -137,7 +141,8 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
                          <span class="n">latex_shape_caption</span><span class="o">=</span><span class="s1">&#39;Mode shapes extracted from test data.&#39;</span><span class="p">,</span>
                          <span class="n">latex_shape_label</span><span class="o">=</span><span class="s1">&#39;fig:modeshapes&#39;</span><span class="p">,</span>
                          <span class="n">latex_shape_table_columns</span><span class="o">=</span><span class="s1">&#39;lllp</span><span class="si">{3.5in}</span><span class="s1">&#39;</span><span class="p">,</span>
-                         <span class="n">latex_shape_table_caption</span><span class="o">=</span><span class="s1">&#39;List of modes extracted from the test data.  Modal parameters are shown along with a brief description of the mode shape.&#39;</span><span class="p">,</span>
+                         <span class="n">latex_shape_table_caption</span><span class="o">=</span><span class="p">(</span>
+                             <span class="s1">&#39;List of modes extracted from the test data.  Modal parameters are shown along with a brief description of the mode shape.&#39;</span><span class="p">),</span>
                          <span class="n">latex_shape_table_label</span><span class="o">=</span><span class="s1">&#39;tab:modelist&#39;</span><span class="p">):</span>
 
     <span class="k">if</span> <span class="n">figure_basename_relative_to_latex</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -216,7 +221,7 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
                          <span class="n">latex_cmif_label</span><span class="p">)</span>
 
     <span class="n">output_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
-<span class="s1">    </span>
+
 <span class="s1">\begin{{figure}}</span>
 <span class="s1">    \centering</span>
 <span class="s1">    \includegraphics[</span><span class="si">{:}</span><span class="s1">]{{</span><span class="si">{:}</span><span class="s1">}}</span>
@@ -229,7 +234,7 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
 
     <span class="c1"># Create a table of natural frequencies, damping values, and comments</span>
     <span class="n">output_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
-<span class="s1">    </span>
+
 <span class="s1">\begin{{table}}</span>
 <span class="s1">    \centering</span>
 <span class="s1">    \caption{{</span><span class="si">{:}</span><span class="s1">}}</span>
@@ -242,7 +247,7 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
         <span class="n">output_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
 <span class="s1">        </span><span class="si">{:}</span><span class="s1"> &amp; </span><span class="si">{:}</span><span class="s1"> &amp; </span><span class="si">{:}</span><span class="s1"> &amp; </span><span class="si">{:}</span><span class="s1"> \\&#39;&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">frequency_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">frequency</span><span class="p">),</span>
                                            <span class="n">damping_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">damping</span> <span class="o">*</span> <span class="mi">100</span><span class="p">),</span> <span class="n">shape</span><span class="o">.</span><span class="n">comment1</span><span class="p">)</span>
-    <span class="n">output_string</span> <span class="o">+=</span> <span class="s1">&#39;&#39;&#39;</span>
+    <span class="n">output_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
 <span class="s1">    \end</span><span class="si">{tabular}</span>
 <span class="s1">    %}</span>
 <span class="s1">\end</span><span class="si">{table}</span><span class="s1">&#39;&#39;&#39;</span>
@@ -256,8 +261,8 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
             <span class="n">output_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
 <span class="s1">\end</span><span class="si">{figure}</span>
 <span class="s1">\begin</span><span class="si">{figure}</span><span class="s1">[h]</span>
-<span class="s1">	\ContinuedFloat</span>
-<span class="s1">	\centering&#39;&#39;&#39;</span>
+<span class="s1">    \ContinuedFloat</span>
+<span class="s1">    \centering&#39;&#39;&#39;</span>
         <span class="n">output_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
 <span class="s1">    \begin{{subfigure}}</span><span class="si">{subfigure_options:}</span>
 <span class="s1">        \centering</span>
@@ -289,6 +294,257 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
     <span class="k">if</span> <span class="n">close</span><span class="p">:</span>
         <span class="n">output_file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
     <span class="k">return</span> <span class="n">output_string</span></div>
+
+<div class="viewcode-block" id="figure"><a class="viewcode-back" href="../../../_autosummary/sdynpy.doc.sdynpy_latex.figure.html#sdynpy.doc.sdynpy_latex.figure">[docs]</a><span class="k">def</span> <span class="nf">figure</span><span class="p">(</span><span class="n">figures</span><span class="p">,</span> <span class="n">figure_label</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">figure_caption</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+           <span class="n">graphics_options</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;width=0.7\linewidth&#39;</span><span class="p">,</span> 
+           <span class="n">animate_graphics_options</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;width=0.7\linewidth,loop&#39;</span><span class="p">,</span>
+           <span class="n">figure_placement</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
+           <span class="n">subfigure_options</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;[t]{0.45\linewidth}&#39;</span><span class="p">,</span> <span class="n">subfigure_labels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
+           <span class="n">subfigure_captions</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_subfigures_per_page</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+           <span class="n">max_subfigures_first_page</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">figure_save_names</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+           <span class="n">latex_root</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;&#39;</span>
+           <span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adds figures, subfigures, and animations to a running latex document.</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    figures : list</span>
+<span class="sd">        Figure or figures that can be inserted into a latex document.  See </span>
+<span class="sd">        note for various figure types and configurations that can be used.</span>
+<span class="sd">    figure_label : str, optional</span>
+<span class="sd">        The label that will be used for the figure in the latex document.</span>
+<span class="sd">        If not specified, the figure will not be labeled.</span>
+<span class="sd">    figure_caption : str, optional</span>
+<span class="sd">        The caption that will be used for the figure in the latex document.</span>
+<span class="sd">        If not specified, the figure will only be captioned with the figure</span>
+<span class="sd">        number.</span>
+<span class="sd">    graphics_options : str, optional</span>
+<span class="sd">        Graphics options that will be used for the figure.  If not specified</span>
+<span class="sd">        this will be r&#39;width=0.7\linewidth&#39;.</span>
+<span class="sd">    animate_graphics_options : str, optional</span>
+<span class="sd">        Graphics options that will be used for an animation.  If not specified</span>
+<span class="sd">        this will be r&#39;width=0.7\linewidth,loop&#39;.</span>
+<span class="sd">    figure_placement : str, optional</span>
+<span class="sd">        Specify the placement of the figure with strings such as &#39;[t]&#39;, &#39;[b]&#39;,</span>
+<span class="sd">        or &#39;[h]&#39;.  If not specified, the figure will have no placement</span>
+<span class="sd">        specified.</span>
+<span class="sd">    subfigure_options : str, optional</span>
+<span class="sd">        The options that will be applied to each subfigure in the figure, if</span>
+<span class="sd">        subfigures are specified.  By default, this will be r&#39;[t]{0.45\linewidth}&#39;</span>
+<span class="sd">    subfigure_labels : str, optional</span>
+<span class="sd">        Labels to apply to the subfigure.  This can either be a list of strings</span>
+<span class="sd">        the same size as the list of figures, or a string with a format specifier</span>
+<span class="sd">        accepting the subfigure index.  If not specified, the subfigures will</span>
+<span class="sd">        not be labeled.</span>
+<span class="sd">    subfigure_captions : list, optional</span>
+<span class="sd">        A list of strings the same length as the list of figures to use as</span>
+<span class="sd">        captions for the subfigures.  If not specified, the subfigures will</span>
+<span class="sd">        only be captioned with the subfigure number.</span>
+<span class="sd">    max_subfigures_per_page : int, optional</span>
+<span class="sd">        The maximum number of subfigures on a page.  Longer figures will be</span>
+<span class="sd">        broken up into multiple figures using \ContinuedFloat.  If not specified,</span>
+<span class="sd">        a single figure environment will be generated.</span>
+<span class="sd">    max_subfigures_first_page : int, optional</span>
+<span class="sd">        The maximum number of subfigures on the first page.  Longer figures will be</span>
+<span class="sd">        broken up into multiple figures using \ContinuedFloat.  If not specified,</span>
+<span class="sd">        the max_subfigures_per_page value will be used if specified, otherwise</span>
+<span class="sd">        a single figure environment will be generated.</span>
+<span class="sd">    figure_save_names : str or list of str, optional</span>
+<span class="sd">        File names to save the figures as.  This can be specified as a string</span>
+<span class="sd">        with a format specifier in it that will accept the figure index, or</span>
+<span class="sd">        a list of strings the same length as the list of figures.</span>
+<span class="sd">        If not specified, files will be specified as &#39;figure_0&#39;,</span>
+<span class="sd">        &#39;figure_1&#39;, etc.  If file names are not present, then the file name</span>
+<span class="sd">        will be automatically selected for the type of figure given.</span>
+<span class="sd">    latex_root : str, optional</span>
+<span class="sd">        Directory in which the latex .tex file will be constructed.  This is</span>
+<span class="sd">        used to create relative paths to the save_figure_names within the latex</span>
+<span class="sd">        document.  If not specified, then the current directory will be assumed.</span>
+<span class="sd">     </span>
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    latex_string : str</span>
+<span class="sd">        The latex source code to insert the figures into the document.</span>
+
+<span class="sd">    Notes</span>
+<span class="sd">    -----</span>
+<span class="sd">    The `figures` argument must be a list of figures.  If only one entry</span>
+<span class="sd">    is present in the list, a figure will be made in the latex document.  If</span>
+<span class="sd">    multiple entries are present, a figure will be made and subfigures will be</span>
+<span class="sd">    made for each entry in the list.  If an entry in the list is also a list,</span>
+<span class="sd">    then that figure or subfigure will be made into an animation.</span>
+<span class="sd">    </span>
+<span class="sd">    The list of figures can contain many types of objects that a figure will be</span>
+<span class="sd">    made from, including:</span>
+<span class="sd">        - A 2D numpy array</span>
+<span class="sd">        - A Matplotlib figure</span>
+<span class="sd">        - A pyqtgraph plotitem</span>
+<span class="sd">        - A bytes object that represents an image</span>
+<span class="sd">        - A string to a file name</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">figures</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">subfigures</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">subfigures</span> <span class="o">=</span> <span class="kc">True</span>
+        
+    <span class="k">if</span> <span class="n">max_subfigures_first_page</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">max_subfigures_first_page</span> <span class="o">=</span> <span class="n">max_subfigures_per_page</span>
+        
+    <span class="k">if</span> <span class="n">figure_save_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">figure_save_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;figure_</span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">figures</span><span class="p">))]</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">figure_save_names</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+        <span class="n">figure_save_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">figure_save_names</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">figures</span><span class="p">))]</span>
+        
+    <span class="n">latex_string</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;\begin</span><span class="si">{figure}</span><span class="s1">&#39;</span><span class="o">+</span><span class="n">figure_placement</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">    </span><span class="se">\\</span><span class="s1">centering&#39;</span>
+    <span class="c1"># Go through and save all of the files out to disk</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">figure</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">figures</span><span class="p">):</span>
+        <span class="c1"># Check the type of figure.  If it&#39;s a list of figures, then it&#39;s an</span>
+        <span class="c1"># animation.</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">figure</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">animate</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">num_frames</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
+        <span class="c1"># Otherwise it&#39;s just a figure, but we turn it into a list anyways to</span>
+        <span class="c1"># make it so we only have to program this once.</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">animate</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">num_frames</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="n">figure</span> <span class="o">=</span> <span class="p">[</span><span class="n">figure</span><span class="p">]</span>
+        <span class="c1"># We need to get the extension of the file name to figure out what</span>
+        <span class="c1"># type of file to save the image to.</span>
+        <span class="n">base</span><span class="p">,</span><span class="n">ext</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">figure_save_names</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="c1"># We also want to get the directory so we can get the relative path to</span>
+        <span class="c1"># the file</span>
+        <span class="n">relpath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">relpath</span><span class="p">(</span><span class="n">base</span><span class="p">,</span><span class="n">latex_root</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\\</span><span class="s1">&#39;</span><span class="p">,</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">this_figure</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">figure</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">animate</span><span class="p">:</span>
+                <span class="n">this_filename</span> <span class="o">=</span> <span class="n">base</span><span class="o">+</span><span class="s1">&#39;_</span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="p">)</span><span class="o">+</span><span class="n">ext</span>
+                <span class="n">relpath</span> <span class="o">+=</span> <span class="s1">&#39;_&#39;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">this_filename</span> <span class="o">=</span> <span class="n">figure_save_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="c1"># Matplotlib Figure</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span><span class="n">plt</span><span class="o">.</span><span class="n">Figure</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                    <span class="n">this_filename</span> <span class="o">+=</span> <span class="s1">&#39;.pdf&#39;</span>
+                <span class="n">this_figure</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">this_filename</span><span class="p">)</span>
+            <span class="c1"># Pyqtgraph PlotItem</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span><span class="n">pqtg</span><span class="o">.</span><span class="n">PlotItem</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                    <span class="n">this_filename</span> <span class="o">+=</span> <span class="s1">&#39;.png&#39;</span>
+                <span class="n">this_figure</span><span class="o">.</span><span class="n">writeImage</span><span class="p">(</span><span class="n">this_filename</span><span class="p">)</span>
+            <span class="c1"># 2D NumpyArray</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                    <span class="n">this_filename</span> <span class="o">+=</span> <span class="s1">&#39;.png&#39;</span>
+                <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">this_figure</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">this_filename</span><span class="p">)</span>
+            <span class="c1"># ShapePlotter</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span> <span class="n">ShapePlotter</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;ShapePlotter is not implemented yet&#39;</span><span class="p">)</span>
+            <span class="c1"># GeometryPlotter</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span> <span class="n">GeometryPlotter</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;GeometryPlotter is not implemented yet&#39;</span><span class="p">)</span>
+            <span class="c1"># Bytes object</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span> <span class="nb">bytes</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                    <span class="n">this_filename</span> <span class="o">+=</span> <span class="s1">&#39;.png&#39;</span>
+                <span class="n">PIL</span><span class="o">.</span><span class="n">Image</span><span class="o">.</span><span class="n">frombytes</span><span class="p">(</span><span class="n">this_figure</span><span class="p">)</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">this_filename</span><span class="p">)</span>
+            <span class="c1"># String to file name</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">ext</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                    <span class="n">this_filename</span> <span class="o">+=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">this_figure</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">copy</span><span class="p">(</span><span class="n">this_figure</span><span class="p">,</span><span class="n">this_filename</span><span class="p">)</span>
+        <span class="c1"># Now we end the figure and create a new one if we are at the right</span>
+        <span class="c1"># subfigure number</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">subfigures</span> <span class="ow">and</span> <span class="n">max_subfigures_per_page</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> 
+            <span class="p">((</span><span class="n">i</span><span class="o">-</span><span class="n">max_subfigures_first_page</span><span class="p">)</span><span class="o">%</span><span class="n">max_subfigures_per_page</span> <span class="o">==</span> <span class="mi">0</span>
+              <span class="ow">and</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)):</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">\end</span><span class="si">{figure}</span>
+<span class="s2">\begin</span><span class="si">{figure}</span><span class="s2">[h]</span>
+<span class="s2">    \ContinuedFloat</span>
+<span class="s2">    \centering&quot;&quot;&quot;</span>
+        <span class="c1"># If we have subfigures we need to stick in the subfigure environment</span>
+        <span class="k">if</span> <span class="n">subfigures</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    \begin</span><span class="si">{subfigure}</span><span class="s2">&quot;&quot;&quot;</span><span class="o">+</span><span class="n">subfigure_options</span><span class="o">+</span><span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">        \centering&quot;&quot;&quot;</span>
+        <span class="c1"># Now we have to insert the includegraphics or animategraphics command</span>
+        <span class="k">if</span> <span class="n">animate</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">        \animategraphics[</span><span class="si">{graphics_options:}</span><span class="s2">]{{</span><span class="si">{num_frames:}</span><span class="s2">}}{{</span><span class="si">{base_name:}</span><span class="s2">}}{{0}}{{</span><span class="si">{end_frame:}</span><span class="s2">}}&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+            <span class="n">graphics_options</span><span class="o">=</span><span class="n">animate_graphics_options</span><span class="p">,</span>
+            <span class="n">num_frames</span><span class="o">=</span><span class="n">num_frames</span><span class="p">,</span>
+            <span class="n">base_name</span><span class="o">=</span><span class="n">relpath</span><span class="p">,</span> <span class="n">end_frame</span><span class="o">=</span><span class="n">num_frames</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">        \includegraphics[</span><span class="si">{:}</span><span class="s2">]{{</span><span class="si">{:}</span><span class="s2">}}&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+        <span class="n">graphics_options</span><span class="p">,</span><span class="n">relpath</span><span class="p">)</span>
+        <span class="c1"># Now add captions and labels if they exist</span>
+        <span class="k">if</span> <span class="n">subfigures</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">        \caption{{</span><span class="si">{:}</span><span class="s2">}}&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">subfigure_captions</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">subfigure_captions</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">subfigure_labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">subfigure_labels</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+                    <span class="n">label</span> <span class="o">=</span> <span class="n">subfigure_labels</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">label</span> <span class="o">=</span> <span class="n">subfigure_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">        \label{{</span><span class="si">{:}</span><span class="s2">}}&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    \end</span><span class="si">{subfigure}</span><span class="s2">&quot;&quot;&quot;</span>
+    <span class="c1"># Add the figure caption and label</span>
+    <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    \caption{{</span><span class="si">{:}</span><span class="s2">}}&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">figure_caption</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">figure_caption</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">figure_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    \label{{</span><span class="si">{:}</span><span class="s2">}}&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">figure_label</span><span class="p">)</span>
+    <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">\end</span><span class="si">{figure}</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">latex_string</span></div>
+
+<div class="viewcode-block" id="table"><a class="viewcode-back" href="../../../_autosummary/sdynpy.doc.sdynpy_latex.table.html#sdynpy.doc.sdynpy_latex.table">[docs]</a><span class="k">def</span> <span class="nf">table</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">justification_string</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> 
+          <span class="n">table_label</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">table_caption</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">longtable</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+          <span class="n">header</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">horizontal_lines</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+    <span class="n">nrows</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">table</span><span class="p">)</span>
+    <span class="n">ncols</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">table</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">justification_string</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">justification_string</span> <span class="o">=</span> <span class="s1">&#39;c&#39;</span><span class="o">*</span><span class="n">ncols</span>
+    <span class="k">if</span> <span class="n">longtable</span><span class="p">:</span>
+        <span class="n">latex_string</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;\begin{{longtable}}{{</span><span class="si">{:}</span><span class="s1">}}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">justification_string</span><span class="p">)</span><span class="o">+</span><span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">    \caption{{</span><span class="si">{:}</span><span class="s1">}}&#39;&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">table_caption</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">table_caption</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">table_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">    \label{{</span><span class="si">{:}</span><span class="s1">}}&#39;&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">table_label</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">latex_string</span> <span class="o">=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;\begin{{table}}</span>
+<span class="s1">    \centering</span>
+<span class="s1">    \caption{{</span><span class="si">{:}</span><span class="s1">}}&#39;&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">table_caption</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">table_caption</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">table_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">    \label{{</span><span class="si">{:}</span><span class="s1">}}&#39;&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">table_label</span><span class="p">)</span>
+        <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">    \begin{{tabular}}{{</span><span class="si">{:}</span><span class="s1">}}&#39;&#39;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">justification_string</span><span class="p">)</span>
+    <span class="c1"># Now create the meat of the table</span>
+    <span class="k">if</span> <span class="n">horizontal_lines</span><span class="p">:</span>
+        <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">        \hline&#39;&#39;&#39;</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nrows</span><span class="p">):</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="s1">&#39;        &#39;</span><span class="o">+</span><span class="s1">&#39; &amp; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">table</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">])</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">)])</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\\\\</span><span class="s1">&#39;</span>
+        <span class="k">if</span> <span class="n">header</span> <span class="ow">and</span> <span class="n">i</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">row</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;\hline&#39;</span>
+        <span class="n">latex_string</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">+</span><span class="n">row</span>
+        <span class="k">if</span> <span class="n">horizontal_lines</span><span class="p">:</span>
+            <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">        \hline&#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="n">longtable</span><span class="p">:</span>
+        <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">\end</span><span class="si">{longtable}</span><span class="s1">&#39;&#39;&#39;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">latex_string</span> <span class="o">+=</span> <span class="sa">r</span><span class="s1">&#39;&#39;&#39;</span>
+<span class="s1">    \end</span><span class="si">{tabular}</span>
+<span class="s1">\end</span><span class="si">{table}</span><span class="s1">&#39;&#39;&#39;</span>
+    <span class="k">return</span> <span class="n">latex_string</span></div>
 </pre></div>
 
            </div>
@@ -298,7 +554,7 @@ <h1>Source code for sdynpy.doc.sdynpy_latex</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebe1edd0>
+<jinja2.runtime.BlockReference object at 0x7f5a26b7e560>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/doc/sdynpy_ppt.html b/_modules/sdynpy/doc/sdynpy_ppt.html
index 20c8b0c..276fd96 100644
--- a/_modules/sdynpy/doc/sdynpy_ppt.html
+++ b/_modules/sdynpy/doc/sdynpy_ppt.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.doc.sdynpy_ppt &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.doc.sdynpy_ppt &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -255,10 +255,10 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
         <span class="n">pic</span><span class="o">.</span><span class="n">width</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">pic</span><span class="o">.</span><span class="n">height</span> <span class="o">*</span> <span class="n">ar</span><span class="p">)</span>
         <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
 
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">exp_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">exp_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
         <span class="n">h1</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">exp_data</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">exp_data</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">fit_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">fit_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">h2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">fit_data</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">fit_data</span><span class="o">.</span><span class="n">ordinate</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
             <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">h1</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">h2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]],</span>
                       <span class="p">[</span><span class="s1">&#39;Experiment&#39;</span><span class="p">,</span> <span class="s1">&#39;Analytic Fit&#39;</span><span class="p">])</span>
@@ -365,9 +365,9 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
     <span class="c1"># Plot the MAC matrix</span>
     <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">mac_subplots_kwargs</span><span class="p">)</span>
     <span class="n">matrix_plot</span><span class="p">(</span><span class="n">mac_matrix</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">matrix_plot_kwargs</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">shapes_1_label</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">shapes_1_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">shapes_1_label</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">shapes_2_label</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">shapes_2_label</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="n">shapes_2_label</span><span class="p">)</span>
     <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
     <span class="c1"># Add a table for the mode shapes</span>
@@ -499,9 +499,9 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
         <span class="n">prs</span> <span class="o">=</span> <span class="n">presentation</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">prs</span> <span class="o">=</span> <span class="n">pptx</span><span class="o">.</span><span class="n">Presentation</span><span class="p">()</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">slide_width</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">slide_width</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">prs</span><span class="o">.</span><span class="n">slide_width</span> <span class="o">=</span> <span class="n">Inches</span><span class="p">(</span><span class="n">slide_width</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">slide_height</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">slide_height</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">prs</span><span class="o">.</span><span class="n">slide_height</span> <span class="o">=</span> <span class="n">Inches</span><span class="p">(</span><span class="n">slide_height</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">position_placeholder</span><span class="p">(</span><span class="n">placeholder</span><span class="p">,</span> <span class="n">left</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@@ -531,7 +531,7 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
         <span class="n">placeholder</span><span class="o">.</span><span class="n">height</span> <span class="o">=</span> <span class="n">height</span>
 
     <span class="c1"># Add Title Slide</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">title</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">title_slide_layout</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slide_layouts</span><span class="p">[</span><span class="n">title_slide_layout_index</span><span class="p">]</span>
         <span class="n">slide</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slides</span><span class="o">.</span><span class="n">add_slide</span><span class="p">(</span><span class="n">title_slide_layout</span><span class="p">)</span>
         <span class="n">slide</span><span class="o">.</span><span class="n">shapes</span><span class="o">.</span><span class="n">title</span><span class="o">.</span><span class="n">text</span> <span class="o">=</span> <span class="n">title</span>
@@ -543,7 +543,7 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
     <span class="n">empty_slide</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slide_layouts</span><span class="p">[</span><span class="n">empty_slide_layout_index</span><span class="p">]</span>
 
     <span class="c1"># Plot the test geometry</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">geometry_plot</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">**</span><span class="n">geometry_plot_kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">slide</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slides</span><span class="o">.</span><span class="n">add_slide</span><span class="p">(</span><span class="n">bullet_slide</span><span class="p">)</span>
         <span class="n">text_placeholder</span> <span class="o">=</span> <span class="n">slide</span><span class="o">.</span><span class="n">placeholders</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
@@ -571,10 +571,10 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
             <span class="n">p</span><span class="o">.</span><span class="n">level</span> <span class="o">=</span> <span class="mi">1</span>
 
     <span class="c1"># Plot the list of shapes</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">max_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">shapes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">max_shapes</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">shapes</span> <span class="o">=</span> <span class="n">shapes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[:</span><span class="n">max_shapes</span><span class="p">]</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">shapes</span> <span class="o">=</span> <span class="n">shapes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="n">shapes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">frequency</span> <span class="o">&lt;</span> <span class="n">max_frequency</span><span class="p">]</span>
         <span class="n">slide</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slides</span><span class="o">.</span><span class="n">add_slide</span><span class="p">(</span><span class="n">empty_slide</span><span class="p">)</span>
 
@@ -602,7 +602,7 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
             <span class="n">table</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">text</span> <span class="o">=</span> <span class="n">damping_format</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">shape</span><span class="o">.</span><span class="n">damping</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
             <span class="n">table</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">text</span> <span class="o">=</span> <span class="n">shape</span><span class="o">.</span><span class="n">comment1</span>
 
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">analytic_cmif</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">analytic_cmif</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
             <span class="n">experimental_cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
             <span class="n">analytic_cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
@@ -621,7 +621,7 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
             <span class="n">pic</span><span class="o">.</span><span class="n">height</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">pic</span><span class="o">.</span><span class="n">width</span> <span class="o">/</span> <span class="n">ar</span><span class="p">)</span>
 
         <span class="c1"># Now we need to plot each shape if possible</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">output_save_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tempfile</span><span class="o">.</span><span class="n">gettempdir</span><span class="p">(),</span>
                                             <span class="s1">&#39;Shape_</span><span class="si">{:}</span><span class="s1">.gif&#39;</span><span class="p">)</span>
             <span class="n">shapes_plot</span> <span class="o">=</span> <span class="n">geometry</span><span class="o">.</span><span class="n">plot_shape</span><span class="p">(</span><span class="n">shapes</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="n">geometry_plot_kwargs</span><span class="p">)</span>
@@ -672,7 +672,7 @@ <h1>Source code for sdynpy.doc.sdynpy_ppt</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba6f880>
+<jinja2.runtime.BlockReference object at 0x7f5a27f4a2f0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fem/sdynpy_beam.html b/_modules/sdynpy/fem/sdynpy_beam.html
index de89842..0eb7f59 100644
--- a/_modules/sdynpy/fem/sdynpy_beam.html
+++ b/_modules/sdynpy/fem/sdynpy_beam.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fem.sdynpy_beam &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fem.sdynpy_beam &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -302,11 +302,11 @@ <h1>Source code for sdynpy.fem.sdynpy_beam</h1><div class="highlight"><pre>
     <span class="k">return</span> <span class="n">K</span><span class="p">,</span> <span class="n">M</span></div>
 
 
-<span class="sd">&#39;&#39;&#39;        </span>
+<span class="sd">&#39;&#39;&#39;</span>
 <span class="sd">node_coords = np.array([np.linspace(0,1,10),</span>
 <span class="sd">                         np.zeros(10),</span>
 <span class="sd">                         np.zeros(10)]).T</span>
-<span class="sd">        </span>
+
 <span class="sd">element_connectivity = np.array([np.arange(0,9),np.arange(1,10)]).T</span>
 
 <span class="sd">bend_direction_1 = np.array([np.zeros(element_connectivity.shape[0]),np.zeros(element_connectivity.shape[0]),np.ones(element_connectivity.shape[0])]).T</span>
@@ -371,11 +371,11 @@ <h1>Source code for sdynpy.fem.sdynpy_beam</h1><div class="highlight"><pre>
 <span class="sd">    -----</span>
 <span class="sd">    Steel : SI</span>
 <span class="sd">        E = 200e9 # [N/m^2],</span>
-<span class="sd">        nu = 0.25 # [-], </span>
+<span class="sd">        nu = 0.25 # [-],</span>
 <span class="sd">        rho = 7850 # [kg/m^3]</span>
 <span class="sd">    Aluminum : SI</span>
-<span class="sd">        E = 69e9 # [N/m^2], </span>
-<span class="sd">        nu = 0.33 # [-], </span>
+<span class="sd">        E = 69e9 # [N/m^2],</span>
+<span class="sd">        nu = 0.33 # [-],</span>
 <span class="sd">        rho = 2830 # [kg/m^3]</span>
 <span class="sd">    &#39;&#39;&#39;</span>
     <span class="k">if</span> <span class="n">h</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -393,7 +393,7 @@ <h1>Source code for sdynpy.fem.sdynpy_beam</h1><div class="highlight"><pre>
     <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;ei2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">E</span> <span class="o">*</span> <span class="n">I2</span>
     <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;mass_per_length&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">rho</span> <span class="o">*</span> <span class="n">A</span>
     <span class="n">return_dict</span><span class="p">[</span><span class="s1">&#39;tmmi_per_length&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">Ixx_per_L</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">nelem</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">nelem</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">return_dict</span><span class="p">:</span>
             <span class="n">return_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">return_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">nelem</span><span class="p">,),</span> <span class="nb">float</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">return_dict</span></div>
@@ -466,7 +466,7 @@ <h1>Source code for sdynpy.fem.sdynpy_beam</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba6cb50>
+<jinja2.runtime.BlockReference object at 0x7f5a265a0700>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fem/sdynpy_dof.html b/_modules/sdynpy/fem/sdynpy_dof.html
index 34afd2b..6f4aa27 100644
--- a/_modules/sdynpy/fem/sdynpy_dof.html
+++ b/_modules/sdynpy/fem/sdynpy_dof.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fem.sdynpy_dof &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fem.sdynpy_dof &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -136,7 +136,7 @@ <h1>Source code for sdynpy.fem.sdynpy_dof</h1><div class="highlight"><pre>
 <span class="sd">    -------</span>
 <span class="sd">    indices : np.array</span>
 <span class="sd">        A 1d array corresponding to the indices to keep in the first dimension</span>
-<span class="sd">        of the shape_matrix array (e.g. new_shape_matrix = </span>
+<span class="sd">        of the shape_matrix array (e.g. new_shape_matrix =</span>
 <span class="sd">        shape_matrix[incies,...])</span>
 <span class="sd">    returned_condition_numbers : list</span>
 <span class="sd">        The condition number at each iteration.  Returned only if</span>
@@ -192,7 +192,7 @@ <h1>Source code for sdynpy.fem.sdynpy_dof</h1><div class="highlight"><pre>
 <span class="sd">    -------</span>
 <span class="sd">    indices : np.array</span>
 <span class="sd">        A 1d array corresponding to the indices to keep in the first dimension</span>
-<span class="sd">        of the shape_matrix array (e.g. new_shape_matrix = </span>
+<span class="sd">        of the shape_matrix array (e.g. new_shape_matrix =</span>
 <span class="sd">        shape_matrix[incies,...])</span>
 <span class="sd">    returned_efi : list</span>
 <span class="sd">        The effective independence at each iteration.  Returned only if</span>
@@ -236,7 +236,7 @@ <h1>Source code for sdynpy.fem.sdynpy_dof</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc4880>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb52d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fem/sdynpy_exodus.html b/_modules/sdynpy/fem/sdynpy_exodus.html
index f319ae6..72247fc 100644
--- a/_modules/sdynpy/fem/sdynpy_exodus.html
+++ b/_modules/sdynpy/fem/sdynpy_exodus.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fem.sdynpy_exodus &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fem.sdynpy_exodus &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -423,9 +423,9 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">num_dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="s1">&#39;num_dim&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">size</span>
 
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">     _   _           _           </span>
-<span class="sd">    | \ | | ___   __| | ___  ___ </span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">     _   _           _</span>
+<span class="sd">    | \ | | ___   __| | ___  ___</span>
 <span class="sd">    |  \| |/ _ \ / _` |/ _ \/ __|</span>
 <span class="sd">    | |\  | (_) | (_| |  __/\__ \</span>
 <span class="sd">    |_| \_|\___/ \__,_|\___||___/</span>
@@ -756,9 +756,9 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">get_node_variable_values</span><span class="p">(</span><span class="n">displacement_name</span> <span class="o">+</span> <span class="p">(</span><span class="n">val</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span> <span class="k">if</span> <span class="n">capital_coordinates</span> <span class="k">else</span> <span class="n">val</span><span class="o">.</span><span class="n">lower</span><span class="p">()))</span>
                          <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="s1">&#39;xyz&#39;</span><span class="p">])</span></div>
 
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;        </span>
-<span class="sd">      _____ _                           _       </span>
-<span class="sd">     | ____| | ___ _ __ ___   ___ _ __ | |_ ___ </span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">      _____ _                           _</span>
+<span class="sd">     | ____| | ___ _ __ ___   ___ _ __ | |_ ___</span>
 <span class="sd">     |  _| | |/ _ \ &#39;_ ` _ \ / _ \ &#39;_ \| __/ __|</span>
 <span class="sd">     | |___| |  __/ | | | | |  __/ | | | |_\__ \</span>
 <span class="sd">     |_____|_|\___|_| |_| |_|\___|_| |_|\__|___/</span>
@@ -978,7 +978,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        id : int </span>
+<span class="sd">        id : int</span>
 <span class="sd">            The block id for which the element connectivity is being assigned</span>
 <span class="sd">        connectivity : 2D array-like</span>
 <span class="sd">            A 2D array of dimension num_elements x num_nodes_per_element</span>
@@ -994,7 +994,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
             <span class="k">raise</span> <span class="n">ExodusError</span><span class="p">(</span><span class="s1">&#39;Invalid Block ID&#39;</span><span class="p">)</span>
         <span class="n">conn_name</span> <span class="o">=</span> <span class="s1">&#39;connect</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_index</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">conn_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">conn_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
             <span class="k">raise</span> <span class="n">ExodusError</span><span class="p">(</span><span class="s1">&#39;Element Block </span><span class="si">{:d}</span><span class="s1"> has not been defined yet&#39;</span><span class="p">)</span>
         <span class="n">connectivity</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="n">conn_name</span><span class="p">][:]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">connectivity</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
@@ -1007,7 +1007,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        d : int </span>
+<span class="sd">        d : int</span>
 <span class="sd">            The block id for which the number of attributes is desired</span>
 
 <span class="sd">        Returns</span>
@@ -1032,7 +1032,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        d : int </span>
+<span class="sd">        d : int</span>
 <span class="sd">            The block id for which the number of attributes is desired</span>
 
 <span class="sd">        Returns</span>
@@ -1055,7 +1055,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 
 <span class="sd">        Parameters</span>
 <span class="sd">        ----------</span>
-<span class="sd">        d : int </span>
+<span class="sd">        d : int</span>
 <span class="sd">            The block id for which the number of attributes is desired</span>
 
 <span class="sd">        Returns</span>
@@ -1114,7 +1114,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
             <span class="k">raise</span> <span class="n">ExodusError</span><span class="p">(</span><span class="s1">&#39;Invalid Block ID&#39;</span><span class="p">)</span>
         <span class="n">attr_name</span> <span class="o">=</span> <span class="s1">&#39;attrib</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">block_index</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">attr_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">attr_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">:</span>
             <span class="k">raise</span> <span class="n">ExodusError</span><span class="p">(</span><span class="s1">&#39;Element Block </span><span class="si">{:d}</span><span class="s1"> has no defined attributes&#39;</span><span class="p">)</span>
         <span class="n">attributes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">attributes</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="n">attr_name</span><span class="p">][:]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">attributes</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
@@ -1435,9 +1435,9 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="Exodus.put_element_property_value"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.put_element_property_value">[docs]</a>    <span class="k">def</span> <span class="nf">put_element_property_value</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;        </span>
-<span class="sd">     _   _           _                _       </span>
-<span class="sd">    | \ | | ___   __| | ___  ___  ___| |_ ___ </span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">     _   _           _                _</span>
+<span class="sd">    | \ | | ___   __| | ___  ___  ___| |_ ___</span>
 <span class="sd">    |  \| |/ _ \ / _` |/ _ \/ __|/ _ \ __/ __|</span>
 <span class="sd">    | |\  | (_) | (_| |  __/\__ \  __/ |_\__ \</span>
 <span class="sd">    |_| \_|\___/ \__,_|\___||___/\___|\__|___/</span>
@@ -1622,7 +1622,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="n">ExodusError</span><span class="p">(</span>
                 <span class="s1">&#39;Information for node set </span><span class="si">{:d}</span><span class="s1"> has already been put to the exodus file.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">id</span><span class="p">))</span>
         <span class="n">nodes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">nodes</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">dist_fact</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dist_fact</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
             <span class="k">if</span> <span class="n">nodes</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">dist_fact</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="n">ExodusError</span><span class="p">(</span><span class="s1">&#39;dist_fact must be the same size as nodes&#39;</span><span class="p">)</span>
@@ -1632,7 +1632,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="n">node_name</span> <span class="o">=</span> <span class="s1">&#39;node_ns</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ns_index</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="n">node_name</span><span class="p">,</span> <span class="s1">&#39;int32&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">num_node_in_ns_name</span><span class="p">,))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="n">node_name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">nodes</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">distfact_name</span> <span class="o">=</span> <span class="s1">&#39;dist_fact_ns</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ns_index</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span>
                 <span class="n">distfact_name</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">floating_point_type</span><span class="p">,</span> <span class="p">(</span><span class="n">num_node_in_ns_name</span><span class="p">,))</span>
@@ -1641,10 +1641,10 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="c1"># actually what it should be, just every exodus file I&#39;ve looked at has</span>
         <span class="c1"># the eb_status as all ones...)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s1">&#39;ns_status&#39;</span><span class="p">][</span><span class="n">ns_index</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span></div>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">       _____ _     _                _       </span>
-<span class="sd">      / ____(_)   | |              | |      </span>
-<span class="sd">     | (___  _  __| | ___  ___  ___| |_ ___ </span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">       _____ _     _                _</span>
+<span class="sd">      / ____(_)   | |              | |</span>
+<span class="sd">     | (___  _  __| | ___  ___  ___| |_ ___</span>
 <span class="sd">      \___ \| |/ _` |/ _ \/ __|/ _ \ __/ __|</span>
 <span class="sd">      ____) | | (_| |  __/\__ \  __/ |_\__ \</span>
 <span class="sd">     |_____/|_|\__,_|\___||___/\___|\__|___/</span>
@@ -1835,7 +1835,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="s1">&#39;Information for side set </span><span class="si">{:d}</span><span class="s1"> has already been put to the exodus file.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">id</span><span class="p">))</span>
         <span class="n">elements</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">elements</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
         <span class="n">sides</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sides</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning:Distribution factors for sidesets have not been thoroughly checked! Use with Caution!&#39;</span><span class="p">)</span>
             <span class="n">dist_fact</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dist_fact</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
             <span class="c1"># TODO: Check if the size of dist_fact is correct</span>
@@ -1850,7 +1850,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">createVariable</span><span class="p">(</span><span class="n">side_name</span><span class="p">,</span> <span class="s1">&#39;int32&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">num_side_in_ss_name</span><span class="p">,))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="n">elem_name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">elements</span> <span class="o">+</span> <span class="mi">1</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="n">side_name</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">sides</span> <span class="o">+</span> <span class="mi">1</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">dist_fact</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">distfact_name</span> <span class="o">=</span> <span class="s1">&#39;dist_fact_ss</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ss_index</span><span class="p">)</span>
             <span class="n">distfact_dim_name</span> <span class="o">=</span> <span class="s1">&#39;num_df_ss</span><span class="si">{:d}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ss_index</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_ncdf_handle</span><span class="o">.</span><span class="n">createDimension</span><span class="p">(</span><span class="n">distfact_dim_name</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">dist_fact</span><span class="p">))</span>
@@ -1865,10 +1865,10 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 <div class="viewcode-block" id="Exodus.get_side_set_node_list"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_side_set_node_list">[docs]</a>    <span class="k">def</span> <span class="nf">get_side_set_node_list</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">id</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
 
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;        </span>
-<span class="sd">       _____ _       _           _  __      __        _       _     _           </span>
-<span class="sd">      / ____| |     | |         | | \ \    / /       (_)     | |   | |          </span>
-<span class="sd">     | |  __| | ___ | |__   __ _| |  \ \  / /_ _ _ __ _  __ _| |__ | | ___  ___ </span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">       _____ _       _           _  __      __        _       _     _</span>
+<span class="sd">      / ____| |     | |         | | \ \    / /       (_)     | |   | |</span>
+<span class="sd">     | |  __| | ___ | |__   __ _| |  \ \  / /_ _ _ __ _  __ _| |__ | | ___  ___</span>
 <span class="sd">     | | |_ | |/ _ \| &#39;_ \ / _` | |   \ \/ / _` | &#39;__| |/ _` | &#39;_ \| |/ _ \/ __|</span>
 <span class="sd">     | |__| | | (_) | |_) | (_| | |    \  / (_| | |  | | (_| | |_) | |  __/\__ \</span>
 <span class="sd">      \_____|_|\___/|_.__/ \__,_|_|     \/ \__,_|_|  |_|\__,_|_.__/|_|\___||___/</span>
@@ -2063,7 +2063,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 <span class="sd">            surface faces of the block.  Values in this array correspond to</span>
 <span class="sd">            indices into the node_indices array.  To recover the connectivity</span>
 <span class="sd">            array in the original node indices of the exodus file, it can be</span>
-<span class="sd">            passed through the node_indices as </span>
+<span class="sd">            passed through the node_indices as</span>
 <span class="sd">            node_indices[block_surface_connectivity].  The</span>
 <span class="sd">            block_surface_original_elements array shows the original element</span>
 <span class="sd">            indices of the block that each surface came from.  This can be</span>
@@ -2480,13 +2480,13 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="n">elem_var_names</span> <span class="o">=</span> <span class="n">exo</span><span class="o">.</span><span class="n">get_elem_variable_names</span><span class="p">()</span>
                 <span class="n">variable_truth_table</span> <span class="o">=</span> <span class="n">exo</span><span class="o">.</span><span class="n">get_elem_variable_table</span><span class="p">()</span><span class="o">.</span><span class="n">data</span>
                 <span class="k">for</span> <span class="n">blk_index</span><span class="p">,</span> <span class="n">blk_id</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">exo</span><span class="o">.</span><span class="n">get_elem_blk_ids</span><span class="p">()):</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">blk_id</span> <span class="ow">in</span> <span class="n">block_ids</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">blk_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">block_ids</span><span class="p">:</span>
                         <span class="k">continue</span>
                     <span class="n">block_dict</span> <span class="o">=</span> <span class="p">{}</span>
                     <span class="n">block_dict</span><span class="p">[</span><span class="s1">&#39;id&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">blk_id</span>
                     <span class="n">block_dict</span><span class="p">[</span><span class="s1">&#39;elem_var_data&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
                     <span class="k">for</span> <span class="n">elem_index</span><span class="p">,</span> <span class="n">elem_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">elem_var_names</span><span class="p">):</span>
-                        <span class="k">if</span> <span class="p">(</span><span class="n">variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="ow">not</span> <span class="n">elem_name</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">):</span>
+                        <span class="k">if</span> <span class="p">(</span><span class="n">variables</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">elem_name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">variables</span><span class="p">):</span>
                             <span class="k">continue</span>
                         <span class="k">if</span> <span class="n">variable_truth_table</span><span class="p">[</span><span class="n">blk_index</span><span class="p">,</span> <span class="n">elem_index</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                             <span class="n">block_dict</span><span class="p">[</span><span class="s1">&#39;elem_var_data&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
@@ -2686,7 +2686,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="c1"># Write the nodes</span>
         <span class="n">exo_out</span><span class="o">.</span><span class="n">put_coord_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">names</span><span class="p">)</span>
         <span class="n">exo_out</span><span class="o">.</span><span class="n">put_coords</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">coordinates</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_node_num_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span><span class="p">)</span>
 
         <span class="c1"># Initialize blocks</span>
@@ -2706,11 +2706,11 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="n">exo_out</span><span class="o">.</span><span class="n">set_elem_attr</span><span class="p">(</span><span class="n">block</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="n">block</span><span class="o">.</span><span class="n">attributes</span><span class="p">)</span>
 
         <span class="c1"># Element Map</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">element_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">element_map</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_elem_num_map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">element_map</span><span class="p">)</span>
 
         <span class="c1"># Nodesets</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodesets</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodesets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_node_set_ids</span><span class="p">([</span><span class="n">ns</span><span class="o">.</span><span class="n">id</span> <span class="k">for</span> <span class="n">ns</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodesets</span><span class="p">])</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_node_set_names</span><span class="p">(</span>
                 <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">nodeset</span><span class="o">.</span><span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">nodeset</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">nodeset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodesets</span><span class="p">])</span>
@@ -2718,7 +2718,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="n">exo_out</span><span class="o">.</span><span class="n">put_node_set_info</span><span class="p">(</span><span class="n">nodeset</span><span class="o">.</span><span class="n">id</span><span class="p">,</span> <span class="n">nodeset</span><span class="o">.</span><span class="n">nodes</span><span class="p">,</span> <span class="n">nodeset</span><span class="o">.</span><span class="n">dist_factors</span><span class="p">)</span>
 
         <span class="c1"># Sidesets</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">sidesets</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sidesets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_side_set_ids</span><span class="p">([</span><span class="n">ns</span><span class="o">.</span><span class="n">id</span> <span class="k">for</span> <span class="n">ns</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sidesets</span><span class="p">])</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_side_set_names</span><span class="p">(</span>
                 <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">if</span> <span class="n">sideset</span><span class="o">.</span><span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">sideset</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">sideset</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sidesets</span><span class="p">])</span>
@@ -2727,21 +2727,21 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                                           <span class="n">sideset</span><span class="o">.</span><span class="n">sides</span><span class="p">,</span> <span class="n">sideset</span><span class="o">.</span><span class="n">dist_factors</span><span class="p">)</span>
 
         <span class="c1"># Global Variables #TODO: Make it so I can write all timesteps at once.</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_global_variable_names</span><span class="p">([</span><span class="n">var</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_vars</span><span class="p">])</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_vars</span><span class="p">):</span>
                 <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">time</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time</span><span class="p">):</span>
                     <span class="n">exo_out</span><span class="o">.</span><span class="n">set_global_variable_values</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_vars</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
 
         <span class="c1"># Nodal Variables #TODO: Make it so I can write all timesteps at once.</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodal_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodal_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">exo_out</span><span class="o">.</span><span class="n">put_node_variable_names</span><span class="p">([</span><span class="n">var</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodal_vars</span><span class="p">])</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nodal_vars</span><span class="p">):</span>
                 <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">time</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time</span><span class="p">):</span>
                     <span class="n">exo_out</span><span class="o">.</span><span class="n">set_node_variable_values</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nodal_vars</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="p">:])</span>
 
         <span class="c1"># Element Variables # TODO: Make it so I can write all timesteps at once</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">elem_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">elem_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">nblocks</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">elem_vars</span><span class="p">)</span>
             <span class="n">nelemvars</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">elem_vars</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">elem_var_data</span><span class="p">)</span>
             <span class="n">elem_table</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">nblocks</span><span class="p">,</span> <span class="n">nelemvars</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
@@ -2837,7 +2837,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 <span class="sd">            surface faces of the block.  Values in this array correspond to</span>
 <span class="sd">            indices into the node_indices array.  To recover the connectivity</span>
 <span class="sd">            array in the original node indices of the exodus file, it can be</span>
-<span class="sd">            passed through the node_indices as </span>
+<span class="sd">            passed through the node_indices as</span>
 <span class="sd">            node_indices[block_surface_connectivity].  The</span>
 <span class="sd">            block_surface_original_elements array shows the original element</span>
 <span class="sd">            indices of the block that each surface came from.  This can be</span>
@@ -3024,9 +3024,9 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
 <span class="sd">           /  |           /  |</span>
 <span class="sd">          5--------------6   |</span>
 <span class="sd">          |   4----------|---3</span>
-<span class="sd">          |  /           |  / </span>
-<span class="sd">          | /            | /  </span>
-<span class="sd">          |/             |/   </span>
+<span class="sd">          |  /           |  /</span>
+<span class="sd">          | /            | /</span>
+<span class="sd">          |/             |/</span>
 <span class="sd">          1--------------2</span>
 
 <span class="sd">    So the 6 faces are as follows::</span>
@@ -3227,7 +3227,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
     <span class="c1"># If the exodus file has element variables, we are only keeping the ones</span>
     <span class="c1"># corresponding to the blocks we are keeping</span>
     <span class="k">if</span> <span class="n">exo_in_mem</span><span class="p">:</span>
-        <span class="n">has_elem_vars</span> <span class="o">=</span> <span class="p">(</span><span class="ow">not</span> <span class="n">fexo</span><span class="o">.</span><span class="n">elem_vars</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">node_variables_to_transform</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
+        <span class="n">has_elem_vars</span> <span class="o">=</span> <span class="p">(</span><span class="n">fexo</span><span class="o">.</span><span class="n">elem_vars</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">node_variables_to_transform</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">has_elem_vars</span> <span class="o">=</span> <span class="n">fexo</span><span class="o">.</span><span class="n">num_elem_variables</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">elem_variables_to_transform</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
@@ -3344,13 +3344,13 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="n">face_elem_vars</span> <span class="o">=</span> <span class="n">fexo</span><span class="o">.</span><span class="n">elem_vars</span><span class="p">[[</span><span class="n">var</span><span class="o">.</span><span class="n">id</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="n">fexo</span><span class="o">.</span><span class="n">elem_vars</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">block_id</span><span class="p">)]</span>
                 <span class="k">for</span> <span class="n">elem_index</span><span class="p">,</span> <span class="n">elem_var_data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">face_elem_vars</span><span class="o">.</span><span class="n">elem_var_data</span><span class="p">):</span>
                     <span class="c1"># Check if we need to skip it</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">elem_index</span> <span class="ow">in</span> <span class="n">elem_variables_to_transform</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">elem_index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">elem_variables_to_transform</span><span class="p">:</span>
                         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Skipping Element Variable </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">elem_index</span><span class="p">))</span>
                         <span class="k">continue</span>
                     <span class="c1"># If the variable doesn&#39;t exist it will be None (for example a</span>
                     <span class="c1"># volume quantity on a surface element).  We only operate if</span>
                     <span class="c1"># the variable exists (is not None)</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">elem_var_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">elem_var_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                         <span class="c1"># We use the face&#39;s original elements vector to map the</span>
                         <span class="c1"># element data to the face connectivity matrix</span>
                         <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
@@ -3372,7 +3372,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="c1"># Loop through and pick element variables that exist in the truth table</span>
                 <span class="k">for</span> <span class="n">elem_index</span><span class="p">,</span> <span class="n">elem_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">elem_var_names</span><span class="p">):</span>
                     <span class="c1"># Check if we need to skip it</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">elem_index</span> <span class="ow">in</span> <span class="n">elem_variables_to_transform</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">elem_index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">elem_variables_to_transform</span><span class="p">:</span>
                         <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
                             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Skipping Element Variable </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">elem_name</span><span class="p">))</span>
                         <span class="k">continue</span>
@@ -3440,7 +3440,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">node_variables_to_transform</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">fexo_out</span><span class="o">.</span><span class="n">nodal_vars</span> <span class="o">=</span> <span class="p">[]</span>
             <span class="k">for</span> <span class="n">node_var_index</span><span class="p">,</span> <span class="n">node_var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">fexo</span><span class="o">.</span><span class="n">nodal_vars</span><span class="p">):</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">node_var_index</span> <span class="ow">in</span> <span class="n">node_variables_to_transform</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">node_var_index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node_variables_to_transform</span><span class="p">:</span>
                     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
                         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Skipping Nodal Variable </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">node_var</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
                     <span class="k">continue</span>
@@ -3454,7 +3454,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
                 <span class="n">fexo_out</span><span class="o">.</span><span class="n">nodal_vars</span> <span class="o">=</span> <span class="p">[]</span>
                 <span class="n">node_vars</span> <span class="o">=</span> <span class="n">fexo</span><span class="o">.</span><span class="n">get_node_variable_names</span><span class="p">()</span>
                 <span class="k">for</span> <span class="n">node_var_index</span><span class="p">,</span> <span class="n">node_var_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">node_vars</span><span class="p">):</span>
-                    <span class="k">if</span> <span class="ow">not</span> <span class="n">node_var_index</span> <span class="ow">in</span> <span class="n">node_variables_to_transform</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">node_var_index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">node_variables_to_transform</span><span class="p">:</span>
                         <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
                             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Skipping Nodal Variable </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">node_var_name</span><span class="p">))</span>
                         <span class="k">continue</span>
@@ -3526,7 +3526,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
     <span class="k">for</span> <span class="n">node_var_index</span><span class="p">,</span> <span class="n">node_var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">edge_fexo</span><span class="o">.</span><span class="n">nodal_vars</span><span class="p">):</span>
         <span class="n">node_var</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">node_var</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="n">keep_nodes</span><span class="p">]</span>
     <span class="n">edge_fexo</span><span class="o">.</span><span class="n">elem_vars</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">edge_fexo</span><span class="o">.</span><span class="n">blocks</span> <span class="o">=</span> <span class="p">[</span><span class="n">block</span> <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">edge_fexo</span><span class="o">.</span><span class="n">blocks</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">block</span><span class="o">.</span><span class="n">id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">]</span>
+    <span class="n">edge_fexo</span><span class="o">.</span><span class="n">blocks</span> <span class="o">=</span> <span class="p">[</span><span class="n">block</span> <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">edge_fexo</span><span class="o">.</span><span class="n">blocks</span> <span class="k">if</span> <span class="n">block</span><span class="o">.</span><span class="n">id</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
     <span class="k">for</span> <span class="n">block</span> <span class="ow">in</span> <span class="n">edge_fexo</span><span class="o">.</span><span class="n">blocks</span><span class="p">:</span>
         <span class="n">block</span><span class="o">.</span><span class="n">connectivity</span> <span class="o">=</span> <span class="n">node_map</span><span class="p">[</span><span class="n">block</span><span class="o">.</span><span class="n">connectivity</span><span class="p">]</span>
     <span class="n">edge_fexo</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span> <span class="o">=</span> <span class="n">surface_fexo</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">node_num_map</span><span class="p">[</span><span class="n">keep_nodes</span><span class="p">]</span>
@@ -3540,7 +3540,7 @@ <h1>Source code for sdynpy.fem.sdynpy_exodus</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbbbf40>
+<jinja2.runtime.BlockReference object at 0x7f5a27f4bdf0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fem/sdynpy_shaker.html b/_modules/sdynpy/fem/sdynpy_shaker.html
index b48f951..fce1c7c 100644
--- a/_modules/sdynpy/fem/sdynpy_shaker.html
+++ b/_modules/sdynpy/fem/sdynpy_shaker.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fem.sdynpy_shaker &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fem.sdynpy_shaker &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -109,6 +109,93 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
+<span class="c1"># def shaker_4dof(m_armature,m_body,m_forcegauge,</span>
+<span class="c1">#                 k_suspension,k_stinger,</span>
+<span class="c1">#                 c_suspension,c_stinger,</span>
+<span class="c1">#                 resistance,inductance,force_factor):</span>
+<span class="c1">#     &#39;&#39;&#39;Creates a four degree of freedom electromechanical model of a shaker</span>
+
+<span class="c1">#     Parameters</span>
+<span class="c1">#     ----------</span>
+<span class="c1">#     m_armature : float</span>
+<span class="c1">#         Mass of the armaturesistance</span>
+<span class="c1">#     m_body : float</span>
+<span class="c1">#         Mass of the body and trunion of the shaker</span>
+<span class="c1">#     m_forcegauge : float</span>
+<span class="c1">#         Mass of the force gauge at the tip of the stinger</span>
+<span class="c1">#     k_suspension : float</span>
+<span class="c1">#         Stiffness of the spring between the body and armature</span>
+<span class="c1">#     k_stinger : float</span>
+<span class="c1">#         Stiffness of the stinger between the armature and force gauge</span>
+<span class="c1">#     c_suspension : float</span>
+<span class="c1">#         Damping of the spring between the body and armature</span>
+<span class="c1">#     c_stinger : float</span>
+<span class="c1">#         Damping of the stinger between the armature and force gauge</span>
+<span class="c1">#     resistance : float</span>
+<span class="c1">#         Coil resistance of the electronics portion of the shaker</span>
+<span class="c1">#     inductance : float</span>
+<span class="c1">#         Coil inductance of the electronics portion of the shaker</span>
+<span class="c1">#     force_factor : float</span>
+<span class="c1">#         Force factor BL of the magnet and coil</span>
+
+<span class="c1">#     Returns</span>
+<span class="c1">#     -------</span>
+<span class="c1">#     M_shaker : ndarray</span>
+<span class="c1">#         The mass matrix of the shaker</span>
+<span class="c1">#     C_shaker : ndarray</span>
+<span class="c1">#         The damping matrix of the shaker</span>
+<span class="c1">#     K_shaker : ndarray</span>
+<span class="c1">#         The stiffness matrix of the shaker</span>
+
+<span class="c1">#     Notes</span>
+<span class="c1">#     -----</span>
+<span class="c1">#     The dof order of the responses are:</span>
+<span class="c1">#     Displacement of Armature,</span>
+<span class="c1">#     Displacement of Body,</span>
+<span class="c1">#     Displacement of Force Gauge,</span>
+<span class="c1">#     Shaker Current.</span>
+
+<span class="c1">#     The dof order of the inputs are:</span>
+<span class="c1">#     Force into Armature</span>
+<span class="c1">#     Force into Body</span>
+<span class="c1">#     Force into Force Gauge</span>
+<span class="c1">#     Shaker Voltage.</span>
+
+<span class="c1">#     The force imparted by the shaker can be computed as the stinger stiffness</span>
+<span class="c1">#     times the difference in displacement of the armature and force gauge plus</span>
+<span class="c1">#     the stinger damping times the difference in the velocity of the armature</span>
+<span class="c1">#     and force gauge.</span>
+<span class="c1">#     &#39;&#39;&#39;</span>
+
+<span class="c1">#     # Cresistanceate the matrices</span>
+<span class="c1">#     M_shaker = np.array([[m_armature,  0,  0,  0],</span>
+<span class="c1">#                          [ 0, m_body,  0,  0],</span>
+<span class="c1">#                          [ 0,  0, m_forcegauge,  0],</span>
+<span class="c1">#                          [ 0,  0,  0,  0]])</span>
+<span class="c1">#     C_shaker = np.array([[(c_suspension+c_stinger), -c_suspension, -c_stinger, 0],</span>
+<span class="c1">#                          [     -c_suspension,  c_suspension,    0, 0],</span>
+<span class="c1">#                          [     -c_stinger,    0,  c_stinger, 0],</span>
+<span class="c1">#                          [       force_factor,  -force_factor, 0,   inductance]])</span>
+<span class="c1">#     K_shaker = np.array([[(k_suspension+k_stinger), -k_suspension, -k_stinger, -force_factor],</span>
+<span class="c1">#                          [     -k_suspension,  k_suspension,    0,  force_factor],</span>
+<span class="c1">#                          [     -k_stinger,    0,  k_stinger,   0],</span>
+<span class="c1">#                          [        0,    0,    0,  resistance]])</span>
+
+<span class="c1">#     return M_shaker,C_shaker,K_shaker</span>
+
+<span class="c1"># test_shaker_dakota = shaker_4dof( m_armature = 0.227, # [kg] ~ armaturesistance mass</span>
+<span class="c1">#                                   m_body = 24.9, # [kg] ~ body mass</span>
+<span class="c1">#                                   k_suspension = 2312, # [N/m] ~ suspension stiffness</span>
+<span class="c1">#                                   c_suspension = 20, # [N/(m/s)] damping ...</span>
+<span class="c1">#                                   k_stinger = 4e6, # [N/m] stiff stinger spring</span>
+<span class="c1">#                                   m_forcegauge = 1e-2, # [kg] force gauge mass</span>
+<span class="c1">#                                   c_stinger = 10, # [N/(m/s)] damping</span>
+<span class="c1">#                                   resistance = 1.3, # [ohm] ~ coil resistance</span>
+<span class="c1">#                                   inductance = 100e-6, # [Henry] ~ coil inductance</span>
+<span class="c1">#                                   force_factor = 30 # [-] ~ Force Factor of magnet &amp; coil</span>
+<span class="c1">#                                 )</span>
+
+
 <div class="viewcode-block" id="Shaker4DoF"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fem.sdynpy_shaker.Shaker4DoF.html#sdynpy.fem.sdynpy_shaker.Shaker4DoF">[docs]</a><span class="k">class</span> <span class="nc">Shaker4DoF</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;A class defining a four degree-of-freedom model of a shaker</span>
 
@@ -169,7 +256,7 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
 
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
-<span class="sd">        The dof order of the responses are: </span>
+<span class="sd">        The dof order of the responses are:</span>
 <span class="sd">        Displacement of Armature,</span>
 <span class="sd">        Displacement of Body,</span>
 <span class="sd">        Displacement of Force Gauge,</span>
@@ -219,10 +306,10 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
 
 <span class="sd">        Notes</span>
 <span class="sd">        -----</span>
-<span class="sd">        The dof order of the state matrix is </span>
+<span class="sd">        The dof order of the state matrix is</span>
 <span class="sd">          0. armature displacement</span>
 <span class="sd">          1. body displacement</span>
-<span class="sd">          2. forcegauge displacement, </span>
+<span class="sd">          2. forcegauge displacement,</span>
 <span class="sd">          3. armature velocity</span>
 <span class="sd">          4. body velocity</span>
 <span class="sd">          5. forcegauge velocity</span>
@@ -256,8 +343,14 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
         <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                       <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                       <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
-                      <span class="p">[(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">-</span>
-                                                                                                                                                        <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">force_factor</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">],</span>
+                      <span class="p">[(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                       <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span>
+                        <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                       <span class="bp">self</span><span class="o">.</span><span class="n">force_factor</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">],</span>
                       <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_body</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_body</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">/</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">m_body</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_body</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">force_factor</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_body</span><span class="p">],</span>
                       <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_forcegauge</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_forcegauge</span><span class="p">,</span>
@@ -274,8 +367,10 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
             <span class="c1"># States: x1, x2, x3, xd1, xd2, xd3, i</span>
                      <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>  <span class="c1"># Displacement of mass 1</span>
                      <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>  <span class="c1"># Velocity of mass 1</span>
-                     <span class="p">[(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span><span class="p">)</span> <span class="o">/</span> \
-                      <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">force_factor</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">],</span>  <span class="c1"># Acceleration of mass 1</span>
+                     <span class="p">[(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                      <span class="bp">self</span><span class="o">.</span><span class="n">k_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span><span class="p">)</span> <span class="o">/</span> \
+                      <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_suspension</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span>
+                      <span class="bp">self</span><span class="o">.</span><span class="n">c_stinger</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">force_factor</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">m_armature</span><span class="p">],</span>  <span class="c1"># Acceleration of mass 1</span>
                      <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>  <span class="c1"># External Force on mass 1</span>
                      <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>  <span class="c1"># Displacement of mass 2</span>
                      <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>  <span class="c1"># Velocity of mass 2</span>
@@ -350,10 +445,10 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
         <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">H</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transfer_function</span><span class="p">(</span><span class="n">frequencies</span><span class="p">)[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span>
         <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">H</span><span class="p">))</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">test_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">test_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">test_data</span><span class="p">))</span>
         <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">H</span><span class="p">))</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">test_data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">test_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">test_data</span><span class="p">))</span></div>
 
 <div class="viewcode-block" id="Shaker4DoF.modal_shop_100lbf"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fem.sdynpy_shaker.Shaker4DoF.html#sdynpy.fem.sdynpy_shaker.Shaker4DoF.modal_shop_100lbf">[docs]</a>    <span class="nd">@classmethod</span>
@@ -392,6 +487,10 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
                    <span class="n">resistance</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
                    <span class="n">inductance</span><span class="o">=</span><span class="mf">6e-4</span><span class="p">,</span>
                    <span class="n">force_factor</span><span class="o">=</span><span class="mi">36</span><span class="p">)</span></div></div>
+
+<span class="c1"># shaker = Shaker4DoF.modal_shop_100lbf()</span>
+<span class="c1"># shaker.m_forcegauge = 2.6</span>
+<span class="c1"># shaker.plot_electrical_impedance()</span>
 </pre></div>
 
            </div>
@@ -401,7 +500,7 @@ <h1>Source code for sdynpy.fem.sdynpy_shaker</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebb5dc60>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb7820>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_rattlesnake.html b/_modules/sdynpy/fileio/sdynpy_rattlesnake.html
index 4ec4b2a..3bcb01b 100644
--- a/_modules/sdynpy/fileio/sdynpy_rattlesnake.html
+++ b/_modules/sdynpy/fileio/sdynpy_rattlesnake.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_rattlesnake &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_rattlesnake &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -108,18 +108,19 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
 
 <span class="kn">import</span> <span class="nn">netCDF4</span> <span class="k">as</span> <span class="nn">nc4</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">..core.sdynpy_coordinate</span> <span class="kn">import</span> <span class="p">(</span><span class="n">coordinate_array</span><span class="p">,</span><span class="n">outer_product</span><span class="p">,</span>
-                                      <span class="n">CoordinateArray</span><span class="p">,</span><span class="n">_string_map</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">..core.sdynpy_coordinate</span> <span class="kn">import</span> <span class="p">(</span><span class="n">coordinate_array</span><span class="p">,</span> <span class="n">outer_product</span><span class="p">,</span>
+                                      <span class="n">CoordinateArray</span><span class="p">,</span> <span class="n">_string_map</span><span class="p">)</span>
 <span class="kn">from</span> <span class="nn">..core.sdynpy_data</span> <span class="kn">import</span> <span class="n">data_array</span><span class="p">,</span> <span class="n">FunctionTypes</span>
 <span class="kn">from</span> <span class="nn">..core.sdynpy_system</span> <span class="kn">import</span> <span class="n">System</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 <span class="kn">import</span> <span class="nn">openpyxl</span> <span class="k">as</span> <span class="nn">opxl</span>
 <span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
 
 
-<div class="viewcode-block" id="read_rattlesnake_output"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_rattlesnake_output.html#sdynpy.fileio.sdynpy_rattlesnake.read_rattlesnake_output">[docs]</a><span class="k">def</span> <span class="nf">read_rattlesnake_output</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_override_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_only_indices</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                            <span class="n">read_variable</span> <span class="o">=</span> <span class="s1">&#39;time_data&#39;</span><span class="p">):</span>
+<div class="viewcode-block" id="read_rattlesnake_output"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_rattlesnake_output.html#sdynpy.fileio.sdynpy_rattlesnake.read_rattlesnake_output">[docs]</a><span class="k">def</span> <span class="nf">read_rattlesnake_output</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_override_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_only_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                            <span class="n">read_variable</span><span class="o">=</span><span class="s1">&#39;time_data&#39;</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Reads in a Rattlesnake data file and returns the time history array as well</span>
 <span class="sd">    as the channel table</span>
@@ -127,7 +128,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    file : str or nc4.Dataset</span>
-<span class="sd">        Path to the file to read in or an already open </span>
+<span class="sd">        Path to the file to read in or an already open</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -182,61 +183,63 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
         <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
     <span class="k">return</span> <span class="n">time_data</span><span class="p">,</span> <span class="n">channel_table</span></div>
 
+
 <div class="viewcode-block" id="read_system_id_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_system_id_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_system_id_data">[docs]</a><span class="k">def</span> <span class="nf">read_system_id_data</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">file</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
     <span class="n">df</span> <span class="o">=</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;sysid_frequency_spacing&#39;</span><span class="p">]</span>
     <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_transformation_matrix&#39;</span><span class="p">]):</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span>
                 <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;channel_node_number&#39;</span><span class="p">][</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_indices&#39;</span><span class="p">]]],</span>
-                 <span class="n">file</span><span class="p">[</span><span class="s1">&#39;channel_node_direction&#39;</span><span class="p">][</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_indices&#39;</span><span class="p">]])</span>
+                <span class="n">file</span><span class="p">[</span><span class="s1">&#39;channel_node_direction&#39;</span><span class="p">][</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_indices&#39;</span><span class="p">]])</span>
         <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
-            <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_indices&#39;</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_indices&#39;</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_transformation_matrix&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_transformation_matrix&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_transformation_matrix&#39;</span><span class="p">]):</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span>
                 <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;channel_node_number&#39;</span><span class="p">][</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_indices&#39;</span><span class="p">]]],</span>
-                 <span class="n">file</span><span class="p">[</span><span class="s1">&#39;channel_node_direction&#39;</span><span class="p">][</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_indices&#39;</span><span class="p">]])</span>
+                <span class="n">file</span><span class="p">[</span><span class="s1">&#39;channel_node_direction&#39;</span><span class="p">][</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_indices&#39;</span><span class="p">]])</span>
         <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
-            <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_indices&#39;</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_indices&#39;</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_transformation_matrix&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;frf_data&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">reference_dofs</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_transformation_matrix&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;frf_data&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">frfs</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
-                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">ordinate</span><span class="p">,</span>
-                      <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">))</span>
-    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_cpsd&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">ordinate</span><span class="p">,</span>
+                      <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">))</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_cpsd&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">response_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">ordinate</span><span class="p">,</span>
-                      <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span><span class="n">response_dofs</span><span class="p">))</span>
-    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_cpsd&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                               <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">ordinate</span><span class="p">,</span>
+                               <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span> <span class="n">response_dofs</span><span class="p">))</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_cpsd&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">reference_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">ordinate</span><span class="p">,</span>
-                      <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">))</span>
-    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_noise_cpsd&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                                <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">ordinate</span><span class="p">,</span>
+                                <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">))</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;response_noise_cpsd&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">response_noise_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">ordinate</span><span class="p">,</span>
-                      <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span><span class="n">response_dofs</span><span class="p">))</span>
-    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_noise_cpsd&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                                     <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">ordinate</span><span class="p">,</span>
+                                     <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span> <span class="n">response_dofs</span><span class="p">))</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;reference_noise_cpsd&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">reference_noise_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">ordinate</span><span class="p">,</span>
-                      <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span><span class="n">reference_dofs</span><span class="p">))</span>
-    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;coherence&#39;</span><span class="p">],</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">ordinate</span><span class="p">,</span>
+                                      <span class="n">outer_product</span><span class="p">(</span><span class="n">reference_dofs</span><span class="p">,</span> <span class="n">reference_dofs</span><span class="p">))</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;coherence&#39;</span><span class="p">],</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">coherence</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">,</span>
-                      <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="n">ordinate</span><span class="p">,</span>
-                      <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">frfs</span><span class="p">,</span><span class="n">response_cpsd</span><span class="p">,</span><span class="n">reference_cpsd</span><span class="p">,</span><span class="n">response_noise_cpsd</span><span class="p">,</span><span class="n">reference_noise_cpsd</span><span class="p">,</span><span class="n">coherence</span></div>
-    
+                           <span class="n">df</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">ordinate</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">ordinate</span><span class="p">,</span>
+                           <span class="n">outer_product</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">frfs</span><span class="p">,</span> <span class="n">response_cpsd</span><span class="p">,</span> <span class="n">reference_cpsd</span><span class="p">,</span> <span class="n">response_noise_cpsd</span><span class="p">,</span> <span class="n">reference_noise_cpsd</span><span class="p">,</span> <span class="n">coherence</span></div>
+
+
 <div class="viewcode-block" id="read_random_spectral_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_random_spectral_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_random_spectral_data">[docs]</a><span class="k">def</span> <span class="nf">read_random_spectral_data</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_override_column</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">ds</span> <span class="o">=</span> <span class="n">nc4</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">nc4</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
         <span class="n">ds</span> <span class="o">=</span> <span class="n">file</span>
     <span class="n">coordinate_override_column</span> <span class="o">=</span> <span class="kc">None</span>
-        
+
     <span class="n">environment</span> <span class="o">=</span> <span class="p">[</span><span class="n">group</span> <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">groups</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">group</span> <span class="o">==</span> <span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
 
     <span class="c1"># Get the channels in the group</span>
@@ -251,7 +254,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
 
     <span class="c1"># Cull down to just those in the environment</span>
     <span class="n">environment_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;environment_names&#39;</span><span class="p">][:]</span> <span class="o">==</span> <span class="n">environment</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">environment_channels</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;environment_active_channels&#39;</span><span class="p">][:,</span><span class="n">environment_index</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span>
+    <span class="n">environment_channels</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;environment_active_channels&#39;</span><span class="p">][:,</span> <span class="n">environment_index</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span>
 
     <span class="n">drives</span> <span class="o">=</span> <span class="n">drives</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
@@ -268,20 +271,20 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
     <span class="n">spec_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span>
         <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;specification_cpsd_matrix_real&#39;</span><span class="p">][:]</span>
                  <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;specification_cpsd_matrix_imag&#39;</span><span class="p">][:]),</span>
-        <span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 
     <span class="n">response_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span>
         <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;response_cpsd_real&#39;</span><span class="p">][:]</span>
                  <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;response_cpsd_imag&#39;</span><span class="p">][:]),</span>
-        <span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 
     <span class="n">drive_cpsd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span>
         <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;drive_cpsd_real&#39;</span><span class="p">][:]</span>
                  <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;drive_cpsd_imag&#39;</span><span class="p">][:]),</span>
-        <span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 
-    <span class="n">response_coordinates</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">control_coordinates</span><span class="p">,</span><span class="n">control_coordinates</span><span class="p">)</span>
-    <span class="n">drive_coordinates</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">drive_coordinates</span><span class="p">,</span><span class="n">drive_coordinates</span><span class="p">)</span>
+    <span class="n">response_coordinates</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">control_coordinates</span><span class="p">,</span> <span class="n">control_coordinates</span><span class="p">)</span>
+    <span class="n">drive_coordinates</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">drive_coordinates</span><span class="p">,</span> <span class="n">drive_coordinates</span><span class="p">)</span>
 
     <span class="n">comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;channel_type&#39;</span><span class="p">][:],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
                                        <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
@@ -297,56 +300,57 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
     <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;serial_number&#39;</span><span class="p">,</span> <span class="s1">&#39;triax_dof&#39;</span><span class="p">):</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">][:],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
-        
+
     <span class="n">comment1</span> <span class="o">=</span> <span class="n">comment1</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">comment2</span> <span class="o">=</span> <span class="n">comment2</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">comment3</span> <span class="o">=</span> <span class="n">comment3</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">comment4</span> <span class="o">=</span> <span class="n">comment4</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">comment5</span> <span class="o">=</span> <span class="n">comment5</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
 
-    <span class="n">comment1_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment2_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment3_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment4_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment5_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">control_indices</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">jdx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">control_indices</span><span class="p">):</span>
-            <span class="n">comment1_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment1</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment1</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment2_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment2</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment2</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment3_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment3</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment3</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment4_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment4</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment4</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment5_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment5</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment5</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            
-    <span class="n">comment1_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment2_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment3_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment4_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-    <span class="n">comment5_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span><span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment1_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment2_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment3_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment4_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment5_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">response_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">control_indices</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">jdx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">control_indices</span><span class="p">):</span>
+            <span class="n">comment1_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment1</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment1</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment2_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment2</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment2</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment3_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment3</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment3</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment4_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment4</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment4</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment5_response</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment5</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment5</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+
+    <span class="n">comment1_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment2_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment3_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment4_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">comment5_drive</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">drive_coordinates</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">comment1</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
     <span class="n">drive_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">drives</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">drive_indices</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">jdx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">drive_indices</span><span class="p">):</span>
-            <span class="n">comment1_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment1</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment1</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment2_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment2</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment2</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment3_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment3</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment3</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment4_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment4</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment4</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
-            <span class="n">comment5_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment5</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment5</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">drive_indices</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">jdx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">drive_indices</span><span class="p">):</span>
+            <span class="n">comment1_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment1</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment1</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment2_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment2</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment2</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment3_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment3</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment3</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment4_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment4</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment4</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
+            <span class="n">comment5_drive</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">comment5</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; // &#39;</span> <span class="o">+</span> <span class="n">comment5</span><span class="p">[</span><span class="n">jdx</span><span class="p">]</span>
 
     <span class="c1"># Save the data to SDynpy objects</span>
     <span class="n">response_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                      <span class="n">frequencies</span><span class="p">,</span><span class="n">response_cpsd</span><span class="p">,</span><span class="n">response_coordinates</span><span class="p">,</span>
-                      <span class="n">comment1_response</span><span class="p">,</span><span class="n">comment2_response</span><span class="p">,</span><span class="n">comment3_response</span><span class="p">,</span>
-                      <span class="n">comment4_response</span><span class="p">,</span><span class="n">comment5_response</span><span class="p">)</span>
+                               <span class="n">frequencies</span><span class="p">,</span> <span class="n">response_cpsd</span><span class="p">,</span> <span class="n">response_coordinates</span><span class="p">,</span>
+                               <span class="n">comment1_response</span><span class="p">,</span> <span class="n">comment2_response</span><span class="p">,</span> <span class="n">comment3_response</span><span class="p">,</span>
+                               <span class="n">comment4_response</span><span class="p">,</span> <span class="n">comment5_response</span><span class="p">)</span>
     <span class="n">spec_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                           <span class="n">frequencies</span><span class="p">,</span><span class="n">spec_cpsd</span><span class="p">,</span><span class="n">response_coordinates</span><span class="p">,</span>
-                           <span class="n">comment1_response</span><span class="p">,</span><span class="n">comment2_response</span><span class="p">,</span><span class="n">comment3_response</span><span class="p">,</span>
-                           <span class="n">comment4_response</span><span class="p">,</span><span class="n">comment5_response</span><span class="p">)</span>
+                           <span class="n">frequencies</span><span class="p">,</span> <span class="n">spec_cpsd</span><span class="p">,</span> <span class="n">response_coordinates</span><span class="p">,</span>
+                           <span class="n">comment1_response</span><span class="p">,</span> <span class="n">comment2_response</span><span class="p">,</span> <span class="n">comment3_response</span><span class="p">,</span>
+                           <span class="n">comment4_response</span><span class="p">,</span> <span class="n">comment5_response</span><span class="p">)</span>
     <span class="n">drive_cpsd</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                      <span class="n">frequencies</span><span class="p">,</span><span class="n">drive_cpsd</span><span class="p">,</span><span class="n">drive_coordinates</span><span class="p">,</span>
-                      <span class="n">comment1_drive</span><span class="p">,</span><span class="n">comment2_drive</span><span class="p">,</span><span class="n">comment3_drive</span><span class="p">,</span>
-                      <span class="n">comment4_drive</span><span class="p">,</span><span class="n">comment5_drive</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">response_cpsd</span><span class="p">,</span><span class="n">spec_cpsd</span><span class="p">,</span><span class="n">drive_cpsd</span></div>
+                            <span class="n">frequencies</span><span class="p">,</span> <span class="n">drive_cpsd</span><span class="p">,</span> <span class="n">drive_coordinates</span><span class="p">,</span>
+                            <span class="n">comment1_drive</span><span class="p">,</span> <span class="n">comment2_drive</span><span class="p">,</span> <span class="n">comment3_drive</span><span class="p">,</span>
+                            <span class="n">comment4_drive</span><span class="p">,</span> <span class="n">comment5_drive</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">response_cpsd</span><span class="p">,</span> <span class="n">spec_cpsd</span><span class="p">,</span> <span class="n">drive_cpsd</span></div>
 
-<div class="viewcode-block" id="read_modal_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_modal_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_modal_data">[docs]</a><span class="k">def</span> <span class="nf">read_modal_data</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_override_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_only_indices</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<div class="viewcode-block" id="read_modal_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_modal_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_modal_data">[docs]</a><span class="k">def</span> <span class="nf">read_modal_data</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_override_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_only_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">ds</span> <span class="o">=</span> <span class="n">nc4</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">nc4</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
@@ -361,7 +365,11 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
     <span class="n">samples_per_frame</span> <span class="o">=</span> <span class="n">group</span><span class="o">.</span><span class="n">samples_per_frame</span>
     <span class="n">num_averages</span> <span class="o">=</span> <span class="n">group</span><span class="o">.</span><span class="n">num_averages</span>
     <span class="c1"># Load in the time data</span>
-    <span class="n">output_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;time_data&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">read_only_indices</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">num_channels</span><span class="p">,</span><span class="n">num_averages</span><span class="p">,</span><span class="n">samples_per_frame</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">output_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;time_data&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">read_only_indices</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">num_channels</span><span class="p">,</span> <span class="n">num_averages</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Number of averages in the time data does not match the number of averages specified in the test settings.  Your test may be incomplete.&#39;</span><span class="p">)</span>
+        <span class="n">output_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;time_data&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">read_only_indices</span><span class="p">])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">num_channels</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
     <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">samples_per_frame</span><span class="p">)</span> <span class="o">/</span> <span class="n">sample_rate</span>
     <span class="k">if</span> <span class="n">coordinate_override_column</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">nodes</span> <span class="o">=</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">char</span> <span class="k">for</span> <span class="n">char</span> <span class="ow">in</span> <span class="n">node</span> <span class="k">if</span> <span class="n">char</span> <span class="ow">in</span> <span class="s1">&#39;0123456789&#39;</span><span class="p">))</span>
@@ -400,10 +408,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
     <span class="n">kept_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_channels</span><span class="p">)[</span><span class="n">read_only_indices</span><span class="p">]</span>
     <span class="n">reference_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s1">&#39;reference_channel_indices&#39;</span><span class="p">][:])</span>
     <span class="n">response_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">variables</span><span class="p">[</span><span class="s1">&#39;response_channel_indices&#39;</span><span class="p">][:])</span>
-    <span class="n">keep_response_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">response_indices</span><span class="p">)</span> <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">kept_indices</span><span class="p">])</span>
-    <span class="n">keep_reference_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">reference_indices</span><span class="p">)</span> <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">kept_indices</span><span class="p">])</span>
+    <span class="n">keep_response_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">response_indices</span><span class="p">)</span> <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">kept_indices</span><span class="p">])</span>
+    <span class="n">keep_reference_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">reference_indices</span><span class="p">)</span> <span class="k">if</span> <span class="n">index</span> <span class="ow">in</span> <span class="n">kept_indices</span><span class="p">])</span>
     <span class="n">frequency_lines</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">group</span><span class="o">.</span><span class="n">dimensions</span><span class="p">[</span><span class="s1">&#39;fft_lines&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">)</span><span class="o">*</span><span class="n">sample_rate</span><span class="o">/</span><span class="n">samples_per_frame</span>
-    <span class="n">coherence_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;coherence&#39;</span><span class="p">][:,</span><span class="n">keep_response_indices</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">coherence_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;coherence&#39;</span><span class="p">][:,</span> <span class="n">keep_response_indices</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
     <span class="n">comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;channel_type&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
                                        <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
                            <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
@@ -419,59 +427,59 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
         <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">coherence_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">MULTIPLE_COHERENCE</span><span class="p">,</span>
-                           <span class="n">frequency_lines</span><span class="p">,</span>
-                           <span class="n">coherence_data</span><span class="p">,</span>
-                           <span class="n">coordinates</span><span class="p">[</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span>
-                           <span class="n">comment1</span><span class="p">,</span>
-                           <span class="n">comment2</span><span class="p">,</span>
-                           <span class="n">comment3</span><span class="p">,</span>
-                           <span class="n">comment4</span><span class="p">,</span>
-                           <span class="n">comment5</span><span class="p">)</span>
+                                <span class="n">frequency_lines</span><span class="p">,</span>
+                                <span class="n">coherence_data</span><span class="p">,</span>
+                                <span class="n">coordinates</span><span class="p">[</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span>
+                                <span class="n">comment1</span><span class="p">,</span>
+                                <span class="n">comment2</span><span class="p">,</span>
+                                <span class="n">comment3</span><span class="p">,</span>
+                                <span class="n">comment4</span><span class="p">,</span>
+                                <span class="n">comment5</span><span class="p">)</span>
     <span class="c1"># Frequency Response Functions</span>
-    <span class="n">frf_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;frf_data_real&#39;</span><span class="p">])[:,</span><span class="n">keep_response_indices</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">keep_reference_indices</span><span class="p">]</span>
-                           <span class="o">+</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;frf_data_imag&#39;</span><span class="p">])[:,</span><span class="n">keep_response_indices</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">keep_reference_indices</span><span class="p">]</span><span class="o">*</span><span class="mi">1</span><span class="n">j</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">frf_coordinate</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="p">[</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">],</span><span class="mi">0</span><span class="p">],</span>
-                                   <span class="n">coordinates</span><span class="p">[</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">],</span><span class="mi">0</span><span class="p">])</span>
+    <span class="n">frf_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;frf_data_real&#39;</span><span class="p">])[:,</span> <span class="n">keep_response_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">keep_reference_indices</span><span class="p">]</span>
+                           <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">group</span><span class="p">[</span><span class="s1">&#39;frf_data_imag&#39;</span><span class="p">])[:,</span> <span class="n">keep_response_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">keep_reference_indices</span><span class="p">]</span><span class="o">*</span><span class="mi">1</span><span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">frf_coordinate</span> <span class="o">=</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="p">[</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">],</span> <span class="mi">0</span><span class="p">],</span>
+                                   <span class="n">coordinates</span><span class="p">[</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">],</span> <span class="mi">0</span><span class="p">])</span>
     <span class="c1"># print(response_indices[keep_response_indices])</span>
     <span class="c1"># print(reference_indices[keep_reference_indices])</span>
     <span class="n">response_comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;channel_type&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
-                                       <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
-                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
+                                                <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
+                                    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">response_comment2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;physical_device&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
-                                       <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
-                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;physical_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
+                                                <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
+                                    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;physical_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">response_comment3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;feedback_device&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
-                                       <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
-                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;feedback_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
+                                                <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
+                                    <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;feedback_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">response_comment4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;comment&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">)</span>
     <span class="n">response_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;make&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;serial_number&#39;</span><span class="p">,</span> <span class="s1">&#39;triax_dof&#39;</span><span class="p">):</span>
         <span class="n">response_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
         <span class="n">response_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">response_indices</span><span class="p">[</span><span class="n">keep_response_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">reference_comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;channel_type&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
-                                       <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
-                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
+                                                 <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
+                                     <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">reference_comment2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;physical_device&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
-                                       <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
-                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;physical_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
+                                                 <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
+                                     <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;physical_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">reference_comment3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;feedback_device&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
-                                       <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
-                           <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;feedback_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
+                                                 <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
+                                     <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;feedback_channel&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
     <span class="n">reference_comment4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;comment&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">)</span>
     <span class="n">reference_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;make&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">)</span>
     <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;serial_number&#39;</span><span class="p">,</span> <span class="s1">&#39;triax_dof&#39;</span><span class="p">):</span>
         <span class="n">reference_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">reference_comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
         <span class="n">reference_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">reference_comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">][</span><span class="o">...</span><span class="p">][</span><span class="n">reference_indices</span><span class="p">[</span><span class="n">keep_reference_indices</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
-    <span class="n">response_comment1</span><span class="p">,</span><span class="n">reference_comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment1</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">reference_comment1</span><span class="p">)</span>
-    <span class="n">comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment1</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span><span class="n">reference_comment1</span><span class="p">)</span>
-    <span class="n">response_comment2</span><span class="p">,</span><span class="n">reference_comment2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment2</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">reference_comment2</span><span class="p">)</span>
-    <span class="n">comment2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span><span class="n">reference_comment2</span><span class="p">)</span>
-    <span class="n">response_comment3</span><span class="p">,</span><span class="n">reference_comment3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment3</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">reference_comment3</span><span class="p">)</span>
-    <span class="n">comment3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment3</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span><span class="n">reference_comment3</span><span class="p">)</span>
-    <span class="n">response_comment4</span><span class="p">,</span><span class="n">reference_comment4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment4</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">reference_comment4</span><span class="p">)</span>
-    <span class="n">comment4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment4</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span><span class="n">reference_comment4</span><span class="p">)</span>
-    <span class="n">response_comment5</span><span class="p">,</span><span class="n">reference_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment5</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">reference_comment5</span><span class="p">)</span>
-    <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment5</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span><span class="n">reference_comment5</span><span class="p">)</span>
+    <span class="n">response_comment1</span><span class="p">,</span> <span class="n">reference_comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment1</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">reference_comment1</span><span class="p">)</span>
+    <span class="n">comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span> <span class="n">reference_comment1</span><span class="p">)</span>
+    <span class="n">response_comment2</span><span class="p">,</span> <span class="n">reference_comment2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment2</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">reference_comment2</span><span class="p">)</span>
+    <span class="n">comment2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span> <span class="n">reference_comment2</span><span class="p">)</span>
+    <span class="n">response_comment3</span><span class="p">,</span> <span class="n">reference_comment3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment3</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">reference_comment3</span><span class="p">)</span>
+    <span class="n">comment3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span> <span class="n">reference_comment3</span><span class="p">)</span>
+    <span class="n">response_comment4</span><span class="p">,</span> <span class="n">reference_comment4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment4</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">reference_comment4</span><span class="p">)</span>
+    <span class="n">comment4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment4</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span> <span class="n">reference_comment4</span><span class="p">)</span>
+    <span class="n">response_comment5</span><span class="p">,</span> <span class="n">reference_comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_arrays</span><span class="p">(</span><span class="n">response_comment5</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">reference_comment5</span><span class="p">)</span>
+    <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">response_comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; / &#39;</span><span class="p">)),</span> <span class="n">reference_comment5</span><span class="p">)</span>
     <span class="n">frf_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
                           <span class="n">frequency_lines</span><span class="p">,</span>
                           <span class="n">frf_data</span><span class="p">,</span>
@@ -481,7 +489,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
                           <span class="n">comment3</span><span class="p">,</span>
                           <span class="n">comment4</span><span class="p">,</span>
                           <span class="n">comment5</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">time_data</span><span class="p">,</span><span class="n">frf_data</span><span class="p">,</span><span class="n">coherence_data</span><span class="p">,</span><span class="n">channel_table</span></div>
+    <span class="k">return</span> <span class="n">time_data</span><span class="p">,</span> <span class="n">frf_data</span><span class="p">,</span> <span class="n">coherence_data</span><span class="p">,</span> <span class="n">channel_table</span></div>
+
 
 <div class="viewcode-block" id="read_transient_control_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_transient_control_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_transient_control_data">[docs]</a><span class="k">def</span> <span class="nf">read_transient_control_data</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_override_column</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
@@ -489,7 +498,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
     <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">nc4</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
         <span class="n">ds</span> <span class="o">=</span> <span class="n">file</span>
     <span class="n">coordinate_override_column</span> <span class="o">=</span> <span class="kc">None</span>
-        
+
     <span class="n">environment</span> <span class="o">=</span> <span class="p">[</span><span class="n">group</span> <span class="k">for</span> <span class="n">group</span> <span class="ow">in</span> <span class="n">ds</span><span class="o">.</span><span class="n">groups</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">group</span> <span class="o">==</span> <span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
 
     <span class="c1"># Get the channels in the group</span>
@@ -504,7 +513,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
 
     <span class="c1"># Cull down to just those in the environment</span>
     <span class="n">environment_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;environment_names&#39;</span><span class="p">][:]</span> <span class="o">==</span> <span class="n">environment</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">environment_channels</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;environment_active_channels&#39;</span><span class="p">][:,</span><span class="n">environment_index</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span>
+    <span class="n">environment_channels</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;environment_active_channels&#39;</span><span class="p">][:,</span> <span class="n">environment_index</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">bool</span><span class="p">)</span>
 
     <span class="n">drives</span> <span class="o">=</span> <span class="n">drives</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">coordinates</span> <span class="o">=</span> <span class="n">coordinates</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
@@ -524,8 +533,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
 
     <span class="n">drive_signal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="n">environment</span><span class="p">][</span><span class="s1">&#39;control_drives&#39;</span><span class="p">][</span><span class="o">...</span><span class="p">])</span>
 
-    <span class="n">response_coordinates</span> <span class="o">=</span> <span class="n">control_coordinates</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-    <span class="n">drive_coordinates</span> <span class="o">=</span> <span class="n">drive_coordinates</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="n">response_coordinates</span> <span class="o">=</span> <span class="n">control_coordinates</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="n">drive_coordinates</span> <span class="o">=</span> <span class="n">drive_coordinates</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
 
     <span class="n">comment1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="s1">&#39;channel_type&#39;</span><span class="p">][:],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">),</span>
                                        <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; :: &#39;</span><span class="p">)),</span>
@@ -541,7 +550,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
     <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">&#39;model&#39;</span><span class="p">,</span> <span class="s1">&#39;serial_number&#39;</span><span class="p">,</span> <span class="s1">&#39;triax_dof&#39;</span><span class="p">):</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">))</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">char</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">comment5</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;channels&#39;</span><span class="p">][</span><span class="n">key</span><span class="p">][:],</span> <span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;&lt;U80&#39;</span><span class="p">))</span>
-        
+
     <span class="n">comment1</span> <span class="o">=</span> <span class="n">comment1</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">comment2</span> <span class="o">=</span> <span class="n">comment2</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
     <span class="n">comment3</span> <span class="o">=</span> <span class="n">comment3</span><span class="p">[</span><span class="n">environment_channels</span><span class="p">]</span>
@@ -550,109 +559,109 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
 
     <span class="c1"># Save the data to SDynpy objects</span>
     <span class="n">response_signal</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
-                      <span class="n">timesteps</span><span class="p">,</span><span class="n">response_signal</span><span class="p">,</span><span class="n">response_coordinates</span><span class="p">,</span>
-                      <span class="n">comment1</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span><span class="n">comment2</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span><span class="n">comment3</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span>
-                      <span class="n">comment4</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span><span class="n">comment5</span><span class="p">[</span><span class="n">control_indices</span><span class="p">])</span>
+                                 <span class="n">timesteps</span><span class="p">,</span> <span class="n">response_signal</span><span class="p">,</span> <span class="n">response_coordinates</span><span class="p">,</span>
+                                 <span class="n">comment1</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span> <span class="n">comment2</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span> <span class="n">comment3</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span>
+                                 <span class="n">comment4</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span> <span class="n">comment5</span><span class="p">[</span><span class="n">control_indices</span><span class="p">])</span>
     <span class="n">spec_signal</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
-                           <span class="n">timesteps</span><span class="p">,</span><span class="n">spec_signal</span><span class="p">,</span><span class="n">response_coordinates</span><span class="p">,</span>
-                           <span class="n">comment1</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span><span class="n">comment2</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span><span class="n">comment3</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span>
-                           <span class="n">comment4</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span><span class="n">comment5</span><span class="p">[</span><span class="n">control_indices</span><span class="p">])</span>
+                             <span class="n">timesteps</span><span class="p">,</span> <span class="n">spec_signal</span><span class="p">,</span> <span class="n">response_coordinates</span><span class="p">,</span>
+                             <span class="n">comment1</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span> <span class="n">comment2</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span> <span class="n">comment3</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span>
+                             <span class="n">comment4</span><span class="p">[</span><span class="n">control_indices</span><span class="p">],</span> <span class="n">comment5</span><span class="p">[</span><span class="n">control_indices</span><span class="p">])</span>
     <span class="n">drive_signal</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span>
-                      <span class="n">timesteps</span><span class="p">,</span><span class="n">drive_signal</span><span class="p">,</span><span class="n">drive_coordinates</span><span class="p">,</span>
-                      <span class="n">comment1</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span><span class="n">comment2</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span><span class="n">comment3</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span>
-                      <span class="n">comment4</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span><span class="n">comment5</span><span class="p">[</span><span class="n">drives</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">response_signal</span><span class="p">,</span><span class="n">spec_signal</span><span class="p">,</span><span class="n">drive_signal</span></div>
-
-<div class="viewcode-block" id="create_synthetic_test"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.create_synthetic_test.html#sdynpy.fileio.sdynpy_rattlesnake.create_synthetic_test">[docs]</a><span class="k">def</span> <span class="nf">create_synthetic_test</span><span class="p">(</span><span class="n">spreadsheet_file_name</span> <span class="p">:</span> <span class="nb">str</span><span class="p">,</span> 
-                          <span class="n">system_filename</span> <span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">system</span> <span class="p">:</span> <span class="n">System</span><span class="p">,</span>
-                          <span class="n">excitation_coordinates</span> <span class="p">:</span> <span class="n">CoordinateArray</span><span class="p">,</span>
-                          <span class="n">response_coordinates</span> <span class="p">:</span> <span class="n">CoordinateArray</span><span class="p">,</span>
-                          <span class="n">rattlesnake_directory</span> <span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-                          <span class="n">displacement_derivative</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
-                          <span class="n">sample_rate</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">time_per_read</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">time_per_write</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">integration_oversample</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
-                          <span class="n">environments</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[],</span>
-                          <span class="n">channel_comment_data</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">channel_serial_number_data</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">channel_triax_dof_data</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">channel_engineering_unit_data</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">channel_warning_level_data</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">channel_abort_level_data</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                          <span class="n">channel_active_in_environment_data</span> <span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span>
+                              <span class="n">timesteps</span><span class="p">,</span> <span class="n">drive_signal</span><span class="p">,</span> <span class="n">drive_coordinates</span><span class="p">,</span>
+                              <span class="n">comment1</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span> <span class="n">comment2</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span> <span class="n">comment3</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span>
+                              <span class="n">comment4</span><span class="p">[</span><span class="n">drives</span><span class="p">],</span> <span class="n">comment5</span><span class="p">[</span><span class="n">drives</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">response_signal</span><span class="p">,</span> <span class="n">spec_signal</span><span class="p">,</span> <span class="n">drive_signal</span></div>
+
+
+<div class="viewcode-block" id="create_synthetic_test"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_rattlesnake.create_synthetic_test.html#sdynpy.fileio.sdynpy_rattlesnake.create_synthetic_test">[docs]</a><span class="k">def</span> <span class="nf">create_synthetic_test</span><span class="p">(</span><span class="n">spreadsheet_file_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+                          <span class="n">system_filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">system</span><span class="p">:</span> <span class="n">System</span><span class="p">,</span>
+                          <span class="n">excitation_coordinates</span><span class="p">:</span> <span class="n">CoordinateArray</span><span class="p">,</span>
+                          <span class="n">response_coordinates</span><span class="p">:</span> <span class="n">CoordinateArray</span><span class="p">,</span>
+                          <span class="n">rattlesnake_directory</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+                          <span class="n">displacement_derivative</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                          <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">time_per_read</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">time_per_write</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">integration_oversample</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
+                          <span class="n">environments</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[],</span>
+                          <span class="n">channel_comment_data</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">channel_serial_number_data</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">channel_triax_dof_data</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">channel_engineering_unit_data</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">channel_warning_level_data</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">channel_abort_level_data</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                          <span class="n">channel_active_in_environment_data</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span>
                           <span class="p">):</span>
-    <span class="n">A</span><span class="p">,</span><span class="n">B</span><span class="p">,</span><span class="n">C</span><span class="p">,</span><span class="n">D</span> <span class="o">=</span> <span class="n">system</span><span class="o">.</span><span class="n">to_state_space</span><span class="p">(</span><span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span>
-                                    <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span>
-                                    <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span>
-                                    <span class="kc">True</span><span class="p">,</span>
-                                    <span class="n">response_coordinates</span><span class="p">,</span>
-                                    <span class="n">excitation_coordinates</span><span class="p">)</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">system_filename</span><span class="p">,</span><span class="n">A</span><span class="o">=</span><span class="n">A</span><span class="p">,</span><span class="n">B</span><span class="o">=</span><span class="n">B</span><span class="p">,</span><span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">,</span><span class="n">D</span><span class="o">=</span><span class="n">D</span><span class="p">)</span>
+    <span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">D</span> <span class="o">=</span> <span class="n">system</span><span class="o">.</span><span class="n">to_state_space</span><span class="p">(</span><span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span>
+                                       <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span>
+                                       <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span>
+                                       <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">response_coordinates</span><span class="p">,</span>
+                                       <span class="n">excitation_coordinates</span><span class="p">)</span>
+    <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">system_filename</span><span class="p">,</span> <span class="n">A</span><span class="o">=</span><span class="n">A</span><span class="p">,</span> <span class="n">B</span><span class="o">=</span><span class="n">B</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="n">C</span><span class="p">,</span> <span class="n">D</span><span class="o">=</span><span class="n">D</span><span class="p">)</span>
     <span class="c1"># Load in Rattlesnake to create a template for the test</span>
-    <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">rattlesnake_directory</span><span class="p">)</span>
+    <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">rattlesnake_directory</span><span class="p">)</span>
     <span class="kn">import</span> <span class="nn">components</span> <span class="k">as</span> <span class="nn">rs</span>
     <span class="n">environment_data</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="n">environment_type</span><span class="p">,</span> <span class="n">environment_name</span> <span class="ow">in</span> <span class="n">environments</span><span class="p">:</span>
         <span class="c1"># Find the identifier</span>
         <span class="n">environment_type</span> <span class="o">=</span> <span class="n">rs</span><span class="o">.</span><span class="n">environments</span><span class="o">.</span><span class="n">ControlTypes</span><span class="p">[</span><span class="n">environment_type</span><span class="o">.</span><span class="n">upper</span><span class="p">()]</span>
-        <span class="n">environment_data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">environment_type</span><span class="p">,</span><span class="n">environment_name</span><span class="p">))</span>
-    <span class="n">rs</span><span class="o">.</span><span class="n">ui_utilities</span><span class="o">.</span><span class="n">save_combined_environments_profile_template</span><span class="p">(</span><span class="n">spreadsheet_file_name</span><span class="p">,</span><span class="n">environment_data</span><span class="p">)</span>
+        <span class="n">environment_data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">environment_type</span><span class="p">,</span> <span class="n">environment_name</span><span class="p">))</span>
+    <span class="n">rs</span><span class="o">.</span><span class="n">ui_utilities</span><span class="o">.</span><span class="n">save_combined_environments_profile_template</span><span class="p">(</span><span class="n">spreadsheet_file_name</span><span class="p">,</span> <span class="n">environment_data</span><span class="p">)</span>
     <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
     <span class="c1"># Populate the channel table</span>
     <span class="n">workbook</span> <span class="o">=</span> <span class="n">opxl</span><span class="o">.</span><span class="n">load_workbook</span><span class="p">(</span><span class="n">spreadsheet_file_name</span><span class="p">)</span>
     <span class="n">worksheet</span> <span class="o">=</span> <span class="n">workbook</span><span class="o">.</span><span class="n">get_sheet_by_name</span><span class="p">(</span><span class="s1">&#39;Channel Table&#39;</span><span class="p">)</span>
     <span class="n">index</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">response_coordinates</span><span class="p">):</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">channel</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">_string_map</span><span class="p">[</span><span class="n">channel</span><span class="o">.</span><span class="n">direction</span><span class="p">])</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="s1">&#39;Virtual&#39;</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">14</span><span class="p">,</span><span class="s1">&#39;Accel&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">response_coordinates</span><span class="p">):</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">channel</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">_string_map</span><span class="p">[</span><span class="n">channel</span><span class="o">.</span><span class="n">direction</span><span class="p">])</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;Virtual&#39;</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="s1">&#39;Accel&#39;</span><span class="p">)</span>
         <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">excitation_coordinates</span><span class="p">):</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">response_coordinates</span><span class="p">)</span><span class="o">+</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">channel</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">_string_map</span><span class="p">[</span><span class="n">channel</span><span class="o">.</span><span class="n">direction</span><span class="p">])</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="s1">&#39;Virtual&#39;</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">14</span><span class="p">,</span><span class="s1">&#39;Force&#39;</span><span class="p">)</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span><span class="mi">20</span><span class="p">,</span><span class="s1">&#39;Shaker&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">excitation_coordinates</span><span class="p">):</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">response_coordinates</span><span class="p">)</span><span class="o">+</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">channel</span><span class="o">.</span><span class="n">node</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">_string_map</span><span class="p">[</span><span class="n">channel</span><span class="o">.</span><span class="n">direction</span><span class="p">])</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;Virtual&#39;</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="s1">&#39;Force&#39;</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="s1">&#39;Shaker&#39;</span><span class="p">)</span>
         <span class="n">index</span> <span class="o">+=</span> <span class="mi">1</span>
     <span class="c1"># Go through the various channel table data that could have been optionally</span>
     <span class="c1"># provided</span>
-    <span class="k">for</span> <span class="n">column</span><span class="p">,</span><span class="n">data</span> <span class="ow">in</span> <span class="p">[(</span><span class="mi">4</span><span class="p">,</span><span class="n">channel_comment_data</span><span class="p">),</span>
-                        <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="n">channel_serial_number_data</span><span class="p">),</span>
-                        <span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="n">channel_triax_dof_data</span><span class="p">),</span>
-                        <span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="n">channel_engineering_unit_data</span><span class="p">),</span>
-                        <span class="p">(</span><span class="mi">22</span><span class="p">,</span><span class="n">channel_warning_level_data</span><span class="p">),</span>
-                        <span class="p">(</span><span class="mi">23</span><span class="p">,</span><span class="n">channel_abort_level_data</span><span class="p">)]:</span>
+    <span class="k">for</span> <span class="n">column</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="p">[(</span><span class="mi">4</span><span class="p">,</span> <span class="n">channel_comment_data</span><span class="p">),</span>
+                         <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">channel_serial_number_data</span><span class="p">),</span>
+                         <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="n">channel_triax_dof_data</span><span class="p">),</span>
+                         <span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="n">channel_engineering_unit_data</span><span class="p">),</span>
+                         <span class="p">(</span><span class="mi">22</span><span class="p">,</span> <span class="n">channel_warning_level_data</span><span class="p">),</span>
+                         <span class="p">(</span><span class="mi">23</span><span class="p">,</span> <span class="n">channel_abort_level_data</span><span class="p">)]:</span>
         <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">continue</span>
-        <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
-            <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="o">+</span><span class="n">row_index</span><span class="p">,</span><span class="n">column</span><span class="p">,</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="o">+</span><span class="n">row_index</span><span class="p">,</span> <span class="n">column</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
     <span class="c1"># Now fill out the environment table</span>
     <span class="k">if</span> <span class="n">channel_active_in_environment_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">environment_index</span><span class="p">,(</span><span class="n">environment_type</span><span class="p">,</span><span class="n">environment_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">environment_data</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span><span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">channel_active_in_environment_data</span><span class="p">[</span><span class="n">environment_name</span><span class="p">]):</span>
+        <span class="k">for</span> <span class="n">environment_index</span><span class="p">,</span> <span class="p">(</span><span class="n">environment_type</span><span class="p">,</span> <span class="n">environment_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">environment_data</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">row_index</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">channel_active_in_environment_data</span><span class="p">[</span><span class="n">environment_name</span><span class="p">]):</span>
                 <span class="k">if</span> <span class="n">value</span><span class="p">:</span>
-                    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="o">+</span><span class="n">row_index</span><span class="p">,</span><span class="mi">24</span><span class="o">+</span><span class="n">environment_index</span><span class="p">,</span><span class="s1">&#39;X&#39;</span><span class="p">)</span>
+                    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="o">+</span><span class="n">row_index</span><span class="p">,</span> <span class="mi">24</span><span class="o">+</span><span class="n">environment_index</span><span class="p">,</span> <span class="s1">&#39;X&#39;</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">environment_index</span><span class="p">,(</span><span class="n">environment_type</span><span class="p">,</span><span class="n">environment_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">environment_data</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">environment_index</span><span class="p">,</span> <span class="p">(</span><span class="n">environment_type</span><span class="p">,</span> <span class="n">environment_name</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">environment_data</span><span class="p">):</span>
             <span class="k">for</span> <span class="n">row_index</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">response_coordinates</span><span class="o">.</span><span class="n">size</span> <span class="o">+</span> <span class="n">excitation_coordinates</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
-                <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="o">+</span><span class="n">row_index</span><span class="p">,</span><span class="mi">24</span><span class="o">+</span><span class="n">environment_index</span><span class="p">,</span><span class="s1">&#39;X&#39;</span><span class="p">)</span>
+                <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="o">+</span><span class="n">row_index</span><span class="p">,</span> <span class="mi">24</span><span class="o">+</span><span class="n">environment_index</span><span class="p">,</span> <span class="s1">&#39;X&#39;</span><span class="p">)</span>
     <span class="n">worksheet</span> <span class="o">=</span> <span class="n">workbook</span><span class="o">.</span><span class="n">get_sheet_by_name</span><span class="p">(</span><span class="s1">&#39;Hardware&#39;</span><span class="p">)</span>
-    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
-    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">system_filename</span><span class="p">))</span>
+    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="n">system_filename</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">sample_rate</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">sample_rate</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">time_per_read</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">time_per_read</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">time_per_read</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">time_per_write</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">time_per_write</span><span class="p">)</span>
-    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">integration_oversample</span><span class="p">)</span>
+        <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">time_per_write</span><span class="p">)</span>
+    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">worksheet</span><span class="o">.</span><span class="n">cell</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">integration_oversample</span><span class="p">)</span>
     <span class="n">workbook</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">spreadsheet_file_name</span><span class="p">)</span></div>
-    
 </pre></div>
 
            </div>
@@ -662,7 +671,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_rattlesnake</h1><div class="highlight">
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc71f0>
+<jinja2.runtime.BlockReference object at 0x7f5a27c1e7a0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_tshaker.html b/_modules/sdynpy/fileio/sdynpy_tshaker.html
index 971eb8f..cfbfa7e 100644
--- a/_modules/sdynpy/fileio/sdynpy_tshaker.html
+++ b/_modules/sdynpy/fileio/sdynpy_tshaker.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_tshaker &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_tshaker &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -113,24 +113,25 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">nptdms</span> <span class="k">as</span> <span class="nn">tdms</span>
-<span class="kn">from</span> <span class="nn">..core.sdynpy_coordinate</span> <span class="kn">import</span> <span class="p">(</span><span class="n">coordinate_array</span><span class="p">,</span><span class="n">outer_product</span><span class="p">,</span>
-                                      <span class="n">CoordinateArray</span><span class="p">,</span><span class="n">_string_map</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">..core.sdynpy_coordinate</span> <span class="kn">import</span> <span class="p">(</span><span class="n">coordinate_array</span><span class="p">,</span> <span class="n">outer_product</span><span class="p">,</span>
+                                      <span class="n">CoordinateArray</span><span class="p">,</span> <span class="n">_string_map</span><span class="p">)</span>
 <span class="kn">from</span> <span class="nn">..core.sdynpy_data</span> <span class="kn">import</span> <span class="n">data_array</span><span class="p">,</span> <span class="n">FunctionTypes</span>
 <span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">from</span> <span class="nn">scipy.io</span> <span class="kn">import</span> <span class="n">loadmat</span>
 
-<div class="viewcode-block" id="read_tdms"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_tdms.html#sdynpy.fileio.sdynpy_tshaker.read_tdms">[docs]</a><span class="k">def</span> <span class="nf">read_tdms</span><span class="p">(</span><span class="n">file</span><span class="p">,</span><span class="n">coordinate_property</span> <span class="o">=</span> <span class="s2">&quot;Ch_Location&quot;</span><span class="p">,</span>
-              <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span> <span class="o">=</span> <span class="s1">&#39;999999&#39;</span><span class="p">,</span>
-              <span class="n">min_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">channel_indices</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<div class="viewcode-block" id="read_tdms"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_tdms.html#sdynpy.fileio.sdynpy_tshaker.read_tdms">[docs]</a><span class="k">def</span> <span class="nf">read_tdms</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">coordinate_property</span><span class="o">=</span><span class="s2">&quot;Ch_Location&quot;</span><span class="p">,</span>
+              <span class="n">coordinate_string_map</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span><span class="o">=</span><span class="s1">&#39;999999&#39;</span><span class="p">,</span>
+              <span class="n">min_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">channel_indices</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="c1"># Read in the file if we&#39;ve passed a string.</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span><span class="n">tdms</span><span class="o">.</span><span class="n">TdmsFile</span><span class="p">):</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">tdms</span><span class="o">.</span><span class="n">TdmsFile</span><span class="p">):</span>
         <span class="n">file</span> <span class="o">=</span> <span class="n">tdms</span><span class="o">.</span><span class="n">TdmsFile</span><span class="o">.</span><span class="n">read</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
-    
+
     <span class="c1"># Set up the coordinate_string_map if necessary</span>
     <span class="k">if</span> <span class="n">coordinate_string_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="p">{}</span>
-    
+
     <span class="c1"># Go through and get the metadata</span>
     <span class="n">dt</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;TH Data Info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">properties</span><span class="p">[</span><span class="s1">&#39;Sample Rate&#39;</span><span class="p">]</span>
     <span class="n">run_number</span> <span class="o">=</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;Test Run Info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">properties</span><span class="p">[</span><span class="s1">&#39;Run Number&#39;</span><span class="p">]</span>
@@ -138,7 +139,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
     <span class="n">test_axis</span> <span class="o">=</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;Test Run Info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">properties</span><span class="p">[</span><span class="s1">&#39;Test Axis&#39;</span><span class="p">]</span>
     <span class="n">test_type</span> <span class="o">=</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;Test Run Info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">properties</span><span class="p">[</span><span class="s1">&#39;Test Type&#39;</span><span class="p">]</span>
     <span class="n">test_title</span> <span class="o">=</span> <span class="n">file</span><span class="p">[</span><span class="s1">&#39;Test Series Info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">properties</span><span class="p">[</span><span class="s1">&#39;Test Series Title&#39;</span><span class="p">]</span>
-    
+
     <span class="c1"># Set up abscissa limits</span>
     <span class="k">if</span> <span class="n">min_time</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">min_index</span> <span class="o">=</span> <span class="mi">0</span>
@@ -148,11 +149,11 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="n">max_index</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">max_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">max_time</span> <span class="o">/</span> <span class="n">dt</span><span class="p">))</span><span class="o">+</span><span class="mi">1</span>
-    <span class="n">abscissa_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">min_index</span><span class="p">,</span><span class="n">max_index</span><span class="p">)</span>
-    
+    <span class="n">abscissa_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">min_index</span><span class="p">,</span> <span class="n">max_index</span><span class="p">)</span>
+
     <span class="c1"># Now let&#39;s go through the channels</span>
     <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;TH Data&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">channels</span><span class="p">()):</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">file</span><span class="p">[</span><span class="s1">&#39;TH Data&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">channels</span><span class="p">()):</span>
         <span class="k">if</span> <span class="n">channel_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">channel_indices</span><span class="p">:</span>
             <span class="k">continue</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Reading </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">channel</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
@@ -178,10 +179,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="c1"># Comment 2 will be DSA_Device DSA_S/N :: DSA_Channel</span>
         <span class="c1"># Comment 3 will be Test Series Title :: Run Number :: Run Description, Test Axis :: Test Type</span>
         <span class="c1"># Comment 5 will be the coordinate value as a string, just in case we can&#39;t convert it to a coordinate</span>
-        <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span><span class="n">ch_eu</span><span class="p">)</span>
+        <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span> <span class="n">ch_eu</span><span class="p">)</span>
         <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;Device: </span><span class="si">{:}</span><span class="s1">; S/N: </span><span class="si">{:}</span><span class="s1">; Ch: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dsa_device</span><span class="p">,</span> <span class="n">dsa_sn</span><span class="p">,</span> <span class="n">dsa_channel</span><span class="p">)</span>
-        <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> Run </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_title</span><span class="p">,</span><span class="n">run_number</span><span class="p">,</span> <span class="n">run_description</span><span class="p">,</span> <span class="n">test_axis</span><span class="p">,</span> <span class="n">test_type</span><span class="p">)</span>
-        <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;Sensor: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_model</span><span class="p">,</span><span class="n">trans_sn</span><span class="p">,</span><span class="n">trans_axis</span><span class="p">)</span>
+        <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> Run </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_title</span><span class="p">,</span> <span class="n">run_number</span><span class="p">,</span> <span class="n">run_description</span><span class="p">,</span> <span class="n">test_axis</span><span class="p">,</span> <span class="n">test_type</span><span class="p">)</span>
+        <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;Sensor: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_model</span><span class="p">,</span> <span class="n">trans_sn</span><span class="p">,</span> <span class="n">trans_axis</span><span class="p">)</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">channel</span><span class="o">.</span><span class="n">properties</span><span class="p">[</span><span class="n">coordinate_property</span><span class="p">])</span>
         <span class="c1"># Now create the data object</span>
         <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">,</span> <span class="n">time_data</span><span class="p">,</span> <span class="n">coordinate</span><span class="p">,</span>
@@ -189,20 +190,21 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
     <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">data</span></div>
 
-<div class="viewcode-block" id="read_mat_time_history"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_time_history.html#sdynpy.fileio.sdynpy_tshaker.read_mat_time_history">[docs]</a><span class="k">def</span> <span class="nf">read_mat_time_history</span><span class="p">(</span><span class="n">data_directory</span><span class="p">,</span><span class="n">coordinate_property</span> <span class="o">=</span> <span class="s2">&quot;Ch_ID&quot;</span><span class="p">,</span>
-              <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span> <span class="o">=</span> <span class="s1">&#39;999999&#39;</span><span class="p">,</span>
-              <span class="n">min_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">file_pattern</span> <span class="o">=</span> <span class="s1">&#39;Ch*_THData.mat&#39;</span><span class="p">,</span>
-              <span class="n">file_sort_key</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;Ch&#39;</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">))):</span>
+
+<div class="viewcode-block" id="read_mat_time_history"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_time_history.html#sdynpy.fileio.sdynpy_tshaker.read_mat_time_history">[docs]</a><span class="k">def</span> <span class="nf">read_mat_time_history</span><span class="p">(</span><span class="n">data_directory</span><span class="p">,</span> <span class="n">coordinate_property</span><span class="o">=</span><span class="s2">&quot;Ch_ID&quot;</span><span class="p">,</span>
+                          <span class="n">coordinate_string_map</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span><span class="o">=</span><span class="s1">&#39;999999&#39;</span><span class="p">,</span>
+                          <span class="n">min_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">file_pattern</span><span class="o">=</span><span class="s1">&#39;Ch*_THData.mat&#39;</span><span class="p">,</span>
+                          <span class="n">file_sort_key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">x</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;Ch&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">))):</span>
     <span class="c1"># Set up the coordinate_string_map if necessary</span>
     <span class="k">if</span> <span class="n">coordinate_string_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="p">{}</span>
-        
+
     <span class="c1"># Find files in the directory</span>
-    <span class="n">mat_files</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_directory</span><span class="p">,</span><span class="n">file_pattern</span><span class="p">))</span>
+    <span class="n">mat_files</span> <span class="o">=</span> <span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data_directory</span><span class="p">,</span> <span class="n">file_pattern</span><span class="p">))</span>
     <span class="c1"># Sort the files by number</span>
     <span class="n">mat_files</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">mat_files</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">file_sort_key</span><span class="p">)</span>
     <span class="n">data_arrays</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">data_file</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mat_files</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data_file</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mat_files</span><span class="p">):</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Reading File </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">data_file</span><span class="p">))</span>
         <span class="n">data</span> <span class="o">=</span> <span class="n">loadmat</span><span class="p">(</span><span class="n">data_file</span><span class="p">)</span>
         <span class="c1"># Get sample rate</span>
@@ -216,7 +218,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
             <span class="n">max_index</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">max_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">max_time</span> <span class="o">/</span> <span class="n">dt</span><span class="p">))</span><span class="o">+</span><span class="mi">1</span>
-        <span class="n">abscissa_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">min_index</span><span class="p">,</span><span class="n">max_index</span><span class="p">)</span>
+        <span class="n">abscissa_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">min_index</span><span class="p">,</span> <span class="n">max_index</span><span class="p">)</span>
         <span class="c1"># Get metadata</span>
         <span class="n">run_number</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Test_RunNum&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
         <span class="n">run_description</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Test_Description&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
@@ -237,10 +239,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="n">time_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;THData&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()[</span><span class="n">abscissa_slice</span><span class="p">]</span>
         <span class="n">num_elements</span> <span class="o">=</span> <span class="n">time_data</span><span class="o">.</span><span class="n">size</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_elements</span><span class="p">)</span><span class="o">*</span><span class="n">dt</span> <span class="o">+</span> <span class="n">min_index</span><span class="o">*</span><span class="n">dt</span>
-        <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span><span class="n">ch_eu</span><span class="p">)</span>
+        <span class="n">comment1</span> <span class="o">=</span> <span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span> <span class="n">ch_eu</span><span class="p">)</span>
         <span class="n">comment2</span> <span class="o">=</span> <span class="s1">&#39;Channel: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dsa_channel</span><span class="p">)</span>
         <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;Run </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">run_number</span><span class="p">,</span> <span class="n">run_description</span><span class="p">,</span> <span class="n">test_axis</span><span class="p">)</span>
-        <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;Sensor: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_model</span><span class="p">,</span><span class="n">trans_sn</span><span class="p">)</span>
+        <span class="n">comment4</span> <span class="o">=</span> <span class="s1">&#39;Sensor: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trans_model</span><span class="p">,</span> <span class="n">trans_sn</span><span class="p">)</span>
         <span class="n">comment5</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">coordinate_property</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()))</span>
         <span class="c1"># Now create the data object</span>
         <span class="n">data_arrays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">,</span> <span class="n">time_data</span><span class="p">,</span> <span class="n">coordinate</span><span class="p">,</span>
@@ -248,9 +250,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
     <span class="n">data_arrays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">data_arrays</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">data_arrays</span></div>
 
-<div class="viewcode-block" id="read_mat_shock"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_shock.html#sdynpy.fileio.sdynpy_tshaker.read_mat_shock">[docs]</a><span class="k">def</span> <span class="nf">read_mat_shock</span><span class="p">(</span><span class="n">data_file</span><span class="p">,</span><span class="n">coordinate_property</span> <span class="o">=</span> <span class="s2">&quot;Ch_Info&quot;</span><span class="p">,</span>
-              <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span> <span class="o">=</span> <span class="s1">&#39;999999&#39;</span><span class="p">,</span>
-              <span class="n">min_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">max_time</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">read_filtered_time_data</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+
+<div class="viewcode-block" id="read_mat_shock"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_shock.html#sdynpy.fileio.sdynpy_tshaker.read_mat_shock">[docs]</a><span class="k">def</span> <span class="nf">read_mat_shock</span><span class="p">(</span><span class="n">data_file</span><span class="p">,</span> <span class="n">coordinate_property</span><span class="o">=</span><span class="s2">&quot;Ch_Info&quot;</span><span class="p">,</span>
+                   <span class="n">coordinate_string_map</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span><span class="o">=</span><span class="s1">&#39;999999&#39;</span><span class="p">,</span>
+                   <span class="n">min_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_time</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">read_filtered_time_data</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
     <span class="c1"># Set up the coordinate_string_map if necessary</span>
     <span class="k">if</span> <span class="n">coordinate_string_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="p">{}</span>
@@ -266,7 +269,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="n">max_index</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">max_index</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">max_time</span> <span class="o">/</span> <span class="n">dt</span><span class="p">))</span><span class="o">+</span><span class="mi">1</span>
-    <span class="n">abscissa_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">min_index</span><span class="p">,</span><span class="n">max_index</span><span class="p">)</span>
+    <span class="n">abscissa_slice</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="n">min_index</span><span class="p">,</span> <span class="n">max_index</span><span class="p">)</span>
     <span class="c1"># Get metadata</span>
     <span class="n">run_number</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Test_RunNum&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
     <span class="n">run_description</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Test_Description&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
@@ -285,25 +288,26 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span>
             <span class="n">coordinate</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">invalid_coordinate_value</span><span class="p">)</span>
         <span class="n">coordinates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coordinate</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
-    <span class="n">coordinates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="n">coordinates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
     <span class="n">time_data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;shk&#39;</span> <span class="k">if</span> <span class="n">read_filtered_time_data</span> <span class="k">else</span> <span class="s1">&#39;unfilshk&#39;</span><span class="p">][</span><span class="n">abscissa_slice</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
     <span class="n">num_elements</span> <span class="o">=</span> <span class="n">time_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
     <span class="n">abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_elements</span><span class="p">)</span><span class="o">*</span><span class="n">dt</span> <span class="o">+</span> <span class="n">min_index</span><span class="o">*</span><span class="n">dt</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">trans_type</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="n">comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">ch_eu</span><span class="p">]</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span><span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span><span class="n">ch_eu</span><span class="p">)]</span>
+        <span class="n">comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span> <span class="n">ch_eu</span><span class="p">)]</span>
     <span class="n">comment2</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Channel: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">dsa_channel</span><span class="p">]</span>
     <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;Run </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">run_number</span><span class="p">,</span> <span class="n">run_description</span><span class="p">,</span> <span class="n">test_axis</span><span class="p">)</span>
     <span class="n">comment5</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">coordinate_property</span><span class="p">]</span>
-    <span class="n">time_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span><span class="n">abscissa</span><span class="p">,</span><span class="n">time_data</span><span class="p">,</span>
-                           <span class="n">coordinates</span><span class="p">,</span><span class="n">comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span>
+    <span class="n">time_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">TIME_RESPONSE</span><span class="p">,</span> <span class="n">abscissa</span><span class="p">,</span> <span class="n">time_data</span><span class="p">,</span>
+                           <span class="n">coordinates</span><span class="p">,</span> <span class="n">comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span>
                            <span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">time_data</span></div>
 
-<div class="viewcode-block" id="read_mat_random"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_random.html#sdynpy.fileio.sdynpy_tshaker.read_mat_random">[docs]</a><span class="k">def</span> <span class="nf">read_mat_random</span><span class="p">(</span><span class="n">data_file</span><span class="p">,</span><span class="n">coordinate_property</span> <span class="o">=</span> <span class="s2">&quot;Ch_Info&quot;</span><span class="p">,</span>
-                    <span class="n">reference_coordinate_property</span> <span class="o">=</span> <span class="s2">&quot;FRF_RefID&quot;</span><span class="p">,</span>
-              <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span> <span class="o">=</span> <span class="s1">&#39;999999&#39;</span><span class="p">):</span>
+
+<div class="viewcode-block" id="read_mat_random"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_random.html#sdynpy.fileio.sdynpy_tshaker.read_mat_random">[docs]</a><span class="k">def</span> <span class="nf">read_mat_random</span><span class="p">(</span><span class="n">data_file</span><span class="p">,</span> <span class="n">coordinate_property</span><span class="o">=</span><span class="s2">&quot;Ch_Info&quot;</span><span class="p">,</span>
+                    <span class="n">reference_coordinate_property</span><span class="o">=</span><span class="s2">&quot;FRF_RefID&quot;</span><span class="p">,</span>
+                    <span class="n">coordinate_string_map</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">invalid_coordinate_value</span><span class="o">=</span><span class="s1">&#39;999999&#39;</span><span class="p">):</span>
     <span class="c1"># Set up the coordinate_string_map if necessary</span>
     <span class="k">if</span> <span class="n">coordinate_string_map</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">coordinate_string_map</span> <span class="o">=</span> <span class="p">{}</span>
@@ -323,7 +327,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">):</span>
             <span class="n">coordinate</span> <span class="o">=</span> <span class="n">coordinate_array</span><span class="p">(</span><span class="n">string_array</span><span class="o">=</span><span class="n">invalid_coordinate_value</span><span class="p">)</span>
         <span class="n">coordinates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">coordinate</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
-    <span class="n">coordinates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="n">coordinates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
     <span class="n">reference_coordinates_raw</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">reference_coordinate_property</span><span class="p">]</span>
     <span class="n">reference_coordinates</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="n">coordinate</span> <span class="ow">in</span> <span class="n">reference_coordinates_raw</span><span class="p">:</span>
@@ -348,22 +352,22 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
         <span class="n">coh_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">ch_eu</span><span class="p">]</span>
         <span class="n">frf_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">ch_eu</span><span class="p">]</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">psd_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">^2/Hz&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span><span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span><span class="n">ch_eu</span><span class="p">)]</span>
-        <span class="n">coh_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span><span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span><span class="n">ch_eu</span><span class="p">)]</span>
-        <span class="n">frf_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span><span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span><span class="n">ch_eu</span><span class="p">)]</span>
+        <span class="n">psd_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">^2/Hz&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span> <span class="n">ch_eu</span><span class="p">)]</span>
+        <span class="n">coh_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">;&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span> <span class="n">ch_eu</span><span class="p">)]</span>
+        <span class="n">frf_comment1</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Type: </span><span class="si">{:}</span><span class="s1">; Unit: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">ch</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">trans_type</span><span class="p">,</span> <span class="n">ch_eu</span><span class="p">)]</span>
     <span class="n">comment2</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Channel: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ch</span><span class="p">)</span> <span class="k">for</span> <span class="n">ch</span> <span class="ow">in</span> <span class="n">dsa_channel</span><span class="p">]</span>
     <span class="n">comment3</span> <span class="o">=</span> <span class="s1">&#39;Run </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1"> </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">run_number</span><span class="p">,</span> <span class="n">run_description</span><span class="p">,</span> <span class="n">test_axis</span><span class="p">)</span>
     <span class="n">comment5</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">coordinate_property</span><span class="p">]</span>
     <span class="n">psd_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">POWER_SPECTRAL_DENSITY</span><span class="p">,</span>
-                          <span class="n">abscissa</span><span class="p">,</span><span class="n">psd</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">coordinates</span><span class="p">,</span><span class="n">coordinates</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
-                          <span class="n">psd_comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
+                          <span class="n">abscissa</span><span class="p">,</span> <span class="n">psd</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">coordinates</span><span class="p">,</span> <span class="n">coordinates</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                          <span class="n">psd_comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
     <span class="n">coh_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">COHERENCE</span><span class="p">,</span>
-                          <span class="n">abscissa</span><span class="p">,</span><span class="n">coherence</span><span class="p">,</span><span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span><span class="n">reference_coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">())[:,</span><span class="mi">0</span><span class="p">,:],</span>
-                          <span class="n">coh_comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
+                          <span class="n">abscissa</span><span class="p">,</span> <span class="n">coherence</span><span class="p">,</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">reference_coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">())[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:],</span>
+                          <span class="n">coh_comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
     <span class="n">frf_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
-                          <span class="n">abscissa</span><span class="p">,</span><span class="n">frf</span><span class="p">,</span><span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span><span class="n">reference_coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">())[:,</span><span class="mi">0</span><span class="p">,:],</span>
-                          <span class="n">frf_comment1</span><span class="p">,</span><span class="n">comment2</span><span class="p">,</span><span class="n">comment3</span><span class="p">,</span><span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">psd_data</span><span class="p">,</span><span class="n">coh_data</span><span class="p">,</span><span class="n">frf_data</span></div>
+                          <span class="n">abscissa</span><span class="p">,</span> <span class="n">frf</span><span class="p">,</span> <span class="n">outer_product</span><span class="p">(</span><span class="n">coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span> <span class="n">reference_coordinates</span><span class="o">.</span><span class="n">flatten</span><span class="p">())[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="p">:],</span>
+                          <span class="n">frf_comment1</span><span class="p">,</span> <span class="n">comment2</span><span class="p">,</span> <span class="n">comment3</span><span class="p">,</span> <span class="n">comment5</span><span class="o">=</span><span class="n">comment5</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">psd_data</span><span class="p">,</span> <span class="n">coh_data</span><span class="p">,</span> <span class="n">frf_data</span></div>
 </pre></div>
 
            </div>
@@ -373,7 +377,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_tshaker</h1><div class="highlight"><pre
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba6c040>
+<jinja2.runtime.BlockReference object at 0x7f5a2765caf0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff.html b/_modules/sdynpy/fileio/sdynpy_uff.html
index 60cb16a..25cfc82 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -88,7 +88,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
 <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">Interface to the universal file format (UFF).</span>
 
-<span class="sd">Using the functions in this module, one can read and write unv files.  </span>
+<span class="sd">Using the functions in this module, one can read and write unv files.</span>
 
 <span class="sd">Copyright 2022 National Technology &amp; Engineering Solutions of Sandia,</span>
 <span class="sd">LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.</span>
@@ -108,6 +108,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
 <span class="sd">along with this program.  If not, see &lt;https://www.gnu.org/licenses/&gt;.</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <div class="viewcode-block" id="UFFReadError"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_uff.UFFReadError.html#sdynpy.fileio.sdynpy_uff.UFFReadError">[docs]</a><span class="k">class</span> <span class="nc">UFFReadError</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Exception to be used when there is an error reading a UNV file&quot;&quot;&quot;</span>
@@ -243,7 +244,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">write_number</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
     <span class="n">non_X_format_specs</span> <span class="o">=</span> <span class="p">[</span>
-        <span class="n">format_spec</span> <span class="k">for</span> <span class="n">format_spec</span> <span class="ow">in</span> <span class="n">format_specs</span> <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;X&#39;</span> <span class="ow">in</span> <span class="n">format_spec</span><span class="o">.</span><span class="n">upper</span><span class="p">()]</span>
+        <span class="n">format_spec</span> <span class="k">for</span> <span class="n">format_spec</span> <span class="ow">in</span> <span class="n">format_specs</span> <span class="k">if</span> <span class="s1">&#39;X&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">format_spec</span><span class="o">.</span><span class="n">upper</span><span class="p">()]</span>
     <span class="n">copies</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">write_number</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="nb">len</span><span class="p">(</span><span class="n">non_X_format_specs</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
     <span class="n">format_specs</span> <span class="o">=</span> <span class="p">(</span><span class="n">copies</span> <span class="o">*</span> <span class="p">(</span><span class="n">format_specs</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">]))</span>
     <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
@@ -273,7 +274,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
             <span class="k">break</span>
     <span class="k">if</span> <span class="n">fill_line</span><span class="p">:</span>
         <span class="n">lines</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;80s}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">l</span><span class="p">)</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
+        <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&lt;80s}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">this_line</span><span class="p">)</span> <span class="k">for</span> <span class="n">this_line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
     <span class="k">return</span> <span class="n">line</span></div>
 
 
@@ -281,16 +282,16 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
 <span class="c1"># and write command, import it here, then add it to the dataset dictionary</span>
 <span class="c1"># using the dataset number as the key.</span>
 
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_55</span> <span class="k">as</span> <span class="n">dataset_55</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_58</span> <span class="k">as</span> <span class="n">dataset_58</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_82</span> <span class="k">as</span> <span class="n">dataset_82</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_151</span> <span class="k">as</span> <span class="n">dataset_151</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_164</span> <span class="k">as</span> <span class="n">dataset_164</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_1858</span> <span class="k">as</span> <span class="n">dataset_1858</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2400</span> <span class="k">as</span> <span class="n">dataset_2400</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2411</span> <span class="k">as</span> <span class="n">dataset_2411</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2412</span> <span class="k">as</span> <span class="n">dataset_2412</span>
-<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2420</span> <span class="k">as</span> <span class="n">dataset_2420</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_55</span> <span class="k">as</span> <span class="n">dataset_55</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_58</span> <span class="k">as</span> <span class="n">dataset_58</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_82</span> <span class="k">as</span> <span class="n">dataset_82</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_151</span> <span class="k">as</span> <span class="n">dataset_151</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_164</span> <span class="k">as</span> <span class="n">dataset_164</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_1858</span> <span class="k">as</span> <span class="n">dataset_1858</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2400</span> <span class="k">as</span> <span class="n">dataset_2400</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2411</span> <span class="k">as</span> <span class="n">dataset_2411</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2412</span> <span class="k">as</span> <span class="n">dataset_2412</span>  <span class="c1"># noqa: E402</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_uff_datasets</span> <span class="kn">import</span> <span class="n">sdynpy_uff_dataset_2420</span> <span class="k">as</span> <span class="n">dataset_2420</span>  <span class="c1"># noqa: E402</span>
 
 <span class="n">dataset_dict</span> <span class="o">=</span> <span class="p">{</span><span class="mi">55</span><span class="p">:</span> <span class="n">dataset_55</span><span class="p">,</span>
                 <span class="mi">58</span><span class="p">:</span> <span class="n">dataset_58</span><span class="p">,</span>
@@ -304,6 +305,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
                 <span class="mi">2420</span><span class="p">:</span> <span class="n">dataset_2420</span><span class="p">}</span>
 
 
+
 <div class="viewcode-block" id="readuff"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_uff.readuff.html#sdynpy.fileio.sdynpy_uff.readuff">[docs]</a><span class="k">def</span> <span class="nf">readuff</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="n">datasets</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Read a universal file</span>
@@ -313,7 +315,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
 <span class="sd">    filename : str</span>
 <span class="sd">        Path to the file that should be read.</span>
 <span class="sd">    datasets : iterable, optional</span>
-<span class="sd">        List of dataset numbers to read. The default is None.</span>
+<span class="sd">        List of dataset id numbers to read. The default is None.</span>
 <span class="sd">    verbose : bool, optional</span>
 <span class="sd">        Output extra information when reading the file. The default is False.</span>
 
@@ -330,22 +332,22 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">return_dict</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
-        <span class="n">line</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+        <span class="n">line</span> <span class="o">=</span> <span class="sa">b</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span>
         <span class="n">line_num</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="n">dataset_line_num</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="c1"># Loop through the file until it is at its end</span>
-        <span class="k">while</span> <span class="n">line</span> <span class="o">!=</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+        <span class="k">while</span> <span class="n">line</span> <span class="o">!=</span> <span class="sa">b</span><span class="s1">&#39;&#39;</span><span class="p">:</span>
             <span class="c1"># Find the first delimiter line</span>
             <span class="c1"># Here we want to find a line that has -1 in the 5th and 6th column,</span>
             <span class="c1"># and make sure that -1 isn&#39;t the only thing in the line to make sure</span>
             <span class="c1"># that any comments at the start of the file don&#39;t accidentally line up</span>
-            <span class="k">while</span> <span class="ow">not</span> <span class="n">line</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;-1&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;-1&#39;</span><span class="p">:</span>
+            <span class="k">while</span> <span class="ow">not</span> <span class="n">line</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;-1&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;-1&#39;</span><span class="p">:</span>
                 <span class="n">line</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
                 <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
-                <span class="k">if</span> <span class="n">line</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">line</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;&#39;</span><span class="p">:</span>
                     <span class="k">break</span>
-            <span class="k">if</span> <span class="n">line</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">line</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;&#39;</span><span class="p">:</span>
                 <span class="k">break</span>
             <span class="n">dataset_line_num</span> <span class="o">=</span> <span class="n">line_num</span>
             <span class="c1"># Load in the dataset specifier</span>
@@ -353,8 +355,16 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
             <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
             <span class="k">try</span><span class="p">:</span>
                 <span class="c1"># Make sure that we can convert it to an integer</span>
-                <span class="n">dataset</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
-            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                <span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">byte_ordering</span><span class="p">,</span> <span class="n">floating_point_format</span><span class="p">,</span>
+                 <span class="n">num_ascii_lines_following</span><span class="p">,</span> <span class="n">num_bytes_following</span><span class="p">,</span> <span class="o">*</span><span class="n">not_used</span><span class="p">)</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span>
+                     <span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">(),</span> <span class="p">[</span><span class="s1">&#39;I6&#39;</span><span class="p">,</span> <span class="s1">&#39;A1&#39;</span><span class="p">,</span> <span class="s1">&#39;I6&#39;</span><span class="p">,</span> <span class="s1">&#39;I6&#39;</span><span class="p">,</span> <span class="s1">&#39;I12&#39;</span><span class="p">,</span> <span class="s1">&#39;I12&#39;</span><span class="p">,</span> <span class="s1">&#39;I6&#39;</span><span class="p">,</span> <span class="s1">&#39;I6&#39;</span><span class="p">,</span> <span class="s1">&#39;I12&#39;</span><span class="p">,</span> <span class="s1">&#39;I12&#39;</span><span class="p">])</span>
+                <span class="n">is_binary</span> <span class="o">=</span> <span class="n">b</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+                <span class="k">if</span> <span class="n">is_binary</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">byte_ordering</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="n">byte_ordering</span> <span class="o">=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="n">num_ascii_lines_following</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                        <span class="n">num_ascii_lines_following</span> <span class="o">=</span> <span class="mi">11</span>
+            <span class="k">except</span> <span class="n">UFFReadError</span><span class="p">:</span>
                 <span class="k">raise</span> <span class="n">UFFReadError</span><span class="p">(</span>
                     <span class="s1">&#39;Improperly formatted dataset specification at line </span><span class="si">{}</span><span class="s1">, </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">line_num</span><span class="p">,</span> <span class="n">line</span><span class="p">))</span>
             <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
@@ -364,25 +374,27 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
             <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
             <span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
             <span class="c1"># Loop through the file until we find the delimiter</span>
-            <span class="k">while</span> <span class="ow">not</span> <span class="n">line</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;-1&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;-1&#39;</span><span class="p">:</span>
+            <span class="k">while</span> <span class="p">(</span><span class="ow">not</span> <span class="p">(</span><span class="ow">not</span> <span class="n">is_binary</span> <span class="ow">and</span> <span class="n">line</span><span class="p">[</span><span class="mi">4</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;-1&#39;</span> <span class="ow">and</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;-1&#39;</span><span class="p">)</span>
+                   <span class="ow">and</span>
+                   <span class="ow">not</span> <span class="p">(</span><span class="n">is_binary</span> <span class="ow">and</span> <span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">()[</span><span class="o">-</span><span class="mi">6</span><span class="p">:]</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;    -1&#39;</span> <span class="ow">and</span> <span class="n">line_num</span> <span class="o">-</span> <span class="n">dataset_line_num</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">&gt;</span> <span class="n">num_ascii_lines_following</span><span class="p">)):</span>
                 <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="p">)</span>
                 <span class="n">line</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
                 <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
                 <span class="k">if</span> <span class="n">line</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
                     <span class="k">raise</span> <span class="n">UFFReadError</span><span class="p">(</span>
                         <span class="s1">&#39;File ended before dataset starting at line </span><span class="si">{}</span><span class="s1"> was ended.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dataset_line_num</span><span class="p">))</span>
+                <span class="k">if</span> <span class="n">is_binary</span> <span class="ow">and</span> <span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">()[</span><span class="o">-</span><span class="mi">6</span><span class="p">:]</span> <span class="o">==</span> <span class="sa">b</span><span class="s1">&#39;    -1&#39;</span> <span class="ow">and</span> <span class="n">line_num</span> <span class="o">-</span> <span class="n">dataset_line_num</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">&gt;</span> <span class="n">num_ascii_lines_following</span><span class="p">:</span>
+                    <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">line</span><span class="o">.</span><span class="n">rstrip</span><span class="p">()[:</span><span class="o">-</span><span class="mi">6</span><span class="p">])</span>
             <span class="k">try</span><span class="p">:</span>
                 <span class="n">read_fn</span> <span class="o">=</span> <span class="n">dataset_dict</span><span class="p">[</span><span class="n">dataset</span><span class="p">]</span><span class="o">.</span><span class="n">read</span>
-            <span class="k">except</span> <span class="ne">KeyError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-                <span class="c1">#                raise e</span>
+            <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
                 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataset </span><span class="si">{}</span><span class="s1"> at line </span><span class="si">{}</span><span class="s1"> is not implemented, skipping...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                     <span class="n">dataset</span><span class="p">,</span> <span class="n">dataset_line_num</span><span class="p">))</span>
                 <span class="c1"># Read the next line in preparation for the next loop of the script</span>
                 <span class="n">line</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
                 <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
                 <span class="k">continue</span>
-            <span class="k">except</span> <span class="ne">AttributeError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-                <span class="c1">#                raise e</span>
+            <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
                 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Dataset </span><span class="si">{}</span><span class="s1"> at line </span><span class="si">{}</span><span class="s1"> read function is not implemented, skipping...&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                     <span class="n">dataset</span><span class="p">,</span> <span class="n">dataset_line_num</span><span class="p">))</span>
                 <span class="c1"># Read the next line in preparation for the next loop of the script</span>
@@ -395,7 +407,12 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
                 <span class="n">line</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
                 <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
                 <span class="k">continue</span>
-            <span class="n">dataset_obj</span> <span class="o">=</span> <span class="n">read_fn</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">is_binary</span><span class="p">:</span>
+                <span class="n">dataset_obj</span> <span class="o">=</span> <span class="n">read_fn</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">is_binary</span><span class="p">,</span> <span class="n">byte_ordering</span><span class="p">,</span>
+                                      <span class="n">floating_point_format</span><span class="p">,</span> <span class="n">num_ascii_lines_following</span><span class="p">,</span>
+                                      <span class="n">num_bytes_following</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">dataset_obj</span> <span class="o">=</span> <span class="n">read_fn</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
             <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataset_obj</span><span class="p">,</span> <span class="n">UFFReadError</span><span class="p">):</span>
                 <span class="k">raise</span> <span class="n">UFFReadError</span><span class="p">(</span><span class="s1">&#39;In dataset starting at line </span><span class="si">{}</span><span class="s1">, </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                     <span class="n">dataset_line_num</span><span class="p">,</span> <span class="n">dataset_obj</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
@@ -406,6 +423,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
             <span class="c1"># Read the next line in preparation for the next loop of the script</span>
             <span class="n">line</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readline</span><span class="p">()</span>
             <span class="n">line_num</span> <span class="o">+=</span> <span class="mi">1</span>
+
     <span class="k">return</span> <span class="n">return_dict</span></div>
 
 
@@ -419,7 +437,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba81510>
+<jinja2.runtime.BlockReference object at 0x7f5a27f49c90>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_151.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_151.html
index 5611dc1..d602b8f 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_151.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_151.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -157,6 +157,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151</h1
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_151.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151.Sdynpy_UFF_Dataset_151.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151.Sdynpy_UFF_Dataset_151.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="n">ds_151</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">()</span>
         <span class="c1"># Record 1:       FORMAT(80A1)</span>
         <span class="c1"># Field 1      -- model file name</span>
@@ -273,7 +275,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151</h1
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba6cd30>
+<jinja2.runtime.BlockReference object at 0x7f5a27c1e890>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_164.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_164.html
index c29162c..b220c62 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_164.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_164.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -127,6 +127,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164</h1
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_164.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164.Sdynpy_UFF_Dataset_164.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164.Sdynpy_UFF_Dataset_164.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="n">ds_164</span> <span class="o">=</span> <span class="bp">cls</span><span class="p">()</span>
         <span class="c1"># Record 1:       FORMAT(I10,20A1,I10)</span>
         <span class="c1"># Field 1      -- units code</span>
@@ -211,7 +213,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164</h1
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc4490>
+<jinja2.runtime.BlockReference object at 0x7f5a276e3250>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_1858.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_1858.html
index 021a916..979a39b 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_1858.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_1858.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_1858 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_1858 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -161,6 +161,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_1858</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_1858.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_1858.Sdynpy_UFF_Dataset_1858.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_1858.Sdynpy_UFF_Dataset_1858.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="c1">#       Record 1:     FORMAT(6I12)</span>
         <span class="c1">#              Field 1       - Set record number</span>
         <span class="c1">#              Field 2       - Octave format</span>
@@ -316,7 +318,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_1858</h
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba822c0>
+<jinja2.runtime.BlockReference object at 0x7f5a2736a890>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2400.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2400.html
index 0404e45..2030e55 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2400.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2400.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -165,6 +165,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_2400.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400.Sdynpy_UFF_Dataset_2400.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400.Sdynpy_UFF_Dataset_2400.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="c1"># Record 1: FORMAT(I12,2I6,I12)</span>
         <span class="c1">#                 Field 1       -- Model UID</span>
         <span class="c1">#                 Field 2       -- Entity type</span>
@@ -344,7 +346,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400</h
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc5900>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb6890>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2411.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2411.html
index af11a4b..bbacbc3 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2411.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2411.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -129,6 +129,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_2411.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411.Sdynpy_UFF_Dataset_2411.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411.Sdynpy_UFF_Dataset_2411.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="n">nnodes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
         <span class="n">node_labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">nnodes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
         <span class="n">export_coordinate_systems</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">nnodes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
@@ -157,7 +159,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_2411.write_string"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411.Sdynpy_UFF_Dataset_2411.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411.Sdynpy_UFF_Dataset_2411.write_string">[docs]</a>    <span class="k">def</span> <span class="nf">write_string</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">return_string</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
-        <span class="k">for</span> <span class="n">node_label</span><span class="p">,</span> <span class="n">export_coordinate_system</span><span class="p">,</span> <span class="n">displacement_coordinate_system</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">coordinates</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node_labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">export_coordinate_systems</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">displacement_coordinate_systems</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">colors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">node_label</span><span class="p">,</span> <span class="n">export_coordinate_system</span><span class="p">,</span> <span class="n">displacement_coordinate_system</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">coordinates</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">node_labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">export_coordinate_systems</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">displacement_coordinate_systems</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">colors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">coordinates</span><span class="p">):</span>
             <span class="n">return_string</span> <span class="o">+=</span> <span class="n">write_uff_line</span><span class="p">([</span><span class="n">node_label</span><span class="p">,</span> <span class="n">export_coordinate_system</span><span class="p">,</span> <span class="n">displacement_coordinate_system</span><span class="p">,</span>
                                             <span class="n">color</span><span class="p">]</span> <span class="o">+</span> <span class="n">coordinates</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="mi">4</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;D25.16&#39;</span><span class="p">])</span>
         <span class="k">return</span> <span class="n">return_string</span></div>
@@ -181,7 +184,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411</h
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebe1eb90>
+<jinja2.runtime.BlockReference object at 0x7f5a27c1cf70>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2412.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2412.html
index dec1821..72528a6 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2412.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2412.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -140,6 +140,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_2412.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412.Sdynpy_UFF_Dataset_2412.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412.Sdynpy_UFF_Dataset_2412.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="n">element_labels</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">fe_descriptor_ids</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="n">physical_property_table_numbers</span> <span class="o">=</span> <span class="p">[]</span>
@@ -210,7 +212,9 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_2412.write_string"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412.Sdynpy_UFF_Dataset_2412.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412.Sdynpy_UFF_Dataset_2412.write_string">[docs]</a>    <span class="k">def</span> <span class="nf">write_string</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">return_string</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
-        <span class="k">for</span> <span class="n">element_label</span><span class="p">,</span> <span class="n">fe_descriptor_id</span><span class="p">,</span> <span class="n">physical_property_table_number</span><span class="p">,</span> <span class="n">material_property_table_number</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">connectivity</span><span class="p">,</span> <span class="n">beam_orientation</span><span class="p">,</span> <span class="n">beam_fore_cross_section_number</span><span class="p">,</span> <span class="n">beam_aft_cross_section_number</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
+        <span class="k">for</span> <span class="p">(</span><span class="n">element_label</span><span class="p">,</span> <span class="n">fe_descriptor_id</span><span class="p">,</span> <span class="n">physical_property_table_number</span><span class="p">,</span>
+             <span class="n">material_property_table_number</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">connectivity</span><span class="p">,</span> <span class="n">beam_orientation</span><span class="p">,</span>
+             <span class="n">beam_fore_cross_section_number</span><span class="p">,</span> <span class="n">beam_aft_cross_section_number</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">element_labels</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">fe_descriptor_ids</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">physical_property_table_numbers</span><span class="p">,</span>
@@ -220,7 +224,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412</h
                 <span class="bp">self</span><span class="o">.</span><span class="n">beam_orientations</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">beam_fore_cross_section_numbers</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">beam_aft_cross_section_numbers</span><span class="p">):</span>
-            <span class="n">return_string</span> <span class="o">+=</span> <span class="n">write_uff_line</span><span class="p">([</span><span class="n">element_label</span><span class="p">,</span> <span class="n">fe_descriptor_id</span><span class="p">,</span> <span class="n">physical_property_table_number</span><span class="p">,</span> <span class="n">material_property_table_number</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)],</span>
+            <span class="n">return_string</span> <span class="o">+=</span> <span class="n">write_uff_line</span><span class="p">([</span><span class="n">element_label</span><span class="p">,</span> <span class="n">fe_descriptor_id</span><span class="p">,</span>
+                                             <span class="n">physical_property_table_number</span><span class="p">,</span>
+                                             <span class="n">material_property_table_number</span><span class="p">,</span>
+                                             <span class="n">color</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">connectivity</span><span class="p">)],</span>
                                             <span class="mi">6</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">])</span>
             <span class="k">if</span> <span class="n">fe_descriptor_id</span> <span class="ow">in</span> <span class="n">_beam_elem_types</span><span class="p">:</span>
                 <span class="n">return_string</span> <span class="o">+=</span> <span class="n">write_uff_line</span><span class="p">(</span>
@@ -247,7 +254,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412</h
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba9a9b0>
+<jinja2.runtime.BlockReference object at 0x7f5a273697e0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2420.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2420.html
index 419c293..2ef832a 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2420.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_2420.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2420 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2420 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -131,6 +131,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2420</h
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_2420.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2420.Sdynpy_UFF_Dataset_2420.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2420.Sdynpy_UFF_Dataset_2420.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="c1">#       Record 1:        FORMAT (1I10)</span>
         <span class="c1">#                 Field 1       -- Part UID</span>
         <span class="n">part_uid</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">])</span>
@@ -213,7 +215,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2420</h
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba83c40>
+<jinja2.runtime.BlockReference object at 0x7f5a265a32e0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_55.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_55.html
index 65ff00b..b0127df 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_55.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_55.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -111,6 +111,14 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55</h1>
 <span class="kn">from</span> <span class="nn">..sdynpy_uff</span> <span class="kn">import</span> <span class="n">UFFReadError</span><span class="p">,</span> <span class="n">parse_uff_line</span><span class="p">,</span> <span class="n">parse_uff_lines</span><span class="p">,</span> <span class="n">write_uff_line</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
+<span class="n">_analysis_types</span> <span class="o">=</span> <span class="p">{</span><span class="mi">0</span><span class="p">:</span><span class="s1">&#39;Unknown&#39;</span><span class="p">,</span>
+                   <span class="mi">1</span><span class="p">:</span><span class="s1">&#39;Static&#39;</span><span class="p">,</span>
+                   <span class="mi">2</span><span class="p">:</span><span class="s1">&#39;Normal Mode&#39;</span><span class="p">,</span>
+                   <span class="mi">3</span><span class="p">:</span><span class="s1">&#39;Complex Mode&#39;</span><span class="p">,</span>
+                   <span class="mi">4</span><span class="p">:</span><span class="s1">&#39;Transient&#39;</span><span class="p">,</span>
+                   <span class="mi">5</span><span class="p">:</span><span class="s1">&#39;Frequency Response&#39;</span><span class="p">,</span>
+                   <span class="mi">6</span><span class="p">:</span><span class="s1">&#39;Buckling&#39;</span><span class="p">,</span>
+                   <span class="mi">7</span><span class="p">:</span><span class="s1">&#39;Complex Mode Second Order&#39;</span><span class="p">}</span>
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_55"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55">[docs]</a><span class="k">class</span> <span class="nc">Sdynpy_UFF_Dataset_55</span><span class="p">:</span>
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_55.__init__"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idline1</span><span class="p">,</span> <span class="n">idline2</span><span class="p">,</span> <span class="n">idline3</span><span class="p">,</span> <span class="n">idline4</span><span class="p">,</span> <span class="n">idline5</span><span class="p">,</span> <span class="n">model_type</span><span class="p">,</span>
@@ -134,263 +142,492 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55</h1>
     <span class="k">def</span> <span class="nf">dataset_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="mi">55</span>
 
+    <span class="c1"># For Analysis Type = 0, Unknown</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1"># </span>
+    <span class="c1">#            FIELD 1:   1</span>
+    <span class="c1">#            FIELD 2:   1</span>
+    <span class="c1">#            FIELD 3:   ID Number - Done</span>
+    <span class="c1"># </span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1"># </span>
+    <span class="c1">#            FIELD 1:   0.0</span>
+    <span class="c1">#</span>
+    <span class="c1"># For Analysis Type = 1, Static</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1">#            FIELD 1:    1</span>
+    <span class="c1">#            FIELD 2:    1</span>
+    <span class="c1">#            FIELD 3:    Load Case Number - Done</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1">#            FIELD 11:    0.0</span>
+    <span class="c1">#</span>
+    <span class="c1"># For Analysis Type = 2, Normal Mode</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1">#</span>
+    <span class="c1">#            FIELD 1:    2</span>
+    <span class="c1">#            FIELD 2:    4</span>
+    <span class="c1">#            FIELD 3:    Load Case Number - Done</span>
+    <span class="c1">#            FIELD 4:    Mode Number - Done</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1">#            FIELD 1:    Frequency (Hertz) - Done</span>
+    <span class="c1">#            FIELD 2:    Modal  - Done</span>
+    <span class="c1">#            FIELD 3:    Modal Viscous Damping Ratio - Done</span>
+    <span class="c1">#            FIELD 4:    Modal Hysteretic Damping Ratio - Done</span>
+    <span class="c1">#</span>
+    <span class="c1"># For Analysis Type = 3, Complex Eigenvalue</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1">#            FIELD 1:    2</span>
+    <span class="c1">#            FIELD 2:    6</span>
+    <span class="c1">#            FIELD 3:    Load Case Number - Done</span>
+    <span class="c1">#            FIELD 4:    Mode Number - Done</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1">#</span>
+    <span class="c1">#            FIELD 1:    Real Part Eigenvalue - Done</span>
+    <span class="c1">#            FIELD 2:    Imaginary Part Eigenvalue - Done</span>
+    <span class="c1">#            FIELD 3:    Real Part Of Modal A - Done</span>
+    <span class="c1">#            FIELD 4:    Imaginary Part Of Modal A - Done</span>
+    <span class="c1">#            FIELD 5:    Real Part Of Modal B</span>
+    <span class="c1">#            FIELD 6:    Imaginary Part Of Modal B</span>
+    <span class="c1">#</span>
+    <span class="c1">#</span>
+    <span class="c1"># For Analysis Type = 4, Transient</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1">#</span>
+    <span class="c1">#            FIELD 1:    2</span>
+    <span class="c1">#            FIELD 2:    1</span>
+    <span class="c1">#            FIELD 3:    Load Case Number - Done</span>
+    <span class="c1">#            FIELD 4:    Time Step Number - Done</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1">#            FIELD 1: Time (Seconds)</span>
+    <span class="c1">#</span>
+    <span class="c1"># For Analysis Type = 5, Frequency Response</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1">#</span>
+    <span class="c1">#            FIELD 1:    2</span>
+    <span class="c1">#            FIELD 2:    1</span>
+    <span class="c1">#            FIELD 3:    Load Case Number - Done</span>
+    <span class="c1">#            FIELD 4:    Frequency Step Number - Done</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1">#            FIELD 1:    Frequency (Hertz) - Done</span>
+    <span class="c1">#</span>
+    <span class="c1"># For Analysis Type = 6, Buckling</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 7:</span>
+    <span class="c1">#</span>
+    <span class="c1">#            FIELD 1:    1</span>
+    <span class="c1">#            FIELD 2:    1</span>
+    <span class="c1">#            FIELD 3:    Load Case Number - Done</span>
+    <span class="c1">#</span>
+    <span class="c1">#       RECORD 8:</span>
+    <span class="c1">#</span>
+    <span class="c1">#            FIELD 1: Eigenvalue - Done</span>
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">id_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;id_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@id_number</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">id_number</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;id_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">load_case_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;load_case_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
 
     <span class="nd">@load_case_number</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">load_case_number</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;load_case_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">mode_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;mode_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@mode_number</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">mode_number</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;mode_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">time_step_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;time_step_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@time_step_number</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">time_step_number</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;time_step_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">frequency_step_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;frequency_step_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@frequency_step_number</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">frequency_step_number</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Integer data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">integer_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;frequency_step_number field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">frequency</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;frequency field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@frequency</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">frequency</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">5</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;frequency field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">modal_mass</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_mass field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@modal_mass</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">modal_mass</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_mass field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">modal_viscous_damping</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_viscous_damping field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@modal_viscous_damping</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">modal_viscous_damping</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">2</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_viscous_damping field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">modal_hysteretic_damping</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">3</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_hysteretic_damping field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@modal_hysteretic_damping</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">modal_hysteretic_damping</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">3</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">real_eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="nd">@real_eigenvalue</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">real_eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">imaginary_eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="nd">@imaginary_eigenvalue</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">imaginary_eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">real_modal_a</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
-
-    <span class="nd">@real_modal_a</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">real_modal_a</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">2</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_hysteretic_damping field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">imaginary_modal_a</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+    <span class="k">def</span> <span class="nf">eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="o">==</span> <span class="mi">6</span><span class="p">:</span>
+                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;eigenvalue field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
+    <span class="nd">@eigenvalue</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span><span class="mi">6</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                        <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="mi">1</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="o">==</span> <span class="mi">6</span><span class="p">:</span>
+                <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+                <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                        <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;eigenvalue field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
-    <span class="nd">@imaginary_modal_a</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">imaginary_modal_a</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">3</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
 
     <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">real_modal_b</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span>
-
-    <span class="nd">@real_modal_b</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">real_modal_b</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">4</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+    <span class="k">def</span> <span class="nf">modal_a</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_a field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
+    <span class="nd">@modal_a</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">modal_a</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="mi">3</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_a field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">imaginary_modal_b</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
-
-    <span class="nd">@imaginary_modal_b</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">imaginary_modal_b</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">5</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+    <span class="k">def</span> <span class="nf">modal_b</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_b field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
+    <span class="nd">@modal_b</span><span class="o">.</span><span class="n">setter</span>
+    <span class="k">def</span> <span class="nf">modal_b</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="mi">5</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">4</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;modal_b field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">time</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">]</span>
+        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;time field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
+            
+    
     <span class="nd">@time</span><span class="o">.</span><span class="n">setter</span>
     <span class="k">def</span> <span class="nf">time</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="n">valid_types</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">]</span>
         <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">buckling_eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="nd">@buckling_eigenvalue</span><span class="o">.</span><span class="n">setter</span>
-    <span class="k">def</span> <span class="nf">buckling_eigenvalue</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-        <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">analysis_type</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+            <span class="k">except</span> <span class="ne">IndexError</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Real data currently only has </span><span class="si">{:}</span><span class="s1"> indices, tried to index </span><span class="si">{:}</span><span class="s1">, expanding to </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">),</span> <span class="n">index</span><span class="p">,</span> <span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span> <span class="o">+=</span> <span class="p">((</span><span class="n">index</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">)))</span> <span class="o">*</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">real_data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
+                <span class="s1">&#39;time field only exists for analysis_types </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="s1">&#39;, &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">_analysis_types</span><span class="p">[</span><span class="n">t</span><span class="p">])</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">valid_types</span><span class="p">])))</span>
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_55.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
         <span class="c1">#        RECORD 1:      Format (40A2)</span>
         <span class="c1">#               FIELD 1:          ID Line 1</span>
         <span class="n">idline1</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
@@ -467,6 +704,27 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55</h1>
 <span class="c1">#               FIELD 6:  Number Of Data Values Per Node (NDV)</span>
         <span class="n">model_type</span><span class="p">,</span> <span class="n">analysis_type</span><span class="p">,</span> <span class="n">data_characteristic</span><span class="p">,</span> <span class="n">specific_data_type</span><span class="p">,</span> <span class="n">data_type</span><span class="p">,</span> <span class="n">num_data_per_node</span> <span class="o">=</span> <span class="p">(</span>
             <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">6</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]))</span>
+        <span class="c1"># Correct the number of values per node</span>
+        <span class="k">if</span> <span class="n">data_characteristic</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">required_data_values</span> <span class="o">=</span> <span class="mi">3</span>
+            <span class="k">if</span> <span class="n">num_data_per_node</span> <span class="o">!=</span> <span class="n">required_data_values</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Dataset 55 has data characteristic &quot;3 DOF Global Translation&quot; but has </span><span class="si">{:}</span><span class="s1"> data values per node (should be </span><span class="si">{:}</span><span class="s1">)</span><span class="se">\n</span><span class="s1">Universal File is formatted incorrectly.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_data_per_node</span><span class="p">,</span><span class="n">required_data_values</span><span class="p">))</span>
+                <span class="n">num_data_per_node</span> <span class="o">=</span> <span class="n">required_data_values</span>
+        <span class="k">elif</span> <span class="n">data_characteristic</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">required_data_values</span> <span class="o">=</span> <span class="mi">6</span>
+            <span class="k">if</span> <span class="n">num_data_per_node</span> <span class="o">!=</span> <span class="n">required_data_values</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Dataset 55 has data characteristic &quot;3 DOF Global Translation &amp; Rotation Vector&quot; but has </span><span class="si">{:}</span><span class="s1"> data values per node (should be </span><span class="si">{:}</span><span class="s1">)</span><span class="se">\n</span><span class="s1">Universal File is formatted incorrectly.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_data_per_node</span><span class="p">,</span><span class="n">required_data_values</span><span class="p">))</span>
+                <span class="n">num_data_per_node</span> <span class="o">=</span> <span class="n">required_data_values</span>
+        <span class="k">elif</span> <span class="n">data_characteristic</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+            <span class="n">required_data_values</span> <span class="o">=</span> <span class="mi">6</span>
+            <span class="k">if</span> <span class="n">num_data_per_node</span> <span class="o">!=</span> <span class="n">required_data_values</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Dataset 55 has data characteristic &quot;Symmetric GLobal Tensor&quot; but has </span><span class="si">{:}</span><span class="s1"> data values per node (should be </span><span class="si">{:}</span><span class="s1">)</span><span class="se">\n</span><span class="s1">Universal File is formatted incorrectly.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_data_per_node</span><span class="p">,</span><span class="n">required_data_values</span><span class="p">))</span>
+                <span class="n">num_data_per_node</span> <span class="o">=</span> <span class="n">required_data_values</span>
+        <span class="k">elif</span> <span class="n">data_characteristic</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
+            <span class="n">required_data_values</span> <span class="o">=</span> <span class="mi">9</span>
+            <span class="k">if</span> <span class="n">num_data_per_node</span> <span class="o">!=</span> <span class="n">required_data_values</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Dataset 55 has data characteristic &quot;General GLobal Tensor&quot; but has </span><span class="si">{:}</span><span class="s1"> data values per node (should be </span><span class="si">{:}</span><span class="s1">)</span><span class="se">\n</span><span class="s1">Universal File is formatted incorrectly.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_data_per_node</span><span class="p">,</span><span class="n">required_data_values</span><span class="p">))</span>
+                <span class="n">num_data_per_node</span> <span class="o">=</span> <span class="n">required_data_values</span>
         <span class="n">data_is_complex</span> <span class="o">=</span> <span class="n">data_type</span> <span class="o">==</span> <span class="mi">5</span>
 <span class="c1">#          Records 7 And 8 Are Analysis Type Specific</span>
 <span class="c1">#</span>
@@ -509,7 +767,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55</h1>
             <span class="n">next_line</span> <span class="o">+=</span> <span class="n">read_lines</span>
             <span class="k">if</span> <span class="n">data_is_complex</span><span class="p">:</span>
                 <span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">real</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span>
-                                              <span class="n">imag</span> <span class="k">for</span> <span class="n">real</span><span class="p">,</span> <span class="n">imag</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])]</span>
+                                              <span class="n">imag</span> <span class="k">for</span> <span class="n">real</span><span class="p">,</span> <span class="n">imag</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">node_data</span><span class="p">[::</span><span class="mi">2</span><span class="p">],</span> <span class="n">node_data</span><span class="p">[</span><span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">])]</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="o">=</span> <span class="n">node_data</span>
 
@@ -549,7 +807,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55</h1>
         <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node_data_dictionary</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
             <span class="n">return_string</span> <span class="o">+=</span> <span class="n">write_uff_line</span><span class="p">([</span><span class="n">node</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">])</span>
             <span class="n">return_string</span> <span class="o">+=</span> <span class="n">write_uff_line</span><span class="p">(</span>
-                <span class="p">[</span><span class="n">fn</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="k">for</span> <span class="n">fn</span> <span class="ow">in</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">)]</span> 
+                <span class="p">[</span><span class="n">fn</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">node</span><span class="p">]</span> <span class="k">for</span> <span class="n">fn</span> <span class="ow">in</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">)]</span>
                 <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">iscomplexobj</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">node</span><span class="p">])</span>
                 <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">node_data_dictionary</span><span class="p">[</span><span class="n">node</span><span class="p">],</span> <span class="mi">6</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">])</span>
         <span class="k">return</span> <span class="n">return_string</span></div>
@@ -573,7 +831,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55</h1>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc78b0>
+<jinja2.runtime.BlockReference object at 0x7f5a27c1e200>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_58.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_58.html
index 1dd46b3..2122278 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_58.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_58.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -110,8 +110,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
 <span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">from</span> <span class="nn">..sdynpy_uff</span> <span class="kn">import</span> <span class="n">parse_uff_line</span><span class="p">,</span> <span class="n">parse_uff_lines</span><span class="p">,</span> <span class="n">write_uff_line</span>
+<span class="kn">from</span> <span class="nn">...core.sdynpy_coordinate</span> <span class="kn">import</span> <span class="n">parse_coordinate_string</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
+<span class="kn">import</span> <span class="nn">struct</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
 
 <div class="viewcode-block" id="is_abscissa_even"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.is_abscissa_even.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.is_abscissa_even">[docs]</a><span class="k">def</span> <span class="nf">is_abscissa_even</span><span class="p">(</span><span class="n">abscissa</span><span class="p">):</span>
     <span class="n">abscissa_inc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diff</span><span class="p">(</span><span class="n">abscissa</span><span class="p">))</span>
@@ -186,7 +188,21 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
         <span class="k">return</span> <span class="mi">58</span>
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_58.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.Sdynpy_UFF_Dataset_58.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">block_lines</span><span class="p">,</span> <span class="n">is_binary</span><span class="p">,</span> <span class="n">byte_ordering</span><span class="p">,</span>
+                            <span class="n">floating_point_format</span><span class="p">,</span> <span class="n">num_ascii_lines_following</span><span class="p">,</span>
+                            <span class="n">num_bytes_following</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Extract function at nodal DOF - data-set 58.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        ds_58</span>
+<span class="sd">            Object with attributes as the dataset fields and values containing the</span>
+<span class="sd">            data from the universal file in those dataset fields.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">num_ascii_lines_following</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">num_ascii_lines_following</span> <span class="o">=</span> <span class="mi">11</span>
+        <span class="n">split_header</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">block_lines</span><span class="p">[:</span><span class="n">num_ascii_lines_following</span><span class="p">]]</span>
         <span class="c1">#        Record 1:     Format(80A1)</span>
         <span class="c1">#                       Field 1    - ID Line 1</span>
         <span class="c1">#</span>
@@ -194,10 +210,10 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
         <span class="c1">#</span>
         <span class="c1">#                           ID Line 1 is generally  used  for  the  function</span>
         <span class="c1">#                           description.</span>
-        <span class="n">idline1</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
+        <span class="n">idline1</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
 <span class="c1">#        Record 2:     Format(80A1)</span>
 <span class="c1">#                       Field 1    - ID Line 2</span>
-        <span class="n">idline2</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
+        <span class="n">idline2</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
 <span class="c1">#        Record 3:     Format(80A1)</span>
 <span class="c1">#                       Field 1    - ID Line 3</span>
 <span class="c1">#</span>
@@ -207,13 +223,13 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
 <span class="c1">#                           function  was  created.  The date is in the form</span>
 <span class="c1">#                           DD-MMM-YY, and the time is in the form HH:MM:SS,</span>
 <span class="c1">#                           with a general Format(9A1,1X,8A1).</span>
-        <span class="n">idline3</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
+        <span class="n">idline3</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
 <span class="c1">#        Record 4:     Format(80A1)</span>
 <span class="c1">#                       Field 1    - ID Line 4</span>
-        <span class="n">idline4</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
+        <span class="n">idline4</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
 <span class="c1">#         Record 5:     Format(80A1)</span>
 <span class="c1">#                       Field 1    - ID Line 5</span>
-        <span class="n">idline5</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
+        <span class="n">idline5</span><span class="p">,</span> <span class="o">=</span> <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;A80&#39;</span><span class="p">])</span>
 <span class="c1">#        Record 6:     Format(2(I5,I10),2(1X,10A1,I10,I4))</span>
 <span class="c1">#                                  DOF Identification</span>
 <span class="c1">#                       Field 1    - Function Type</span>
@@ -271,7 +287,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
         <span class="p">(</span><span class="n">function_type</span><span class="p">,</span> <span class="n">function_id</span><span class="p">,</span> <span class="n">version_number</span><span class="p">,</span> <span class="n">load_case</span><span class="p">,</span>
          <span class="n">response_entity_name</span><span class="p">,</span> <span class="n">response_node</span><span class="p">,</span> <span class="n">response_direction</span><span class="p">,</span>
          <span class="n">reference_entity_name</span><span class="p">,</span> <span class="n">reference_node</span><span class="p">,</span> <span class="n">reference_direction</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">,</span> <span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A10&#39;</span><span class="p">,</span> <span class="s1">&#39;I10&#39;</span><span class="p">,</span> <span class="s1">&#39;I4&#39;</span><span class="p">]))</span>
+            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">,</span> <span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A10&#39;</span><span class="p">,</span> <span class="s1">&#39;I10&#39;</span><span class="p">,</span> <span class="s1">&#39;I4&#39;</span><span class="p">]))</span>
+
 <span class="c1">#        Record 7:     Format(3I10,3E13.5)</span>
 <span class="c1">#                                  Data Form</span>
 <span class="c1">#                       Field 1    - Ordinate Data Type</span>
@@ -290,7 +307,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
 <span class="c1">#                       Field 6    - Z-axis value (0.0 if unused)</span>
         <span class="p">(</span><span class="n">ordinate_data_type</span><span class="p">,</span> <span class="n">num_data</span><span class="p">,</span> <span class="n">abscissa_spacing</span><span class="p">,</span> <span class="n">abscissa_minimum</span><span class="p">,</span>
          <span class="n">abscissa_increment</span><span class="p">,</span> <span class="n">zaxis_value</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">]))</span>
+            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">6</span><span class="p">],</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">]))</span>
 <span class="c1">#        Record 8:     Format(I10,3I5,2(1X,20A1))</span>
 <span class="c1">#                                  Abscissa Data Characteristics</span>
 <span class="c1">#                       Field 1    - Specific Data Type</span>
@@ -338,25 +355,25 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
         <span class="p">(</span><span class="n">abscissa_data_type</span><span class="p">,</span> <span class="n">abscissa_length_exponent</span><span class="p">,</span>
          <span class="n">abscissa_force_exponent</span><span class="p">,</span> <span class="n">abscissa_temp_exponent</span><span class="p">,</span>
          <span class="n">abscissa_axis_label</span><span class="p">,</span> <span class="n">abscissa_units_label</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
+            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
 <span class="c1">#         Record 9:     Format(I10,3I5,2(1X,20A1))</span>
 <span class="c1">#                       Ordinate (or ordinate numerator) Data Characteristics</span>
         <span class="p">(</span><span class="n">ordinate_num_data_type</span><span class="p">,</span> <span class="n">ordinate_num_length_exponent</span><span class="p">,</span>
          <span class="n">ordinate_num_force_exponent</span><span class="p">,</span> <span class="n">ordinate_num_temp_exponent</span><span class="p">,</span>
          <span class="n">ordinate_num_axis_label</span><span class="p">,</span> <span class="n">ordinate_num_units_label</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
+            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">8</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
 <span class="c1">#         Record 10:    Format(I10,3I5,2(1X,20A1))</span>
 <span class="c1">#                       Ordinate Denominator Data Characteristics</span>
         <span class="p">(</span><span class="n">ordinate_den_data_type</span><span class="p">,</span> <span class="n">ordinate_den_length_exponent</span><span class="p">,</span>
          <span class="n">ordinate_den_force_exponent</span><span class="p">,</span> <span class="n">ordinate_den_temp_exponent</span><span class="p">,</span>
          <span class="n">ordinate_den_axis_label</span><span class="p">,</span> <span class="n">ordinate_den_units_label</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
+            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">9</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
 <span class="c1">#         Record 11:    Format(I10,3I5,2(1X,20A1))</span>
 <span class="c1">#                       Z-axis Data Characteristics</span>
         <span class="p">(</span><span class="n">zaxis_data_type</span><span class="p">,</span> <span class="n">zaxis_length_exponent</span><span class="p">,</span>
          <span class="n">zaxis_force_exponent</span><span class="p">,</span> <span class="n">zaxis_temp_exponent</span><span class="p">,</span>
          <span class="n">zaxis_axis_label</span><span class="p">,</span> <span class="n">zaxis_units_label</span><span class="p">)</span> <span class="o">=</span> <span class="p">(</span>
-            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
+            <span class="n">parse_uff_line</span><span class="p">(</span><span class="n">split_header</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="p">[</span><span class="s1">&#39;I10&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">3</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;I5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;X1&#39;</span><span class="p">,</span> <span class="s1">&#39;A20&#39;</span><span class="p">]))</span>
 <span class="c1">#         Record 12:</span>
 <span class="c1">#                                   Data Values</span>
 <span class="c1">#</span>
@@ -372,32 +389,53 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
 <span class="c1">#               7     complex    double        even         4E20.12</span>
 <span class="c1">#               8     complex    double       uneven      E13.5,2E20.12</span>
 <span class="c1">#           --------------------------------------------------------------</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">]):</span>   <span class="c1"># Single, regardless of abscissa or complexity</span>
-            <span class="n">read_format</span> <span class="o">=</span> <span class="mi">6</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">]</span>
-            <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>   <span class="c1"># Double precision</span>
-              <span class="ow">and</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">1</span><span class="p">):</span>   <span class="c1"># Even spacing</span>
-            <span class="n">read_format</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E20.12&#39;</span><span class="p">]</span>
-            <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float64&#39;</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="o">==</span> <span class="mi">4</span>       <span class="c1"># Real double precision</span>
-              <span class="ow">and</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>   <span class="c1"># Uneven spacing</span>
-            <span class="n">read_format</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">,</span> <span class="s1">&#39;E20.12&#39;</span><span class="p">]</span>
-            <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float64&#39;</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="o">==</span> <span class="mi">6</span>       <span class="c1"># Complex double precision</span>
-              <span class="ow">and</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>   <span class="c1"># Uneven spacing</span>
-            <span class="n">read_format</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E20.12&#39;</span><span class="p">]</span>
-            <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float64&#39;</span>
-        <span class="c1"># Number of data points to read</span>
+        
+        <span class="c1"># Number of data points to read per item</span>
         <span class="n">read_multiplication_factor</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># Base value is just one value</span>
         <span class="k">if</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>       <span class="c1"># If abscissa spacing is uneven</span>
             <span class="n">read_multiplication_factor</span> <span class="o">+=</span> <span class="mi">1</span>  <span class="c1"># Will need to read that too</span>
         <span class="k">if</span> <span class="n">ordinate_data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]:</span>  <span class="c1"># If complex,</span>
             <span class="n">read_multiplication_factor</span> <span class="o">+=</span> <span class="mi">1</span>  <span class="c1"># Will need to read 2 ordinates</span>
-        <span class="n">total_number_of_entries</span> <span class="o">=</span> <span class="n">read_multiplication_factor</span> <span class="o">*</span> <span class="n">num_data</span>
-        <span class="c1"># Now read the data</span>
-        <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-            <span class="n">parse_uff_lines</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">11</span><span class="p">:],</span> <span class="n">read_format</span><span class="p">,</span> <span class="n">total_number_of_entries</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
-            <span class="n">dtype</span><span class="o">=</span><span class="n">ordinate_data_dtype</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">read_multiplication_factor</span><span class="p">)</span>
+
+        <span class="c1"># reading Record 12 if encoded in binary</span>
+        <span class="k">if</span> <span class="n">is_binary</span><span class="p">:</span>
+            <span class="n">split_data</span> <span class="o">=</span> <span class="sa">b</span><span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">block_lines</span><span class="p">[</span><span class="n">num_ascii_lines_following</span><span class="p">:])</span>
+            <span class="k">if</span> <span class="n">byte_ordering</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">bo</span> <span class="o">=</span> <span class="s1">&#39;&lt;&#39;</span>
+            <span class="k">elif</span> <span class="n">byte_ordering</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">bo</span> <span class="o">=</span> <span class="s1">&#39;&gt;&#39;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;UFF file does not contain byte_ordering parameter, assuming Little Endian (DEC VMS &amp; ULTRIX, WIN NT)&#39;</span><span class="p">,</span> <span class="ne">EncodingWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                <span class="n">bo</span> <span class="o">=</span> <span class="s1">&#39;&lt;&#39;</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">]):</span>
+                <span class="c1"># single precision - 4 bytes</span>
+                <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">struct</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%c%s</span><span class="s1">f&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">bo</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">split_data</span><span class="p">)</span> <span class="o">/</span> <span class="mi">4</span><span class="p">)),</span> <span class="n">split_data</span><span class="p">),</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">read_multiplication_factor</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># double precision - 8 bytes</span>
+                <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">struct</span><span class="o">.</span><span class="n">unpack</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%c%s</span><span class="s1">d&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">bo</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">split_data</span><span class="p">)</span> <span class="o">/</span> <span class="mi">8</span><span class="p">)),</span> <span class="n">split_data</span><span class="p">),</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">read_multiplication_factor</span><span class="p">)</span>
+
+        <span class="c1"># reading Record 12 if encoded as ascii</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">]):</span>   <span class="c1"># Single, regardless of abscissa or complexity</span>
+                <span class="n">read_format</span> <span class="o">=</span> <span class="mi">6</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">]</span>
+                <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float32&#39;</span>
+            <span class="k">elif</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>   <span class="c1"># Double precision</span>
+                <span class="ow">and</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">1</span><span class="p">):</span>   <span class="c1"># Even spacing</span>
+                <span class="n">read_format</span> <span class="o">=</span> <span class="mi">4</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E20.12&#39;</span><span class="p">]</span>
+                <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float64&#39;</span>
+            <span class="k">elif</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="o">==</span> <span class="mi">4</span>       <span class="c1"># Real double precision</span>
+                <span class="ow">and</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>   <span class="c1"># Uneven spacing</span>
+                <span class="n">read_format</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">,</span> <span class="s1">&#39;E20.12&#39;</span><span class="p">]</span>
+                <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float64&#39;</span>
+            <span class="k">elif</span> <span class="p">(</span><span class="n">ordinate_data_type</span> <span class="o">==</span> <span class="mi">6</span>       <span class="c1"># Complex double precision</span>
+                <span class="ow">and</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>   <span class="c1"># Uneven spacing</span>
+                <span class="n">read_format</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;E13.5&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">[</span><span class="s1">&#39;E20.12&#39;</span><span class="p">]</span>
+                <span class="n">ordinate_data_dtype</span> <span class="o">=</span> <span class="s1">&#39;float64&#39;</span>
+            <span class="n">total_number_of_entries</span> <span class="o">=</span> <span class="n">read_multiplication_factor</span> <span class="o">*</span> <span class="n">num_data</span>
+            <span class="c1"># Now read the data</span>
+            <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
+                <span class="n">parse_uff_lines</span><span class="p">([</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">block_lines</span><span class="p">[</span><span class="n">num_ascii_lines_following</span><span class="p">:]],</span> <span class="n">read_format</span><span class="p">,</span> <span class="n">total_number_of_entries</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="n">ordinate_data_dtype</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">read_multiplication_factor</span><span class="p">)</span>
         <span class="c1"># Now parse the data into the right format</span>
         <span class="k">if</span> <span class="n">abscissa_spacing</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># If abscissa spacing is uneven</span>
             <span class="n">abscissa</span> <span class="o">=</span> <span class="n">ordinate</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float32&#39;</span><span class="p">)</span>
@@ -535,8 +573,11 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
             <span class="k">return</span> <span class="s1">&#39;Dataset 58: Data</span><span class="se">\n</span><span class="s1">  &#39;</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">  &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">lines</span><span class="p">)</span></div>
 
 
-<div class="viewcode-block" id="read"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read">[docs]</a><span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">Sdynpy_UFF_Dataset_58</span><span class="o">.</span><span class="n">from_uff_data_array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
+<div class="viewcode-block" id="read"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58.read">[docs]</a><span class="k">def</span> <span class="nf">read</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="n">is_binary</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">byte_ordering</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">floating_point_format</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+         <span class="n">num_ascii_lines_following</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">num_bytes_following</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">Sdynpy_UFF_Dataset_58</span><span class="o">.</span><span class="n">from_uff_data_array</span><span class="p">(</span>
+        <span class="n">data</span><span class="p">,</span> <span class="n">is_binary</span><span class="p">,</span> <span class="n">byte_ordering</span><span class="p">,</span> <span class="n">floating_point_format</span><span class="p">,</span>
+        <span class="n">num_ascii_lines_following</span><span class="p">,</span> <span class="n">num_bytes_following</span><span class="p">)</span></div>
 </pre></div>
 
            </div>
@@ -546,7 +587,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_58</h1>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb97b190>
+<jinja2.runtime.BlockReference object at 0x7f5a276e2770>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_82.html b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_82.html
index edb0e27..5e5d5ef 100644
--- a/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_82.html
+++ b/_modules/sdynpy/fileio/sdynpy_uff_datasets/sdynpy_uff_dataset_82.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82 &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82 &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../../_static/plot_directive.css" />
@@ -130,11 +130,8 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82</h1>
 
 <div class="viewcode-block" id="Sdynpy_UFF_Dataset_82.from_uff_data_array"><a class="viewcode-back" href="../../../../_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_82.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82.Sdynpy_UFF_Dataset_82.from_uff_data_array">[docs]</a>    <span class="nd">@classmethod</span>
     <span class="k">def</span> <span class="nf">from_uff_data_array</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
-        <span class="n">ids</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">nodes</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">descriptions</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1"># Transform from binary to ascii</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">line</span><span class="o">.</span><span class="n">decode</span><span class="p">()</span> <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
 <span class="c1">#            Record 1: FORMAT(3I10)</span>
 <span class="c1">#                       Field 1 -    trace line number</span>
 <span class="c1">#                       Field 2 -    number of nodes defining trace line</span>
@@ -188,7 +185,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_82</h1>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb9d2800>
+<jinja2.runtime.BlockReference object at 0x7f5a27088550>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/fileio/sdynpy_vic.html b/_modules/sdynpy/fileio/sdynpy_vic.html
index 4258ba9..f2e5203 100644
--- a/_modules/sdynpy/fileio/sdynpy_vic.html
+++ b/_modules/sdynpy/fileio/sdynpy_vic.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.fileio.sdynpy_vic &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.fileio.sdynpy_vic &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -117,6 +117,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_vic</h1><div class="highlight"><pre>
 
 <span class="n">REPORT_TIME</span> <span class="o">=</span> <span class="mi">5</span>
 
+
 <div class="viewcode-block" id="extract_vic_cal_parameters"><a class="viewcode-back" href="../../../_autosummary/sdynpy.fileio.sdynpy_vic.extract_vic_cal_parameters.html#sdynpy.fileio.sdynpy_vic.extract_vic_cal_parameters">[docs]</a><span class="k">def</span> <span class="nf">extract_vic_cal_parameters</span><span class="p">(</span><span class="n">z3d_file</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Extracts camera calibration parameters from a VIC3D .z3d file.</span>
@@ -357,7 +358,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_vic</h1><div class="highlight"><pre>
 <span class="sd">    time_data_3D : TimeHistoryArray</span>
 <span class="sd">        3D Displacement data from the test.  Only returned if read_3D is True.</span>
 <span class="sd">    geometry_2D : Geometry</span>
-<span class="sd">        Geometry object consisting of the 2D (pixel) node positions from the </span>
+<span class="sd">        Geometry object consisting of the 2D (pixel) node positions from the</span>
 <span class="sd">        test.  Only returned if read_2D is True</span>
 <span class="sd">    time_data_2D : TimeHistoryArray</span>
 <span class="sd">        2D Pixel Displacement data from the test.  Only returned if read_2D is</span>
@@ -368,7 +369,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_vic</h1><div class="highlight"><pre>
 <span class="sd">        positions over time for the right image.  Only returned if read_2D is</span>
 <span class="sd">        True.</span>
 <span class="sd">    sigma_data : TimeHistoryArray</span>
-<span class="sd">        Data quality metric for the test over time.  Only returned if </span>
+<span class="sd">        Data quality metric for the test over time.  Only returned if</span>
 <span class="sd">        read_quality is True</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -513,7 +514,7 @@ <h1>Source code for sdynpy.fileio.sdynpy_vic</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc6350>
+<jinja2.runtime.BlockReference object at 0x7f5a276e1f30>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/modal/sdynpy_ccmif.html b/_modules/sdynpy/modal/sdynpy_ccmif.html
index 1c5ca7d..7c898dd 100644
--- a/_modules/sdynpy/modal/sdynpy_ccmif.html
+++ b/_modules/sdynpy/modal/sdynpy_ccmif.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_ccmif &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_ccmif &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -122,12 +122,12 @@ <h1>Source code for sdynpy.modal.sdynpy_ccmif</h1><div class="highlight"><pre>
 <span class="kn">from</span> <span class="nn">qtpy.QtGui</span> <span class="kn">import</span> <span class="n">QIcon</span><span class="p">,</span> <span class="n">QFont</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtCore</span> <span class="kn">import</span> <span class="n">Qt</span><span class="p">,</span> <span class="n">QCoreApplication</span><span class="p">,</span> <span class="n">QRect</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtWidgets</span> <span class="kn">import</span> <span class="p">(</span><span class="n">QToolTip</span><span class="p">,</span> <span class="n">QLabel</span><span class="p">,</span> <span class="n">QPushButton</span><span class="p">,</span> <span class="n">QApplication</span><span class="p">,</span>
-                             <span class="n">QGroupBox</span><span class="p">,</span> <span class="n">QWidget</span><span class="p">,</span> <span class="n">QMessageBox</span><span class="p">,</span> <span class="n">QHBoxLayout</span><span class="p">,</span>
-                             <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="p">,</span> <span class="n">QMainWindow</span><span class="p">,</span>
-                             <span class="n">QFileDialog</span><span class="p">,</span> <span class="n">QErrorMessage</span><span class="p">,</span> <span class="n">QListWidget</span><span class="p">,</span> <span class="n">QListWidgetItem</span><span class="p">,</span>
-                             <span class="n">QLineEdit</span><span class="p">,</span>
-                             <span class="n">QDockWidget</span><span class="p">,</span> <span class="n">QGridLayout</span><span class="p">,</span> <span class="n">QButtonGroup</span><span class="p">,</span> <span class="n">QDialog</span><span class="p">,</span>
-                             <span class="n">QCheckBox</span><span class="p">,</span> <span class="n">QRadioButton</span><span class="p">,</span> <span class="n">QMenuBar</span><span class="p">,</span> <span class="n">QMenu</span><span class="p">)</span>
+                            <span class="n">QGroupBox</span><span class="p">,</span> <span class="n">QWidget</span><span class="p">,</span> <span class="n">QMessageBox</span><span class="p">,</span> <span class="n">QHBoxLayout</span><span class="p">,</span>
+                            <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="p">,</span> <span class="n">QMainWindow</span><span class="p">,</span>
+                            <span class="n">QFileDialog</span><span class="p">,</span> <span class="n">QErrorMessage</span><span class="p">,</span> <span class="n">QListWidget</span><span class="p">,</span> <span class="n">QListWidgetItem</span><span class="p">,</span>
+                            <span class="n">QLineEdit</span><span class="p">,</span>
+                            <span class="n">QDockWidget</span><span class="p">,</span> <span class="n">QGridLayout</span><span class="p">,</span> <span class="n">QButtonGroup</span><span class="p">,</span> <span class="n">QDialog</span><span class="p">,</span>
+                            <span class="n">QCheckBox</span><span class="p">,</span> <span class="n">QRadioButton</span><span class="p">,</span> <span class="n">QMenuBar</span><span class="p">,</span> <span class="n">QMenu</span><span class="p">)</span>
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">qtpy.QtGui</span> <span class="kn">import</span> <span class="n">QAction</span>
 <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
@@ -270,7 +270,7 @@ <h1>Source code for sdynpy.modal.sdynpy_ccmif</h1><div class="highlight"><pre>
             <span class="n">item</span><span class="o">.</span><span class="n">setSelected</span><span class="p">(</span><span class="ow">not</span> <span class="n">item</span><span class="o">.</span><span class="n">isSelected</span><span class="p">())</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">set_properties</span><span class="p">(</span><span class="n">file_index</span><span class="p">,</span> <span class="n">shape_index</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">update_shape_list</span><span class="p">(</span><span class="n">no_load</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+        <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
             <span class="nb">print</span><span class="p">(</span><span class="n">traceback</span><span class="o">.</span><span class="n">format_exc</span><span class="p">())</span></div>
 
 <div class="viewcode-block" id="ColoredCMIF.set_properties"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.set_properties">[docs]</a>    <span class="k">def</span> <span class="nf">set_properties</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_index</span><span class="p">,</span> <span class="n">shape_index</span><span class="p">):</span>
@@ -369,7 +369,7 @@ <h1>Source code for sdynpy.modal.sdynpy_ccmif</h1><div class="highlight"><pre>
                 <span class="n">frf</span> <span class="o">=</span> <span class="n">file_data</span><span class="p">[</span><span class="s1">&#39;frfs&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">TransferFunctionArray</span><span class="p">)</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
                 <span class="n">shapes</span> <span class="o">=</span> <span class="n">file_data</span><span class="p">[</span><span class="s1">&#39;shapes&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">ShapeArray</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
                 <span class="c1"># Check sizes of things</span>
-                <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">frf_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frf_array</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
+                <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frf_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frf_array</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
                     <span class="k">if</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">setdiff1d</span><span class="p">(</span><span class="n">frf</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frf_array</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">)</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span>
                             <span class="n">np</span><span class="o">.</span><span class="n">setdiff1d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frf_array</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">,</span> <span class="n">frf</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">)</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
                         <span class="n">QMessageBox</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;Bad Response Data&#39;</span><span class="p">,</span>
@@ -408,7 +408,7 @@ <h1>Source code for sdynpy.modal.sdynpy_ccmif</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">file_selector</span><span class="o">.</span><span class="n">blockSignals</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">update_shape_list</span><span class="p">()</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">compute_and_plot_ccmif</span><span class="p">()</span>
-            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+            <span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
                 <span class="nb">print</span><span class="p">(</span><span class="n">traceback</span><span class="o">.</span><span class="n">format_exc</span><span class="p">())</span></div>
 
 <div class="viewcode-block" id="ColoredCMIF.plot_ccmif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.plot_ccmif">[docs]</a>    <span class="k">def</span> <span class="nf">plot_ccmif</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -729,7 +729,7 @@ <h1>Source code for sdynpy.modal.sdynpy_ccmif</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebe1c670>
+<jinja2.runtime.BlockReference object at 0x7f5a27c1f7c0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/modal/sdynpy_modal_test.html b/_modules/sdynpy/modal/sdynpy_modal_test.html
new file mode 100644
index 0000000..682f79c
--- /dev/null
+++ b/_modules/sdynpy/modal/sdynpy_modal_test.html
@@ -0,0 +1,995 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" />
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+             
+  <h1>Source code for sdynpy.modal.sdynpy_modal_test</h1><div class="highlight"><pre>
+<span></span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Class defining the typical data for a modal test used to automatically create</span>
+<span class="sd">reports and plots</span>
+
+<span class="sd">Copyright 2022 National Technology &amp; Engineering Solutions of Sandia,</span>
+<span class="sd">LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.</span>
+<span class="sd">Government retains certain rights in this software.</span>
+
+<span class="sd">This program is free software: you can redistribute it and/or modify</span>
+<span class="sd">it under the terms of the GNU General Public License as published by</span>
+<span class="sd">the Free Software Foundation, either version 3 of the License, or</span>
+<span class="sd">(at your option) any later version.</span>
+
+<span class="sd">This program is distributed in the hope that it will be useful,</span>
+<span class="sd">but WITHOUT ANY WARRANTY; without even the implied warranty of</span>
+<span class="sd">MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span>
+<span class="sd">GNU General Public License for more details.</span>
+
+<span class="sd">You should have received a copy of the GNU General Public License</span>
+<span class="sd">along with this program.  If not, see &lt;https://www.gnu.org/licenses/&gt;.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">tempfile</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">..core.sdynpy_shape</span> <span class="kn">import</span> <span class="n">ShapeArray</span><span class="p">,</span> <span class="n">mac</span><span class="p">,</span> <span class="n">matrix_plot</span><span class="p">,</span> <span class="n">rigid_body_check</span>
+<span class="kn">from</span> <span class="nn">..core.sdynpy_data</span> <span class="kn">import</span> <span class="p">(</span><span class="n">TransferFunctionArray</span><span class="p">,</span> <span class="n">CoherenceArray</span><span class="p">,</span>
+                                <span class="n">MultipleCoherenceArray</span><span class="p">,</span> <span class="n">PowerSpectralDensityArray</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_signal_processing_gui</span> <span class="kn">import</span> <span class="n">SignalProcessingGUI</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_polypy</span> <span class="kn">import</span> <span class="n">PolyPy_GUI</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_smac</span> <span class="kn">import</span> <span class="n">SMAC_GUI</span>
+<span class="kn">from</span> <span class="nn">..doc.sdynpy_latex</span> <span class="kn">import</span> <span class="n">figure</span> <span class="k">as</span> <span class="n">latex_figure</span><span class="p">,</span> <span class="n">table</span> <span class="k">as</span> <span class="n">latex_table</span>
+<span class="kn">from</span> <span class="nn">qtpy.QtCore</span> <span class="kn">import</span> <span class="n">Qt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<div class="viewcode-block" id="read_modal_fit_data"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html#sdynpy.modal.sdynpy_modal_test.read_modal_fit_data">[docs]</a><span class="k">def</span> <span class="nf">read_modal_fit_data</span><span class="p">(</span><span class="n">modal_fit_data</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Reads Modal Fit Data from PolyPy_GUI which contains modes and FRF data</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    modal_fit_data : str or NpzFile</span>
+<span class="sd">        Filename to or data loaded from a Modal Fit Data .npz file that is</span>
+<span class="sd">        saved from SDynPy&#39;s mode fitters.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    shapes : ShapeArray</span>
+<span class="sd">        The modes fit to the structure</span>
+<span class="sd">    experimental_frfs : TransferFunctionArray</span>
+<span class="sd">        The FRFs to which the modes were fit</span>
+<span class="sd">    resynthesized_frfs : TransferFunctionArray</span>
+<span class="sd">        FRFs resynthesized from the fit modes</span>
+<span class="sd">    residual_frfs : TransferFunctionArray</span>
+<span class="sd">        FRF contribution from the residual terms in the modal fit.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">modal_fit_data</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+        <span class="n">modal_fit_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">modal_fit_data</span><span class="p">)</span>
+    <span class="n">shapes</span> <span class="o">=</span> <span class="n">modal_fit_data</span><span class="p">[</span><span class="s1">&#39;shapes&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">ShapeArray</span><span class="p">)</span>
+    <span class="n">experimental_frfs</span> <span class="o">=</span> <span class="n">modal_fit_data</span><span class="p">[</span><span class="s1">&#39;frfs&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">TransferFunctionArray</span><span class="p">)</span>
+    <span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="n">modal_fit_data</span><span class="p">[</span><span class="s1">&#39;frfs_resynth&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">TransferFunctionArray</span><span class="p">)</span>
+    <span class="n">residual_frfs</span> <span class="o">=</span> <span class="n">modal_fit_data</span><span class="p">[</span><span class="s1">&#39;frfs_residual&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">TransferFunctionArray</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">experimental_frfs</span><span class="p">,</span> <span class="n">resynthesized_frfs</span><span class="p">,</span> <span class="n">residual_frfs</span></div>
+
+<div class="viewcode-block" id="ModalTest"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest">[docs]</a><span class="k">class</span> <span class="nc">ModalTest</span><span class="p">:</span>
+    
+<div class="viewcode-block" id="ModalTest.__init__"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                 <span class="n">geometry</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">time_histories</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">autopower_spectra</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">frfs</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">coherence</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">fit_modes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">response_unit</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">reference_unit</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">rigid_body_shapes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                 <span class="n">channel_table</span> <span class="o">=</span> <span class="kc">None</span>
+                 <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="o">=</span> <span class="n">response_unit</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="o">=</span> <span class="n">reference_unit</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="n">geometry</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span> <span class="o">=</span> <span class="n">time_histories</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span> <span class="o">=</span> <span class="n">autopower_spectra</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="o">=</span> <span class="n">frfs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="o">=</span> <span class="n">coherence</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span> <span class="o">=</span> <span class="n">fit_modes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="n">resynthesized_frfs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rigid_body_shapes</span> <span class="o">=</span> <span class="n">rigid_body_shapes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channel_table</span> <span class="o">=</span> <span class="n">channel_table</span>
+        <span class="c1"># Handles for GUIs that exist</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">smacgui</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">modeshape_plotter</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">deflectionshape_plotter</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Quantities of interest for computing spectral quantities</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_samples_per_frame</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_averages</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">start_time</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">end_time</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_channel_index</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_slope</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_level</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pretrigger</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">overlap</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frf_estimator</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Quantities of interest from Curve Fitters</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes_information</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="c1"># Figures to save to documentation</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span> <span class="o">=</span> <span class="p">{}</span></div>
+        
+<div class="viewcode-block" id="ModalTest.set_rigid_body_shapes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes">[docs]</a>    <span class="k">def</span> <span class="nf">set_rigid_body_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">rigid_body_shapes</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rigid_body_shapes</span> <span class="o">=</span> <span class="n">rigid_body_shapes</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_units"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_units">[docs]</a>    <span class="k">def</span> <span class="nf">set_units</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">response_unit</span><span class="p">,</span> <span class="n">reference_unit</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="o">=</span> <span class="n">response_unit</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="o">=</span> <span class="n">reference_unit</span></div>
+        
+<div class="viewcode-block" id="ModalTest.set_geometry"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry">[docs]</a>    <span class="k">def</span> <span class="nf">set_geometry</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">geometry</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="n">geometry</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_time_histories"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories">[docs]</a>    <span class="k">def</span> <span class="nf">set_time_histories</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">time_histories</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span> <span class="o">=</span> <span class="n">time_histories</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_autopower_spectra"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra">[docs]</a>    <span class="k">def</span> <span class="nf">set_autopower_spectra</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">autopower_spectra</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span> <span class="o">=</span> <span class="n">autopower_spectra</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_frfs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs">[docs]</a>    <span class="k">def</span> <span class="nf">set_frfs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">frfs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="o">=</span> <span class="n">frfs</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_coherence"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence">[docs]</a>    <span class="k">def</span> <span class="nf">set_coherence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coherence</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="o">=</span> <span class="n">coherence</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_fit_modes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes">[docs]</a>    <span class="k">def</span> <span class="nf">set_fit_modes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fit_modes</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span> <span class="o">=</span> <span class="n">fit_modes</span></div>
+        
+<div class="viewcode-block" id="ModalTest.set_resynthesized_frfs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs">[docs]</a>    <span class="k">def</span> <span class="nf">set_resynthesized_frfs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resynthesized_frfs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="n">resynthesized_frfs</span></div>
+    
+<div class="viewcode-block" id="ModalTest.set_channel_table"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table">[docs]</a>    <span class="k">def</span> <span class="nf">set_channel_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">channel_table</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">channel_table</span> <span class="o">=</span> <span class="n">channel_table</span></div>
+    
+<div class="viewcode-block" id="ModalTest.compute_spectral_quantities_SignalProcessingGUI"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI">[docs]</a>    <span class="k">def</span> <span class="nf">compute_spectral_quantities_SignalProcessingGUI</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Time Histories must be defined in order to compute spectral quantities&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span> <span class="o">=</span> <span class="n">SignalProcessingGUI</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">geometry</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span></div>
+    
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">response_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">response_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="o">.</span><span class="n">size</span><span class="p">)[</span>
+            <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">in1d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="o">.</span><span class="n">size</span><span class="p">),</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span><span class="p">)]</span>
+        <span class="k">return</span> <span class="n">response_indices</span>
+    
+<div class="viewcode-block" id="ModalTest.retrieve_spectral_quantities_SignalProcessingGUI"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI">[docs]</a>    <span class="k">def</span> <span class="nf">retrieve_spectral_quantities_SignalProcessingGUI</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">referencesSelector</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="p">(</span>
+            <span class="n">Qt</span><span class="o">.</span><span class="n">UserRole</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">referencesSelector</span><span class="o">.</span><span class="n">count</span><span class="p">())])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_samples_per_frame</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">frameSizeSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_averages</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">framesSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">start_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">startTimeDoubleSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">end_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">endTimeDoubleSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">typeComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_channel_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">channelComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_slope</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">slopeComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_level</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">levelDoubleSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pretrigger</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">pretriggerDoubleSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">overlap</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">overlapDoubleSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span><span class="o">/</span><span class="mi">100</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">windowComboBox</span><span class="o">.</span><span class="n">currentText</span><span class="p">()</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frf_estimator</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentText</span><span class="p">()</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">frfCheckBox</span><span class="o">.</span><span class="n">setChecked</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">autospectraCheckBox</span><span class="o">.</span><span class="n">setChecked</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">coherenceCheckBox</span><span class="o">.</span><span class="n">setChecked</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">compute</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">autospectra_data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra_reference_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">frf_data</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">spgui</span><span class="o">.</span><span class="n">coherence_data</span></div>
+        
+<div class="viewcode-block" id="ModalTest.compute_spectral_quantities"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities">[docs]</a>    <span class="k">def</span> <span class="nf">compute_spectral_quantities</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span> <span class="n">reference_indices</span><span class="p">,</span> <span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">,</span> <span class="n">num_samples_per_frame</span><span class="p">,</span>
+            <span class="n">overlap</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">frf_estimator</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Time Histories must be defined in order to compute spectral quantities&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span> <span class="o">=</span> <span class="n">reference_indices</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra_reference_indices</span> <span class="o">=</span> <span class="n">reference_indices</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_samples_per_frame</span> <span class="o">=</span> <span class="n">num_samples_per_frame</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">start_time</span> <span class="o">=</span> <span class="n">start_time</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">end_time</span> <span class="o">=</span> <span class="n">end_time</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_channel_index</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_slope</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_level</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pretrigger</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">overlap</span> <span class="o">=</span> <span class="n">overlap</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="n">window</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frf_estimator</span> <span class="o">=</span> <span class="n">frf_estimator</span>
+        
+        <span class="c1"># Separate into references and responses</span>
+        <span class="n">time_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="o">.</span><span class="n">extract_elements_by_abscissa</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">)</span>
+        <span class="n">references</span> <span class="o">=</span> <span class="n">time_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span><span class="p">]</span>
+        
+        <span class="n">responses</span> <span class="o">=</span> <span class="n">time_data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">response_indices</span><span class="p">]</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_averages</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">time_data</span><span class="o">.</span><span class="n">num_elements</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">overlap</span><span class="p">)</span><span class="o">*</span><span class="n">num_samples_per_frame</span><span class="p">)</span><span class="o">//</span><span class="n">num_samples_per_frame</span> <span class="o">+</span> <span class="mi">1</span>
+        
+        <span class="c1"># Compute FRFs, Coherence, and Autospectra</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="o">=</span> <span class="n">TransferFunctionArray</span><span class="o">.</span><span class="n">from_time_data</span><span class="p">(</span>
+            <span class="n">references</span><span class="p">,</span> <span class="n">responses</span><span class="p">,</span> <span class="n">num_samples_per_frame</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span> <span class="n">frf_estimator</span><span class="p">,</span>
+            <span class="n">window</span><span class="p">)</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span> <span class="o">=</span> <span class="n">PowerSpectralDensityArray</span><span class="o">.</span><span class="n">from_time_data</span><span class="p">(</span>
+            <span class="n">time_data</span><span class="p">,</span> <span class="n">num_samples_per_frame</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">only_asds</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="o">=</span> <span class="n">MultipleCoherenceArray</span><span class="o">.</span><span class="n">from_time_data</span><span class="p">(</span>
+                <span class="n">responses</span><span class="p">,</span> <span class="n">num_samples_per_frame</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> 
+                <span class="n">references</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="o">=</span> <span class="n">CoherenceArray</span><span class="o">.</span><span class="n">from_time_data</span><span class="p">(</span>
+                <span class="n">responses</span><span class="p">,</span> <span class="n">num_samples_per_frame</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">references</span><span class="p">)</span></div>
+        
+<div class="viewcode-block" id="ModalTest.define_spectral_processing_parameters"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">define_spectral_processing_parameters</span><span class="p">(</span>
+            <span class="bp">self</span><span class="p">,</span> <span class="n">reference_indices</span><span class="p">,</span> <span class="n">num_samples_per_frame</span><span class="p">,</span> <span class="n">num_averages</span><span class="p">,</span> 
+            <span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">,</span> <span class="n">trigger</span><span class="p">,</span> <span class="n">trigger_channel_index</span><span class="p">,</span>
+            <span class="n">trigger_slope</span><span class="p">,</span> <span class="n">trigger_level</span><span class="p">,</span> <span class="n">pretrigger</span><span class="p">,</span> <span class="n">overlap</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span>
+            <span class="n">frf_estimator</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span> <span class="o">=</span> <span class="n">reference_indices</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra_reference_indices</span> <span class="o">=</span> <span class="n">reference_indices</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_samples_per_frame</span> <span class="o">=</span> <span class="n">num_samples_per_frame</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_averages</span> <span class="o">=</span> <span class="n">num_averages</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">start_time</span> <span class="o">=</span> <span class="n">start_time</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">end_time</span> <span class="o">=</span> <span class="n">end_time</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger</span> <span class="o">=</span> <span class="n">trigger</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_channel_index</span> <span class="o">=</span> <span class="n">trigger_channel_index</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_slope</span> <span class="o">=</span> <span class="n">trigger_slope</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trigger_level</span> <span class="o">=</span> <span class="n">trigger_level</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pretrigger</span> <span class="o">=</span> <span class="n">pretrigger</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">overlap</span> <span class="o">=</span> <span class="n">overlap</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="n">window</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frf_estimator</span> <span class="o">=</span> <span class="n">frf_estimator</span></div>
+    
+<div class="viewcode-block" id="ModalTest.fit_modes_PolyPy"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy">[docs]</a>    <span class="k">def</span> <span class="nf">fit_modes_PolyPy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs must be defined in order to fit modes&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span> <span class="o">=</span> <span class="n">PolyPy_GUI</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.retrieve_modes_PolyPy"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy">[docs]</a>    <span class="k">def</span> <span class="nf">retrieve_modes_PolyPy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">compute_shapes</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">shapes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes_information</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;info&#39;</span><span class="p">:[</span>
+            <span class="s1">&#39;Modes were fit to the data in </span><span class="si">{:}</span><span class="s1"> bands.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">stability_diagrams</span><span class="p">))]}</span>
+        <span class="n">figure_index</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="c1"># Now go through and get polynomial data from each frequency range</span>
+        <span class="k">for</span> <span class="n">stability_diagram</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">stability_diagrams</span><span class="p">:</span>
+            <span class="c1"># First get data about the stabilization diagram</span>
+            <span class="n">min_frequency</span> <span class="o">=</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">polypy</span><span class="o">.</span><span class="n">min_frequency</span>
+            <span class="n">max_frequency</span> <span class="o">=</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">polypy</span><span class="o">.</span><span class="n">max_frequency</span>
+            <span class="n">min_order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">stability_diagram</span><span class="o">.</span><span class="n">polypy</span><span class="o">.</span><span class="n">polynomial_orders</span><span class="p">)</span>
+            <span class="n">max_order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">stability_diagram</span><span class="o">.</span><span class="n">polypy</span><span class="o">.</span><span class="n">polynomial_orders</span><span class="p">)</span>
+            <span class="n">num_selected_poles</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">stability_diagram</span><span class="o">.</span><span class="n">selected_poles</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes_information</span><span class="p">[</span><span class="s1">&#39;info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="p">(</span><span class="s1">&#39;The frequency band from </span><span class="si">{:0.2f}</span><span class="s1"> to </span><span class="si">{:0.2f}</span><span class="s1"> was analyzed with polynomials from order </span><span class="si">{:}</span><span class="s1"> to </span><span class="si">{:}</span><span class="s1">.  &#39;</span><span class="o">+</span>
+                 <span class="s1">&#39;</span><span class="si">{:}</span><span class="s1"> poles were selected from this band.  The stabilization diagram is shown in {{figure</span><span class="si">{:}</span><span class="s1">ref:}}.&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">min_frequency</span><span class="p">,</span> <span class="n">max_frequency</span><span class="p">,</span><span class="n">min_order</span><span class="p">,</span><span class="n">max_order</span><span class="p">,</span><span class="n">num_selected_poles</span><span class="p">,</span><span class="n">figure_index</span><span class="p">))</span>
+            <span class="c1"># Go through and save out a figure for each stabilization diagram</span>
+            <span class="n">tempdir</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">mkdtemp</span><span class="p">()</span>
+            <span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tempdir</span><span class="p">,</span><span class="s1">&#39;stability_diagram.png&#39;</span><span class="p">)</span>
+            <span class="c1"># Turn off last highlighted closest mode</span>
+            <span class="k">if</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">previous_closest_marker_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">previous_closest_marker_index</span> <span class="ow">in</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">selected_poles</span><span class="p">:</span>
+                    <span class="n">order_index</span><span class="p">,</span> <span class="n">pole_index</span> <span class="o">=</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">pole_indices</span><span class="p">[</span><span class="n">stability_diagram</span><span class="o">.</span><span class="n">previous_closest_marker_index</span><span class="p">]</span>
+                    <span class="n">pole</span> <span class="o">=</span> <span class="n">stability_diagram</span><span class="o">.</span><span class="n">polypy</span><span class="o">.</span><span class="n">pole_list</span><span class="p">[</span><span class="n">order_index</span><span class="p">][</span><span class="n">pole_index</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">pole</span><span class="p">[</span><span class="s1">&#39;part_stable&#39;</span><span class="p">]:</span>
+                        <span class="n">brush</span> <span class="o">=</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+                    <span class="k">elif</span> <span class="n">pole</span><span class="p">[</span><span class="s1">&#39;damp_stable&#39;</span><span class="p">]</span> <span class="ow">or</span> <span class="n">pole</span><span class="p">[</span><span class="s1">&#39;freq_stable&#39;</span><span class="p">]:</span>
+                        <span class="n">brush</span> <span class="o">=</span> <span class="s1">&#39;b&#39;</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">brush</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span>
+                    <span class="n">stability_diagram</span><span class="o">.</span><span class="n">pole_markers</span><span class="p">[</span><span class="n">stability_diagram</span><span class="o">.</span><span class="n">previous_closest_marker_index</span><span class="p">]</span><span class="o">.</span><span class="n">setBrush</span><span class="p">(</span><span class="n">brush</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">stability_diagram</span><span class="o">.</span><span class="n">pole_markers</span><span class="p">[</span><span class="n">stability_diagram</span><span class="o">.</span><span class="n">previous_closest_marker_index</span><span class="p">]</span><span class="o">.</span><span class="n">setBrush</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+            <span class="n">stability_diagram</span><span class="o">.</span><span class="n">pole_plot</span><span class="o">.</span><span class="n">writeImage</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">filename</span><span class="p">,</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="n">image_bytes</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes_information</span><span class="p">[</span><span class="s1">&#39;figure&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">figure_index</span><span class="p">)]</span> <span class="o">=</span> <span class="n">image_bytes</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes_information</span><span class="p">[</span><span class="s1">&#39;figure&#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">figure_index</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;caption&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="s1">&#39;Stabilization Diagram from </span><span class="si">{:0.2f}</span><span class="s1"> to </span><span class="si">{:0.2f}</span><span class="s1"> Hz.  &#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">min_frequency</span><span class="p">,</span><span class="n">max_frequency</span><span class="p">)</span> <span class="o">+</span> 
+                    <span class="s1">&#39;Red Xs represent unstable poles.  &#39;</span> <span class="o">+</span> 
+                    <span class="s1">&#39;Blue Triangles represent that the frequency has stablized.  &#39;</span> <span class="o">+</span> 
+                    <span class="s1">&#39;Blue Squares represent that the frequency and damping have stablized.  &#39;</span> <span class="o">+</span> 
+                    <span class="s1">&#39;Green circles represent that the frequency, damping, and shape have stablized.  &#39;</span> <span class="o">+</span> 
+                    <span class="s1">&#39;Solid markers are poles that were selected in the final mode set.&#39;</span><span class="p">)</span>
+            <span class="n">figure_index</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">removedirs</span><span class="p">(</span><span class="n">tempdir</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes_information</span><span class="p">[</span><span class="s1">&#39;info&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="p">(</span><span class="s1">&#39;Complex Modes&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">complex_modes_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;Normal Modes&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; were fit to the data.  &#39;</span> <span class="o">+</span> 
+            <span class="p">(</span><span class="s1">&#39;Residuals&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">use_residuals_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;No residuals&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; were used when fitting mode shapes.  &#39;</span> <span class="o">+</span>
+            <span class="p">(</span><span class="s1">&#39;All frequency lines were used to fit mode shapes.&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">all_frequency_lines_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">()</span> <span class="k">else</span>
+            <span class="s1">&#39;Mode shapes were fit using </span><span class="si">{:}</span><span class="s1"> frequency lines around each resonance, and </span><span class="si">{:}</span><span class="s1"> frequency lines were used to fit residuals.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">lines_at_resonance_spinbox</span><span class="o">.</span><span class="n">value</span><span class="p">(),</span><span class="bp">self</span><span class="o">.</span><span class="n">ppgui</span><span class="o">.</span><span class="n">lines_at_residuals_spinbox</span><span class="o">.</span><span class="n">value</span><span class="p">())))</span></div>
+    
+<div class="viewcode-block" id="ModalTest.fit_modes_SMAC"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC">[docs]</a>    <span class="k">def</span> <span class="nf">fit_modes_SMAC</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;SMAC has not been implemented yet&#39;</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.retrieve_modes_SMAC"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC">[docs]</a>    <span class="k">def</span> <span class="nf">retrieve_modes_SMAC</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;SMAC has not been implemented yet&#39;</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.edit_mode_comments"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments">[docs]</a>    <span class="k">def</span> <span class="nf">edit_mode_comments</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Modes have not yet been fit or assigned.&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">edit_comments</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.compute_resynthesized_frfs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs">[docs]</a>    <span class="k">def</span> <span class="nf">compute_resynthesized_frfs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">compute_frf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
+                                                             <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">),</span>
+                                                             <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">),</span>
+                                                             <span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_reference_autospectra"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra">[docs]</a>    <span class="k">def</span> <span class="nf">plot_reference_autospectra</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Autopower Spectra have not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="n">reference_apsd</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra_reference_indices</span><span class="p">]</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">reference_apsd</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">one_axis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">():</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">a</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">get_ylabel</span><span class="p">()</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">(</span><span class="si">{:}</span><span class="s1">$^2$/Hz)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+            <span class="n">a</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_drive_point_frfs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs">[docs]</a>    <span class="k">def</span> <span class="nf">plot_drive_point_frfs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">part</span><span class="o">=</span><span class="s1">&#39;imag&#39;</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">get_drive_points</span><span class="p">()</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">one_axis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">part</span><span class="o">=</span><span class="n">part</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">():</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">a</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">get_ylabel</span><span class="p">()</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">(</span><span class="si">{:}</span><span class="s1">/</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+            <span class="n">a</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_reciprocal_frfs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs">[docs]</a>    <span class="k">def</span> <span class="nf">plot_reciprocal_frfs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="n">reciprocal_frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">get_reciprocal_data</span><span class="p">()</span>
+        <span class="n">axes</span> <span class="o">=</span> <span class="n">reciprocal_frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">one_axis</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot_kwargs</span><span class="o">=</span><span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">original_frf</span><span class="p">,</span> <span class="n">reciprocal_frf</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span><span class="o">*</span><span class="n">reciprocal_frfs</span><span class="p">):</span>
+            <span class="n">reciprocal_frf</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="o">**</span><span class="n">plot_kwargs</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span>
+                <span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">coord</span><span class="p">)</span> <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="n">original_frf</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]),</span>
+                <span class="s1">&#39;/&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">coord</span><span class="p">)</span> <span class="k">for</span> <span class="n">coord</span> <span class="ow">in</span> <span class="n">reciprocal_frf</span><span class="o">.</span><span class="n">coordinate</span><span class="p">])])</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">ax</span><span class="o">.</span><span class="n">get_ylabel</span><span class="p">()</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">(</span><span class="si">{:}</span><span class="s1">/</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">axes</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span><span class="n">axes</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_coherence_image"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image">[docs]</a>    <span class="k">def</span> <span class="nf">plot_coherence_image</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Coherence has not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span><span class="o">.</span><span class="n">plot_image</span><span class="p">(</span><span class="n">colorbar_min</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="n">colorbar_max</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Degree of Freedom&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ax</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">ax</span></div>
+        
+<div class="viewcode-block" id="ModalTest.plot_drive_point_frf_coherence"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence">[docs]</a>    <span class="k">def</span> <span class="nf">plot_drive_point_frf_coherence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Coherence has not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="n">frf_ax</span><span class="p">,</span> <span class="n">coh_ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">get_drive_points</span><span class="p">()</span><span class="o">.</span><span class="n">plot_with_coherence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">coherence</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">plot_kwargs</span><span class="p">,</span> <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">frf_ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">():</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">a</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">get_ylabel</span><span class="p">()</span><span class="o">+</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">(</span><span class="si">{:}</span><span class="s1">/</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+            <span class="n">a</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">frf_ax</span><span class="o">.</span><span class="n">flatten</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">frf_ax</span><span class="p">,</span> <span class="n">coh_ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_cmif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif">[docs]</a>    <span class="k">def</span> <span class="nf">plot_cmif</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">measured</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">resynthesized</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">mark_modes</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                  <span class="n">measured_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">resynthesized_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                  <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Plots the complex mode indicator function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        measured : bool, optional</span>
+<span class="sd">            If True, plots the measured MIF. The default is True.</span>
+<span class="sd">        resynthesized : bool, optional</span>
+<span class="sd">            If True, plots resynthesized MIF. The default is False.</span>
+<span class="sd">        mark_modes : bool, optional</span>
+<span class="sd">            If True, plots a vertical line at the frequency of each mode. The</span>
+<span class="sd">            default is False.</span>
+<span class="sd">        measured_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the measured</span>
+<span class="sd">            data is plotted. The default is {}.</span>
+<span class="sd">        resynthesized_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the </span>
+<span class="sd">            resynthesized data is plotted. The default is {}.</span>
+<span class="sd">        subplots_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the figure</span>
+<span class="sd">            and axes are created.  This is passed to the plt.subplots function.</span>
+<span class="sd">            The default is {}.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if a required data has not been computed or assigned yet.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        fig : matplotlib.figure.Figure</span>
+<span class="sd">            A reference to the figure on which the plot is plotted.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            A reference to the axes on which the plot is plotted.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">()</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">measured_plot_kwargs</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Resynthesized FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">()</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">resynthesized_plot_kwargs</span><span class="p">,</span>
+                      <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">frequency</span>
+                      <span class="k">if</span> <span class="n">mark_modes</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;linestyle&#39;</span><span class="p">:</span><span class="s1">&#39;--&#39;</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">},</span>
+                      <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{abscissa:0.1f}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span> <span class="ow">and</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="n">cmif</span><span class="o">.</span><span class="n">size</span><span class="p">]],[</span><span class="s1">&#39;Measured&#39;</span><span class="p">,</span><span class="s1">&#39;Resynthesized&#39;</span><span class="p">],</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Complex Mode Indicator Function (</span><span class="si">{:}</span><span class="s1">/</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Complex Mode Indicator Function&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_qmif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif">[docs]</a>    <span class="k">def</span> <span class="nf">plot_qmif</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">measured</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">resynthesized</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">mark_modes</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                  <span class="n">measured_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">resynthesized_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                  <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Plots the complex mode indicator function computed from the imaginary</span>
+<span class="sd">        part of the FRFs</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        measured : bool, optional</span>
+<span class="sd">            If True, plots the measured MIF. The default is True.</span>
+<span class="sd">        resynthesized : bool, optional</span>
+<span class="sd">            If True, plots resynthesized MIF. The default is False.</span>
+<span class="sd">        mark_modes : bool, optional</span>
+<span class="sd">            If True, plots a vertical line at the frequency of each mode. The</span>
+<span class="sd">            default is False.</span>
+<span class="sd">        measured_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the measured</span>
+<span class="sd">            data is plotted. The default is {}.</span>
+<span class="sd">        resynthesized_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the </span>
+<span class="sd">            resynthesized data is plotted. The default is {}.</span>
+<span class="sd">        subplots_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the figure</span>
+<span class="sd">            and axes are created.  This is passed to the plt.subplots function.</span>
+<span class="sd">            The default is {}.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if a required data has not been computed or assigned yet.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        fig : matplotlib.figure.Figure</span>
+<span class="sd">            A reference to the figure on which the plot is plotted.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            A reference to the axes on which the plot is plotted.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span><span class="n">part</span><span class="o">=</span><span class="s1">&#39;imag&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">measured_plot_kwargs</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Resynthesized FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span><span class="n">part</span><span class="o">=</span><span class="s1">&#39;imag&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">resynthesized_plot_kwargs</span><span class="p">,</span>
+                      <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">frequency</span>
+                      <span class="k">if</span> <span class="n">mark_modes</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;linestyle&#39;</span><span class="p">:</span><span class="s1">&#39;--&#39;</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">},</span>
+                      <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{abscissa:0.1f}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span> <span class="ow">and</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="n">cmif</span><span class="o">.</span><span class="n">size</span><span class="p">]],[</span><span class="s1">&#39;Measured&#39;</span><span class="p">,</span><span class="s1">&#39;Resynthesized&#39;</span><span class="p">],</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Quadrature Mode Indicator Function (</span><span class="si">{:}</span><span class="s1">/</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Quadrature Mode Indicator Function&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_psmif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif">[docs]</a>    <span class="k">def</span> <span class="nf">plot_psmif</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">measured</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">resynthesized</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">mark_modes</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                   <span class="n">measured_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">resynthesized_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                   <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Plots the first singular value of the complex mode indicator function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        measured : bool, optional</span>
+<span class="sd">            If True, plots the measured MIF. The default is True.</span>
+<span class="sd">        resynthesized : bool, optional</span>
+<span class="sd">            If True, plots resynthesized MIF. The default is False.</span>
+<span class="sd">        mark_modes : bool, optional</span>
+<span class="sd">            If True, plots a vertical line at the frequency of each mode. The</span>
+<span class="sd">            default is False.</span>
+<span class="sd">        measured_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the measured</span>
+<span class="sd">            data is plotted. The default is {}.</span>
+<span class="sd">        resynthesized_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the </span>
+<span class="sd">            resynthesized data is plotted. The default is {}.</span>
+<span class="sd">        subplots_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the figure</span>
+<span class="sd">            and axes are created.  This is passed to the plt.subplots function.</span>
+<span class="sd">            The default is {}.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if a required data has not been computed or assigned yet.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        fig : matplotlib.figure.Figure</span>
+<span class="sd">            A reference to the figure on which the plot is plotted.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            A reference to the axes on which the plot is plotted.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">()[:</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">measured_plot_kwargs</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Resynthesized FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">()[:</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">resynthesized_plot_kwargs</span><span class="p">,</span>
+                      <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">frequency</span>
+                      <span class="k">if</span> <span class="n">mark_modes</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;linestyle&#39;</span><span class="p">:</span><span class="s1">&#39;--&#39;</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">},</span>
+                      <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{abscissa:0.1f}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span> <span class="ow">and</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="n">cmif</span><span class="o">.</span><span class="n">size</span><span class="p">]],[</span><span class="s1">&#39;Measured&#39;</span><span class="p">,</span><span class="s1">&#39;Resynthesized&#39;</span><span class="p">],</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Principal Singular Value</span><span class="se">\n</span><span class="s1">Mode Indicator Function (</span><span class="si">{:}</span><span class="s1">/</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">response_unit</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_unit</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Principal Singular Value</span><span class="se">\n</span><span class="s1">Mode Indicator Function&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_nmif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif">[docs]</a>    <span class="k">def</span> <span class="nf">plot_nmif</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">measured</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">resynthesized</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">mark_modes</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                  <span class="n">measured_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">resynthesized_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                  <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Plots the normal mode indicator function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        measured : bool, optional</span>
+<span class="sd">            If True, plots the measured MIF. The default is True.</span>
+<span class="sd">        resynthesized : bool, optional</span>
+<span class="sd">            If True, plots resynthesized MIF. The default is False.</span>
+<span class="sd">        mark_modes : bool, optional</span>
+<span class="sd">            If True, plots a vertical line at the frequency of each mode. The</span>
+<span class="sd">            default is False.</span>
+<span class="sd">        measured_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the measured</span>
+<span class="sd">            data is plotted. The default is {}.</span>
+<span class="sd">        resynthesized_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the </span>
+<span class="sd">            resynthesized data is plotted. The default is {}.</span>
+<span class="sd">        subplots_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the figure</span>
+<span class="sd">            and axes are created.  This is passed to the plt.subplots function.</span>
+<span class="sd">            The default is {}.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if a required data has not been computed or assigned yet.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        fig : matplotlib.figure.Figure</span>
+<span class="sd">            A reference to the figure on which the plot is plotted.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            A reference to the axes on which the plot is plotted.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">()</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">measured_plot_kwargs</span><span class="p">)</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Resynthesized FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">()</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">resynthesized_plot_kwargs</span><span class="p">,</span>
+                      <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">frequency</span>
+                      <span class="k">if</span> <span class="n">mark_modes</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;linestyle&#39;</span><span class="p">:</span><span class="s1">&#39;--&#39;</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">},</span>
+                      <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{abscissa:0.1f}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span> <span class="ow">and</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="n">cmif</span><span class="o">.</span><span class="n">size</span><span class="p">]],[</span><span class="s1">&#39;Measured&#39;</span><span class="p">,</span><span class="s1">&#39;Resynthesized&#39;</span><span class="p">],</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Normal Mode Indicator Function&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+
+<div class="viewcode-block" id="ModalTest.plot_mmif"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif">[docs]</a>    <span class="k">def</span> <span class="nf">plot_mmif</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">measured</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">resynthesized</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">mark_modes</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                  <span class="n">measured_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span> <span class="n">resynthesized_plot_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                  <span class="n">subplots_kwargs</span> <span class="o">=</span> <span class="p">{}):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Plots the multi mode indicator function</span>
+
+<span class="sd">        Parameters</span>
+<span class="sd">        ----------</span>
+<span class="sd">        measured : bool, optional</span>
+<span class="sd">            If True, plots the measured MIF. The default is True.</span>
+<span class="sd">        resynthesized : bool, optional</span>
+<span class="sd">            If True, plots resynthesized MIF. The default is False.</span>
+<span class="sd">        mark_modes : bool, optional</span>
+<span class="sd">            If True, plots a vertical line at the frequency of each mode. The</span>
+<span class="sd">            default is False.</span>
+<span class="sd">        measured_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the measured</span>
+<span class="sd">            data is plotted. The default is {}.</span>
+<span class="sd">        resynthesized_plot_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the </span>
+<span class="sd">            resynthesized data is plotted. The default is {}.</span>
+<span class="sd">        subplots_kwargs : dict, optional</span>
+<span class="sd">            Dictionary containing keyword arguments to specify how the figure</span>
+<span class="sd">            and axes are created.  This is passed to the plt.subplots function.</span>
+<span class="sd">            The default is {}.</span>
+
+<span class="sd">        Raises</span>
+<span class="sd">        ------</span>
+<span class="sd">        ValueError</span>
+<span class="sd">            Raised if a required data has not been computed or assigned yet.</span>
+
+<span class="sd">        Returns</span>
+<span class="sd">        -------</span>
+<span class="sd">        fig : matplotlib.figure.Figure</span>
+<span class="sd">            A reference to the figure on which the plot is plotted.</span>
+<span class="sd">        ax : matplotlib.axes.Axes</span>
+<span class="sd">            A reference to the axes on which the plot is plotted.</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="o">**</span><span class="n">subplots_kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_mmif</span><span class="p">()</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">measured_plot_kwargs</span><span class="p">)</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ylim</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Resynthesized FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+            <span class="n">cmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">coordinate</span><span class="p">]</span><span class="o">.</span><span class="n">compute_mmif</span><span class="p">()</span>
+            <span class="n">cmif</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">plot_kwargs</span> <span class="o">=</span> <span class="n">resynthesized_plot_kwargs</span><span class="p">,</span>
+                      <span class="n">abscissa_markers</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="o">.</span><span class="n">frequency</span>
+                      <span class="k">if</span> <span class="n">mark_modes</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span> <span class="n">abscissa_marker_plot_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;linewidth&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;linestyle&#39;</span><span class="p">:</span><span class="s1">&#39;--&#39;</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;alpha&#39;</span><span class="p">:</span><span class="mf">0.5</span><span class="p">,</span>
+                                                                            <span class="s1">&#39;color&#39;</span><span class="p">:</span><span class="s1">&#39;k&#39;</span><span class="p">},</span>
+                      <span class="n">abscissa_marker_labels</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{abscissa:0.1f}</span><span class="s1">&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">ylim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="n">ylim</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">measured</span> <span class="ow">and</span> <span class="n">resynthesized</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ax</span><span class="o">.</span><span class="n">lines</span><span class="p">[</span><span class="n">cmif</span><span class="o">.</span><span class="n">size</span><span class="p">]],[</span><span class="s1">&#39;Measured&#39;</span><span class="p">,</span><span class="s1">&#39;Resynthesized&#39;</span><span class="p">],</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Multi Mode Indicator Function&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_deflection_shapes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes">[docs]</a>    <span class="k">def</span> <span class="nf">plot_deflection_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;FRFs have not yet been computed or assigned&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Geometry must be assigned to plot deflection shapes&#39;</span><span class="p">)</span>
+        <span class="n">frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">reshape_to_matrix</span><span class="p">()</span>
+        <span class="n">plotters</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">frf</span> <span class="ow">in</span> <span class="n">frfs</span><span class="o">.</span><span class="n">T</span><span class="p">:</span>
+            <span class="n">plotters</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">plot_deflection_shape</span><span class="p">(</span><span class="n">frf</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">plotters</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_mac"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac">[docs]</a>    <span class="k">def</span> <span class="nf">plot_mac</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">matrix_plot_args</span><span class="p">,</span> <span class="o">**</span><span class="n">matrix_plot_kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Modes have not yet been fit or assigned.&#39;</span><span class="p">)</span>
+        <span class="n">mac_matrix</span> <span class="o">=</span> <span class="n">mac</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">matrix_plot</span><span class="p">(</span><span class="n">mac_matrix</span><span class="p">,</span> <span class="o">*</span><span class="n">matrix_plot_args</span><span class="p">,</span> <span class="o">**</span><span class="n">matrix_plot_kwargs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ax</span><span class="o">.</span><span class="n">figure</span><span class="p">,</span> <span class="n">ax</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_modeshape"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape">[docs]</a>    <span class="k">def</span> <span class="nf">plot_modeshape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Modes have not yet been fit or assigned.&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Geometry must be assigned to plot deflection shapes&#39;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">plot_shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_modes</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.plot_figures_for_documentation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation">[docs]</a>    <span class="k">def</span> <span class="nf">plot_figures_for_documentation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+                                       <span class="n">plot_geometry</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">geometry_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_coordinate</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">coordinate_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_rigid_body_checks</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">rigid_body_checks_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_reference_autospectra</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">reference_autospectra_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_drive_point_frfs</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> 
+                                       <span class="n">drive_point_frfs_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_reciprocal_frfs</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">reciprocal_frfs_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_frf_coherence</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">frf_coherence_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_coherence_image</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">coherence_image_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_cmif</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">cmif_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_qmif</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                                       <span class="n">qmif_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_nmif</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                                       <span class="n">nmif_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_mmif</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                                       <span class="n">mmif_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_modeshapes</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                       <span class="n">modeshape_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="n">plot_mac</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> 
+                                       <span class="n">mac_kwargs</span> <span class="o">=</span> <span class="p">{},</span>
+                                       <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">if</span> <span class="n">plot_geometry</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot geometry; geometry is undefined.&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">plotter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">**</span><span class="n">geometry_kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;geometry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plotter</span>
+        <span class="k">if</span> <span class="n">plot_coordinate</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot coordinates; geometry is undefined.&#39;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot coordinates; time_histories is undefined&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="s1">&#39;plot_kwargs&#39;</span> <span class="ow">in</span> <span class="n">coordinate_kwargs</span><span class="p">:</span>
+                    <span class="n">coordinate_kwargs</span><span class="p">[</span><span class="s1">&#39;plot_kwargs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">geometry_kwargs</span>
+                <span class="c1"># Check and see if the references are defined</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">plotter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">plot_coordinate</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">response_indices</span><span class="p">]</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+                        <span class="o">**</span><span class="n">coordinate_kwargs</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;response_coordinate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plotter</span>
+                    <span class="n">plotter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">plot_coordinate</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_indices</span><span class="p">]</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+                        <span class="o">**</span><span class="n">coordinate_kwargs</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;reference_coordinate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plotter</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">plotter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="o">.</span><span class="n">plot_coordinate</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">time_histories</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
+                        <span class="o">**</span><span class="n">coordinate_kwargs</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;coordinate&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">plotter</span>
+        <span class="k">if</span> <span class="n">plot_rigid_body_checks</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">geometry</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot rigid body checks; geometry is undefined.&#39;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">rigid_body_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot rigid body checks; rigid_body_shapes is undefined.&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">supicious_channels</span><span class="p">,</span> <span class="o">*</span><span class="n">figures</span> <span class="o">=</span> <span class="n">rigid_body_check</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">geometry</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">rigid_body_shapes</span><span class="p">,</span> 
+                                                                <span class="n">return_figures</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">rigid_body_checks_kwargs</span><span class="p">)</span>
+                <span class="n">num_figs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rigid_body_shapes</span><span class="o">.</span><span class="n">size</span><span class="o">+</span><span class="mi">1</span>
+                <span class="n">figures</span> <span class="o">=</span> <span class="n">figures</span><span class="p">[</span><span class="o">-</span><span class="n">num_figs</span><span class="p">:]</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_figs</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;rigid_body_complex_plane_</span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span> <span class="o">=</span> <span class="n">figures</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;rigid_body_residuals&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">figures</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">plot_reference_autospectra</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">autopower_spectra</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot reference autospectra; autopower_spectra is undefined&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">plot_reference_autospectra</span><span class="p">(</span><span class="o">**</span><span class="n">reference_autospectra_kwargs</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;reference_autospectra&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span>
+        <span class="k">if</span> <span class="n">plot_drive_point_frfs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot drive point FRFs; frfs is undefined&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;part&#39;</span><span class="p">:</span><span class="s1">&#39;imag&#39;</span><span class="p">}</span>
+                <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">drive_point_frfs_kwargs</span><span class="p">)</span>
+                <span class="n">fig</span><span class="p">,</span><span class="n">ax</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">plot_drive_point_frfs</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">documentation_figures</span><span class="p">[</span><span class="s1">&#39;drive_point_frf&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fig</span>
+        <span class="k">if</span> <span class="n">plot_frf_coherence</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot FRF coherence; frfs is undefined&#39;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">coherence</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Could not plot FRF coherence; coherence is undefined&#39;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">pass</span></div>
+
+<div class="viewcode-block" id="ModalTest.create_documentation_latex"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex">[docs]</a>    <span class="k">def</span> <span class="nf">create_documentation_latex</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mif_plots</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;cmif&#39;</span><span class="p">],</span> 
+                                   <span class="n">latex_root</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">figure_folder</span> <span class="o">=</span> <span class="s1">&#39;figures&#39;</span><span class="p">,</span>
+                                   <span class="n">document_filename</span> <span class="o">=</span> <span class="s1">&#39;include.tex&#39;</span><span class="p">,</span>
+                                   <span class="n">appendix_filename</span> <span class="o">=</span> <span class="s1">&#39;appendix.tex&#39;</span><span class="p">,</span>
+                                   <span class="n">frequency_format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.1f}</span><span class="s1">&#39;</span><span class="p">,</span>
+                                   <span class="n">damping_format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="se">\\</span><span class="s1">%&#39;</span><span class="p">,</span>
+                                   <span class="n">comment1_header</span> <span class="o">=</span> <span class="s1">&#39;Description&#39;</span><span class="p">,</span>
+                                   <span class="n">comment2_header</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                                   <span class="n">comment3_header</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                                   <span class="n">comment4_header</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                                   <span class="n">comment5_header</span> <span class="o">=</span> <span class="kc">None</span>
+                                   
+                                   <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Not Implemented Yet&#39;</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.create_documentation_word"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word">[docs]</a>    <span class="k">def</span> <span class="nf">create_documentation_word</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Not Implemented Yet&#39;</span><span class="p">)</span></div>
+    
+<div class="viewcode-block" id="ModalTest.create_documentation_pptx"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx">[docs]</a>    <span class="k">def</span> <span class="nf">create_documentation_pptx</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Not Implemented Yet&#39;</span><span class="p">)</span></div></div>
+    
+    
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f5a265a2500>
+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/_modules/sdynpy/modal/sdynpy_modeshape.html b/_modules/sdynpy/modal/sdynpy_modeshape.html
index 7a6c47a..5d4735d 100644
--- a/_modules/sdynpy/modal/sdynpy_modeshape.html
+++ b/_modules/sdynpy/modal/sdynpy_modeshape.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_modeshape &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_modeshape &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -112,6 +112,7 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
 <span class="kn">from</span> <span class="nn">..core.sdynpy_shape</span> <span class="kn">import</span> <span class="n">shape_array</span>
 <span class="kn">from</span> <span class="nn">..core.sdynpy_coordinate</span> <span class="kn">import</span> <span class="n">CoordinateArray</span>
 <span class="kn">from</span> <span class="nn">..signal_processing.sdynpy_complex</span> <span class="kn">import</span> <span class="n">collapse_complex_to_real</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
 
 
 <div class="viewcode-block" id="ShapeSelection"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modeshape.ShapeSelection.html#sdynpy.modal.sdynpy_modeshape.ShapeSelection">[docs]</a><span class="k">class</span> <span class="nc">ShapeSelection</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
@@ -119,6 +120,7 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
     <span class="n">DRIVE_POINT_COEFFICIENT</span> <span class="o">=</span> <span class="mi">1</span>
     <span class="n">PARTICIPATION_FACTOR</span> <span class="o">=</span> <span class="mi">2</span></div>
 
+
 <div class="viewcode-block" id="compute_residues"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modeshape.compute_residues.html#sdynpy.modal.sdynpy_modeshape.compute_residues">[docs]</a><span class="k">def</span> <span class="nf">compute_residues</span><span class="p">(</span><span class="n">experimental_frf</span><span class="p">:</span> <span class="n">TransferFunctionArray</span><span class="p">,</span>
                      <span class="n">natural_frequencies</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
                      <span class="n">damping_ratios</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
@@ -366,12 +368,14 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
                          <span class="n">comment1</span><span class="o">=</span><span class="n">coordinates</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">string_array</span><span class="p">()[</span><span class="n">shape_selection_indices</span><span class="p">])</span>
     <span class="k">return</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">negative_drive_points</span></div>
 
+
 <div class="viewcode-block" id="compute_shapes_multireference"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html#sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference">[docs]</a><span class="k">def</span> <span class="nf">compute_shapes_multireference</span><span class="p">(</span><span class="n">experimental_frf</span><span class="p">:</span> <span class="n">TransferFunctionArray</span><span class="p">,</span>
                                   <span class="n">natural_frequencies</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
                                   <span class="n">damping_ratios</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
                                   <span class="n">participation_factors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
                                   <span class="n">real_modes</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
-                                  <span class="n">residuals</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                  <span class="n">lower_residuals</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                                  <span class="n">upper_residuals</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
                                   <span class="n">min_frequency</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
                                   <span class="n">max_frequency</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
                                   <span class="n">displacement_derivative</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
@@ -379,7 +383,7 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
                                   <span class="n">frequency_lines_for_residuals</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes mode shapes from multireference datasets.</span>
-<span class="sd">    </span>
+
 <span class="sd">    Uses the modal participation factor as a constraint on the mode shapes to</span>
 <span class="sd">    solve for the shapes in one pass, rather than solving for residues and</span>
 <span class="sd">    subsequently solving for shapes.</span>
@@ -397,8 +401,10 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
 <span class="sd">        Should have shape (n_modes x n_inputs)</span>
 <span class="sd">    real_modes : bool, optional</span>
 <span class="sd">        Specifies whether to solve for real modes or complex modes (default).</span>
-<span class="sd">    residuals : bool, optional</span>
-<span class="sd">        Use residuals in the FRF fit. The default is True.</span>
+<span class="sd">    lower_residuals : bool, optional</span>
+<span class="sd">        Use lower residuals in the FRF fit. The default is True.</span>
+<span class="sd">    upper_residuals : bool, optional</span>
+<span class="sd">        Use upper residuals in the FRF fit. The default is True.</span>
 <span class="sd">    min_frequency : float, optional</span>
 <span class="sd">        Minimum frequency to use in the shape fit. The default is the lowest</span>
 <span class="sd">        frequency in the experimental FRF.</span>
@@ -433,6 +439,9 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
 <span class="sd">        FRFs resynthesized from the fit shapes and residuals</span>
 <span class="sd">    residual_frfs : TransferFunctionArray</span>
 <span class="sd">        FRFs resynthesized only from the residuals used in the calculation</span>
+<span class="sd">    kernel_frfs: TransferFunctionArray</span>
+<span class="sd">        FRFs synthesized by the kernel solution without taking into account</span>
+<span class="sd">        construction of mode shapes and reciprocity with participation factors</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">original_coordinates</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">coordinate</span>
     <span class="k">if</span> <span class="ow">not</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
@@ -440,8 +449,10 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
     <span class="n">abs_coordinate</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">original_coordinates</span><span class="p">)</span>
     <span class="n">experimental_frf</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="p">[</span><span class="n">abs_coordinate</span><span class="p">]</span>
     <span class="c1"># Also need to adjust the participation factors</span>
-    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">original_coordinates</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
-    <span class="n">frequencies</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">original_coordinates</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">direction</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">real_modes</span><span class="p">:</span>
+        <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">collapse_complex_to_real</span><span class="p">(</span><span class="n">participation_factors</span><span class="p">)</span>
+    <span class="n">frequencies</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">min_frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">frequencies</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -452,171 +463,470 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
     <span class="n">frequencies</span> <span class="o">=</span> <span class="n">frequencies</span><span class="p">[</span><span class="n">abscissa_indices</span><span class="p">]</span>
     <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">abscissa_indices</span><span class="p">]</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">angular_frequencies</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">frequencies</span>
+    <span class="n">reconstruction_angular_frequencies</span> <span class="o">=</span> <span class="n">frequencies</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
     <span class="n">angular_natural_frequencies</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">natural_frequencies</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
     <span class="n">damping_ratios</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">damping_ratios</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-    
+
     <span class="c1"># Reduce to the kept frequency lines</span>
     <span class="k">if</span> <span class="n">frequency_lines_at_resonance</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">solve_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">angular_natural_frequencies</span> <span class="o">-</span> <span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">solve_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">angular_natural_frequencies</span> <span class="o">-</span> <span class="n">angular_frequencies</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
         <span class="c1"># print(solve_indices)</span>
         <span class="n">solve_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span>
             <span class="n">solve_indices</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">frequency_lines_at_resonance</span><span class="p">)</span> <span class="o">-</span> <span class="n">frequency_lines_at_resonance</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
         <span class="n">solve_indices</span> <span class="o">=</span> <span class="n">solve_indices</span><span class="p">[(</span><span class="n">solve_indices</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span>
             <span class="n">solve_indices</span> <span class="o">&lt;</span> <span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">)]</span>
         <span class="c1"># Add the residual indices</span>
-        <span class="k">if</span> <span class="n">residuals</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
             <span class="k">if</span> <span class="n">frequency_lines_for_residuals</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">low_freq_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span> <span class="o">//</span> <span class="mi">10</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">low_freq_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">frequency_lines_for_residuals</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">low_freq_indices</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">frequency_lines_for_residuals</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">high_freq_indices</span> <span class="o">=</span> <span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> \
                     <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span> <span class="o">//</span> <span class="mi">10</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
             <span class="k">else</span><span class="p">:</span>
-                <span class="n">low_freq_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">frequency_lines_for_residuals</span><span class="p">)</span>
                 <span class="n">high_freq_indices</span> <span class="o">=</span> <span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> \
                     <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">frequency_lines_for_residuals</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
-            <span class="n">solve_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
-                <span class="p">(</span><span class="n">solve_indices</span><span class="p">,</span> <span class="n">low_freq_indices</span><span class="p">,</span> <span class="n">high_freq_indices</span><span class="p">)))</span>
-        <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">frf_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">solve_indices</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">high_freq_indices</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">solve_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">solve_indices</span><span class="p">,</span> <span class="n">low_freq_indices</span><span class="p">,</span> <span class="n">high_freq_indices</span><span class="p">)))</span>
+        <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">frf_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">solve_indices</span><span class="p">]</span>
         <span class="n">angular_frequencies</span> <span class="o">=</span> <span class="n">angular_frequencies</span><span class="p">[</span><span class="n">solve_indices</span><span class="p">]</span>
-    
+
+    <span class="n">poles</span> <span class="o">=</span> <span class="o">-</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_natural_frequencies</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">damping_ratios</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">angular_natural_frequencies</span>
+
     <span class="k">if</span> <span class="n">real_modes</span><span class="p">:</span>
-        <span class="n">denominator</span> <span class="o">=</span> <span class="n">angular_natural_frequencies</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="n">j</span><span class="o">*</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_natural_frequencies</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-        <span class="n">l</span> <span class="o">=</span> <span class="n">collapse_complex_to_real</span><span class="p">(</span><span class="n">participation_factors</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">gamma</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">denominator</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">coef</span> <span class="o">=</span> <span class="p">(</span><span class="n">l</span><span class="nd">@gamma</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        
-        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">coef</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">coef</span><span class="o">.</span><span class="n">imag</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">generate_kernel_real</span><span class="p">(</span>
+            <span class="n">angular_frequencies</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span>
+            <span class="n">participation_factors</span><span class="p">,</span> <span class="n">lower_residuals</span><span class="p">,</span> 
+            <span class="n">upper_residuals</span><span class="p">,</span> <span class="n">displacement_derivative</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">reconstruction_angular_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+            <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="n">generate_kernel_real</span><span class="p">(</span>
+                <span class="n">reconstruction_angular_frequencies</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span>
+                <span class="n">participation_factors</span><span class="p">,</span> <span class="n">lower_residuals</span><span class="p">,</span> 
+                <span class="n">upper_residuals</span><span class="p">,</span> <span class="n">displacement_derivative</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="n">kernel</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="c1"># Now set up the kernel</span>
-        <span class="n">poles</span> <span class="o">=</span> <span class="o">-</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_natural_frequencies</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">damping_ratios</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">angular_natural_frequencies</span>
-        <span class="n">poles_conj</span> <span class="o">=</span> <span class="o">-</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_natural_frequencies</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">damping_ratios</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">angular_natural_frequencies</span>
-        
-        <span class="n">gamma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles</span><span class="p">))[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-        <span class="n">gamma_conj</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles_conj</span><span class="p">))[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-        
-        <span class="n">l</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">l_conj</span> <span class="o">=</span> <span class="n">l</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span>
-        
-        <span class="n">coef</span> <span class="o">=</span> <span class="p">(</span><span class="n">l</span><span class="nd">@gamma</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">coef_conj</span> <span class="o">=</span> <span class="p">(</span><span class="n">l_conj</span><span class="nd">@gamma_conj</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    
-        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([</span>
-            <span class="p">[</span><span class="n">coef</span><span class="o">.</span><span class="n">real</span> <span class="o">+</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">imag</span> <span class="o">-</span> <span class="n">coef</span><span class="o">.</span><span class="n">imag</span><span class="p">],</span>
-            <span class="p">[</span><span class="n">coef</span><span class="o">.</span><span class="n">imag</span> <span class="o">+</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="n">coef</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">real</span><span class="p">],</span>
-            <span class="p">])</span>
-        
-    <span class="k">if</span> <span class="n">residuals</span><span class="p">:</span>
-        <span class="n">lr</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="n">angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">ur</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">zeros</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">lr</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-
-        <span class="n">residual_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">lr</span><span class="p">,</span><span class="n">zeros</span><span class="p">,</span><span class="n">ur</span><span class="p">,</span><span class="n">zeros</span><span class="p">],</span>
-                                   <span class="p">[</span><span class="n">zeros</span><span class="p">,</span><span class="n">lr</span><span class="p">,</span><span class="n">zeros</span><span class="p">,</span><span class="n">ur</span><span class="p">]])</span>
-
-        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">kernel</span><span class="p">,</span><span class="n">residual_block</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        
-    <span class="c1"># Convert to accelerations to do the solve</span>
-    <span class="n">omega_scale_factor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">-</span><span class="n">angular_frequencies</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">kernel</span> <span class="o">*=</span> <span class="n">omega_scale_factor</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-    <span class="n">frf_matrix</span> <span class="o">*=</span> <span class="p">(</span><span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">angular_frequencies</span><span class="p">)</span><span class="o">**</span><span class="p">(</span><span class="mi">2</span> <span class="o">-</span> <span class="n">displacement_derivative</span><span class="p">)</span>
-    
-    <span class="c1"># TODO: See about putting weighting in here</span>
-    
-    <span class="c1"># Now assemble the FRF matrix to fit.</span>
-    <span class="n">frf_to_fit</span> <span class="o">=</span> <span class="n">frf_matrix</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">experimental_frf</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">frf_to_fit</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">frf_to_fit</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">frf_to_fit</span><span class="o">.</span><span class="n">imag</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    
-    <span class="c1"># Solve the least squares problem</span>
-    <span class="n">shape_coefficients</span><span class="p">,</span><span class="o">*</span><span class="n">metrics</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="n">frf_to_fit</span><span class="p">)</span>
-    
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">generate_kernel_complex</span><span class="p">(</span>
+            <span class="n">angular_frequencies</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span> <span class="n">participation_factors</span><span class="p">,</span> <span class="n">lower_residuals</span><span class="p">,</span>
+            <span class="n">upper_residuals</span><span class="p">,</span> <span class="n">displacement_derivative</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">angular_frequencies</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">reconstruction_angular_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
+            <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="n">generate_kernel_complex</span><span class="p">(</span>
+                <span class="n">reconstruction_angular_frequencies</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span> <span class="n">participation_factors</span><span class="p">,</span> <span class="n">lower_residuals</span><span class="p">,</span> 
+                <span class="n">upper_residuals</span><span class="p">,</span> <span class="n">displacement_derivative</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="n">kernel</span>
+
+    <span class="n">coefficients</span><span class="p">,</span> <span class="p">(</span><span class="n">full_reconstruction</span><span class="p">,</span> <span class="n">residual_reconstruction</span><span class="p">)</span> <span class="o">=</span> <span class="n">stack_and_lstsq</span><span class="p">(</span>
+        <span class="n">frf_matrix</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span>
+        <span class="n">return_reconstruction</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+        <span class="n">reconstruction_partitions</span> <span class="o">=</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">),</span>
+                                     <span class="nb">slice</span><span class="p">(</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="n">real_modes</span> <span class="k">else</span> <span class="mi">2</span><span class="p">),</span><span class="kc">None</span><span class="p">)],</span>
+        <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="n">reconstruction_kernel</span><span class="p">)</span>
+
+    <span class="n">residual_frfs</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="o">.</span><span class="n">extract_elements</span><span class="p">(</span><span class="n">abscissa_indices</span><span class="p">)</span>
+    <span class="n">residual_frfs</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">residual_reconstruction</span>
+    <span class="n">kernel_frfs</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="o">.</span><span class="n">extract_elements</span><span class="p">(</span><span class="n">abscissa_indices</span><span class="p">)</span>
+    <span class="n">kernel_frfs</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">full_reconstruction</span>
+
     <span class="c1"># Extract the shapes and residues</span>
     <span class="k">if</span> <span class="n">real_modes</span><span class="p">:</span>
-        <span class="n">shapes</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape_coefficients</span><span class="p">[:</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span><span class="o">.</span><span class="n">T</span>
+        <span class="n">shapes</span> <span class="o">=</span> <span class="p">(</span><span class="n">coefficients</span><span class="p">[:</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">shapes</span> <span class="o">=</span> <span class="p">(</span><span class="n">shape_coefficients</span><span class="p">[:</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">shape_coefficients</span><span class="p">[</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span><span class="mi">2</span><span class="o">*</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]])</span><span class="o">.</span><span class="n">T</span>
-    
-    <span class="n">reconstruction_angular_frequencies</span> <span class="o">=</span> <span class="n">frequencies</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
-    <span class="n">residual_frfs</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span><span class="o">.</span><span class="n">extract_elements</span><span class="p">(</span><span class="n">abscissa_indices</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">residuals</span><span class="p">:</span>
-        <span class="n">lr</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="n">reconstruction_angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">ur</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">reconstruction_angular_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">zeros</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">lr</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="n">shapes</span> <span class="o">=</span> <span class="p">(</span><span class="n">coefficients</span><span class="p">[:</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">coefficients</span><span class="p">[</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">:</span><span class="mi">2</span><span class="o">*</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">])</span><span class="o">.</span><span class="n">T</span>
 
-        <span class="n">residual_reconstruction_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">lr</span><span class="p">,</span><span class="n">zeros</span><span class="p">,</span><span class="n">ur</span><span class="p">,</span><span class="n">zeros</span><span class="p">],</span>
-                                                  <span class="p">[</span><span class="n">zeros</span><span class="p">,</span><span class="n">lr</span><span class="p">,</span><span class="n">zeros</span><span class="p">,</span><span class="n">ur</span><span class="p">]])</span>
-    
-        <span class="n">residual_reconstruction</span> <span class="o">=</span> <span class="p">(</span><span class="n">residual_reconstruction_block</span> <span class="o">@</span> <span class="n">shape_coefficients</span><span class="p">[</span><span class="o">-</span><span class="mi">4</span><span class="o">*</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:])</span>
-        <span class="n">residual_reconstruction</span> <span class="o">=</span> <span class="p">(</span><span class="n">residual_reconstruction</span><span class="p">[:</span><span class="n">residual_reconstruction</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> 
-                                   <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">residual_reconstruction</span><span class="p">[</span><span class="n">residual_reconstruction</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:])</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">reconstruction_angular_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">residual_frfs</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="n">residual_reconstruction</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">reconstruction_angular_frequencies</span><span class="p">)</span><span class="o">**</span><span class="n">displacement_derivative</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">residual_frfs</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="mi">0</span>
-    
     <span class="c1"># Now we have to go in and find the scale factor to scale the shapes correctly</span>
     <span class="n">drive_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">experimental_frf</span><span class="o">.</span><span class="n">response_coordinate</span> <span class="o">==</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">drive_points</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning, Drive Points Not Found in Dataset, Shapes are Unscaled.&#39;</span><span class="p">)</span>
-        <span class="n">output_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">abs_coordinate</span><span class="p">[:,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">shapes</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Drive Points Not Found in Dataset, Shapes are Unscaled.&#39;</span><span class="p">)</span>
+        <span class="n">output_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">abs_coordinate</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">shapes</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
                                    <span class="n">angular_natural_frequencies</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">),</span>
                                    <span class="n">damping_ratios</span><span class="p">,</span>
                                    <span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">real_modes</span><span class="p">:</span>
-            <span class="n">denominator</span> <span class="o">=</span> <span class="n">angular_natural_frequencies</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">reconstruction_angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="n">j</span><span class="o">*</span><span class="n">damping_ratios</span><span class="o">*</span><span class="n">angular_natural_frequencies</span><span class="o">*</span><span class="n">reconstruction_angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-            <span class="n">l</span> <span class="o">=</span> <span class="n">collapse_complex_to_real</span><span class="p">(</span><span class="n">participation_factors</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">gamma</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">denominator</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">coef</span> <span class="o">=</span> <span class="p">(</span><span class="n">l</span><span class="nd">@gamma</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-            
-            <span class="n">full_kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">coef</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">coef</span><span class="o">.</span><span class="n">imag</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">gamma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">reconstruction_angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles</span><span class="p">))[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-            <span class="n">gamma_conj</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">reconstruction_angular_frequencies</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">poles_conj</span><span class="p">))[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-            
-            <span class="n">l</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">T</span>
-            <span class="n">l_conj</span> <span class="o">=</span> <span class="n">l</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span>
-            
-            <span class="n">coef</span> <span class="o">=</span> <span class="p">(</span><span class="n">l</span><span class="nd">@gamma</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">coef_conj</span> <span class="o">=</span> <span class="p">(</span><span class="n">l_conj</span><span class="nd">@gamma_conj</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">gamma</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    
-            <span class="n">full_kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([</span>
-                <span class="p">[</span><span class="n">coef</span><span class="o">.</span><span class="n">real</span> <span class="o">+</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">imag</span> <span class="o">-</span> <span class="n">coef</span><span class="o">.</span><span class="n">imag</span><span class="p">],</span>
-                <span class="p">[</span><span class="n">coef</span><span class="o">.</span><span class="n">imag</span> <span class="o">+</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">imag</span><span class="p">,</span> <span class="n">coef</span><span class="o">.</span><span class="n">real</span> <span class="o">-</span> <span class="n">coef_conj</span><span class="o">.</span><span class="n">real</span><span class="p">],</span>
-                <span class="p">])</span>
-        <span class="n">reconstruction</span> <span class="o">=</span> <span class="n">full_kernel</span><span class="nd">@shape_coefficients</span><span class="p">[:</span><span class="o">-</span><span class="mi">4</span><span class="o">*</span><span class="n">l</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:]</span>
-        <span class="n">reconstruction</span> <span class="o">=</span> <span class="n">reconstruction</span><span class="p">[:</span><span class="n">reconstruction</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">reconstruction</span><span class="p">[</span><span class="n">reconstruction</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span>
-        
-        <span class="n">frfs_resynthesized</span> <span class="o">=</span> <span class="n">residual_frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">frfs_resynthesized</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">+=</span> <span class="p">(</span><span class="n">reconstruction</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">reconstruction</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+        <span class="n">frfs_resynthesized</span> <span class="o">=</span> <span class="n">kernel_frfs</span>
     <span class="k">else</span><span class="p">:</span>
-        <span class="n">residues</span> <span class="o">=</span> <span class="n">l</span><span class="p">[</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">*</span><span class="n">shapes</span><span class="p">[</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="n">max_residue</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">residues</span> <span class="k">if</span> <span class="n">real_modes</span> <span class="k">else</span> <span class="nb">abs</span><span class="p">(</span><span class="n">residues</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">scale_pre_sqrt</span> <span class="o">=</span> <span class="n">l</span><span class="p">[</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span><span class="o">/</span><span class="n">shapes</span><span class="p">[</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="n">scale_pre_sqrt</span> <span class="o">=</span> <span class="n">scale_pre_sqrt</span><span class="p">[</span><span class="n">max_residue</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">max_residue</span><span class="o">.</span><span class="n">size</span><span class="p">)]</span>
-        <span class="k">if</span> <span class="n">real_modes</span><span class="p">:</span>
-            <span class="n">negative_drive_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">scale_pre_sqrt</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">negative_drive_points</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Negative Drive Point for Modes </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">negative_drive_points</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;These shapes will not be scaled correctly!&#39;</span><span class="p">)</span>
-            <span class="n">scale_pre_sqrt</span><span class="p">[</span><span class="n">negative_drive_points</span><span class="p">]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
-        <span class="n">shape_scaling</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">scale_pre_sqrt</span><span class="p">)</span>
-        <span class="n">shape_scaling</span> <span class="o">=</span> <span class="n">shape_scaling</span>
-        <span class="n">shapes</span> <span class="o">*=</span> <span class="n">shape_scaling</span>
-        
-        <span class="n">output_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">abs_coordinate</span><span class="p">[:,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span> <span class="n">shapes</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+        <span class="n">shapes_normalized</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">drive_residues</span> <span class="o">=</span> <span class="n">shapes</span><span class="p">[</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">T</span><span class="o">*</span><span class="n">participation_factors</span><span class="p">[:,</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">drive_residue</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">drive_residues</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">real_modes</span><span class="p">:</span>
+                <span class="c1"># Throw away negative drive points</span>
+                <span class="n">bad_indices</span> <span class="o">=</span> <span class="n">drive_residue</span> <span class="o">&lt;</span> <span class="mi">0</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="c1"># Throw away non-negative imaginary parts</span>
+                <span class="n">bad_indices_positive</span> <span class="o">=</span> <span class="n">drive_residue</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mi">0</span>
+                <span class="c1"># Throw away small values compared to the average value (only considering negative imaginary parts)</span>
+                <span class="n">bad_indices_small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">drive_residue</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">0.01</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">drive_residue</span><span class="p">[</span><span class="o">~</span><span class="n">bad_indices_positive</span><span class="p">]))</span>
+                <span class="c1"># Combine into a single criteria</span>
+                <span class="n">bad_indices</span> <span class="o">=</span> <span class="n">bad_indices_positive</span> <span class="o">|</span> <span class="n">bad_indices_small</span>
+            <span class="c1"># Get the good indices that are remaining</span>
+            <span class="n">remaining_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="o">~</span><span class="n">bad_indices</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">remaining_indices</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Mode </span><span class="si">{:}</span><span class="s1"> had no valid drive points and is therefore unscaled.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+                <span class="n">shapes_normalized</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shapes</span><span class="p">[:,</span><span class="n">k</span><span class="p">])</span>
+                <span class="k">continue</span>
+            <span class="c1"># We will then construct the least squares solution</span>
+            <span class="n">shape_coefficients</span> <span class="o">=</span> <span class="n">shapes</span><span class="p">[</span><span class="n">drive_points</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">remaining_indices</span><span class="p">],</span><span class="n">k</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">residue_coefficients</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">drive_residue</span><span class="p">[</span><span class="n">remaining_indices</span><span class="p">])[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="c1"># Before we compute the scale, we need to make sure that we have all of the signs the same way.</span>
+            <span class="c1"># This is because the square root can give you +/- root where root**2 = complex number</span>
+            <span class="c1"># This will mess up the least squares at it will try to find something between the</span>
+            <span class="c1"># two vectors.</span>
+            <span class="n">scale_vector</span> <span class="o">=</span> <span class="p">(</span><span class="n">residue_coefficients</span><span class="o">/</span><span class="n">shape_coefficients</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+            <span class="n">sign_vector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">scale_vector</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">scale_vector</span><span class="o">.</span><span class="n">imag</span><span class="p">))</span>
+            <span class="c1"># Get the signs</span>
+            <span class="n">signs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">sign_vector</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span><span class="n">sign_vector</span><span class="p">))</span>
+            <span class="n">residue_coefficients</span> <span class="o">=</span> <span class="n">residue_coefficients</span><span class="o">*</span><span class="n">signs</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="c1"># Now compute the least-squares solution</span>
+            <span class="n">scale</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">shape_coefficients</span><span class="p">,</span><span class="n">residue_coefficients</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Scale for mode </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">scale</span><span class="p">))</span>
+            <span class="n">shapes_normalized</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shapes</span><span class="p">[:,</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">scale</span><span class="p">)</span>
+        <span class="n">shapes_normalized</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">shapes_normalized</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+
+        <span class="n">output_shape</span> <span class="o">=</span> <span class="n">shape_array</span><span class="p">(</span><span class="n">abs_coordinate</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">shapes_normalized</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
                                    <span class="n">angular_natural_frequencies</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">),</span>
                                    <span class="n">damping_ratios</span><span class="p">,</span>
                                    <span class="mi">1</span><span class="p">)</span>
-        
+
         <span class="n">frfs_resynthesized</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_shape</span><span class="o">.</span><span class="n">compute_frf</span><span class="p">(</span>
             <span class="n">frequencies</span><span class="p">,</span>
-            <span class="n">responses</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span>
-            <span class="n">references</span> <span class="o">=</span> <span class="n">experimental_frf</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">,:],</span>
-            <span class="n">displacement_derivative</span> <span class="o">=</span> <span class="n">displacement_derivative</span><span class="p">)</span>
+            <span class="n">responses</span><span class="o">=</span><span class="n">experimental_frf</span><span class="o">.</span><span class="n">response_coordinate</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+            <span class="n">references</span><span class="o">=</span><span class="n">experimental_frf</span><span class="o">.</span><span class="n">reference_coordinate</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">:],</span>
+            <span class="n">displacement_derivative</span><span class="o">=</span><span class="n">displacement_derivative</span><span class="p">)</span>
             <span class="o">+</span> <span class="n">residual_frfs</span><span class="p">)</span>
 
     <span class="n">frfs_resynthesized</span> <span class="o">=</span> <span class="n">frfs_resynthesized</span><span class="p">[</span><span class="n">original_coordinates</span><span class="p">]</span>
     <span class="n">residual_frfs</span> <span class="o">=</span> <span class="n">residual_frfs</span><span class="p">[</span><span class="n">original_coordinates</span><span class="p">]</span>
+    <span class="n">kernel_frfs</span> <span class="o">=</span> <span class="n">kernel_frfs</span><span class="p">[</span><span class="n">original_coordinates</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">output_shape</span><span class="p">,</span> <span class="n">frfs_resynthesized</span><span class="p">,</span> <span class="n">residual_frfs</span><span class="p">,</span> <span class="n">kernel_frfs</span></div>
+
+<div class="viewcode-block" id="generate_kernel_complex"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html#sdynpy.modal.sdynpy_modeshape.generate_kernel_complex">[docs]</a><span class="k">def</span> <span class="nf">generate_kernel_complex</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span> <span class="n">participation_factors</span><span class="p">,</span>
+                            <span class="n">lower_residuals</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">upper_residuals</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                            <span class="n">displacement_derivative</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    </span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    omegas : np.ndarray</span>
+<span class="sd">        The angular frequencies (in radians/s) at which the kernel matrix will</span>
+<span class="sd">        be computed.  This should be a 1D array with length num_freqs.</span>
+<span class="sd">    poles : np.ndarray</span>
+<span class="sd">        An array of poles corresponding to the modes of the structure.  This</span>
+<span class="sd">        should be a 1D array with length num_modes.</span>
+<span class="sd">    participation_factors : np.ndarray</span>
+<span class="sd">        A 2D array of participation factors corresponding to the reference</span>
+<span class="sd">        degrees of freedom.  This should have shape (num_modes, num_inputs).</span>
+<span class="sd">    lower_residuals : bool, optional</span>
+<span class="sd">        If True, construct the kernel matrix such that lower residuals will be</span>
+<span class="sd">        computed in the least-squares operation.  The default is False.</span>
+<span class="sd">    upper_residuals : bool, optional</span>
+<span class="sd">        If True, construct the kernel matrix such that upper residuals will be</span>
+<span class="sd">        computed in the least-squares operation.  The default is False.</span>
+<span class="sd">    displacement_derivative : int, optional</span>
+<span class="sd">        The derivative of displacement used to construct the frequency response</span>
+<span class="sd">        functions.  Should be 0 for a receptance (displacement/force) frf, 1</span>
+<span class="sd">        for a mobility (velocity/force) frf, or 2 for an accelerance</span>
+<span class="sd">        (acceleration/force) frf.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    kernel_matrix : np.ndarray</span>
+<span class="sd">        A 3D matrix that represents the kernel that can be inverted to solve</span>
+<span class="sd">        for mode shapes.  The size of the output array will be num_inputs x</span>
+<span class="sd">        num_freq*2 x num_modes*2.  The top partition of the num_freq dimension</span>
+<span class="sd">        corresponds to the real part of the frf, and the</span>
+<span class="sd">        bottom portion corresponds to the imaginary part of the frf.  The top</span>
+<span class="sd">        partition of the num_modes dimension corresponds to the real part of</span>
+<span class="sd">        the mode shape matrix, and the bottom partition corresponds to the</span>
+<span class="sd">        imaginary part.  If residuals are included, there will be an extra two</span>
+<span class="sd">        entries along the num_modes dimension corresponding to real and</span>
+<span class="sd">        imaginary parts of the residual matrix for each of the residuals</span>
+<span class="sd">        included.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">omegas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">omegas</span><span class="p">)</span>
+    <span class="n">poles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">poles</span><span class="p">)</span>
+    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">participation_factors</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">omegas</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`omegas` should be 1-dimensional&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">poles</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`poles` should be 1-dimensional&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`participation_factors` should be 2-dimensional (num_modes x num_inputs)&#39;</span><span class="p">)</span>
+    <span class="n">n_modes</span><span class="p">,</span><span class="n">n_inputs</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">if</span> <span class="n">poles</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">n_modes</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;number of poles (</span><span class="si">{:}</span><span class="s1">) is not equal to the number of rows in the participation_factors array (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+            <span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="n">n_modes</span><span class="p">))</span>
+    <span class="c1"># Set up broadcasting</span>
+    <span class="c1"># We want the output array to be n_input x n_freq*2 x n_modes*2</span>
+    <span class="c1"># So let&#39;s adjust the terms so they have the right shapes</span>
+    <span class="c1"># We want frequency lines to be the middle dimension</span>
+    <span class="n">omega</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="c1"># We want inputs to be the first dimension and modes the</span>
+    <span class="c1"># last dimension</span>
+    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">T</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+    <span class="c1"># Split up terms into real and imaginary parts</span>
+    <span class="n">p_r</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">p_i</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">imag</span>
+    <span class="n">l_r</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">l_i</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">imag</span>
     
-    <span class="k">return</span> <span class="n">output_shape</span><span class="p">,</span><span class="n">frfs_resynthesized</span><span class="p">,</span><span class="n">residual_frfs</span></div>
+    <span class="c1"># Now go through the different derivatives and compute the kernel plus any</span>
+    <span class="c1"># residuals</span>
+    <span class="k">if</span> <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+            <span class="p">[(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> 
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+             <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">p_r</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)],</span>
+            <span class="p">[(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">p_r</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">p_r</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+             <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> 
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span> <span class="o">+</span> <span class="n">l_i</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)]])</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RL</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RU</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+            <span class="p">[(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+             <span class="p">(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)],</span>
+            <span class="p">[(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+             <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)]])</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RL</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span> <span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RU</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span> <span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+            <span class="p">[(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+             <span class="p">(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)],</span>
+            <span class="p">[(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+             <span class="p">(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">-</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+              <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_i</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">p_r</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">p_i</span> <span class="o">+</span> <span class="n">p_i</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">p_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)]])</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RL</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RU</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]],</span>
+                       <span class="p">[</span><span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]]])</span>
+    <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">kernel</span><span class="p">,</span><span class="n">kernel_RL</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">kernel</span><span class="p">,</span><span class="n">kernel_RU</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">kernel</span></div>
+
+<div class="viewcode-block" id="generate_kernel_real"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html#sdynpy.modal.sdynpy_modeshape.generate_kernel_real">[docs]</a><span class="k">def</span> <span class="nf">generate_kernel_real</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span> <span class="n">participation_factors</span><span class="p">,</span>
+                         <span class="n">lower_residuals</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">upper_residuals</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                         <span class="n">displacement_derivative</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    </span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    omegas : np.ndarray</span>
+<span class="sd">        The angular frequencies (in radians/s) at which the kernel matrix will</span>
+<span class="sd">        be computed.  This should be a 1D array with length num_freqs.</span>
+<span class="sd">    poles : np.ndarray</span>
+<span class="sd">        An array of poles corresponding to the modes of the structure.  This</span>
+<span class="sd">        should be a 1D array with length num_modes.</span>
+<span class="sd">    participation_factors : np.ndarray</span>
+<span class="sd">        A 2D array of participation factors corresponding to the reference</span>
+<span class="sd">        degrees of freedom.  This should have shape (num_modes, num_inputs).</span>
+<span class="sd">    lower_residuals : bool, optional</span>
+<span class="sd">        If True, construct the kernel matrix such that lower residuals will be</span>
+<span class="sd">        computed in the least-squares operation.  The default is False.</span>
+<span class="sd">    upper_residuals : bool, optional</span>
+<span class="sd">        If True, construct the kernel matrix such that upper residuals will be</span>
+<span class="sd">        computed in the least-squares operation.  The default is False.</span>
+<span class="sd">    displacement_derivative : int, optional</span>
+<span class="sd">        The derivative of displacement used to construct the frequency response</span>
+<span class="sd">        functions.  Should be 0 for a receptance (displacement/force) frf, 1</span>
+<span class="sd">        for a mobility (velocity/force) frf, or 2 for an accelerance</span>
+<span class="sd">        (acceleration/force) frf.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    kernel_matrix : np.ndarray</span>
+<span class="sd">        A 3D matrix that represents the kernel that can be inverted to solve</span>
+<span class="sd">        for mode shapes.  The size of the output array will be num_inputs x</span>
+<span class="sd">        num_freq*2 x num_modes.  The top partition of the num_freq dimension</span>
+<span class="sd">        corresponds to the real part of the frf, and the</span>
+<span class="sd">        bottom portion corresponds to the imaginary part of the frf.</span>
+<span class="sd">        If residuals are included, there will be an extra two</span>
+<span class="sd">        entries along the num_modes dimension corresponding to real and</span>
+<span class="sd">        imaginary parts of the residual matrix for each of the residuals</span>
+<span class="sd">        included.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">omegas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">omegas</span><span class="p">)</span>
+    <span class="n">poles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">poles</span><span class="p">)</span>
+    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">participation_factors</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">omegas</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`omegas` should be 1-dimensional&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">poles</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`poles` should be 1-dimensional&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`participation_factors` should be 2-dimensional (num_modes x num_inputs)&#39;</span><span class="p">)</span>
+    <span class="n">n_modes</span><span class="p">,</span><span class="n">n_inputs</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">shape</span>
+    <span class="k">if</span> <span class="n">poles</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">n_modes</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;number of poles (</span><span class="si">{:}</span><span class="s1">) is not equal to the number of rows in the participation_factors array (</span><span class="si">{:}</span><span class="s1">)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+            <span class="n">poles</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="n">n_modes</span><span class="p">))</span>
+    <span class="c1"># Set up broadcasting</span>
+    <span class="c1"># We want the output array to be n_input x n_freq*2 x n_modes*2</span>
+    <span class="c1"># So let&#39;s adjust the terms so they have the right shapes</span>
+    <span class="c1"># We want frequency lines to be the middle dimension</span>
+    <span class="n">omega</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="c1"># We want inputs to be the first dimension and modes the</span>
+    <span class="c1"># last dimension</span>
+    <span class="n">l_r</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">T</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+    <span class="c1"># Split up terms into real and imaginary parts</span>
+    <span class="n">omega_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">poles</span><span class="p">)</span>
+    <span class="n">zeta_r</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">poles</span><span class="p">)</span><span class="o">/</span><span class="n">omega_r</span>
+    
+    <span class="c1"># Now go through the different derivatives and compute the kernel plus any</span>
+    <span class="c1"># residuals</span>
+    <span class="k">if</span> <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[(</span><span class="o">-</span><span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span>
+                             <span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_r</span><span class="o">**</span><span class="mi">4</span><span class="p">)],</span>
+                           <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">omega_r</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">/</span>
+                             <span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_r</span><span class="o">**</span><span class="mi">4</span><span class="p">)]])</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RL</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RU</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">2</span><span class="o">*</span><span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">/</span>
+                             <span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_r</span><span class="o">**</span><span class="mi">4</span><span class="p">)],</span>
+                           <span class="p">[(</span><span class="o">-</span><span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span> <span class="o">+</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span>
+                             <span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_r</span><span class="o">**</span><span class="mi">4</span><span class="p">)]])</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RL</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span> <span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RU</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span> <span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">displacement_derivative</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[(</span><span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">-</span> <span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span>
+                             <span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_r</span><span class="o">**</span><span class="mi">4</span><span class="p">)],</span>
+                           <span class="p">[</span><span class="mi">2</span><span class="o">*</span><span class="n">l_r</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">3</span><span class="o">*</span><span class="n">omega_r</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">/</span>
+                             <span class="p">(</span><span class="n">omega</span><span class="o">**</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">4</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">zeta_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">omega</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">omega_r</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_r</span><span class="o">**</span><span class="mi">4</span><span class="p">)]])</span>
+        <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RL</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+            <span class="n">kernel_RU</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">kernel</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]],</span>
+                       <span class="p">[</span><span class="n">kernel</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]]])</span>
+    <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">kernel</span><span class="p">,</span><span class="n">kernel_RL</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+        <span class="n">kernel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">kernel</span><span class="p">,</span><span class="n">kernel_RU</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    
+    <span class="k">return</span> <span class="n">kernel</span></div>
+
+<div class="viewcode-block" id="stack_and_lstsq"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html#sdynpy.modal.sdynpy_modeshape.stack_and_lstsq">[docs]</a><span class="k">def</span> <span class="nf">stack_and_lstsq</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span> <span class="n">kernel_matrix</span><span class="p">,</span> <span class="n">return_reconstruction</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                    <span class="n">reconstruction_partitions</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                    <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Stacks the frf and kernel matrices appropriately,</span>
+<span class="sd">    then solves the least-squares problem</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    frf_matrix : np.ndarray</span>
+<span class="sd">        A num_outputs x num_inputs x num_frequencies array representing the</span>
+<span class="sd">        frequency response function matrix.</span>
+<span class="sd">    kernel_matrix : np.ndarray</span>
+<span class="sd">        A num_inputs x 2*num_freq x num_coefficients kernel matrix from generated</span>
+<span class="sd">        from generate_kernel_complex or generate_kernel_real functions.</span>
+<span class="sd">    return_reconstruction : bool</span>
+<span class="sd">        If True, a reconstruction of the frequency response function matrix</span>
+<span class="sd">        using the kernel and the computed shape coefficients.  Default is False.</span>
+<span class="sd">    reconstruction_paritions : list</span>
+<span class="sd">        If specified, a list of reconstructions will be returned.  For each</span>
+<span class="sd">        entry in this list, a frf matrix will be reconstructed using the entry</span>
+<span class="sd">        as indices into the coefficient matrix&#39;s rows and the kernel matrix&#39;s</span>
+<span class="sd">        columns.  This allows, for example, reconstruction of the frf matrix</span>
+<span class="sd">        using only the residual terms or only the shape terms.  If not</span>
+<span class="sd">        specified when `return_reconstruction` is True, only one frf matrix will</span>
+<span class="sd">        be returned using all shape coefficients.</span>
+<span class="sd">    reconstruction_kernel : np.ndarray</span>
+<span class="sd">        A separate kernel matrix used for reconstruction.  If not specified, the</span>
+<span class="sd">        kernel matrix used to solve the least-squares problem will be used for</span>
+<span class="sd">        the reconstruction.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    coefficients : np.ndarray</span>
+<span class="sd">        A num_coefficients x num_outputs array of solution values to the</span>
+<span class="sd">        least-squares problem.</span>
+<span class="sd">    reconstruction : np.ndarray or list of np.ndarray</span>
+<span class="sd">        A frf matrix reconstructed from the kernel and the shape coefficients.</span>
+<span class="sd">        If multiple `reconstruction_partitions` are specified in a list, then</span>
+<span class="sd">        this return value will also be a list of reconstructions corresponding</span>
+<span class="sd">        to those partitions.  This will only be returned if</span>
+<span class="sd">        `return_reconstruction` is True.</span>
+<span class="sd">        </span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">H</span> <span class="o">=</span> <span class="n">frf_matrix</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># input, freq, output</span>
+    <span class="n">H</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">H</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">H</span><span class="o">.</span><span class="n">imag</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># Stack real/imag along the freq axis</span>
+    <span class="k">if</span> <span class="n">H</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> <span class="o">!=</span> <span class="n">kernel_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`frf_matrix` </span><span class="si">{:}</span><span class="s1"> does not have consistent dimensions with the kernel matrix </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+            <span class="n">H</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">kernel_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+    <span class="n">H</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">H</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># Stack input and freq along the same dimension</span>
+    <span class="n">kernel</span> <span class="o">=</span> <span class="n">kernel_matrix</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">kernel_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># Stack input and freq</span>
+    <span class="n">coefficients</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">kernel</span><span class="p">,</span><span class="n">H</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">return_reconstruction</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coefficients</span>
+    <span class="k">if</span> <span class="n">reconstruction_kernel</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">reconstruction_kernel</span> <span class="o">=</span> <span class="n">kernel_matrix</span>
+    <span class="k">if</span> <span class="n">reconstruction_partitions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">H_reconstructed</span> <span class="o">=</span> <span class="n">reconstruction_kernel</span><span class="nd">@coefficients</span>
+        <span class="n">frf_matrix_reconstructed</span> <span class="o">=</span> <span class="n">H_reconstructed</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># output, input, freq</span>
+        <span class="n">frf_matrix_reconstructed</span> <span class="o">=</span> <span class="p">(</span><span class="n">frf_matrix_reconstructed</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="n">frf_matrix_reconstructed</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> 
+                                    <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">frf_matrix_reconstructed</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frf_matrix_reconstructed</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">frf_matrix_reconstructed</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">partition</span> <span class="ow">in</span> <span class="n">reconstruction_partitions</span><span class="p">:</span>
+            <span class="n">recon</span> <span class="o">=</span> <span class="p">(</span><span class="n">reconstruction_kernel</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">partition</span><span class="p">]</span><span class="nd">@coefficients</span><span class="p">[</span><span class="n">partition</span><span class="p">])</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># output, input, freq</span>
+            <span class="n">recon</span> <span class="o">=</span> <span class="n">recon</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="n">recon</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">recon</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">recon</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span>
+            <span class="n">frf_matrix_reconstructed</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">recon</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">coefficients</span><span class="p">,</span> <span class="n">frf_matrix_reconstructed</span></div>
 </pre></div>
 
            </div>
@@ -626,7 +936,7 @@ <h1>Source code for sdynpy.modal.sdynpy_modeshape</h1><div class="highlight"><pr
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb9d16c0>
+<jinja2.runtime.BlockReference object at 0x7f5a27c1dc30>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/modal/sdynpy_polypy.html b/_modules/sdynpy/modal/sdynpy_polypy.html
index b2485f3..3c7a4c4 100644
--- a/_modules/sdynpy/modal/sdynpy_polypy.html
+++ b/_modules/sdynpy/modal/sdynpy_polypy.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_polypy &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_polypy &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -134,9 +134,9 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span> <span class="o">=</span> <span class="n">max_frequency</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">displacement_derivative</span> <span class="o">=</span> <span class="n">displacement_derivative</span>
         <span class="n">abscissa_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span> <span class="o">&gt;=</span> <span class="n">min_frequency</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span> <span class="o">&lt;=</span> <span class="n">max_frequency</span><span class="p">)</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span><span class="p">[</span><span class="n">abscissa_indices</span><span class="p">]</span>
         <span class="n">freq_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">abscissa</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">abscissa</span><span class="p">)))</span>
@@ -174,7 +174,7 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">modal_participation_threshold</span> <span class="o">=</span> <span class="n">modal_participation_threshold</span>
 
         <span class="n">pole_dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">([(</span><span class="s1">&#39;omega&#39;</span><span class="p">,</span> <span class="nb">float</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;zeta&#39;</span><span class="p">,</span> <span class="nb">float</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;Lr_real&#39;</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="p">(</span><span class="n">num_input</span><span class="p">,)),</span>
-                               <span class="p">(</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">,</span> <span class="nb">complex</span><span class="p">,</span> <span class="p">(</span><span class="n">num_input</span><span class="p">,)),</span> <span class="p">(</span><span class="s1">&#39;freq_stable&#39;</span><span class="p">,</span><span class="nb">bool</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;damp_stable&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">),</span>
+                               <span class="p">(</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">,</span> <span class="nb">complex</span><span class="p">,</span> <span class="p">(</span><span class="n">num_input</span><span class="p">,)),</span> <span class="p">(</span><span class="s1">&#39;freq_stable&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;damp_stable&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">),</span>
                                <span class="p">(</span><span class="s1">&#39;part_stable&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)])</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">pole_list</span> <span class="o">=</span> <span class="p">[]</span>
@@ -264,7 +264,9 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
                             <span class="n">poles</span><span class="p">[</span><span class="s1">&#39;damp_stable&#39;</span><span class="p">][</span><span class="n">i_pole</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
                             <span class="c1"># Now check participation factor</span>
                             <span class="n">previous_Lr</span> <span class="o">=</span> <span class="n">last_poles</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">][</span><span class="n">closest_freq_index</span><span class="p">]</span>
-                            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">pole</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">previous_Lr</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">previous_Lr</span><span class="p">))</span> <span class="o">&lt;</span> <span class="n">modal_participation_threshold</span><span class="p">:</span>
+                            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span>
+                                    <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">pole</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">previous_Lr</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">previous_Lr</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">modal_participation_threshold</span><span class="p">)</span> <span class="o">|</span> 
+                                    <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">pole</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">])</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">previous_Lr</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">*</span><span class="mf">1e-4</span><span class="p">)):</span> <span class="c1"># Added this to remove divide by zero issues</span>
                                 <span class="n">poles</span><span class="p">[</span><span class="s1">&#39;part_stable&#39;</span><span class="p">][</span><span class="n">i_pole</span><span class="p">]</span> <span class="o">=</span> <span class="kc">True</span>
             <span class="n">last_poles</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span></div>
 
@@ -282,7 +284,7 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
         <span class="n">ax_poles</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
 
         <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="n">poles</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">polynomial_orders</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pole_list</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">order_range</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">order_range</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">order</span> <span class="o">&lt;</span> <span class="n">order_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
                     <span class="k">continue</span>
                 <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="n">order_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
@@ -367,22 +369,23 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
             <span class="n">a</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="PolyPy.compute_shapes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.html#sdynpy.modal.sdynpy_polypy.PolyPy.compute_shapes">[docs]</a>    <span class="k">def</span> <span class="nf">compute_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span>
-                         <span class="n">residuals</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">real_modes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                         <span class="n">frequency_lines_at_resonance</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                         <span class="n">frequency_lines_for_residuals</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span> 
-                         <span class="p">):</span>
+                       <span class="n">low_residuals</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">high_residuals</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">real_modes</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                       <span class="n">frequency_lines_at_resonance</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                       <span class="n">frequency_lines_for_residuals</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span>
+                       <span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">natural_frequencies</span> <span class="o">=</span> <span class="n">poles</span><span class="p">[</span><span class="s1">&#39;omega&#39;</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">damping_ratios</span> <span class="o">=</span> <span class="n">poles</span><span class="p">[</span><span class="s1">&#39;zeta&#39;</span><span class="p">]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">participation_factors</span> <span class="o">=</span> <span class="n">poles</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_residuals</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_kernel</span> <span class="o">=</span> <span class="p">(</span>
             <span class="n">compute_shapes_multireference</span><span class="p">(</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">natural_frequencies</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">damping_ratios</span><span class="p">,</span> 
-                <span class="bp">self</span><span class="o">.</span><span class="n">participation_factors</span><span class="p">,</span> <span class="n">real_modes</span><span class="p">,</span> <span class="n">residuals</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">natural_frequencies</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">damping_ratios</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">participation_factors</span><span class="p">,</span> <span class="n">real_modes</span><span class="p">,</span>
+                <span class="n">low_residuals</span><span class="p">,</span> <span class="n">high_residuals</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">min_frequency</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span><span class="p">,</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">displacement_derivative</span><span class="p">,</span>
                 <span class="n">frequency_lines_at_resonance</span><span class="p">,</span>
                 <span class="n">frequency_lines_for_residuals</span><span class="p">))</span></div>
-            
+
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">frequencies</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">abscissa</span>
@@ -539,7 +542,7 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
                 <span class="c1"># print(&#39;Closest Marker {:}&#39;.format(self.pole_positions[closest_marker]))</span>
             <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
                 <span class="k">return</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">previous_closest_marker_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">previous_closest_marker_index</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">previous_closest_marker_index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">selected_poles</span><span class="p">:</span>
                     <span class="n">order_index</span><span class="p">,</span> <span class="n">pole_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pole_indices</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">previous_closest_marker_index</span><span class="p">]</span>
                     <span class="n">pole</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">polypy</span><span class="o">.</span><span class="n">pole_list</span><span class="p">[</span><span class="n">order_index</span><span class="p">][</span><span class="n">pole_index</span><span class="p">]</span>
@@ -626,6 +629,7 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span> <span class="o">=</span> <span class="n">ShapeArray</span><span class="p">((</span><span class="mi">0</span><span class="p">,),</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">first_stability</span> <span class="o">=</span> <span class="kc">True</span>
 
@@ -687,9 +691,9 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
     <span class="nd">@property</span>
     <span class="k">def</span> <span class="nf">frequency_slice</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">abscissa_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_frequency</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span> <span class="o">&lt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span><span class="p">)</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span><span class="p">[</span><span class="n">abscissa_indices</span><span class="p">]</span>
         <span class="k">if</span> <span class="n">abscissa</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
@@ -721,7 +725,10 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">all_frequency_lines_checkbox</span><span class="o">.</span><span class="n">stateChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pole_selection_changed</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">lines_at_resonance_spinbox</span><span class="o">.</span><span class="n">valueChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pole_selection_changed</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">lines_at_residuals_spinbox</span><span class="o">.</span><span class="n">valueChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pole_selection_changed</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">use_residuals_checkbox</span><span class="o">.</span><span class="n">stateChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pole_selection_changed</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_low_residuals_checkbox</span><span class="o">.</span><span class="n">stateChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pole_selection_changed</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">use_high_residuals_checkbox</span><span class="o">.</span><span class="n">stateChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pole_selection_changed</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode_display_selector</span><span class="o">.</span><span class="n">currentIndexChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reconstruction_mode_selector</span><span class="o">.</span><span class="n">currentIndexChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">export_fit_data_button</span><span class="o">.</span><span class="n">clicked</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">export_fit_data</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">all_frequency_lines_checkbox</span><span class="o">.</span><span class="n">stateChanged</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">show_line_selectors</span><span class="p">)</span></div>
 
@@ -786,19 +793,20 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="PolyPy_GUI.create_frf_window"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.create_frf_window">[docs]</a>    <span class="k">def</span> <span class="nf">create_frf_window</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="p">(</span><span class="s1">&#39;frf&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">,</span> <span class="n">Fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">)))</span>
+            <span class="p">(</span><span class="s1">&#39;frf&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">,</span> <span class="n">Fit</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">)))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="PolyPy_GUI.create_cmif_window"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.create_cmif_window">[docs]</a>    <span class="k">def</span> <span class="nf">create_cmif_window</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="p">(</span><span class="s1">&#39;cmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(),</span> <span class="n">Fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">())))</span>
+            <span class="p">(</span><span class="s1">&#39;cmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(),</span> <span class="n">Fit</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">())))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate_log</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">actionOrdinate_Log</span><span class="o">.</span><span class="n">setChecked</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="PolyPy_GUI.create_qmif_window"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.create_qmif_window">[docs]</a>    <span class="k">def</span> <span class="nf">create_qmif_window</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="p">(</span><span class="s1">&#39;qmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span><span class="n">part</span><span class="o">=</span><span class="s1">&#39;real&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">datatype_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;imag&#39;</span><span class="p">),</span> <span class="n">Fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span><span class="n">part</span><span class="o">=</span><span class="s1">&#39;imag&#39;</span><span class="p">))))</span>
+            <span class="p">(</span><span class="s1">&#39;qmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span><span class="n">part</span><span class="o">=</span><span class="s1">&#39;real&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">datatype_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;imag&#39;</span><span class="p">),</span>
+                             <span class="n">Fit</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span><span class="n">part</span><span class="o">=</span><span class="s1">&#39;imag&#39;</span><span class="p">))))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate_log</span> <span class="o">=</span> <span class="kc">True</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">actionOrdinate_Log</span><span class="o">.</span><span class="n">setChecked</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">()</span></div>
@@ -811,12 +819,12 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
             <span class="n">mmif</span> <span class="o">=</span> <span class="n">test_mmif</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
             <span class="n">mmif</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="mi">1</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="p">(</span><span class="s1">&#39;mmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span> <span class="o">=</span> <span class="n">test_mmif</span><span class="p">,</span> <span class="n">Fit</span> <span class="o">=</span> <span class="n">mmif</span><span class="p">)))</span>
+            <span class="p">(</span><span class="s1">&#39;mmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span><span class="o">=</span><span class="n">test_mmif</span><span class="p">,</span> <span class="n">Fit</span><span class="o">=</span><span class="n">mmif</span><span class="p">)))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="PolyPy_GUI.create_nmif_window"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.create_nmif_window">[docs]</a>    <span class="k">def</span> <span class="nf">create_nmif_window</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-            <span class="p">(</span><span class="s1">&#39;nmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">(),</span> <span class="n">Fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">())))</span>
+            <span class="p">(</span><span class="s1">&#39;nmif&#39;</span><span class="p">,</span> <span class="n">GUIPlot</span><span class="p">(</span><span class="n">Data</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">(),</span> <span class="n">Fit</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">())))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">update_resynthesis</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="PolyPy_GUI.update_resynthesis"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.update_resynthesis">[docs]</a>    <span class="k">def</span> <span class="nf">update_resynthesis</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -824,24 +832,34 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span> <span class="o">=</span> <span class="p">[(</span><span class="n">function_type</span><span class="p">,</span> <span class="n">window</span><span class="p">)</span> <span class="k">for</span> <span class="n">function_type</span><span class="p">,</span>
                                   <span class="n">window</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span> <span class="k">if</span> <span class="n">window</span><span class="o">.</span><span class="n">isVisible</span><span class="p">()]</span>
         <span class="c1"># Now go and update the resynthesized FRFs</span>
-        <span class="n">data_computed</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;frf&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reconstruction_mode_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">data_computed</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;frf&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reconstruction_mode_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">data_computed</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;frf&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_kernel</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reconstruction_mode_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">data_computed</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;frf&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_residual</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}</span>
         <span class="k">for</span> <span class="n">function_type</span><span class="p">,</span> <span class="n">window</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesis_plots</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">function_type</span> <span class="ow">in</span> <span class="n">data_computed</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">function_type</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data_computed</span><span class="p">:</span>
                 <span class="k">if</span> <span class="n">function_type</span> <span class="o">==</span> <span class="s1">&#39;cmif&#39;</span><span class="p">:</span>
-                    <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+                        <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_computed</span><span class="p">[</span><span class="s1">&#39;frf&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
                 <span class="k">elif</span> <span class="n">function_type</span> <span class="o">==</span> <span class="s1">&#39;qmif&#39;</span><span class="p">:</span>
-                    <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span>
+                    <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_computed</span><span class="p">[</span><span class="s1">&#39;frf&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">compute_cmif</span><span class="p">(</span>
                         <span class="n">part</span><span class="o">=</span><span class="s1">&#39;real&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">datatype_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;imag&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
                 <span class="k">elif</span> <span class="n">function_type</span> <span class="o">==</span> <span class="s1">&#39;mmif&#39;</span><span class="p">:</span>
                     <span class="k">try</span><span class="p">:</span>
-                        <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_mmif</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+                        <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_computed</span><span class="p">[</span><span class="s1">&#39;frf&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">compute_mmif</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
                     <span class="k">except</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">LinAlgError</span><span class="p">:</span>
                         <span class="n">test_mmif</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">compute_mmif</span><span class="p">()</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
                         <span class="n">test_mmif</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="mi">1</span>
                         <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="n">test_mmif</span>
                 <span class="k">elif</span> <span class="n">function_type</span> <span class="o">==</span> <span class="s1">&#39;nmif&#39;</span><span class="p">:</span>
-                    <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+                    <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_computed</span><span class="p">[</span><span class="s1">&#39;frf&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">compute_nmif</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
             <span class="n">window</span><span class="o">.</span><span class="n">data_dictionary</span><span class="p">[</span><span class="s1">&#39;Fit&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">data_computed</span><span class="p">[</span><span class="n">function_type</span><span class="p">]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mode_display_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">symbol</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="s1">&#39;vline&#39;</span><span class="p">,</span><span class="s1">&#39;o&#39;</span><span class="p">,</span><span class="s1">&#39;x&#39;</span><span class="p">,</span><span class="s1">&#39;+&#39;</span><span class="p">,</span><span class="s1">&#39;star&#39;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">mode_display_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()]</span>
+                <span class="n">window</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="s1">&#39;Fit&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="o">.</span><span class="n">frequency</span><span class="p">,</span> <span class="s1">&#39;</span><span class="si">{index:}</span><span class="s1">: </span><span class="si">{abscissa:0.2f}</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">symbol</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">window</span><span class="o">.</span><span class="n">marker_data</span><span class="p">[</span><span class="s1">&#39;Fit&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
             <span class="n">window</span><span class="o">.</span><span class="n">update</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="PolyPy_GUI.compute_shapes"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.compute_shapes">[docs]</a>    <span class="k">def</span> <span class="nf">compute_shapes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -856,6 +874,7 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
                 <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">ordinate</span> <span class="o">=</span> <span class="mi">0</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span> <span class="o">=</span> <span class="n">ShapeArray</span><span class="p">((</span><span class="mi">0</span><span class="p">,),</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">negative_drive_points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">0</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
                 <span class="k">return</span>
@@ -864,12 +883,13 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
             <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">poles</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">]</span>
             <span class="n">H</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_slice</span><span class="p">]</span>
             <span class="n">num_outputs</span><span class="p">,</span> <span class="n">num_inputs</span><span class="p">,</span> <span class="n">num_elements</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">shape</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_residual</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shapes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">resynthesized_frfs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_residual</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfs_synth_kernel</span> <span class="o">=</span> <span class="p">(</span>
                 <span class="n">compute_shapes_multireference</span><span class="p">(</span>
                     <span class="bp">self</span><span class="o">.</span><span class="n">frfs</span><span class="p">,</span> <span class="n">natural_frequencies</span><span class="p">,</span> <span class="n">damping_ratios</span><span class="p">,</span>
-                    <span class="n">participation_factors</span><span class="p">,</span> 
+                    <span class="n">participation_factors</span><span class="p">,</span>
                     <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">complex_modes_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">(),</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">use_residuals_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">(),</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_low_residuals_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">(),</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">use_high_residuals_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">(),</span>
                     <span class="bp">self</span><span class="o">.</span><span class="n">min_frequency</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span><span class="p">,</span>
                     <span class="bp">self</span><span class="o">.</span><span class="n">datatype_selector</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">(),</span>
                     <span class="kc">None</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_frequency_lines_checkbox</span><span class="o">.</span><span class="n">isChecked</span><span class="p">()</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">lines_at_resonance_spinbox</span><span class="o">.</span><span class="n">value</span><span class="p">(),</span>
@@ -961,7 +981,7 @@ <h1>Source code for sdynpy.modal.sdynpy_polypy</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba6c430>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb6b00>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/modal/sdynpy_signal_processing_gui.html b/_modules/sdynpy/modal/sdynpy_signal_processing_gui.html
index f5f1b02..d31a22c 100644
--- a/_modules/sdynpy/modal/sdynpy_signal_processing_gui.html
+++ b/_modules/sdynpy/modal/sdynpy_signal_processing_gui.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_signal_processing_gui &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_signal_processing_gui &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -118,12 +118,12 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
 <span class="kn">from</span> <span class="nn">qtpy.QtGui</span> <span class="kn">import</span> <span class="n">QIcon</span><span class="p">,</span> <span class="n">QFont</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtCore</span> <span class="kn">import</span> <span class="n">Qt</span><span class="p">,</span> <span class="n">QCoreApplication</span><span class="p">,</span> <span class="n">QRect</span>
 <span class="kn">from</span> <span class="nn">qtpy.QtWidgets</span> <span class="kn">import</span> <span class="p">(</span><span class="n">QToolTip</span><span class="p">,</span> <span class="n">QLabel</span><span class="p">,</span> <span class="n">QPushButton</span><span class="p">,</span> <span class="n">QApplication</span><span class="p">,</span>
-                             <span class="n">QGroupBox</span><span class="p">,</span> <span class="n">QWidget</span><span class="p">,</span> <span class="n">QMessageBox</span><span class="p">,</span> <span class="n">QHBoxLayout</span><span class="p">,</span>
-                             <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="p">,</span> <span class="n">QMainWindow</span><span class="p">,</span>
-                             <span class="n">QFileDialog</span><span class="p">,</span> <span class="n">QErrorMessage</span><span class="p">,</span> <span class="n">QListWidget</span><span class="p">,</span> <span class="n">QListWidgetItem</span><span class="p">,</span>
-                             <span class="n">QLineEdit</span><span class="p">,</span>
-                             <span class="n">QDockWidget</span><span class="p">,</span> <span class="n">QGridLayout</span><span class="p">,</span> <span class="n">QButtonGroup</span><span class="p">,</span> <span class="n">QDialog</span><span class="p">,</span>
-                             <span class="n">QCheckBox</span><span class="p">,</span> <span class="n">QRadioButton</span><span class="p">,</span> <span class="n">QMenuBar</span><span class="p">,</span> <span class="n">QMenu</span><span class="p">)</span>
+                            <span class="n">QGroupBox</span><span class="p">,</span> <span class="n">QWidget</span><span class="p">,</span> <span class="n">QMessageBox</span><span class="p">,</span> <span class="n">QHBoxLayout</span><span class="p">,</span>
+                            <span class="n">QVBoxLayout</span><span class="p">,</span> <span class="n">QSizePolicy</span><span class="p">,</span> <span class="n">QMainWindow</span><span class="p">,</span>
+                            <span class="n">QFileDialog</span><span class="p">,</span> <span class="n">QErrorMessage</span><span class="p">,</span> <span class="n">QListWidget</span><span class="p">,</span> <span class="n">QListWidgetItem</span><span class="p">,</span>
+                            <span class="n">QLineEdit</span><span class="p">,</span>
+                            <span class="n">QDockWidget</span><span class="p">,</span> <span class="n">QGridLayout</span><span class="p">,</span> <span class="n">QButtonGroup</span><span class="p">,</span> <span class="n">QDialog</span><span class="p">,</span>
+                            <span class="n">QCheckBox</span><span class="p">,</span> <span class="n">QRadioButton</span><span class="p">,</span> <span class="n">QMenuBar</span><span class="p">,</span> <span class="n">QMenu</span><span class="p">)</span>
 <span class="k">try</span><span class="p">:</span>
     <span class="kn">from</span> <span class="nn">qtpy.QtGui</span> <span class="kn">import</span> <span class="n">QAction</span>
 <span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
@@ -134,6 +134,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
 <span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">get_window</span>
 <span class="kn">import</span> <span class="nn">traceback</span>
 
+
 <div class="viewcode-block" id="SignalProcessingGUI"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI">[docs]</a><span class="k">class</span> <span class="nc">SignalProcessingGUI</span><span class="p">(</span><span class="n">QMainWindow</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;An iteractive window allowing users to compute FRFs&quot;&quot;&quot;</span>
 
@@ -238,7 +239,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
                        <span class="bp">self</span><span class="o">.</span><span class="n">hysteresis_level_selector_references</span><span class="p">]:</span>
             <span class="n">widget</span><span class="o">.</span><span class="n">setVisible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">connect_callbacks</span><span class="p">()</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">time_history_array</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">time_history_array</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">time_history_data</span> <span class="o">=</span> <span class="n">time_history_array</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">initialize_ui</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">setWindowTitle</span><span class="p">(</span><span class="s1">&#39;Graphical Signal Processing Tool&#39;</span><span class="p">)</span>
@@ -767,7 +768,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
             <span class="n">pen</span> <span class="o">=</span> <span class="n">pqtg</span><span class="o">.</span><span class="n">mkPen</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">(</span><span class="n">j</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span><span class="p">)])</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">referencesPlot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data_entry</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
                                      <span class="n">y</span><span class="o">=</span><span class="n">data_entry</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">text</span><span class="p">,</span> <span class="n">pen</span><span class="o">=</span><span class="n">pen</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">xrange</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">xrange</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">referencesPlot</span><span class="o">.</span><span class="n">setXRange</span><span class="p">(</span><span class="o">*</span><span class="n">xrange</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">referencesPlot</span><span class="o">.</span><span class="n">addItem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_selector_references</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">referencesPlot</span><span class="o">.</span><span class="n">addItem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trigger_level_selector_references</span><span class="p">)</span>
@@ -801,7 +802,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
             <span class="c1"># print(pen)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">responsesPlot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">data_entry</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span>
                                     <span class="n">y</span><span class="o">=</span><span class="n">data_entry</span><span class="o">.</span><span class="n">ordinate</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">text</span><span class="p">,</span> <span class="n">pen</span><span class="o">=</span><span class="n">pen</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">xrange</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">xrange</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">responsesPlot</span><span class="o">.</span><span class="n">setXRange</span><span class="p">(</span><span class="o">*</span><span class="n">xrange</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mf">0.0</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">responsesPlot</span><span class="o">.</span><span class="n">addItem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">time_selector_responses</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">responsesPlot</span><span class="o">.</span><span class="n">addItem</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">trigger_level_selector_responses</span><span class="p">)</span>
@@ -1012,7 +1013,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
             <span class="n">frequency_spacing</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequencySpacingDoubleSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
             <span class="n">frame_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frameSizeSpinBox</span><span class="o">.</span><span class="n">value</span><span class="p">()</span>
             <span class="n">frame_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">start_index</span> <span class="k">for</span> <span class="n">frame_number</span><span class="p">,</span> <span class="n">start_index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frame_start_indices</span><span class="p">)</span>
-                                      <span class="k">if</span> <span class="ow">not</span> <span class="n">frame_number</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_frames</span><span class="p">])[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">frame_size</span><span class="p">)</span>
+                                      <span class="k">if</span> <span class="n">frame_number</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ignore_frames</span><span class="p">])[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">frame_size</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">frame_indices</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">QMessageBox</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;Invalid Frames&#39;</span><span class="p">,</span>
                                      <span class="s1">&#39;At least one measurement frame must be selected&#39;</span><span class="p">)</span>
@@ -1095,7 +1096,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
                     <span class="bp">self</span><span class="o">.</span><span class="n">frf_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
                                                <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">frf_coordinate</span><span class="p">)</span>
                     <span class="n">success</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># H2</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>  <span class="c1"># H2</span>
                     <span class="k">if</span> <span class="p">(</span><span class="n">response_fft</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">reference_fft</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
                         <span class="n">QMessageBox</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;Bad FRF Shape&#39;</span><span class="p">,</span>
                                              <span class="s1">&#39;For H2, Number of inputs must equal number of outputs&#39;</span><span class="p">)</span>
@@ -1110,7 +1111,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
                         <span class="bp">self</span><span class="o">.</span><span class="n">frf_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
                                                    <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">frf_coordinate</span><span class="p">)</span>
                         <span class="n">success</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> <span class="c1"># H3</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>  <span class="c1"># H3</span>
                     <span class="k">if</span> <span class="p">(</span><span class="n">response_fft</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">reference_fft</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
                         <span class="n">QMessageBox</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;Bad FRF Shape&#39;</span><span class="p">,</span>
                                              <span class="s1">&#39;For H3, Number of inputs must equal number of outputs&#39;</span><span class="p">)</span>
@@ -1130,7 +1131,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
                         <span class="bp">self</span><span class="o">.</span><span class="n">frf_data</span> <span class="o">=</span> <span class="n">data_array</span><span class="p">(</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
                                                    <span class="n">freq</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">frf_coordinate</span><span class="p">)</span>
                         <span class="n">success</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># Hv</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">frfComboBox</span><span class="o">.</span><span class="n">currentIndex</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># Hv</span>
                     <span class="n">Gxx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...iaf,...iaf-&gt;...if&#39;</span><span class="p">,</span> <span class="n">response_fft</span><span class="p">,</span>
                                     <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">response_fft</span><span class="p">))[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
                     <span class="n">Gxf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...iaf,...jaf-&gt;...ifj&#39;</span><span class="p">,</span> <span class="n">response_fft</span><span class="p">,</span>
@@ -1141,7 +1142,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
                     <span class="c1"># print(Gxf.shape)</span>
                     <span class="c1"># print(Gff.shape)</span>
                     <span class="c1"># Broadcast over all responses</span>
-                    <span class="n">Gff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">Gff</span><span class="p">,</span><span class="n">Gxx</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="n">Gff</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:])</span>
+                    <span class="n">Gff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">Gff</span><span class="p">,</span> <span class="n">Gxx</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="n">Gff</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:])</span>
                     <span class="n">Gffx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">Gff</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">Gxf</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))],</span>
                                      <span class="p">[</span><span class="n">Gxf</span><span class="p">,</span> <span class="n">Gxx</span><span class="p">]])</span>
                     <span class="c1"># Compute eigenvalues</span>
@@ -1353,7 +1354,7 @@ <h1>Source code for sdynpy.modal.sdynpy_signal_processing_gui</h1><div class="hi
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc5c90>
+<jinja2.runtime.BlockReference object at 0x7f5a27366200>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/modal/sdynpy_smac.html b/_modules/sdynpy/modal/sdynpy_smac.html
index 5237a5e..9ed578d 100644
--- a/_modules/sdynpy/modal/sdynpy_smac.html
+++ b/_modules/sdynpy/modal/sdynpy_smac.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.modal.sdynpy_smac &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.modal.sdynpy_smac &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -125,6 +125,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
 <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">setConfigOption</span><span class="p">(</span><span class="s1">&#39;background&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
 <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">setConfigOption</span><span class="p">(</span><span class="s1">&#39;foreground&#39;</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
 
+
 <div class="viewcode-block" id="correlation_coefficient"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_smac.correlation_coefficient.html#sdynpy.modal.sdynpy_smac.correlation_coefficient">[docs]</a><span class="k">def</span> <span class="nf">correlation_coefficient</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
     <span class="n">mx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">my</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -149,9 +150,9 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">min_frequency</span> <span class="o">=</span> <span class="n">min_frequency</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">max_frequency</span> <span class="o">=</span> <span class="n">max_frequency</span>
         <span class="n">abscissa_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">bool</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">min_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span> <span class="o">&gt;=</span> <span class="n">min_frequency</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">abscissa_indices</span> <span class="o">&amp;=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span> <span class="o">&lt;=</span> <span class="n">max_frequency</span><span class="p">)</span>
         <span class="n">abscissa</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequencies</span><span class="p">[</span><span class="n">abscissa_indices</span><span class="p">]</span>
         <span class="n">freq_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">abscissa</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">abscissa</span><span class="p">)))</span>
@@ -295,7 +296,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">reference_index</span><span class="p">,</span> <span class="p">(</span><span class="n">matrix</span><span class="p">,</span> <span class="n">peaklist</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_correlation_matrix</span><span class="p">,</span> <span class="n">peaklists</span><span class="p">)):</span>
             <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">peaklist</span><span class="p">):</span>
                 <span class="n">value</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">root_index_list</span> <span class="ow">or</span> <span class="n">root_index_list</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">value</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">root_index_list</span> <span class="ow">or</span> <span class="n">root_index_list</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">value</span><span class="p">:</span>
                     <span class="n">root_index_list</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">reference_index</span><span class="p">)</span>
         <span class="n">root_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">root_index_list</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;frequency&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">),</span>
                                                             <span class="p">(</span><span class="s1">&#39;damping&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">),</span>
@@ -339,7 +340,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
                             <span class="k">if</span> <span class="p">((</span><span class="mi">1</span> <span class="o">-</span> <span class="n">mmif</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="n">peaks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">peak_index</span><span class="p">]])</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">mmif</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="n">peaks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">peak_index</span><span class="p">]])</span> <span class="o">&gt;=</span> <span class="mf">0.6</span>
                                     <span class="ow">and</span> <span class="n">mmif</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="n">peaks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">peak_index</span><span class="p">]]</span> <span class="o">&lt;</span> <span class="mf">0.9</span>
                                     <span class="c1"># and nroots[j] &gt; 0</span>
-                                    <span class="p">):</span>
+                                    <span class="p">):</span>  <span class="c1"># noqa: E124</span>
                                 <span class="n">nroots</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
                         <span class="k">else</span><span class="p">:</span>
                             <span class="n">nroots</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
@@ -366,7 +367,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
                             <span class="c1"># The primary mif must also have a peak - I don&#39;t think this is true</span>
                             <span class="k">if</span> <span class="p">((</span><span class="n">cmif</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="n">peaks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">peak_index</span><span class="p">]])</span> <span class="o">/</span> <span class="p">(</span><span class="n">cmif</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">ordinate</span><span class="p">[</span><span class="n">peaks</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">peak_index</span><span class="p">]])</span> <span class="o">&gt;=</span> <span class="mf">0.1</span>
                                     <span class="c1"># and nroots[j] &gt; 0</span>
-                                    <span class="p">):</span>
+                                    <span class="p">):</span>  <span class="c1"># noqa: E124</span>
                                 <span class="n">nroots</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
                         <span class="k">else</span><span class="p">:</span>
                             <span class="n">nroots</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
@@ -1095,7 +1096,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="SMAC_GUI.add_root"><a class="viewcode-back" href="../../../_autosummary/sdynpy.modal.sdynpy_smac.SMAC_GUI.html#sdynpy.modal.sdynpy_smac.SMAC_GUI.add_root">[docs]</a>    <span class="k">def</span> <span class="nf">add_root</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">freq</span><span class="p">,</span> <span class="n">damp</span><span class="p">,</span> <span class="n">corr</span> <span class="o">=</span> <span class="n">AddRootDialog</span><span class="o">.</span><span class="n">add_root</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">freq</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">freq</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="c1"># add root to the table</span>
             <span class="n">new_root</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rootlist</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
             <span class="n">new_root</span><span class="p">[</span><span class="s1">&#39;frequency&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">freq</span>
@@ -1188,7 +1189,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
         <span class="k">for</span> <span class="n">reference_index</span><span class="p">,</span> <span class="p">(</span><span class="n">matrix</span><span class="p">,</span> <span class="n">peaklist</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_correlation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">peaklists</span><span class="p">)):</span>
             <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">peaklist</span><span class="p">):</span>
                 <span class="n">value</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
-                <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">root_index_list</span> <span class="ow">or</span> <span class="n">root_index_list</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">value</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">key</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">root_index_list</span> <span class="ow">or</span> <span class="n">root_index_list</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">value</span><span class="p">:</span>
                     <span class="n">root_index_list</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">reference_index</span><span class="p">)</span>
         <span class="n">root_list</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">root_index_list</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="p">[(</span><span class="s1">&#39;frequency&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">),</span>
                                                             <span class="p">(</span><span class="s1">&#39;damping&#39;</span><span class="p">,</span> <span class="s1">&#39;float64&#39;</span><span class="p">),</span>
@@ -1266,7 +1267,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
             <span class="n">pen</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">mkPen</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">(</span><span class="mi">0</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span><span class="p">)])</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">mif_plot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">mif</span><span class="o">.</span><span class="n">abscissa</span><span class="p">,</span> <span class="n">mif</span><span class="o">.</span><span class="n">ordinate</span><span class="p">)</span>
         <span class="c1"># Now plot the correlation coefficient</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial_correlation</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial_correlation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">curve</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_correlation</span><span class="p">):</span>
                 <span class="n">pen</span> <span class="o">=</span> <span class="n">pyqtgraph</span><span class="o">.</span><span class="n">mkPen</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mi">255</span> <span class="o">*</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm</span><span class="p">(</span><span class="n">i</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">cm_mod</span><span class="p">)])</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">correlation_plot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_correlation_frequencies</span><span class="p">,</span> <span class="n">curve</span><span class="p">,</span> <span class="n">pen</span><span class="o">=</span><span class="n">pen</span><span class="p">)</span>
@@ -1352,7 +1353,7 @@ <h1>Source code for sdynpy.modal.sdynpy_smac</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb97a620>
+<jinja2.runtime.BlockReference object at 0x7f5a27366a10>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_camera.html b/_modules/sdynpy/signal_processing/sdynpy_camera.html
index 4b6a8c9..b9d090a 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_camera.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_camera.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_camera &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_camera &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -115,7 +115,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_camera</h1><div class="highl
 <div class="viewcode-block" id="camera_matrix_from_image"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_camera.camera_matrix_from_image.html#sdynpy.signal_processing.sdynpy_camera.camera_matrix_from_image">[docs]</a><span class="k">def</span> <span class="nf">camera_matrix_from_image</span><span class="p">(</span><span class="n">image_points</span><span class="p">,</span> <span class="n">spatial_points</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&#39;&#39;&#39;Computes camera matrices from at least six points</span>
 
-<span class="sd">    This function computes camera matrices K (intrinic parameters) and R|T </span>
+<span class="sd">    This function computes camera matrices K (intrinic parameters) and R|T</span>
 <span class="sd">    (extrinsic parameters) from a set of points defined on the image (u,v) and</span>
 <span class="sd">    in 3D space (x,y,z).  It sets up a system of homogeneous linear equations</span>
 <span class="sd">    for the camera parameters and solves for them using the singular value</span>
@@ -129,7 +129,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_camera</h1><div class="highl
 <span class="sd">        A n x 2 array where n is the number of points being correlated.  The</span>
 <span class="sd">        first column is the horizontal pixel index from the left side of the</span>
 <span class="sd">        image, and the second column is the vertical pixel index from the top</span>
-<span class="sd">        of the image.  Pixel locations can be extracted from software such as </span>
+<span class="sd">        of the image.  Pixel locations can be extracted from software such as</span>
 <span class="sd">        Paint or GIMP, or if the image is plotted using</span>
 <span class="sd">        matplotlib.pyplot.imshow.</span>
 <span class="sd">    spatial_points : ndarray</span>
@@ -267,17 +267,47 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_camera</h1><div class="highl
 <span class="sd">    displacement_derivatives.py.</span>
 <span class="sd">    &#39;&#39;&#39;</span>
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
-        <span class="p">[[(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
-          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span>
-                                                                                                                                                                                                                                                        <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
-          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span>
-                                                                                                                                                                                                                                                        <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span>
+        <span class="p">[[(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+          <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+          <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+          <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span>
           <span class="p">],</span>
-         <span class="p">[(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
-          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-                                                                                                                                                                                                              <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
-          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
-                                                                                                                                                                                                              <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span>
+         <span class="p">[(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+          <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+          <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span>
+          <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
+          <span class="o">-</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+             <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+          <span class="o">/</span> <span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span> <span class="o">*</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">K</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="n">RT</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span><span class="o">**</span><span class="mi">2</span>
           <span class="p">]]</span>
     <span class="p">)</span></div>
 
@@ -443,7 +473,8 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_camera</h1><div class="highl
         <span class="n">valid_plane_points</span> <span class="o">=</span> <span class="n">plane_points_h</span><span class="p">[:,</span> <span class="n">mask</span><span class="p">]</span>
 
         <span class="k">def</span> <span class="nf">optfunc</span><span class="p">(</span><span class="n">h_vect</span><span class="p">):</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">valid_image_points</span> <span class="o">-</span> <span class="p">(</span><span class="n">h_vect</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="o">@</span> <span class="n">valid_plane_points</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">h_vect</span><span class="p">[</span><span class="mi">6</span><span class="p">:</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="o">@</span> <span class="n">valid_plane_points</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">valid_image_points</span> <span class="o">-</span> <span class="p">(</span><span class="n">h_vect</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="o">@</span> <span class="n">valid_plane_points</span><span class="p">)</span>
+                                  <span class="o">/</span> <span class="p">(</span><span class="n">h_vect</span><span class="p">[</span><span class="mi">6</span><span class="p">:</span><span class="mi">9</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span> <span class="o">@</span> <span class="n">valid_plane_points</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
         <span class="n">soln</span> <span class="o">=</span> <span class="n">least_squares</span><span class="p">(</span><span class="n">optfunc</span><span class="p">,</span> <span class="n">homography_guesses</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;lm&#39;</span><span class="p">)</span>
         <span class="n">homography</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">soln</span><span class="o">.</span><span class="n">x</span><span class="p">)</span>
 
@@ -747,9 +778,9 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_camera</h1><div class="highl
     <span class="n">K_h</span> <span class="o">=</span> <span class="n">Q</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">cholesky</span><span class="p">(</span><span class="n">P_b</span><span class="p">)</span> <span class="o">@</span> <span class="n">Q</span>
     <span class="n">K</span> <span class="o">=</span> <span class="n">K_h</span> <span class="o">/</span> <span class="n">K_h</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
     <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span> <span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">K</span><span class="p">)</span> <span class="o">@</span> <span class="n">M</span>
-    <span class="n">l</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">A</span><span class="p">))</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
-    <span class="n">R</span> <span class="o">=</span> <span class="n">l</span> <span class="o">*</span> <span class="n">A</span>
-    <span class="n">t</span> <span class="o">=</span> <span class="n">l</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">K</span><span class="p">)</span> <span class="o">@</span> <span class="n">P</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
+    <span class="n">L</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">A</span><span class="p">))</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="n">L</span> <span class="o">*</span> <span class="n">A</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="n">L</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">K</span><span class="p">)</span> <span class="o">@</span> <span class="n">P</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
     <span class="k">return</span> <span class="n">K</span><span class="p">,</span> <span class="n">R</span><span class="p">,</span> <span class="n">t</span></div>
 </pre></div>
 
@@ -760,7 +791,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_camera</h1><div class="highl
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb87c460>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb5660>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_complex.html b/_modules/sdynpy/signal_processing/sdynpy_complex.html
index a150ab2..1574b3f 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_complex.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_complex.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_complex &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_complex &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -111,7 +111,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_complex</h1><div class="high
 
 
 <div class="viewcode-block" id="collapse_complex_to_real"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_complex.collapse_complex_to_real.html#sdynpy.signal_processing.sdynpy_complex.collapse_complex_to_real">[docs]</a><span class="k">def</span> <span class="nf">collapse_complex_to_real</span><span class="p">(</span><span class="n">vector</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">preserve_magnitude</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                             <span class="n">force_angle</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+                             <span class="n">force_angle</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">vector</span><span class="p">)</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">vector</span><span class="p">)</span>
     <span class="n">slope</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">y</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">x</span> <span class="o">*</span> <span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -144,7 +144,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_complex</h1><div class="high
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb97ace0>
+<jinja2.runtime.BlockReference object at 0x7f5a270b6290>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_correlation.html b/_modules/sdynpy/signal_processing/sdynpy_correlation.html
index e6ebf09..2961474 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_correlation.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_correlation.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_correlation &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_correlation &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -131,8 +131,9 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_correlation</h1><div class="
     <span class="n">fft_1_original_shape</span> <span class="o">=</span> <span class="n">fft_1</span><span class="o">.</span><span class="n">shape</span>
     <span class="n">fft_1_flattened</span> <span class="o">=</span> <span class="n">fft_1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">fft_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
     <span class="n">fft_2_flattened</span> <span class="o">=</span> <span class="n">fft_2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">fft_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">frac</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fft_1_flattened</span> <span class="o">*</span> <span class="n">fft_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fft_1_flattened</span> <span class="o">*</span>
-                                                                                           <span class="n">fft_1_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fft_2_flattened</span> <span class="o">*</span> <span class="n">fft_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+    <span class="n">frac</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fft_1_flattened</span> <span class="o">*</span> <span class="n">fft_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span>
+            <span class="o">/</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fft_1_flattened</span> <span class="o">*</span> <span class="n">fft_1_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+               <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fft_2_flattened</span> <span class="o">*</span> <span class="n">fft_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
     <span class="k">return</span> <span class="n">frac</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">fft_1_original_shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
 
 
@@ -142,25 +143,28 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_correlation</h1><div class="
     <span class="n">th_1_original_shape</span> <span class="o">=</span> <span class="n">th_1</span><span class="o">.</span><span class="n">shape</span>
     <span class="n">th_1_flattened</span> <span class="o">=</span> <span class="n">th_1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">th_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
     <span class="n">th_2_flattened</span> <span class="o">=</span> <span class="n">th_2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">th_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-    <span class="n">trac</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">th_1_flattened</span> <span class="o">*</span> <span class="n">th_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span> <span class="o">/</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">th_1_flattened</span> <span class="o">*</span>
-                                                                                         <span class="n">th_1_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">th_2_flattened</span> <span class="o">*</span> <span class="n">th_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+    <span class="n">trac</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">th_1_flattened</span> <span class="o">*</span> <span class="n">th_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span><span class="o">**</span><span class="mi">2</span>
+            <span class="o">/</span> <span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">th_1_flattened</span> <span class="o">*</span> <span class="n">th_1_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+               <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">th_2_flattened</span> <span class="o">*</span> <span class="n">th_2_flattened</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
     <span class="k">return</span> <span class="n">trac</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">th_1_original_shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
- 
-<div class="viewcode-block" id="msf"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_correlation.msf.html#sdynpy.signal_processing.sdynpy_correlation.msf">[docs]</a><span class="k">def</span> <span class="nf">msf</span><span class="p">(</span><span class="n">shapes</span><span class="p">,</span><span class="n">reference_shapes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+
+<div class="viewcode-block" id="msf"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_correlation.msf.html#sdynpy.signal_processing.sdynpy_correlation.msf">[docs]</a><span class="k">def</span> <span class="nf">msf</span><span class="p">(</span><span class="n">shapes</span><span class="p">,</span> <span class="n">reference_shapes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">reference_shapes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">reference_shapes</span> <span class="o">=</span> <span class="n">shapes</span>
-    <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...ij,...ij-&gt;...j&#39;</span><span class="p">,</span><span class="n">shapes</span><span class="p">,</span><span class="n">reference_shapes</span><span class="o">.</span><span class="n">conj</span><span class="p">())</span><span class="o">/</span>
-              <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...ij,...ij-&gt;...j&#39;</span><span class="p">,</span><span class="n">reference_shapes</span><span class="p">,</span><span class="n">reference_shapes</span><span class="o">.</span><span class="n">conj</span><span class="p">()))</span>
+    <span class="n">output</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...ij,...ij-&gt;...j&#39;</span><span class="p">,</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">reference_shapes</span><span class="o">.</span><span class="n">conj</span><span class="p">())</span> <span class="o">/</span>
+              <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...ij,...ij-&gt;...j&#39;</span><span class="p">,</span> <span class="n">reference_shapes</span><span class="p">,</span> <span class="n">reference_shapes</span><span class="o">.</span><span class="n">conj</span><span class="p">()))</span>
     <span class="k">return</span> <span class="n">output</span></div>
 
-<div class="viewcode-block" id="orthog"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html#sdynpy.signal_processing.sdynpy_correlation.orthog">[docs]</a><span class="k">def</span> <span class="nf">orthog</span><span class="p">(</span><span class="n">shapes_1</span><span class="p">,</span><span class="n">mass_matrix</span><span class="p">,</span><span class="n">shapes_2</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span><span class="n">scaling</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">scaling</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;none&#39;</span><span class="p">,</span><span class="s1">&#39;unity&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">]:</span>
+
+<div class="viewcode-block" id="orthog"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html#sdynpy.signal_processing.sdynpy_correlation.orthog">[docs]</a><span class="k">def</span> <span class="nf">orthog</span><span class="p">(</span><span class="n">shapes_1</span><span class="p">,</span> <span class="n">mass_matrix</span><span class="p">,</span> <span class="n">shapes_2</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scaling</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">scaling</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;none&#39;</span><span class="p">,</span> <span class="s1">&#39;unity&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid scaling, should be one of &quot;none&quot;, &quot;unity&quot;, or None&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">shapes_2</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">shapes_2</span> <span class="o">=</span> <span class="n">shapes_1</span>
-    <span class="n">mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">shapes_1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">@</span> <span class="n">mass_matrix</span> <span class="o">@</span> <span class="n">shapes_2</span>
+    <span class="n">mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">shapes_1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">@</span> <span class="n">mass_matrix</span> <span class="o">@</span> <span class="n">shapes_2</span>
     <span class="k">if</span> <span class="n">scaling</span> <span class="o">==</span> <span class="s1">&#39;unity&#39;</span><span class="p">:</span>
-        <span class="n">diagonal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...ii-&gt;...i&#39;</span><span class="p">,</span><span class="n">mat</span><span class="p">)</span>
+        <span class="n">diagonal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...ii-&gt;...i&#39;</span><span class="p">,</span> <span class="n">mat</span><span class="p">)</span>
         <span class="n">scaling</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">diagonal</span><span class="p">)</span>
         <span class="n">scaling_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">mat</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
         <span class="n">scaling_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span>
@@ -169,6 +173,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_correlation</h1><div class="
         <span class="n">mat</span> <span class="o">=</span> <span class="n">scaling_matrix</span> <span class="o">@</span> <span class="n">mat</span> <span class="o">@</span> <span class="n">scaling_matrix</span>
     <span class="k">return</span> <span class="n">mat</span></div>
 
+
 <div class="viewcode-block" id="matrix_plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_correlation.matrix_plot.html#sdynpy.signal_processing.sdynpy_correlation.matrix_plot">[docs]</a><span class="k">def</span> <span class="nf">matrix_plot</span><span class="p">(</span><span class="n">shape_matrix</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">display_values</span><span class="o">=</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.1</span><span class="p">),</span> <span class="n">text_size</span><span class="o">=</span><span class="mi">12</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                 <span class="n">boundaries</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">boundaries</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -197,7 +202,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_correlation</h1><div class="
         <span class="nd">@ticker</span><span class="o">.</span><span class="n">FuncFormatter</span>
         <span class="k">def</span> <span class="nf">major_formatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">pos</span><span class="p">):</span>
             <span class="n">x</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">index_map</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">index_map</span><span class="p">:</span>
                 <span class="k">return</span> <span class="s1">&#39;&#39;</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">x</span> <span class="o">=</span> <span class="n">index_map</span><span class="p">[</span><span class="n">x</span><span class="p">]</span>
@@ -217,7 +222,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_correlation</h1><div class="
     <span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">ticker</span><span class="o">.</span><span class="n">MaxNLocator</span><span class="p">(</span><span class="n">integer</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Shape Number&#39;</span><span class="p">)</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Shape Number&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">display_values</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">display_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">ndenumerate</span><span class="p">(</span><span class="n">shape_matrix</span><span class="p">):</span>
             <span class="k">if</span> <span class="p">((</span><span class="kc">True</span> <span class="k">if</span> <span class="n">display_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">(</span><span class="n">val</span> <span class="o">&gt;</span> <span class="n">display_values</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
                 <span class="ow">and</span>
@@ -234,7 +239,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_correlation</h1><div class="
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc6950>
+<jinja2.runtime.BlockReference object at 0x7f5a2706d7e0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_cpsd.html b/_modules/sdynpy/signal_processing/sdynpy_cpsd.html
index 20ace35..60b064f 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_cpsd.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_cpsd.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_cpsd &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_cpsd &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -146,7 +146,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
 <div class="viewcode-block" id="cpsd"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_cpsd.cpsd.html#sdynpy.signal_processing.sdynpy_cpsd.cpsd">[docs]</a><span class="k">def</span> <span class="nf">cpsd</span><span class="p">(</span><span class="n">signals</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
          <span class="n">samples_per_frame</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">overlap</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
          <span class="n">window</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">averages_to_keep</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-         <span class="n">only_asds</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+         <span class="n">only_asds</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">reference_signals</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Compute cpsd from signals</span>
 
@@ -162,12 +162,18 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
 <span class="sd">    overlap : float</span>
 <span class="sd">        Overlap as a fraction of the frame (e.g. 0.5 not 50).</span>
 <span class="sd">    window : str</span>
-<span class="sd">        Name of a window function in scipy.signal.windows.</span>
+<span class="sd">        Name of a window function in scipy.signal.windows, or a NumPy ndarray</span>
+<span class="sd">        with the window coefficients</span>
 <span class="sd">    averages_to_keep : int, optional</span>
 <span class="sd">        Optional number of averages to use. The default is None.</span>
 <span class="sd">    only_asds : bool, optional</span>
 <span class="sd">        If True, only compute autospectral densities, otherwise compute the</span>
 <span class="sd">        full CPSD matrix</span>
+<span class="sd">    reference_signals : np.ndarray</span>
+<span class="sd">        If specified, these signals will be used for the columns of the CPSD</span>
+<span class="sd">        matrix.  If not specified, the signals passed to the `signals` argument</span>
+<span class="sd">        will be used for both the rows and columns of the CPSD matrix.  Cannot</span>
+<span class="sd">        be used simultaneously with the `only_asds` argument being set to True.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -180,7 +186,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
 <span class="sd">    &quot;&quot;&quot;</span>
 
     <span class="n">samples_per_acquire</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">samples_per_frame</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">overlap</span><span class="p">))</span>
-    <span class="n">window_array</span> <span class="o">=</span> <span class="n">sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">get_window</span><span class="p">(</span><span class="n">window</span><span class="o">.</span><span class="n">lower</span><span class="p">(),</span> <span class="n">samples_per_frame</span><span class="p">,</span> <span class="n">fftbins</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="nb">str</span><span class="p">):</span> <span class="c1"># This will pass if it is a string or a container with a string as the first value</span>
+        <span class="n">window_array</span> <span class="o">=</span> <span class="n">sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">get_window</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="n">samples_per_frame</span><span class="p">,</span> <span class="n">fftbins</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">window_array</span> <span class="o">=</span> <span class="n">window</span>
     <span class="n">window_correction</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">window_array</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
     <span class="n">frequency_spacing</span> <span class="o">=</span> <span class="n">sample_rate</span> <span class="o">/</span> <span class="n">samples_per_frame</span>
     <span class="n">time_starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">signals</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">samples_per_frame</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">samples_per_acquire</span><span class="p">)</span>
@@ -190,20 +199,27 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
     <span class="n">response_fft_array</span> <span class="o">=</span> <span class="n">signals</span><span class="p">[:,</span> <span class="n">time_indices_for_averages</span><span class="p">]</span>
     <span class="n">response_fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">response_fft_array</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">window_array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">only_asds</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">reference_signals</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Cannot specify `only_asds` equal to True and simultaneously provide reference signals&#39;</span><span class="p">)</span>
         <span class="n">response_spectral_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span>
             <span class="s1">&#39;iaf,iaf-&gt;fi&#39;</span><span class="p">,</span> <span class="n">response_fft</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">response_fft</span><span class="p">))</span> <span class="o">/</span> <span class="n">response_fft</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
     <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">reference_signals</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">reference_fft</span> <span class="o">=</span> <span class="n">response_fft</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">reference_fft_array</span> <span class="o">=</span> <span class="n">reference_signals</span><span class="p">[:,</span><span class="n">time_indices_for_averages</span><span class="p">]</span>
+            <span class="n">reference_fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">reference_fft_array</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">*</span> <span class="n">window_array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">response_spectral_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span>
-            <span class="s1">&#39;iaf,jaf-&gt;fij&#39;</span><span class="p">,</span> <span class="n">response_fft</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">response_fft</span><span class="p">))</span> <span class="o">/</span> <span class="n">response_fft</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="s1">&#39;iaf,jaf-&gt;fij&#39;</span><span class="p">,</span> <span class="n">response_fft</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">reference_fft</span><span class="p">))</span> <span class="o">/</span> <span class="n">response_fft</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
     <span class="c1"># Normalize</span>
     <span class="n">response_spectral_matrix</span> <span class="o">*=</span> <span class="p">(</span><span class="n">frequency_spacing</span> <span class="o">*</span> <span class="n">window_correction</span> <span class="o">/</span>
                                  <span class="n">sample_rate</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
     <span class="n">response_spectral_matrix</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*=</span> <span class="mi">2</span>
     <span class="k">return</span> <span class="n">frequency_spacing</span><span class="p">,</span> <span class="n">response_spectral_matrix</span></div>
 
-
 <div class="viewcode-block" id="dB_pow"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_cpsd.dB_pow.html#sdynpy.signal_processing.sdynpy_cpsd.dB_pow">[docs]</a><span class="k">def</span> <span class="nf">dB_pow</span><span class="p">(</span><span class="n">x</span><span class="p">):</span> <span class="k">return</span> <span class="mi">10</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
 
+
 <div class="viewcode-block" id="db2scale"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_cpsd.db2scale.html#sdynpy.signal_processing.sdynpy_cpsd.db2scale">[docs]</a><span class="k">def</span> <span class="nf">db2scale</span><span class="p">(</span><span class="n">dB</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot; Converts a decibel value to a scale factor</span>
 
@@ -211,16 +227,17 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
 <span class="sd">    ----------</span>
 <span class="sd">    dB : float :</span>
 <span class="sd">        Value in decibels</span>
-<span class="sd">        </span>
+
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
 <span class="sd">    scale : float :</span>
 <span class="sd">        Value in linear</span>
-<span class="sd">    </span>
+
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">dB</span><span class="o">/</span><span class="mi">20</span><span class="p">)</span></div>
 
+
 <div class="viewcode-block" id="rms"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rms.html#sdynpy.signal_processing.sdynpy_cpsd.rms">[docs]</a><span class="k">def</span> <span class="nf">rms</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">))</span></div>
 
@@ -413,10 +430,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
 
     <span class="c1"># Truncate to the minimum frequency</span>
     <span class="n">cpsd</span><span class="p">[</span><span class="n">all_freqs</span> <span class="o">&lt;</span> <span class="n">min_frequency</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">max_frequency</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">cpsd</span><span class="p">[</span><span class="n">all_freqs</span> <span class="o">&gt;</span> <span class="n">max_frequency</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
 
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">target_rms</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">target_rms</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">cpsd_rms</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">cpsd</span><span class="p">)</span> <span class="o">*</span> <span class="n">frequency_spacing</span><span class="p">)</span>
         <span class="n">cpsd</span> <span class="o">*=</span> <span class="p">(</span><span class="n">target_rms</span> <span class="o">/</span> <span class="n">cpsd_rms</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
 
@@ -429,26 +446,117 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
 
     <span class="k">return</span> <span class="n">full_cpsd</span></div>
 
+<div class="viewcode-block" id="nth_octave_freqs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html#sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs">[docs]</a><span class="k">def</span> <span class="nf">nth_octave_freqs</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span><span class="n">oct_order</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get N-th octave band frequencies</span>
 
-<span class="c1"># frequency = np.ogrid[min_freq:bandwidth:frequency_spacing]</span>
-
-<span class="c1"># cpsd_channel = np.ones(len(frequency))</span>
-
-<span class="c1"># cpsd_rms = np.sqrt(np.sum(cpsd_channel)*frequency_spacing)</span>
-<span class="c1"># print(&#39;Original CPSD RMS = {:}&#39;.format(cpsd_rms))</span>
-
-<span class="c1"># cpsd_channel *= (target_rms/cpsd_rms)**2</span>
-
-<span class="c1"># cpsd_rms = np.sqrt(np.sum(cpsd_channel)*frequency_spacing)</span>
-<span class="c1"># print(&#39;Scaled CPSD RMS = {:}&#39;.format(cpsd_rms))</span>
-
-<span class="c1"># # Now create the full cpsd matrix</span>
-
-<span class="c1"># cpsd = np.zeros((len(frequency),nchannels,nchannels),dtype=&#39;complex128&#39;)</span>
-
-<span class="c1"># cpsd[:,np.arange(nchannels),np.arange(nchannels)] = cpsd_channel[:,np.newaxis]</span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    freq : ndarray</span>
+<span class="sd">        array of frequency values, either including all freqs or only min and max</span>
+<span class="sd">    oct_order : int, optional</span>
+<span class="sd">        octave type, 1/octave order. 3 represents 1/3 octave bands. default is 1</span>
 
-<span class="c1"># np.savez(&#39;Flat_Force_Specification.npz&#39;,cpsd=cpsd,f=frequency)</span>
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    nominal_band_centers : ndarray</span>
+<span class="sd">        rounded band center frequencies using ANSI standers. Used to report</span>
+<span class="sd">        results, not to compute band limits</span>
+<span class="sd">    band_lb : ndarray</span>
+<span class="sd">        lower band frequencies in Hz</span>
+<span class="sd">    band_ub : ndarray</span>
+<span class="sd">        upper band frequencies in Hz</span>
+<span class="sd">    band_centers : ndarray</span>
+<span class="sd">        exact computed octave center band frequencies</span>
+<span class="sd">        </span>
+<span class="sd">    Notes</span>
+<span class="sd">    -------</span>
+<span class="sd">    Uses equations in ANSI S1.11-2014 &quot;Electroacoustics – Octave-band and </span>
+<span class="sd">    Fractionaloctave-band Filters – Part 1: Specifications&quot;</span>
+<span class="sd">    </span>
+<span class="sd">    Uses the min and max freq lines in the provided freq to determine the upper</span>
+<span class="sd">    and lower bands</span>
+<span class="sd">    </span>
+<span class="sd">    Uses 1000 Hz as the reference frequency (as per the ANSI standard)</span>
+<span class="sd">    </span>
+<span class="sd">    Computes the 1/Nth band center frequencies, using different equations</span>
+<span class="sd">    depending on the octave order. Orders with odd numbers (1, 1/3, etc.) </span>
+<span class="sd">    use ANSI eqn 2. Order with even numbers (1/6, 1/12, etc.) use eqn 3. </span>
+<span class="sd">    This causes any sub-bands to fit entirely within the full octave band</span>
+<span class="sd">    </span>
+<span class="sd">    Note: even-numbered sub-bands will not share center frequencies with </span>
+<span class="sd">    the full octave band, but will share the upper and lower limits.</span>
+<span class="sd">    ANSI S1.11 Annex A gives instructions for rounding to make the nominal</span>
+<span class="sd">    band center freqs. If left-most digit is 1-4, round to 3 significant</span>
+<span class="sd">    digits. If left-most digit is 5-9, round to 2 significant digits. For</span>
+<span class="sd">    example, 41.567 rounds to 41.6. 8785.2 rounds to 8800. </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    
+    <span class="c1"># Process inputs</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">freq</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+        <span class="n">freq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">freq</span><span class="p">])</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    
+    <span class="c1"># Setup</span>
+    <span class="n">oct_ratio</span> <span class="o">=</span> <span class="mi">10</span><span class="o">**</span><span class="mf">0.3</span>
+    <span class="n">f_ref</span> <span class="o">=</span> <span class="mi">1000</span>
+    <span class="n">bands</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">200</span><span class="p">,</span><span class="mi">201</span><span class="p">)</span>
+    
+    <span class="c1"># Compute exact center freqs and edges</span>
+    <span class="c1"># equation is different if using odd octOrder (1, 3) vs. even octOrder</span>
+    <span class="c1"># (6, 12). Note: bandNumbers = x and octOrder = b in the ANSI eqns</span>
+    <span class="nb">pow</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">2</span><span class="o">*</span><span class="n">bands</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">oct_order</span><span class="p">),</span> <span class="n">bands</span><span class="o">/</span><span class="n">oct_order</span><span class="p">]</span>
+    <span class="n">band_centers</span> <span class="o">=</span> <span class="n">f_ref</span> <span class="o">*</span> <span class="n">oct_ratio</span><span class="o">**</span><span class="nb">pow</span><span class="p">[</span><span class="n">oct_order</span><span class="o">%</span><span class="mi">2</span><span class="p">]</span>
+    <span class="n">band_lb</span> <span class="o">=</span> <span class="p">(</span> <span class="n">band_centers</span> <span class="o">*</span> <span class="n">oct_ratio</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">oct_order</span><span class="p">))</span> <span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span> <span class="c1"># round slightly</span>
+    <span class="n">band_ub</span> <span class="o">=</span> <span class="p">(</span> <span class="n">band_centers</span> <span class="o">*</span> <span class="n">oct_ratio</span><span class="o">**</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">oct_order</span><span class="p">))</span> <span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">12</span><span class="p">)</span> <span class="c1"># round slightly</span>
+    
+    <span class="c1"># ONLY KEEP BANDS IN THE FREQ RANGE OF INTEREST: </span>
+    <span class="c1"># lower limit of lowest band is above min freq</span>
+    <span class="c1"># upper limit of highest band is below max freq</span>
+    <span class="c1"># rationale: want to compute oct-ave using all the data, so include </span>
+    <span class="c1"># the band the includes the minimum frequency and the band that includes</span>
+    <span class="c1"># the maximum frequency.</span>
+    <span class="c1"># Note that this will result in an under-estimate of the first and last</span>
+    <span class="c1"># band levels since some of the band is empty.</span>
+    <span class="n">inds</span> <span class="o">=</span> <span class="p">(</span><span class="nb">max</span><span class="p">([</span><span class="n">freq</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span><span class="mi">10</span><span class="p">])</span> <span class="o">&lt;</span> <span class="n">band_ub</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">freq</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">band_lb</span><span class="p">)</span>
+    <span class="n">band_centers</span><span class="p">,</span><span class="n">band_lb</span><span class="p">,</span><span class="n">band_ub</span> <span class="o">=</span> <span class="p">[</span><span class="n">band_centers</span><span class="p">[</span><span class="n">inds</span><span class="p">],</span><span class="n">band_lb</span><span class="p">[</span><span class="n">inds</span><span class="p">],</span><span class="n">band_ub</span><span class="p">[</span><span class="n">inds</span><span class="p">]]</span>
+    
+
+    <span class="c1"># GET THE NOMINAL BAND CENTER FREQUENCIES BY ROUNDING</span>
+    <span class="c1"># These are just &quot;nice&quot; numbers to make it easier to identify the bands </span>
+    <span class="c1"># edges are still calculated from the exact center freqs</span>
+    <span class="c1"># method used here is based on:</span>
+    <span class="c1"># https://www.mathworks.com/matlabcentral/fileexchange/26212-round-with-significant-digits</span>
+    <span class="c1"># freqBandCenters = [ 41.567, 8785.2 ] should give: [ 41.6, 8800]</span>
+    <span class="n">pow_10</span> <span class="o">=</span> <span class="mi">10</span><span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">band_centers</span><span class="p">))</span>
+    <span class="n">left_digit</span> <span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">band_centers</span><span class="o">/</span><span class="n">pow_10</span><span class="p">)</span>
+    <span class="n">sig_digits</span> <span class="o">=</span> <span class="mi">3</span><span class="o">*</span><span class="p">(</span><span class="n">left_digit</span><span class="o">&lt;=</span><span class="mi">4</span><span class="p">)</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">left_digit</span><span class="o">&gt;</span><span class="mi">4</span><span class="p">)</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="mi">10</span> <span class="o">**</span> <span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">band_centers</span><span class="p">))</span> <span class="o">-</span> <span class="n">sig_digits</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">nominal_band_centers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">band_centers</span><span class="o">/</span><span class="n">tmp</span><span class="p">)</span><span class="o">*</span><span class="n">tmp</span>
+    
+    <span class="c1"># Replace calculated nominal band centers with ANSI nominal band centers if</span>
+    <span class="c1"># doing full or 1/3rd octaves:</span>
+    <span class="c1"># ANSI only gives freqs from 25 to 20000, so don&#39;t correct outside that range</span>
+    <span class="c1"># find nearest ANSI nominal center, then verify it is between the band</span>
+    <span class="c1"># limits. If the ANSI nominal is between the band limits, replace the </span>
+    <span class="c1"># calculated nominal with the ANSI value.</span>
+    <span class="k">if</span> <span class="n">oct_order</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">ansi_nominal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">31.5</span> <span class="p">,</span> <span class="mi">63</span> <span class="p">,</span> <span class="mi">125</span> <span class="p">,</span> <span class="mi">250</span> <span class="p">,</span> <span class="mi">500</span> <span class="p">,</span> <span class="mi">1000</span> <span class="p">,</span> <span class="mi">2000</span> <span class="p">,</span> <span class="mi">4000</span> <span class="p">,</span> <span class="mi">8000</span> <span class="p">,</span> <span class="mi">16000</span><span class="p">])</span>
+    <span class="k">elif</span> <span class="n">oct_order</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">ansi_nominal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">25</span> <span class="p">,</span> <span class="mf">31.5</span> <span class="p">,</span> <span class="mi">40</span> <span class="p">,</span> <span class="mi">50</span> <span class="p">,</span> <span class="mi">63</span> <span class="p">,</span> <span class="mi">80</span> <span class="p">,</span> <span class="mi">100</span> <span class="p">,</span> <span class="mi">125</span> <span class="p">,</span> <span class="mi">160</span> <span class="p">,</span> <span class="mi">200</span> <span class="p">,</span>
+                                   <span class="mi">250</span> <span class="p">,</span> <span class="mi">315</span> <span class="p">,</span> <span class="mi">400</span> <span class="p">,</span> <span class="mi">500</span> <span class="p">,</span> <span class="mi">630</span> <span class="p">,</span> <span class="mi">800</span> <span class="p">,</span> <span class="mi">1000</span> <span class="p">,</span> <span class="mi">1250</span> <span class="p">,</span> <span class="mi">1600</span> <span class="p">,</span> <span class="mi">2000</span> <span class="p">,</span>
+                                   <span class="mi">2500</span> <span class="p">,</span> <span class="mi">3150</span> <span class="p">,</span> <span class="mi">4000</span> <span class="p">,</span> <span class="mi">5000</span> <span class="p">,</span> <span class="mi">8000</span> <span class="p">,</span> <span class="mi">10000</span> <span class="p">,</span> <span class="mi">12500</span> <span class="p">,</span> <span class="mi">16000</span> <span class="p">,</span> <span class="mi">20000</span><span class="p">])</span>
+    
+    <span class="k">if</span> <span class="n">oct_order</span><span class="o">==</span><span class="mi">1</span> <span class="ow">or</span> <span class="n">oct_order</span><span class="o">==</span><span class="mi">3</span><span class="p">:</span>
+        <span class="n">dists</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">ansi_nominal</span> <span class="o">-</span> <span class="n">band_centers</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span> <span class="c1"># dist from center freq to nominal</span>
+        <span class="n">contained</span> <span class="o">=</span> <span class="p">(</span><span class="n">ansi_nominal</span> <span class="o">&lt;</span> <span class="n">band_ub</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">ansi_nominal</span> <span class="o">&gt;</span> <span class="n">band_lb</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span> <span class="c1"># bands contain nominals</span>
+        <span class="n">contained_dists</span> <span class="o">=</span> <span class="n">contained</span><span class="o">*</span><span class="n">dists</span> <span class="o">+</span> <span class="mf">1e20</span><span class="o">*</span><span class="p">(</span><span class="o">~</span><span class="n">contained</span><span class="p">)</span> <span class="c1"># product is infty if not contained</span>
+        
+        <span class="n">assign_inds</span> <span class="o">=</span> <span class="n">contained_dists</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># nominal inds to assign    </span>
+        <span class="n">assign_bools</span> <span class="o">=</span> <span class="n">contained_dists</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">1e20</span> <span class="c1"># center freqs inds to assign</span>
+        
+        <span class="n">nominal_band_centers</span><span class="p">[</span><span class="n">assign_bools</span><span class="p">]</span> <span class="o">=</span> <span class="n">ansi_nominal</span><span class="p">[</span><span class="n">assign_inds</span><span class="p">[</span><span class="n">assign_bools</span><span class="p">]]</span>
+        
+    <span class="k">return</span> <span class="n">nominal_band_centers</span><span class="p">,</span> <span class="n">band_lb</span><span class="p">,</span> <span class="n">band_ub</span><span class="p">,</span> <span class="n">band_centers</span></div>
 </pre></div>
 
            </div>
@@ -458,7 +566,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_cpsd</h1><div class="highlig
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba6e500>
+<jinja2.runtime.BlockReference object at 0x7f5a270b6aa0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_frf.html b/_modules/sdynpy/signal_processing/sdynpy_frf.html
index 32f47c1..b94c38b 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_frf.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_frf.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -109,6 +109,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">scipy.signal</span> <span class="k">as</span> <span class="nn">sig</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">.sdynpy_lrm</span> <span class="kn">import</span> <span class="n">frf_local_model</span>
 
 
 <div class="viewcode-block" id="sysmat2frf"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf.sysmat2frf.html#sdynpy.signal_processing.sdynpy_frf.sysmat2frf">[docs]</a><span class="k">def</span> <span class="nf">sysmat2frf</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">M</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">frf_type</span><span class="o">=</span><span class="s1">&#39;disp&#39;</span><span class="p">):</span>
@@ -265,7 +266,8 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
                  <span class="n">overlap</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;H1&#39;</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,)),</span>
                  <span class="n">response_fft</span><span class="o">=</span><span class="k">lambda</span> <span class="n">array</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
                  <span class="n">reference_fft</span><span class="o">=</span><span class="k">lambda</span> <span class="n">array</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
-                 <span class="n">response_fft_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reference_fft_array</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+                 <span class="n">response_fft_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reference_fft_array</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                 <span class="n">return_model_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">lrm_kwargs</span><span class="p">):</span>
     <span class="c1"># TODO Update DOCSTRING with changes after they are all made.</span>
 <span class="w">    </span><span class="sd">&#39;&#39;&#39;Creates an FRF matrix given time histories of responses and references</span>
 
@@ -291,7 +293,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
 <span class="sd">    overlap : float</span>
 <span class="sd">        The overlap as a fraction of the frame (e.g. 0.5 specifies 50% overlap).</span>
 <span class="sd">        If not specified, no overlap is used.</span>
-<span class="sd">    method : str in [&#39;H1&#39;,&#39;H2&#39;,&#39;Hv&#39;,&#39;Hs&#39;]</span>
+<span class="sd">    method : str in [&#39;H1&#39;,&#39;H2&#39;,&#39;Hv&#39;,&#39;Hs&#39;,&#39;LRM&#39;]</span>
 <span class="sd">        The method for creating the frequency response function. &#39;H1&#39; is </span>
 <span class="sd">        default if not specified.</span>
 <span class="sd">    window : ndarray or str</span>
@@ -306,6 +308,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
 <span class="sd">    reference_fft_array : np.ndarray</span>
 <span class="sd">        Array to store the data into before taking the FFT.  Should be</span>
 <span class="sd">        size number_of_references, n_averages, samples_per_average</span>
+<span class="sd">    return_model_data : boolean</span>
+<span class="sd">        Wheter to return selected model orders used in the &#39;LRM&#39; method.</span>
+<span class="sd">        default is False        </span>
+<span class="sd">    **lrm_kwargs</span>
+<span class="sd">        Additional keyword arguments specify parameters if method == &#39;LRM&#39;.</span>
+<span class="sd">        Possible arguments are: f_out (ndarray), bandwidth (float), transient</span>
+<span class="sd">        (boolean), modelset (iterable of (3,1) ndarray), export_ratio (float), </span>
+<span class="sd">        max_parallel (int), print_time (bool). See sdynpy_lrm.frf_local_model.</span>
+<span class="sd">        samples_per_average, overlap, and window are void if method==&#39;LRM&#39;.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -314,7 +325,9 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
 <span class="sd">    H : ndarray</span>
 <span class="sd">        A nf x no x ni array where nf is the number of frequency lines, no is</span>
 <span class="sd">        the number of outputs, and ni is the number of inputs.</span>
-
+<span class="sd">    model_data: dict</span>
+<span class="sd">        Contains None if method != &#39;LRM&#39;. See sdynpy_lrm.frf_local_model.</span>
+<span class="sd">        </span>
 <span class="sd">    Notes</span>
 <span class="sd">    -----</span>
 <span class="sd">    There are requirements for the shapes of references and responses for some</span>
@@ -324,7 +337,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
     <span class="k">if</span> <span class="n">references</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">!=</span> <span class="n">responses</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
             <span class="s1">&#39;reference and responses must have the same number of time steps (last dimension)!&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">method</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;H1&#39;</span><span class="p">,</span> <span class="s1">&#39;H2&#39;</span><span class="p">,</span> <span class="s1">&#39;H3&#39;</span><span class="p">,</span> <span class="s1">&#39;Hs&#39;</span><span class="p">,</span> <span class="s1">&#39;Hv&#39;</span><span class="p">,</span> <span class="s1">&#39;Hcd&#39;</span><span class="p">]:</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">method</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;H1&#39;</span><span class="p">,</span> <span class="s1">&#39;H2&#39;</span><span class="p">,</span> <span class="s1">&#39;H3&#39;</span><span class="p">,</span> <span class="s1">&#39;Hs&#39;</span><span class="p">,</span> <span class="s1">&#39;Hv&#39;</span><span class="p">,</span> <span class="s1">&#39;Hcd&#39;</span><span class="p">,</span><span class="s1">&#39;LRM&#39;</span><span class="p">]:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;method parameter must be one of [&quot;H1&quot;,&quot;H2&quot;,&quot;H3&quot;,&quot;Hv&quot;,&quot;Hcd&quot;]&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">references</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
         <span class="n">references</span> <span class="o">=</span> <span class="n">references</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
@@ -332,12 +345,14 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
         <span class="n">responses</span> <span class="o">=</span> <span class="n">responses</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
     <span class="c1"># Set up time indices</span>
     <span class="n">ntimes_total</span> <span class="o">=</span> <span class="n">references</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">if</span> <span class="n">samples_per_average</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">samples_per_average</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;LRM&#39;</span><span class="p">:</span>
         <span class="n">samples_per_average</span> <span class="o">=</span> <span class="n">ntimes_total</span>
     <span class="n">overlap_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">samples_per_average</span> <span class="o">*</span> <span class="n">overlap</span><span class="p">)</span>
     <span class="n">time_starts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ntimes_total</span> <span class="o">-</span> <span class="n">samples_per_average</span> <span class="o">+</span>
                             <span class="mi">1</span><span class="p">,</span> <span class="n">samples_per_average</span> <span class="o">-</span> <span class="n">overlap_samples</span><span class="p">)</span>
     <span class="n">time_indices_for_averages</span> <span class="o">=</span> <span class="n">time_starts</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">samples_per_average</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;LRM&#39;</span><span class="p">:</span>
+        <span class="n">window</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mf">1.0</span><span class="p">,))</span> <span class="c1"># local modeling does not use windowing</span>
     <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">window</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
         <span class="n">window</span> <span class="o">=</span> <span class="n">sig</span><span class="o">.</span><span class="n">windows</span><span class="o">.</span><span class="n">get_window</span><span class="p">(</span><span class="n">window</span><span class="o">.</span><span class="n">lower</span><span class="p">(),</span> <span class="n">samples_per_average</span><span class="p">,</span> <span class="n">fftbins</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="c1"># Sort the references and responses into their time arrays</span>
@@ -353,7 +368,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
     <span class="n">frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">samples_per_average</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
     <span class="n">response_ffts</span> <span class="o">=</span> <span class="n">response_fft</span><span class="p">(</span><span class="n">response_fft_array</span><span class="p">)</span>
     <span class="n">reference_ffts</span> <span class="o">=</span> <span class="n">reference_fft</span><span class="p">(</span><span class="n">reference_fft_array</span><span class="p">)</span>
-    <span class="c1"># Now start computing FFTs</span>
+    <span class="c1"># Now start computing FRFs</span>
     <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;H1&#39;</span><span class="p">:</span>
         <span class="c1"># We want to compute X*F^H = [X1;X2;X3][F1^H F2^H F3^H]</span>
         <span class="n">Gxf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...iaf,...jaf-&gt;...fij&#39;</span><span class="p">,</span> <span class="n">response_ffts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">reference_ffts</span><span class="p">))</span>
@@ -412,12 +427,21 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
         <span class="n">evect</span> <span class="o">=</span> <span class="n">evect</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>  <span class="c1"># Assumes evals are sorted ascending</span>
         <span class="n">H</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="o">-</span><span class="n">evect</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">/</span> <span class="n">evect</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">:],</span>  <span class="c1"># Scale so last value is -1</span>
                         <span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;LRM&#39;</span><span class="p">:</span>
+        <span class="n">frequencies</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">model_data</span> <span class="o">=</span> <span class="n">frf_local_model</span><span class="p">(</span><span class="n">reference_ffts</span><span class="p">,</span><span class="n">response_ffts</span><span class="p">,</span>
+                                                         <span class="n">frequencies</span><span class="p">,</span><span class="o">**</span><span class="n">lrm_kwargs</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Method </span><span class="si">{:}</span><span class="s1"> has not been implemented yet!&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">method</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">H</span></div>
+        
+    <span class="k">if</span> <span class="n">return_model_data</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">method</span> <span class="o">!=</span> <span class="s1">&#39;LRM&#39;</span><span class="p">:</span>
+            <span class="n">model_data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;info&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span><span class="s1">&#39;modelset&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span><span class="s1">&#39;model_selected&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span>
+        <span class="k">return</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">model_data</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">H</span></div>
 
 
-<div class="viewcode-block" id="fft2frf"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html#sdynpy.signal_processing.sdynpy_frf.fft2frf">[docs]</a><span class="k">def</span> <span class="nf">fft2frf</span><span class="p">(</span><span class="n">references</span><span class="p">,</span> <span class="n">responses</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;H1&#39;</span><span class="p">):</span>
+<div class="viewcode-block" id="fft2frf"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html#sdynpy.signal_processing.sdynpy_frf.fft2frf">[docs]</a><span class="k">def</span> <span class="nf">fft2frf</span><span class="p">(</span><span class="n">references</span><span class="p">,</span> <span class="n">responses</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;H1&#39;</span><span class="p">,</span><span class="n">freqs_in</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&#39;&#39;&#39;Creates an FRF matrix given ffts of responses and references</span>
 
 <span class="sd">    This function creates a nf x no x ni FRF matrix from the ffts</span>
@@ -431,8 +455,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
 <span class="sd">    responses : ndarray</span>
 <span class="sd">        A no x nf or nf array where no is the number of responses and nf is the</span>
 <span class="sd">        number of time frequencies in the fft.</span>
-<span class="sd">    method : str in [&#39;H1&#39;,&#39;H2&#39;,&#39;Hv&#39;,&#39;Hs&#39;]</span>
+<span class="sd">    method : str in [&#39;H1&#39;,&#39;H2&#39;,&#39;Hv&#39;,&#39;Hs&#39;,&#39;LRM&#39;]</span>
 <span class="sd">        The method for creating the frequency response function.</span>
+<span class="sd">    freqs_in : ndarray</span>
+<span class="sd">        Only used if method == &#39;LRM&#39;. A nf or nf x 1 array of frquency values.</span>
+<span class="sd">    **lrm_kwargs</span>
+<span class="sd">        Additional keyword arguments specify parameters if method == &#39;LRM&#39;.</span>
+<span class="sd">        Possible arguments are: f_out (ndarray), bandwidth (float), transient</span>
+<span class="sd">        (boolean), modelset (iterable of (3,1) ndarray), max_parallel (int), </span>
+<span class="sd">        export_ratio (float). See sdynpy_lrm.frf_local_model.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -440,6 +471,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
 <span class="sd">        A nf x no x ni array where nf is the number of frequency lines, no is</span>
 <span class="sd">        the number of outputs, and ni is the number of inputs.  The output</span>
 <span class="sd">        frequency lines will correspond to the frequency lines in the ffts.</span>
+<span class="sd">    freqs_out : None or ndarray</span>
+<span class="sd">        None unless method == &#39;LRM&#39;. See sdynpy_lrm.frf_local_model.</span>
+<span class="sd">    model_data: None dict</span>
+<span class="sd">        None unless method == &#39;LRM&#39;. See sdynpy_lrm.frf_local_model.</span>
 
 <span class="sd">    Notes</span>
 <span class="sd">    -----</span>
@@ -455,8 +490,8 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
         <span class="n">responses</span> <span class="o">=</span> <span class="n">responses</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span>
     <span class="k">elif</span> <span class="n">responses</span><span class="o">.</span><span class="n">ndim</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;responses should be at maximum a 2 dimensional array&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">method</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;H1&#39;</span><span class="p">,</span> <span class="s1">&#39;H2&#39;</span><span class="p">,</span> <span class="s1">&#39;Hs&#39;</span><span class="p">,</span> <span class="s1">&#39;Hv&#39;</span><span class="p">]:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;method parameter must be one of [&quot;H1&quot;,&quot;H2&quot;,&quot;Hs&quot;,&quot;Hv&quot;]&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">method</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;H1&#39;</span><span class="p">,</span> <span class="s1">&#39;H2&#39;</span><span class="p">,</span> <span class="s1">&#39;Hs&#39;</span><span class="p">,</span> <span class="s1">&#39;Hv&#39;</span><span class="p">,</span> <span class="s1">&#39;LRM&#39;</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;method parameter must be one of [&quot;H1&quot;,&quot;H2&quot;,&quot;Hs&quot;,&quot;Hv&quot;,&quot;LRM&quot;]&#39;</span><span class="p">)</span>
 <span class="c1">#    H = np.zeros((references.shape[1],responses.shape[0],references.shape[0]),dtype=complex)</span>
 <span class="c1">#    for i in range(H.shape[1]):</span>
 <span class="c1">#            for j in range(H.shape[2]):</span>
@@ -468,9 +503,18 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
         <span class="n">Gxf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;if,jf-&gt;fij&#39;</span><span class="p">,</span> <span class="n">responses</span><span class="p">,</span> <span class="n">references</span><span class="o">.</span><span class="n">conj</span><span class="p">())</span>
         <span class="n">Gff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;if,jf-&gt;fij&#39;</span><span class="p">,</span> <span class="n">references</span><span class="p">,</span> <span class="n">references</span><span class="o">.</span><span class="n">conj</span><span class="p">())</span>
         <span class="n">H</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">Gff</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">Gxf</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;LRM&#39;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">freqs_in</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;if method == </span><span class="se">\&#39;</span><span class="s1">LRM</span><span class="se">\&#39;</span><span class="s1">, freqs_in must be specified&#39;</span><span class="p">)</span>
+        <span class="kn">import</span> <span class="nn">sdynpy.signal_processing.sdynpy_lrm</span> <span class="k">as</span> <span class="nn">frflm</span>
+        <span class="n">freqs_out</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">model_data</span> <span class="o">=</span> <span class="n">frflm</span><span class="o">.</span><span class="n">frf_local_model</span><span class="p">(</span><span class="n">references</span><span class="p">,</span> <span class="n">responses</span><span class="p">,</span> 
+                                                         <span class="n">freqs_in</span><span class="p">,</span> <span class="o">**</span><span class="n">lrm_kwargs</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;Method </span><span class="si">{:}</span><span class="s1"> has not been implemented&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">method</span><span class="p">))</span>
-    <span class="k">return</span> <span class="n">H</span></div>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">!=</span> <span class="s1">&#39;LRM&#39;</span><span class="p">:</span>
+        <span class="n">freqs_out</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">model_data</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">return</span> <span class="n">H</span><span class="p">,</span> <span class="n">freqs_out</span><span class="p">,</span> <span class="n">model_data</span></div>
 
 
 <div class="viewcode-block" id="plot"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf.plot.html#sdynpy.signal_processing.sdynpy_frf.plot">[docs]</a><span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="n">H</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">responses</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">references</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">real_imag</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
@@ -540,7 +584,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf</h1><div class="highligh
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc6f20>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb5750>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_frf_inverse.html b/_modules/sdynpy/signal_processing/sdynpy_frf_inverse.html
index 5658520..72d806f 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_frf_inverse.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_frf_inverse.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_frf_inverse &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_frf_inverse &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -110,15 +110,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
 
+
 <div class="viewcode-block" id="frf_inverse"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html#sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse">[docs]</a><span class="k">def</span> <span class="nf">frf_inverse</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
-                <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;standard&#39;</span><span class="p">,</span>
-                <span class="n">response_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                <span class="n">reference_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                <span class="n">cond_num_threshold</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                <span class="n">num_retained_values</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span>
+                <span class="n">method</span><span class="o">=</span><span class="s1">&#39;standard&#39;</span><span class="p">,</span>
+                <span class="n">response_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">reference_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">regularization_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">cond_num_threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                <span class="n">num_retained_values</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes the inverse of an FRF matrix for source estimation problems.</span>
 
@@ -126,9 +126,9 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="sd">    ----------</span>
 <span class="sd">    frf_matrix : NDArray</span>
 <span class="sd">        Transfer function as an np.ndarray, should be organized such that the</span>
-<span class="sd">        size is [number of lines, number of responses, number of references] </span>
+<span class="sd">        size is [number of lines, number of responses, number of references]</span>
 <span class="sd">    method : str, optional</span>
-<span class="sd">            The method to be used for the FRF matrix inversions. The available </span>
+<span class="sd">            The method to be used for the FRF matrix inversions. The available</span>
 <span class="sd">            methods are:</span>
 <span class="sd">                - standard - basic pseudo-inverse via numpy.linalg.pinv with the</span>
 <span class="sd">                  default rcond parameter, this is the default method</span>
@@ -136,48 +136,48 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="sd">                  condition number threshold</span>
 <span class="sd">                - tikhonov - pseudo-inverse using the Tikhonov regularization method</span>
 <span class="sd">                - truncation - pseudo-inverse where a fixed number of singular values</span>
-<span class="sd">                  are retained for the inverse </span>
+<span class="sd">                  are retained for the inverse</span>
 <span class="sd">        response_weighting_matrix : sdpy.Matrix or np.ndarray, optional</span>
-<span class="sd">            Diagonal matrix used to weight response degrees of freedom (to solve the </span>
-<span class="sd">            problem as a weight least squares) by multiplying the rows of the FRF </span>
-<span class="sd">            matrix by a scalar weights. This matrix can also be a 3D matrix such that </span>
-<span class="sd">            the the weights are different for each frequency line. The matrix should </span>
-<span class="sd">            be sized [number of lines, number of references, number of references], </span>
-<span class="sd">            where the number of lines either be one (the same weights at all frequencies) </span>
+<span class="sd">            Diagonal matrix used to weight response degrees of freedom (to solve the</span>
+<span class="sd">            problem as a weight least squares) by multiplying the rows of the FRF</span>
+<span class="sd">            matrix by a scalar weights. This matrix can also be a 3D matrix such that</span>
+<span class="sd">            the the weights are different for each frequency line. The matrix should</span>
+<span class="sd">            be sized [number of lines, number of references, number of references],</span>
+<span class="sd">            where the number of lines either be one (the same weights at all frequencies)</span>
 <span class="sd">            or the length of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">        reference_weighting_matrix : sdpy.Matrix or np.ndarray, optional</span>
-<span class="sd">            Diagonal matrix used to weight reference degrees of freedom (generally for </span>
-<span class="sd">            normalization) by multiplying the columns of the FRF matrix by a scalar weights. </span>
+<span class="sd">            Diagonal matrix used to weight reference degrees of freedom (generally for</span>
+<span class="sd">            normalization) by multiplying the columns of the FRF matrix by a scalar weights.</span>
 <span class="sd">            This matrix can also be a 3D matrix such that the the weights are different</span>
-<span class="sd">            for each frequency line. The matrix should be sized </span>
-<span class="sd">            [number of lines, number of references, number of references], where the number </span>
+<span class="sd">            for each frequency line. The matrix should be sized</span>
+<span class="sd">            [number of lines, number of references, number of references], where the number</span>
 <span class="sd">            of lines either be one (the same weights at all frequencies) or the length</span>
 <span class="sd">            of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">        regularization_weighting_matrix : sdpy.Matrix or np.ndarray, optional</span>
-<span class="sd">            Matrix used to weight input degrees of freedom via Tikhonov regularization. </span>
+<span class="sd">            Matrix used to weight input degrees of freedom via Tikhonov regularization.</span>
 <span class="sd">            This matrix can also be a 3D matrix such that the the weights are different</span>
-<span class="sd">            for each frequency line. The matrix should be sized </span>
-<span class="sd">            [number of lines, number of references, number of references], where the number </span>
+<span class="sd">            for each frequency line. The matrix should be sized</span>
+<span class="sd">            [number of lines, number of references, number of references], where the number</span>
 <span class="sd">            of lines either be one (the same weights at all frequencies) or the length</span>
 <span class="sd">            of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">        regularization_parameter : float or np.ndarray, optional</span>
 <span class="sd">            Scaling parameter used on the regularization weighting matrix when the tikhonov</span>
-<span class="sd">            method is chosen. A vector of regularization parameters can be provided so the </span>
-<span class="sd">            regularization is different at each frequency line. The vector must match the </span>
+<span class="sd">            method is chosen. A vector of regularization parameters can be provided so the</span>
+<span class="sd">            regularization is different at each frequency line. The vector must match the</span>
 <span class="sd">            length of the abscissa in this case (either be size [num_lines,] or [num_lines, 1]).</span>
 <span class="sd">        cond_num_threshold : float or np.ndarray, optional</span>
-<span class="sd">            Condition number used for SVD truncation when the threshold method is chosen. </span>
-<span class="sd">            A vector of condition numbers can be provided so it varies as a function of </span>
+<span class="sd">            Condition number used for SVD truncation when the threshold method is chosen.</span>
+<span class="sd">            A vector of condition numbers can be provided so it varies as a function of</span>
 <span class="sd">            frequency. The vector must match the length of the abscissa in this case.</span>
 <span class="sd">        num_retained_values : float or np.ndarray, optional</span>
-<span class="sd">            Number of singular values to retain in the pseudo-inverse when the truncation </span>
-<span class="sd">            method is chosen. A vector of can be provided so the number of retained values </span>
-<span class="sd">            can change as a function of frequency. The vector must match the length of the </span>
+<span class="sd">            Number of singular values to retain in the pseudo-inverse when the truncation</span>
+<span class="sd">            method is chosen. A vector of can be provided so the number of retained values</span>
+<span class="sd">            can change as a function of frequency. The vector must match the length of the</span>
 <span class="sd">            abscissa in this case.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
-<span class="sd">    np.ndarray </span>
+<span class="sd">    np.ndarray</span>
 <span class="sd">        Inverse of the supplied FRF matrix</span>
 
 <span class="sd">    Raises</span>
@@ -191,7 +191,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 
 <span class="sd">    Notes</span>
 <span class="sd">    -----</span>
-<span class="sd">    This function solves the inverse problem for the supplied FRF matrix. All of the inverse </span>
+<span class="sd">    This function solves the inverse problem for the supplied FRF matrix. All of the inverse</span>
 <span class="sd">    methods use the SVD (or modified SVD) to compute the pseudo-inverse.</span>
 
 <span class="sd">    References</span>
@@ -199,14 +199,14 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="sd">    .. [1] Wikipedia, &quot;Moore-Penrose inverse&quot;.</span>
 <span class="sd">           https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse</span>
 <span class="sd">    .. [2] A.N. Tithe, D.J. Thompson, The quantification of structure-borne transmission pathsby inverse methods. Part 2: Use of regularization techniques,</span>
-<span class="sd">           Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X, </span>
+<span class="sd">           Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X,</span>
 <span class="sd">           https://doi.org/10.1016/S0022-460X(02)01203-8.</span>
 <span class="sd">    .. [3] Wikipedia, &quot;Ridge regression&quot;.</span>
 <span class="sd">           https://en.wikipedia.org/wiki/Ridge_regression</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="n">regularization_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">method</span> <span class="o">!=</span> <span class="s1">&#39;tikhonov&#39;</span><span class="p">:</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Regularization weighting matrix is only used during Tikhonov regularization and is not being used&quot;</span><span class="p">)</span>
-    
+
     <span class="k">if</span> <span class="n">response_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">frf_matrix</span> <span class="o">=</span> <span class="n">response_weighting_matrix</span><span class="nd">@frf_matrix</span>
     <span class="k">if</span> <span class="n">reference_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -219,13 +219,13 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
             <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;A condition number threshold must be supplied when using the threshold method&#39;</span><span class="p">)</span>
         <span class="n">rcond_thresh</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">cond_num_threshold</span>
         <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span> <span class="n">rcond</span><span class="o">=</span><span class="n">rcond_thresh</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;tikhonov&#39;</span><span class="p">:</span>     
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;tikhonov&#39;</span><span class="p">:</span>
         <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">pinv_by_tikhonov</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
-                                    <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">regularization_weighting_matrix</span><span class="p">,</span> 
-                                    <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">regularization_parameter</span><span class="p">)</span>
+                                    <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="n">regularization_weighting_matrix</span><span class="p">,</span>
+                                    <span class="n">regularization_parameter</span><span class="o">=</span><span class="n">regularization_parameter</span><span class="p">)</span>
     <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;truncation&#39;</span><span class="p">:</span>
-        <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">pinv_by_truncation</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span> 
-                                      <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">num_retained_values</span><span class="p">)</span>
+        <span class="n">frf_pinv</span> <span class="o">=</span> <span class="n">pinv_by_truncation</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
+                                      <span class="n">num_retained_values</span><span class="o">=</span><span class="n">num_retained_values</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">error_string_start</span> <span class="o">=</span> <span class="s1">&#39;Declared method &quot;&#39;</span>
         <span class="n">error_string_end</span> <span class="o">=</span> <span class="s1">&#39;&quot; is not an available option&#39;</span>
@@ -233,10 +233,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 
     <span class="k">return</span> <span class="n">frf_pinv</span></div>
 
-<div class="viewcode-block" id="pinv_by_tikhonov"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html#sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov">[docs]</a><span class="k">def</span> <span class="nf">pinv_by_tikhonov</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
-                     <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                     <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
 
+<div class="viewcode-block" id="pinv_by_tikhonov"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov.html#sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_tikhonov">[docs]</a><span class="k">def</span> <span class="nf">pinv_by_tikhonov</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
+                     <span class="n">regularization_weighting_matrix</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                     <span class="n">regularization_parameter</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes the pseudo-inverse of an FRF matrix via Tikhonov regularization</span>
 
@@ -244,24 +244,24 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="sd">    ----------</span>
 <span class="sd">    frf_matrix : NDArray</span>
 <span class="sd">        Transfer function as an np.ndarray, should be organized such that the</span>
-<span class="sd">        frequency is the first axis, the responses are the second axis, and the </span>
+<span class="sd">        frequency is the first axis, the responses are the second axis, and the</span>
 <span class="sd">        references are the third axis</span>
 <span class="sd">    regularization_weighting_matrix : sdpy.Matrix or np.ndarray, optional</span>
-<span class="sd">        Matrix used to weight input degrees of freedom in the regularization, the </span>
-<span class="sd">        default is identity. This can be a 3D matrix such that the the weights are </span>
-<span class="sd">        different for each frequency line. The matrix should be sized </span>
-<span class="sd">        [number of lines, number of references, number of references], where the number </span>
+<span class="sd">        Matrix used to weight input degrees of freedom in the regularization, the</span>
+<span class="sd">        default is identity. This can be a 3D matrix such that the the weights are</span>
+<span class="sd">        different for each frequency line. The matrix should be sized</span>
+<span class="sd">        [number of lines, number of references, number of references], where the number</span>
 <span class="sd">        of lines either be one (the same weights at all frequencies) or the length</span>
 <span class="sd">        of the abscissa (for the case where a 3D matrix is supplied).</span>
 <span class="sd">    regularization_parameter : float or np.ndarray, optional</span>
-<span class="sd">        Scaling parameter used on the regularization weighting matrix. A vector of </span>
-<span class="sd">        regularization parameters can be provided so the regularization is different at </span>
-<span class="sd">        each frequency line. The vector must match the length of the abscissa in this </span>
+<span class="sd">        Scaling parameter used on the regularization weighting matrix. A vector of</span>
+<span class="sd">        regularization parameters can be provided so the regularization is different at</span>
+<span class="sd">        each frequency line. The vector must match the length of the abscissa in this</span>
 <span class="sd">        case (either be size [num_lines,] or [num_lines, 1]).</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
-<span class="sd">    np.ndarray </span>
+<span class="sd">    np.ndarray</span>
 <span class="sd">        Inverse of the supplied FRF matrix</span>
 
 <span class="sd">    Raises</span>
@@ -273,13 +273,13 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 
 <span class="sd">    Notes</span>
 <span class="sd">    -----</span>
-<span class="sd">    Tikhonov regularization is being done via the SVD method (details on Wikipedia or </span>
+<span class="sd">    Tikhonov regularization is being done via the SVD method (details on Wikipedia or</span>
 <span class="sd">    in the paper by Tithe and Thompson).</span>
 
 <span class="sd">    References</span>
 <span class="sd">    ----------</span>
 <span class="sd">    .. [1] A.N. Tithe, D.J. Thompson, The quantification of structure-borne transmission pathsby inverse methods. Part 2: Use of regularization techniques,</span>
-<span class="sd">           Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X, </span>
+<span class="sd">           Journal of Sound and Vibration, Volume 264, Issue 2, 2003, Pages 433-451, ISSN 0022-460X,</span>
 <span class="sd">           https://doi.org/10.1016/S0022-460X(02)01203-8.</span>
 <span class="sd">    .. [2] Wikipedia, &quot;Ridge regression&quot;.</span>
 <span class="sd">           https://en.wikipedia.org/wiki/Ridge_regression</span>
@@ -288,14 +288,14 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
     <span class="k">if</span> <span class="n">regularization_parameter</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;A regularization parameter must be supplied when using Tikhonov regularization&#39;</span><span class="p">)</span>
 
-    <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+    <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
 
     <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
     <span class="n">vh_conjT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">vh</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">u_conjT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 
-    <span class="c1"># Need to add a second axis to the regularization parameter if it&#39;s a vector </span>
-    <span class="c1"># so it broadcasts appropriately.  </span>
+    <span class="c1"># Need to add a second axis to the regularization parameter if it&#39;s a vector</span>
+    <span class="c1"># so it broadcasts appropriately.</span>
     <span class="k">if</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
         <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">regularization_parameter</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
 
@@ -303,24 +303,29 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The regularization parameter is not a scalar or vector and cannot be interpreted&#39;</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">frf_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The regularization parameter is a vector but the size does not match the number of frequency lines and cannot be broadcasted to the FRFs&#39;</span><span class="p">)</span> 
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The regularization parameter is a vector but the size&#39;</span>
+                         <span class="o">+</span> <span class="s1">&#39; does not match the number of frequency lines and cannot be broadcasted to the FRFs&#39;</span><span class="p">)</span>
 
-    <span class="c1"># This is adding a third dimension to the regularization weighting matrix (if </span>
-    <span class="c1"># it doesn&#39;t already exist) in the case that the regularization parameter is a </span>
+    <span class="c1"># This is adding a third dimension to the regularization weighting matrix (if</span>
+    <span class="c1"># it doesn&#39;t already exist) in the case that the regularization parameter is a</span>
     <span class="c1"># vector, this is required so the weighting matrix and parameter will broadcast</span>
     <span class="c1"># together appropriately.</span>
     <span class="k">if</span> <span class="n">regularization_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">regularization_weighting_matrix</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The regularization weighting matrix is 2D while the regularization parameter is a vector, the regularization weighting matrix is being expanded to 3D. Consider adding a third dimension to the regularization weighting matrix as appropriate&#39;</span><span class="p">)</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The regularization weighting matrix is 2D while the regularization&#39;</span>
+                      <span class="o">+</span> <span class="s1">&#39; parameter is a vector, the regularization weighting matrix is &#39;</span>
+                      <span class="o">+</span> <span class="s1">&#39;being expanded to 3D. Consider adding a third dimension to the regularization weighting matrix as appropriate&#39;</span><span class="p">)</span>
         <span class="n">desired_shape</span> <span class="o">=</span> <span class="p">[</span><span class="n">regularization_parameter</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">regularization_weighting_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">regularization_weighting_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
         <span class="n">regularization_weighting_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">regularization_weighting_matrix</span><span class="p">,</span> <span class="n">desired_shape</span><span class="p">)</span>
 
-    <span class="c1"># This is adding a third dimension to the regularization parameter vector when a </span>
-    <span class="c1"># regularization weighting matrix is being used. This is required so the broadcasting </span>
+    <span class="c1"># This is adding a third dimension to the regularization parameter vector when a</span>
+    <span class="c1"># regularization weighting matrix is being used. This is required so the broadcasting</span>
     <span class="c1"># works correctly. I.E., if the regularization weighting matrix is sized</span>
     <span class="c1"># [num_lines, num_refs, num_refs] (which it always is), the regularization parameter</span>
     <span class="c1"># vector has to be sized [num_lines, 1, 1]. This is not required when the regularization</span>
     <span class="c1"># parameter is a scaler.</span>
-    <span class="k">if</span> <span class="n">regularization_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">regularization_weighting_matrix</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+    <span class="k">if</span> <span class="p">(</span><span class="n">regularization_weighting_matrix</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="ow">and</span> <span class="n">regularization_weighting_matrix</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span>
+            <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">):</span>
         <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">regularization_parameter</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
 
     <span class="k">if</span> <span class="n">regularization_weighting_matrix</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -335,7 +340,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
         <span class="k">if</span> <span class="n">frf_matrix</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
             <span class="c1"># This turns s into a 3D matrix with the singular values on the diagonal</span>
             <span class="n">s</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">apply_along_axis</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
-            <span class="n">regularized_sInv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">s</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="nd">@s</span><span class="o">+</span><span class="n">regularization_weighting_matrix</span><span class="o">*</span><span class="n">regularization_parameter</span><span class="p">)</span><span class="nd">@np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">s</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">regularized_sInv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="nd">@s</span><span class="o">+</span><span class="n">regularization_weighting_matrix</span><span class="o">*</span><span class="n">regularization_parameter</span><span class="p">)</span><span class="nd">@np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="c1"># This is for the case where a single frequency line is supplied for the FRF</span>
             <span class="n">regularized_sInv</span> <span class="o">=</span> <span class="n">s</span><span class="o">/</span><span class="p">(</span><span class="n">s</span><span class="o">*</span><span class="n">s</span><span class="o">+</span><span class="n">regularization_parameter</span><span class="o">*</span><span class="n">regularization_weighting_matrix</span><span class="p">)</span>
@@ -344,9 +349,9 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 
     <span class="k">return</span> <span class="n">frf_pinv</span></div>
 
-<div class="viewcode-block" id="pinv_by_truncation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html#sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation">[docs]</a><span class="k">def</span> <span class="nf">pinv_by_truncation</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span> 
+
+<div class="viewcode-block" id="pinv_by_truncation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation.html#sdynpy.signal_processing.sdynpy_frf_inverse.pinv_by_truncation">[docs]</a><span class="k">def</span> <span class="nf">pinv_by_truncation</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span>
                        <span class="n">num_retained_values</span><span class="p">):</span>
-<span class="w">    </span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes the pseudo-inverse of an FRF matrix where a fixed number of singular</span>
 <span class="sd">    values are retained for the inverse</span>
@@ -355,17 +360,17 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="sd">    ----------</span>
 <span class="sd">    frf_matrix : NDArray</span>
 <span class="sd">        Transfer function as an np.ndarray, should be organized such that the</span>
-<span class="sd">        frequency is the first axis, the responses are the second axis, and the </span>
+<span class="sd">        frequency is the first axis, the responses are the second axis, and the</span>
 <span class="sd">        references are the third axis</span>
 <span class="sd">    num_retained_values : float or np.ndarray, optional</span>
-<span class="sd">        Number of singular values to retain in the pseudo-inverse when the truncation </span>
-<span class="sd">        method is chosen. A vector of can be provided so the number of retained values </span>
-<span class="sd">        can change as a function of frequency. The vector must match the length of the </span>
+<span class="sd">        Number of singular values to retain in the pseudo-inverse when the truncation</span>
+<span class="sd">        method is chosen. A vector of can be provided so the number of retained values</span>
+<span class="sd">        can change as a function of frequency. The vector must match the length of the</span>
 <span class="sd">        abscissa in this case.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
-<span class="sd">    np.ndarray </span>
+<span class="sd">    np.ndarray</span>
 <span class="sd">        Inverse of the supplied FRF matrix</span>
 
 <span class="sd">    Raises</span>
@@ -379,10 +384,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
     <span class="k">if</span> <span class="n">num_retained_values</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of retained values must be supplied when using the truncation method&#39;</span><span class="p">)</span>
 
-    <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">num_retained_values</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
-    
-    <span class="c1"># Need to add a second axis to the number of retained values if it&#39;s a vector </span>
-    <span class="c1"># so it broadcasts appropriately.  </span>
+    <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">num_retained_values</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">intc</span><span class="p">)</span>
+
+    <span class="c1"># Need to add a second axis to the number of retained values if it&#39;s a vector</span>
+    <span class="c1"># so it broadcasts appropriately.</span>
     <span class="k">if</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
         <span class="n">num_retained_values</span> <span class="o">=</span> <span class="n">num_retained_values</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
 
@@ -390,19 +395,21 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of retained values parameter is not a scalar or vector and cannot be interpreted&#39;</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="n">frf_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of retained values parameter is a vector but the size does not match the number of frequency lines and cannot be broadcasted to the FRFs&#39;</span><span class="p">)</span> 
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of retained values parameter is a vector but &#39;</span>
+                         <span class="o">+</span> <span class="s1">&#39;the size does not match the number of frequency lines and cannot be broadcasted to the FRFs&#39;</span><span class="p">)</span>
 
     <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">frf_matrix</span><span class="p">,</span> <span class="n">full_matrices</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
     <span class="n">vh_conjT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">vh</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">u_conjT</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">conj</span><span class="p">(),</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
 
     <span class="c1"># The weird looking logic is to handle if num_retained_values is either a string</span>
-    <span class="c1"># or a scaler. It also changes the number of retained values in the case that </span>
-    <span class="c1"># it is greater than the actual number of singular values (it changes to retain </span>
+    <span class="c1"># or a scaler. It also changes the number of retained values in the case that</span>
+    <span class="c1"># it is greater than the actual number of singular values (it changes to retain</span>
     <span class="c1"># all the singular values).</span>
     <span class="k">if</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span><span class="n">num_retained_values</span> <span class="o">&gt;</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="ow">or</span> <span class="n">num_retained_values</span><span class="o">.</span><span class="n">size</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">num_retained_values</span> <span class="o">&gt;</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The requested number of retained singular values is greater than the actual number of singular values (for at least one frequency line). All singular values are being retained.&#39;</span><span class="p">)</span>
-        <span class="n">num_retained_values</span><span class="p">[</span><span class="n">num_retained_values</span><span class="o">&gt;</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;The requested number of retained singular values is greater than the &#39;</span>
+                      <span class="o">+</span> <span class="s1">&#39;actual number of singular values (for at least one frequency line). All singular values are being retained.&#39;</span><span class="p">)</span>
+        <span class="n">num_retained_values</span><span class="p">[</span><span class="n">num_retained_values</span> <span class="o">&gt;</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
 
     <span class="n">s_truncated_inv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
     <span class="n">num_lines</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -423,11 +430,12 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 
     <span class="k">return</span> <span class="n">frf_pinv</span></div>
 
+
 <div class="viewcode-block" id="compute_tikhonov_modified_singular_values"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values.html#sdynpy.signal_processing.sdynpy_frf_inverse.compute_tikhonov_modified_singular_values">[docs]</a><span class="k">def</span> <span class="nf">compute_tikhonov_modified_singular_values</span><span class="p">(</span><span class="n">original_singular_values</span><span class="p">,</span> <span class="n">regularization_parameter</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes the modified singular values that would be seen in Tikhonov</span>
 <span class="sd">    regularization. This is only intended for visualization purposes</span>
-<span class="sd">    only. </span>
+<span class="sd">    only.</span>
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -436,7 +444,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 <span class="sd">        [frequency lines, singular values.]</span>
 <span class="sd">    regularization_parameter : float or np.ndarray</span>
 <span class="sd">        The regularization parameter that would be used in an inverse</span>
-<span class="sd">        via Tikhonov regularization. </span>
+<span class="sd">        via Tikhonov regularization.</span>
 
 <span class="sd">    Returns</span>
 <span class="sd">    -------</span>
@@ -445,15 +453,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
 
 <span class="sd">    Notes</span>
 <span class="sd">    -----</span>
-<span class="sd">    This is not a mathematically rigorous version of the modified singular </span>
-<span class="sd">    values and should only be used for subjective evaluation of the effect of </span>
-<span class="sd">    the regularization parameter. </span>
+<span class="sd">    This is not a mathematically rigorous version of the modified singular</span>
+<span class="sd">    values and should only be used for subjective evaluation of the effect of</span>
+<span class="sd">    the regularization parameter.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">,</span> <span class="n">dtype</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
+    <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">regularization_parameter</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">regularization_parameter</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
         <span class="n">regularization_parameter</span> <span class="o">=</span> <span class="n">regularization_parameter</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-        
+
     <span class="n">modified_singular_values</span> <span class="o">=</span> <span class="p">(</span><span class="n">original_singular_values</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">regularization_parameter</span><span class="p">)</span><span class="o">/</span><span class="n">original_singular_values</span>
 
     <span class="k">return</span> <span class="n">modified_singular_values</span></div>
@@ -466,7 +474,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_frf_inverse</h1><div class="
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eba80a00>
+<jinja2.runtime.BlockReference object at 0x7f5a27365c60>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_generator.html b/_modules/sdynpy/signal_processing/sdynpy_generator.html
index 32352f8..3a255c7 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_generator.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_generator.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_generator &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_generator &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -146,7 +146,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_generator</h1><div class="hi
 <span class="sd">        pseudorandom input, each frame will be a replica of the first.</span>
 <span class="sd">    shape_function : function</span>
 <span class="sd">        A shaping that can be applied to the signal per frequency line.  If</span>
-<span class="sd">        specified, it should be a function that takes in one argument (the </span>
+<span class="sd">        specified, it should be a function that takes in one argument (the</span>
 <span class="sd">        frequency in Hz) and returns a single argument (the amplitude of the</span>
 <span class="sd">        sine wave at that frequency).</span>
 
@@ -188,13 +188,13 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_generator</h1><div class="hi
 <div class="viewcode-block" id="random"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_generator.random.html#sdynpy.signal_processing.sdynpy_generator.random">[docs]</a><span class="k">def</span> <span class="nf">random</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">,</span> <span class="n">rms</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">dt</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
            <span class="n">low_frequency_cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">high_frequency_cutoff</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="n">signal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">*</span><span class="n">shape</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">)</span>
-    <span class="k">if</span> <span class="p">(</span><span class="ow">not</span> <span class="n">low_frequency_cutoff</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="ow">not</span> <span class="n">high_frequency_cutoff</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
+    <span class="k">if</span> <span class="p">(</span><span class="n">low_frequency_cutoff</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">high_frequency_cutoff</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">):</span>
         <span class="n">freqs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">dt</span><span class="p">)</span>
         <span class="n">signal_fft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfft</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">low_frequency_cutoff</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">low_frequency_cutoff</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">zero_indices</span> <span class="o">=</span> <span class="n">freqs</span> <span class="o">&lt;</span> <span class="n">low_frequency_cutoff</span>
             <span class="n">signal_fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">zero_indices</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">high_frequency_cutoff</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">high_frequency_cutoff</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">zero_indices</span> <span class="o">=</span> <span class="n">freqs</span> <span class="o">&gt;</span> <span class="n">high_frequency_cutoff</span>
             <span class="n">signal_fft</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">zero_indices</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="n">signal</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">signal_fft</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
@@ -255,6 +255,119 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_generator</h1><div class="hi
         <span class="n">argument</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="n">period</span> <span class="o">*</span> <span class="p">(</span><span class="n">abscissa</span> <span class="o">-</span> <span class="n">time</span><span class="p">)</span>
         <span class="n">signal</span> <span class="o">+=</span> <span class="n">peak</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">argument</span><span class="p">)</span><span class="o">**</span><span class="n">sine_exponent</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">argument</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">signal</span></div>
+
+<div class="viewcode-block" id="sine_sweep"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.html#sdynpy.signal_processing.sdynpy_generator.sine_sweep">[docs]</a><span class="k">def</span> <span class="nf">sine_sweep</span><span class="p">(</span><span class="n">dt</span><span class="p">,</span> <span class="n">frequencies</span><span class="p">,</span> <span class="n">sweep_rates</span><span class="p">,</span> <span class="n">sweep_types</span><span class="p">,</span> <span class="n">amplitudes</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">phases</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generates a sweeping sine wave with linear or logarithmic sweep rate</span>
+
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    dt : float</span>
+<span class="sd">        The time step of the output signal</span>
+<span class="sd">    frequencies : iterable</span>
+<span class="sd">        A list of frequency breakpoints for the sweep.  Can be ascending or</span>
+<span class="sd">        decending or both.  Frequencies are specified in Hz, not rad/s.</span>
+<span class="sd">    sweep_rates : iterable</span>
+<span class="sd">        A list of sweep rates between the breakpoints.  This array should have</span>
+<span class="sd">        one fewer element than the `frequencies` array.  The ith element of this</span>
+<span class="sd">        array specifies the sweep rate between `frequencies[i]` and</span>
+<span class="sd">        `frequencies[i+1]`. For a linear sweep,</span>
+<span class="sd">        the rate is in Hz/s.  For a logarithmic sweep, the rate is in octave/s.</span>
+<span class="sd">    sweep_types : iterable or str</span>
+<span class="sd">        The type of sweep to perform between each frequency breakpoint.  Can be</span>
+<span class="sd">        &#39;lin&#39; or &#39;log&#39;.  If a string is specified, it will be used for all</span>
+<span class="sd">        breakpoints.  Otherwise it should be an array containing strings with</span>
+<span class="sd">        one fewer element than that of the `frequencies` array.</span>
+<span class="sd">    amplitudes : iterable or float, optional</span>
+<span class="sd">        Amplitude of the sine wave at each of the frequency breakpoints.  Can</span>
+<span class="sd">        be specified as a single floating point value, or as an array with a</span>
+<span class="sd">        value specified for each breakpoint. The default is 1.</span>
+<span class="sd">    phases : iterable or float, optional</span>
+<span class="sd">        Phases of the sine wave at each of the frequency breakpoints.  Can</span>
+<span class="sd">        be specified as a single floating point value, or as an array with a</span>
+<span class="sd">        value specified for each breakpoint. Be aware that modifying the phase</span>
+<span class="sd">        between breakpoints will effectively change the frequency of the signal,</span>
+<span class="sd">        because the phase will change over time.  The default is 0.</span>
+
+<span class="sd">    Raises</span>
+<span class="sd">    ------</span>
+<span class="sd">    ValueError</span>
+<span class="sd">        If the sweep rate and start and end frequency would result in a negative</span>
+<span class="sd">        sweep time, for example if the start frequency is above the end frequency</span>
+<span class="sd">        and a positive sweep rate is specified.</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    ordinate : np.ndarray</span>
+<span class="sd">        A numpy array consisting of the generated sine sweep signal.  The length</span>
+<span class="sd">        of the signal will be determined by the frequency breakpoints and sweep</span>
+<span class="sd">        rates.</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">last_phase</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">abscissa</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="c1"># Go through each section</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">frequencies</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+        <span class="c1"># Extract the terms</span>
+        <span class="n">start_frequency</span> <span class="o">=</span> <span class="n">frequencies</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="n">end_frequency</span> <span class="o">=</span> <span class="n">frequencies</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">omega_start</span> <span class="o">=</span> <span class="n">start_frequency</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">sweep_rate</span> <span class="o">=</span> <span class="n">sweep_rates</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">sweep_rate</span> <span class="o">=</span> <span class="n">sweep_rates</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sweep_types</span><span class="p">,</span><span class="nb">str</span><span class="p">):</span>
+            <span class="n">sweep_type</span> <span class="o">=</span> <span class="n">sweep_types</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">sweep_type</span> <span class="o">=</span> <span class="n">sweep_types</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">start_amplitude</span> <span class="o">=</span> <span class="n">amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">end_amplitude</span> <span class="o">=</span> <span class="n">amplitudes</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">start_amplitude</span> <span class="o">=</span> <span class="n">amplitudes</span>
+            <span class="n">end_amplitude</span> <span class="o">=</span> <span class="n">amplitudes</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">start_phase</span> <span class="o">=</span> <span class="n">phases</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">180</span>
+            <span class="n">end_phase</span> <span class="o">=</span> <span class="n">phases</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">180</span>
+        <span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
+            <span class="n">start_phase</span> <span class="o">=</span> <span class="n">phases</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">180</span>
+            <span class="n">end_phase</span> <span class="o">=</span> <span class="n">phases</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">180</span>
+        <span class="c1"># Compute the length of this portion of the signal</span>
+        <span class="k">if</span> <span class="n">sweep_type</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;lin&#39;</span><span class="p">,</span><span class="s1">&#39;linear&#39;</span><span class="p">]:</span>
+            <span class="n">sweep_time</span> <span class="o">=+</span> <span class="p">(</span><span class="n">end_frequency</span><span class="o">-</span><span class="n">start_frequency</span><span class="p">)</span><span class="o">/</span><span class="n">sweep_rate</span>
+        <span class="k">elif</span> <span class="n">sweep_type</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;log&#39;</span><span class="p">,</span><span class="s1">&#39;logarithmic&#39;</span><span class="p">]:</span>
+            <span class="n">sweep_time</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">end_frequency</span><span class="o">/</span><span class="n">start_frequency</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">sweep_rate</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">sweep_time</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Sweep time for segment index </span><span class="si">{:}</span><span class="s1"> is negative.  Check sweep rate.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+        <span class="n">sweep_samples</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">sweep_time</span><span class="o">/</span><span class="n">dt</span><span class="p">))</span>
+        <span class="c1"># Construct the abscissa</span>
+        <span class="n">this_abscissa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">sweep_samples</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">dt</span>
+        <span class="c1"># Compute the phase over time</span>
+        <span class="k">if</span> <span class="n">sweep_type</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;lin&#39;</span><span class="p">,</span><span class="s1">&#39;linear&#39;</span><span class="p">]:</span>
+            <span class="n">this_argument</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">sweep_rate</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">*</span><span class="n">this_abscissa</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">omega_start</span><span class="o">*</span><span class="n">this_abscissa</span>
+            <span class="n">this_frequency</span> <span class="o">=</span> <span class="p">(</span><span class="n">sweep_rate</span><span class="p">)</span><span class="o">*</span><span class="n">this_abscissa</span> <span class="o">+</span> <span class="n">omega_start</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">sweep_type</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;log&#39;</span><span class="p">,</span><span class="s1">&#39;logarithmic&#39;</span><span class="p">]:</span>
+            <span class="n">this_argument</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">sweep_rate</span><span class="o">*</span><span class="n">this_abscissa</span><span class="p">)</span><span class="o">*</span><span class="n">omega_start</span><span class="o">/</span><span class="p">(</span><span class="n">sweep_rate</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span> <span class="o">-</span> <span class="n">omega_start</span><span class="o">/</span><span class="p">(</span><span class="n">sweep_rate</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+            <span class="n">this_frequency</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="p">(</span><span class="n">sweep_rate</span><span class="o">*</span><span class="n">this_abscissa</span><span class="p">)</span><span class="o">*</span><span class="n">omega_start</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+        <span class="c1"># Compute the phase at each time step</span>
+        <span class="k">if</span> <span class="n">end_frequency</span> <span class="o">&gt;</span> <span class="n">start_frequency</span><span class="p">:</span>
+            <span class="n">freq_interp</span> <span class="o">=</span> <span class="p">[</span><span class="n">start_frequency</span><span class="p">,</span><span class="n">end_frequency</span><span class="p">]</span>
+            <span class="n">phase_interp</span> <span class="o">=</span> <span class="p">[</span><span class="n">start_phase</span><span class="p">,</span><span class="n">end_phase</span><span class="p">]</span>
+            <span class="n">amp_interp</span> <span class="o">=</span> <span class="p">[</span><span class="n">start_amplitude</span><span class="p">,</span><span class="n">end_amplitude</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">freq_interp</span> <span class="o">=</span> <span class="p">[</span><span class="n">end_frequency</span><span class="p">,</span> <span class="n">start_frequency</span><span class="p">]</span>
+            <span class="n">phase_interp</span> <span class="o">=</span> <span class="p">[</span><span class="n">end_phase</span><span class="p">,</span><span class="n">start_phase</span><span class="p">]</span>
+            <span class="n">amp_interp</span> <span class="o">=</span> <span class="p">[</span><span class="n">end_amplitude</span><span class="p">,</span><span class="n">start_amplitude</span><span class="p">]</span>
+        <span class="n">this_phases</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">this_frequency</span><span class="p">,</span><span class="n">freq_interp</span><span class="p">,</span><span class="n">phase_interp</span><span class="p">)</span>
+        <span class="c1"># Compute the amplitude at each time step</span>
+        <span class="n">this_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">this_frequency</span><span class="p">,</span><span class="n">freq_interp</span><span class="p">,</span><span class="n">amp_interp</span><span class="p">)</span>
+        <span class="n">this_ordinate</span> <span class="o">=</span> <span class="n">this_amplitudes</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">this_argument</span><span class="o">+</span><span class="n">this_phases</span><span class="o">+</span><span class="n">last_phase</span><span class="p">)</span>
+        <span class="n">last_phase</span> <span class="o">+=</span> <span class="n">this_argument</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">abscissa</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_abscissa</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">ordinate</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">this_ordinate</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+    <span class="n">ordinate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">ordinate</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ordinate</span></div>
 </pre></div>
 
            </div>
@@ -264,7 +377,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_generator</h1><div class="hi
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb82eb60>
+<jinja2.runtime.BlockReference object at 0x7f5a265a2650>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_geometry_fitting.html b/_modules/sdynpy/signal_processing/sdynpy_geometry_fitting.html
index cd8b021..9e03e8b 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_geometry_fitting.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_geometry_fitting.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_geometry_fitting &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_geometry_fitting &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -114,101 +114,118 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_geometry_fitting</h1><div cl
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">scipy.optimize</span> <span class="k">as</span> <span class="nn">opt</span>
 
-<div class="viewcode-block" id="cone_fit"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit">[docs]</a><span class="k">def</span> <span class="nf">cone_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">origin</span><span class="p">,</span><span class="n">direction</span><span class="p">,</span><span class="n">angle</span><span class="p">):</span>
+
+<div class="viewcode-block" id="cone_fit"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cone_fit">[docs]</a><span class="k">def</span> <span class="nf">cone_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">direction</span><span class="p">,</span> <span class="n">angle</span><span class="p">):</span>
     <span class="n">a</span> <span class="o">=</span> <span class="n">origin</span>
     <span class="n">p</span> <span class="o">=</span> <span class="n">points</span>
     <span class="n">n</span> <span class="o">=</span> <span class="n">direction</span>
-    <span class="n">height</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...i,i-&gt;...&#39;</span><span class="p">,(</span><span class="n">a</span> <span class="o">-</span> <span class="n">p</span><span class="p">),</span><span class="n">n</span><span class="p">)</span>
-    <span class="n">distance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">((</span><span class="n">a</span><span class="o">-</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">height</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span><span class="o">*</span><span class="n">n</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">height</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...i,i-&gt;...&#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">a</span> <span class="o">-</span> <span class="n">p</span><span class="p">),</span> <span class="n">n</span><span class="p">)</span>
+    <span class="n">distance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">((</span><span class="n">a</span><span class="o">-</span><span class="n">p</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="n">height</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span><span class="o">*</span><span class="n">n</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">expected_distance</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span><span class="o">*</span><span class="n">height</span>
     <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">((</span><span class="n">distance</span><span class="o">-</span><span class="n">expected_distance</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="create_cone"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html#sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone">[docs]</a><span class="k">def</span> <span class="nf">create_cone</span><span class="p">(</span><span class="n">ncircum</span><span class="p">,</span><span class="n">naxial</span><span class="p">,</span><span class="n">height</span><span class="p">,</span><span class="n">angle</span><span class="p">,</span><span class="n">origin</span><span class="p">,</span><span class="n">direction</span><span class="p">):</span>
-    <span class="n">circum_angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">ncircum</span><span class="p">,</span><span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">height</span><span class="p">,</span><span class="n">naxial</span><span class="o">+</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
+
+<div class="viewcode-block" id="create_cone"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html#sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone">[docs]</a><span class="k">def</span> <span class="nf">create_cone</span><span class="p">(</span><span class="n">ncircum</span><span class="p">,</span> <span class="n">naxial</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">angle</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">direction</span><span class="p">):</span>
+    <span class="n">circum_angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">ncircum</span><span class="p">,</span> <span class="n">endpoint</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">heights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">naxial</span><span class="o">+</span><span class="mi">1</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
     <span class="n">points</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">cangle</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circum_angles</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">j</span><span class="p">,</span><span class="n">h</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">heights</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">cangle</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">circum_angles</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">h</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">heights</span><span class="p">):</span>
             <span class="n">radius</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">angle</span><span class="p">)</span><span class="o">*</span><span class="n">h</span>
-            <span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">cangle</span><span class="p">)</span><span class="o">*</span><span class="n">radius</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">cangle</span><span class="p">)</span><span class="o">*</span><span class="n">radius</span><span class="p">,</span><span class="n">h</span><span class="p">])</span>
+            <span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">cangle</span><span class="p">)</span><span class="o">*</span><span class="n">radius</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">cangle</span><span class="p">)</span><span class="o">*</span><span class="n">radius</span><span class="p">,</span> <span class="n">h</span><span class="p">])</span>
     <span class="n">points</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
     <span class="n">rotation_matrix_z</span> <span class="o">=</span> <span class="n">direction</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">direction</span><span class="p">)</span>
-    <span class="n">rotation_matrix_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_matrix_z</span><span class="p">,[</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">])</span>
+    <span class="n">rotation_matrix_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_matrix_z</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
     <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">rotation_matrix_y</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">1e-10</span><span class="p">:</span>
-        <span class="n">rotation_matrix_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">0.0</span><span class="p">],</span><span class="n">rotation_matrix_z</span><span class="p">)</span>
+        <span class="n">rotation_matrix_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">([</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">],</span> <span class="n">rotation_matrix_z</span><span class="p">)</span>
         <span class="n">rotation_matrix_x</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">rotation_matrix_x</span><span class="p">)</span>
-        <span class="n">rotation_matrix_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_matrix_z</span><span class="p">,</span><span class="n">rotation_matrix_x</span><span class="p">)</span>
+        <span class="n">rotation_matrix_y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_matrix_z</span><span class="p">,</span> <span class="n">rotation_matrix_x</span><span class="p">)</span>
         <span class="n">rotation_matrix_y</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">rotation_matrix_y</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">rotation_matrix_y</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">rotation_matrix_y</span><span class="p">)</span>
-        <span class="n">rotation_matrix_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_matrix_y</span><span class="p">,</span><span class="n">rotation_matrix_z</span><span class="p">)</span>
+        <span class="n">rotation_matrix_x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">(</span><span class="n">rotation_matrix_y</span><span class="p">,</span> <span class="n">rotation_matrix_z</span><span class="p">)</span>
         <span class="n">rotation_matrix_x</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">rotation_matrix_x</span><span class="p">)</span>
-    <span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">rotation_matrix_x</span><span class="p">,</span><span class="n">rotation_matrix_y</span><span class="p">,</span><span class="n">rotation_matrix_z</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">rotation_matrix_x</span><span class="p">,</span> <span class="n">rotation_matrix_y</span><span class="p">,</span> <span class="n">rotation_matrix_z</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
     <span class="n">new_points</span> <span class="o">=</span> <span class="p">((</span><span class="n">R</span><span class="nd">@points</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="o">+</span><span class="n">origin</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">new_points</span></div>
 
-<div class="viewcode-block" id="cone_error_fn_fixed_angle"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle">[docs]</a><span class="k">def</span> <span class="nf">cone_error_fn_fixed_angle</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">angle</span><span class="p">):</span>
-    <span class="k">return</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">cone_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:],</span><span class="n">angle</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="cone_error_fn_fixed_angle"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_fixed_angle">[docs]</a><span class="k">def</span> <span class="nf">cone_error_fn_fixed_angle</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">angle</span><span class="p">):</span>
+    <span class="k">return</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">cone_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:],</span> <span class="n">angle</span><span class="p">)</span></div>
+
 
 <div class="viewcode-block" id="cone_error_fn_free_angle"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cone_error_fn_free_angle">[docs]</a><span class="k">def</span> <span class="nf">cone_error_fn_free_angle</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
-    <span class="k">return</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">cone_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span></div>
+    <span class="k">return</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">cone_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span></div>
 
-<div class="viewcode-block" id="fit_cone_fixed_angle"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle.html#sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle">[docs]</a><span class="k">def</span> <span class="nf">fit_cone_fixed_angle</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">angle</span><span class="p">):</span>
-    <span class="n">opt_fn</span> <span class="o">=</span> <span class="n">cone_error_fn_fixed_angle</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">angle</span><span class="p">)</span>
-    <span class="n">origin_guess</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">u</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
+
+<div class="viewcode-block" id="fit_cone_fixed_angle"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle.html#sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cone_fixed_angle">[docs]</a><span class="k">def</span> <span class="nf">fit_cone_fixed_angle</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">angle</span><span class="p">):</span>
+
+    <span class="k">def</span> <span class="nf">constraint_eqn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:])</span>
+
+    <span class="n">opt_fn</span> <span class="o">=</span> <span class="n">cone_error_fn_fixed_angle</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
+    <span class="n">origin_guess</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
     <span class="n">direction_guess</span> <span class="o">=</span> <span class="n">vh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">x0_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">origin_guess</span><span class="p">,</span><span class="n">direction_guess</span><span class="p">))</span>
-    <span class="n">x0_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">origin_guess</span><span class="p">,</span><span class="o">-</span><span class="n">direction_guess</span><span class="p">))</span>
+    <span class="n">x0_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">origin_guess</span><span class="p">,</span> <span class="n">direction_guess</span><span class="p">))</span>
+    <span class="n">x0_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">origin_guess</span><span class="p">,</span> <span class="o">-</span><span class="n">direction_guess</span><span class="p">))</span>
     <span class="c1"># Constrain to a unit vector</span>
-    <span class="n">constraint_eqn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:])</span>
-    <span class="n">constraint</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">NonlinearConstraint</span><span class="p">(</span><span class="n">constraint_eqn</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
-    <span class="n">result_1</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">opt_fn</span><span class="p">,</span><span class="n">x0_1</span><span class="p">,</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;trust-constr&#39;</span><span class="p">,</span><span class="n">constraints</span> <span class="o">=</span> <span class="n">constraint</span><span class="p">)</span>
-    <span class="n">result_2</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">opt_fn</span><span class="p">,</span><span class="n">x0_2</span><span class="p">,</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;trust-constr&#39;</span><span class="p">,</span><span class="n">constraints</span> <span class="o">=</span> <span class="n">constraint</span><span class="p">)</span>
+    <span class="n">constraint</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">NonlinearConstraint</span><span class="p">(</span><span class="n">constraint_eqn</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">result_1</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">opt_fn</span><span class="p">,</span> <span class="n">x0_1</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;trust-constr&#39;</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="n">constraint</span><span class="p">)</span>
+    <span class="n">result_2</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">opt_fn</span><span class="p">,</span> <span class="n">x0_2</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;trust-constr&#39;</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="n">constraint</span><span class="p">)</span>
     <span class="c1"># Figure out which is the better one</span>
     <span class="n">result</span> <span class="o">=</span> <span class="n">result_1</span> <span class="k">if</span> <span class="n">result_1</span><span class="o">.</span><span class="n">fun</span> <span class="o">&lt;</span> <span class="n">result_2</span><span class="o">.</span><span class="n">fun</span> <span class="k">else</span> <span class="n">result_2</span>
     <span class="n">cone_origin</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
     <span class="n">cone_direction</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:]</span>
-    <span class="k">return</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">,</span><span class="n">cone_origin</span><span class="p">,</span><span class="n">cone_direction</span></div>
+    <span class="k">return</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">,</span> <span class="n">cone_origin</span><span class="p">,</span> <span class="n">cone_direction</span></div>
+
 
-<div class="viewcode-block" id="distance_point_line"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line.html#sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line">[docs]</a><span class="k">def</span> <span class="nf">distance_point_line</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">origin</span><span class="p">,</span><span class="n">direction</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">((</span><span class="n">points</span><span class="o">-</span><span class="n">origin</span><span class="p">),</span><span class="n">direction</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">direction</span><span class="p">)</span></div>
+<div class="viewcode-block" id="distance_point_line"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line.html#sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_line">[docs]</a><span class="k">def</span> <span class="nf">distance_point_line</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">direction</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">cross</span><span class="p">((</span><span class="n">points</span><span class="o">-</span><span class="n">origin</span><span class="p">),</span> <span class="n">direction</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">direction</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="distance_point_plane"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane.html#sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane">[docs]</a><span class="k">def</span> <span class="nf">distance_point_plane</span><span class="p">(</span><span class="n">point</span><span class="p">,</span><span class="n">plane_point</span><span class="p">,</span><span class="n">plane_direction</span><span class="p">):</span>
-    <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...i,...i-&gt;...&#39;</span><span class="p">,</span><span class="n">point</span><span class="o">-</span><span class="n">plane_point</span><span class="p">,</span><span class="n">plane_direction</span><span class="p">))</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">plane_direction</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="cylinder_fit"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit">[docs]</a><span class="k">def</span> <span class="nf">cylinder_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">origin</span><span class="p">,</span><span class="n">direction</span><span class="p">,</span><span class="n">radius</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">distance_point_line</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">origin</span><span class="p">,</span><span class="n">direction</span><span class="p">)</span> <span class="o">-</span> <span class="n">radius</span><span class="p">)</span></div>
+<div class="viewcode-block" id="distance_point_plane"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane.html#sdynpy.signal_processing.sdynpy_geometry_fitting.distance_point_plane">[docs]</a><span class="k">def</span> <span class="nf">distance_point_plane</span><span class="p">(</span><span class="n">point</span><span class="p">,</span> <span class="n">plane_point</span><span class="p">,</span> <span class="n">plane_direction</span><span class="p">):</span>
+    <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;...i,...i-&gt;...&#39;</span><span class="p">,</span> <span class="n">point</span><span class="o">-</span><span class="n">plane_point</span><span class="p">,</span> <span class="n">plane_direction</span><span class="p">))</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">plane_direction</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="cylinder_fit"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit.html#sdynpy.signal_processing.sdynpy_geometry_fitting.cylinder_fit">[docs]</a><span class="k">def</span> <span class="nf">cylinder_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">direction</span><span class="p">,</span> <span class="n">radius</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">distance_point_line</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">direction</span><span class="p">)</span> <span class="o">-</span> <span class="n">radius</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="fit_cylinder"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder.html#sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder">[docs]</a><span class="k">def</span> <span class="nf">fit_cylinder</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">origin_guess</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">direction_guess</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">radius_guess</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">opt_fn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">cylinder_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">:</span><span class="mi">7</span><span class="p">])</span>
+
+    <span class="k">def</span> <span class="nf">constraint_eqn</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">])</span>
 
-<div class="viewcode-block" id="fit_cylinder"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder.html#sdynpy.signal_processing.sdynpy_geometry_fitting.fit_cylinder">[docs]</a><span class="k">def</span> <span class="nf">fit_cylinder</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">origin_guess</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span><span class="n">direction_guess</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">radius_guess</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
-    <span class="n">opt_fn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">cylinder_fit</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">],</span><span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">:</span><span class="mi">7</span><span class="p">])</span>
     <span class="k">if</span> <span class="n">origin_guess</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">origin_guess</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">origin_guess</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">direction_guess</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">direction_guess</span> <span class="o">==</span> <span class="s1">&#39;largest&#39;</span><span class="p">:</span>
-        <span class="n">u</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
+        <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
         <span class="n">direction_guess</span> <span class="o">=</span> <span class="n">vh</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
     <span class="k">elif</span> <span class="n">direction_guess</span> <span class="o">==</span> <span class="s1">&#39;smallest&#39;</span><span class="p">:</span>
-        <span class="n">u</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
+        <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
         <span class="n">direction_guess</span> <span class="o">=</span> <span class="n">vh</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
     <span class="k">if</span> <span class="n">radius_guess</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">u</span><span class="p">,</span><span class="n">s</span><span class="p">,</span><span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
+        <span class="n">u</span><span class="p">,</span> <span class="n">s</span><span class="p">,</span> <span class="n">vh</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">points</span><span class="o">-</span><span class="n">origin_guess</span><span class="p">)</span>
         <span class="n">radius_guess</span> <span class="o">=</span> <span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span>
-    <span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">origin_guess</span><span class="p">,</span><span class="n">direction_guess</span><span class="p">,[</span><span class="n">radius_guess</span><span class="p">]))</span>
+    <span class="n">x0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">origin_guess</span><span class="p">,</span> <span class="n">direction_guess</span><span class="p">,</span> <span class="p">[</span><span class="n">radius_guess</span><span class="p">]))</span>
     <span class="c1"># Constrain to a unit vector</span>
-    <span class="n">constraint_eqn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">])</span>
-    <span class="n">constraint</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">NonlinearConstraint</span><span class="p">(</span><span class="n">constraint_eqn</span><span class="p">,</span><span class="mf">1.0</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
-    <span class="n">result</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">opt_fn</span><span class="p">,</span><span class="n">x0</span><span class="p">,</span><span class="n">method</span><span class="o">=</span><span class="s1">&#39;trust-constr&#39;</span><span class="p">,</span><span class="n">constraints</span> <span class="o">=</span> <span class="n">constraint</span><span class="p">)</span>
+    <span class="n">constraint</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">NonlinearConstraint</span><span class="p">(</span><span class="n">constraint_eqn</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+    <span class="n">result</span> <span class="o">=</span> <span class="n">opt</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">opt_fn</span><span class="p">,</span> <span class="n">x0</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;trust-constr&#39;</span><span class="p">,</span> <span class="n">constraints</span><span class="o">=</span><span class="n">constraint</span><span class="p">)</span>
     <span class="n">origin</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
     <span class="n">direction</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:</span><span class="mi">6</span><span class="p">]</span>
     <span class="n">radius</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">,</span><span class="n">origin</span><span class="p">,</span><span class="n">direction</span><span class="p">,</span><span class="n">radius</span></div>
-    
+    <span class="k">return</span> <span class="n">result</span><span class="o">.</span><span class="n">fun</span><span class="p">,</span> <span class="n">origin</span><span class="p">,</span> <span class="n">direction</span><span class="p">,</span> <span class="n">radius</span></div>
+
+
 <div class="viewcode-block" id="fit_line_point_cloud"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud.html#sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud">[docs]</a><span class="k">def</span> <span class="nf">fit_line_point_cloud</span><span class="p">(</span><span class="n">points</span><span class="p">):</span>
-    <span class="n">center</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">points</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">center</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">points</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">shifted_points</span> <span class="o">=</span> <span class="n">points</span><span class="o">-</span><span class="n">center</span>
     <span class="n">cov</span> <span class="o">=</span> <span class="n">points</span><span class="o">.</span><span class="n">T</span><span class="nd">@shifted_points</span>
-    <span class="n">evals</span><span class="p">,</span><span class="n">evects</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span>
-    <span class="n">direction</span> <span class="o">=</span> <span class="n">evects</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">evals</span><span class="p">)]</span>
+    <span class="n">evals</span><span class="p">,</span> <span class="n">evects</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span>
+    <span class="n">direction</span> <span class="o">=</span> <span class="n">evects</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">evals</span><span class="p">)]</span>
     <span class="n">direction</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">direction</span><span class="p">)</span>
     <span class="k">return</span> <span class="k">lambda</span> <span class="n">t</span><span class="p">:</span> <span class="n">center</span><span class="o">+</span><span class="n">t</span><span class="o">*</span><span class="n">direction</span></div>
 </pre></div>
@@ -220,7 +237,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_geometry_fitting</h1><div cl
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb82f550>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb7040>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_harmonic.html b/_modules/sdynpy/signal_processing/sdynpy_harmonic.html
index 95775ad..ba55bcc 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_harmonic.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_harmonic.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_harmonic &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_harmonic &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -131,7 +131,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_harmonic</h1><div class="hig
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebbc4670>
+<jinja2.runtime.BlockReference object at 0x7f5a270b7340>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_integration.html b/_modules/sdynpy/signal_processing/sdynpy_integration.html
index f228e16..0e68e53 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_integration.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_integration.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_integration &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_integration &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -150,7 +150,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_integration</h1><div class="
 <div class="viewcode-block" id="MCK_incomplete_to_StateSpace"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_integration.MCK_incomplete_to_StateSpace.html#sdynpy.signal_processing.sdynpy_integration.MCK_incomplete_to_StateSpace">[docs]</a><span class="k">def</span> <span class="nf">MCK_incomplete_to_StateSpace</span><span class="p">(</span><span class="n">M</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">K</span><span class="p">,</span> <span class="n">massless_dofs</span><span class="o">=</span><span class="p">[]):</span>
     <span class="n">nomass</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">massless_dofs</span><span class="p">)</span>
     <span class="n">mass</span> <span class="o">=</span> <span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">nomass</span>
-    <span class="n">mass_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">massless_dofs</span><span class="p">])</span>
+    <span class="n">mass_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">if</span> <span class="n">v</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">massless_dofs</span><span class="p">])</span>
     <span class="n">massless_dofs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">massless_dofs</span><span class="p">)</span>
 
     <span class="n">Kmm</span> <span class="o">=</span> <span class="n">K</span><span class="p">[</span><span class="n">mass_dofs</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">mass_dofs</span><span class="p">]</span>
@@ -269,7 +269,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_integration</h1><div class="
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb8b60e0>
+<jinja2.runtime.BlockReference object at 0x7f5a27083430>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_lrm.html b/_modules/sdynpy/signal_processing/sdynpy_lrm.html
new file mode 100644
index 0000000..acc7c3b
--- /dev/null
+++ b/_modules/sdynpy/signal_processing/sdynpy_lrm.html
@@ -0,0 +1,583 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>sdynpy.signal_processing.sdynpy_lrm &mdash; SDynPy 0.14.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/copybutton.css" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/custom.css" />
+
+  
+  <!--[if lt IE 9]>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+        <script src="../../../_static/jquery.js"></script>
+        <script src="../../../_static/underscore.js"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
+        <script src="../../../_static/doctools.js"></script>
+        <script src="../../../_static/sphinx_highlight.js"></script>
+        <script src="../../../_static/clipboard.min.js"></script>
+        <script src="../../../_static/copybutton.js"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
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+          
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../usage.html">Usage</a></li>
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+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">sdynpy.signal_processing.sdynpy_lrm</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for sdynpy.signal_processing.sdynpy_lrm</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
+<span class="kn">import</span> <span class="nn">math</span> <span class="k">as</span> <span class="nn">mt</span>
+<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
+<span class="kn">import</span> <span class="nn">psutil</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
+
+<div class="viewcode-block" id="frf_local_model"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.html#sdynpy.signal_processing.sdynpy_lrm.frf_local_model">[docs]</a><span class="k">def</span> <span class="nf">frf_local_model</span><span class="p">(</span><span class="n">references</span><span class="p">,</span><span class="n">responses</span><span class="p">,</span><span class="n">abscissa</span><span class="p">,</span><span class="n">f_out</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">bandwidth</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="n">transient</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">modelset</span><span class="o">=</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">]],</span>
+                    <span class="n">export_ratio</span><span class="o">=</span><span class="mf">.1</span><span class="p">,</span><span class="n">max_parallel</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span><span class="n">print_time</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Local modeling FRF estimator for MIMO systems.</span>
+<span class="sd">    This function can be called directly or using</span>
+<span class="sd">    frf.timedata2frf(...,method=&#39;LRM&#39;) or frf.fft2frf(...,method=&#39;LRM&#39;).</span>
+<span class="sd">    </span>
+<span class="sd">    Parameters</span>
+<span class="sd">    ----------</span>
+<span class="sd">    references : ndarray (N_in, N_freqs) or (N_in, 1, N_freqs)</span>
+<span class="sd">        REFERENCES FREQUENCY-DOMAIN DATA.</span>
+<span class="sd">    responses : ndarray, shape (N_out, N_freqs) or (N_out, 1, N_freqs)</span>
+<span class="sd">        RESPONSES FREQUENCY-DOMAIN DATA.</span>
+<span class="sd">    abcissa : ndarray (N_freqs,)</span>
+<span class="sd">        INPUT FREQUENCY VECTOR.</span>
+<span class="sd">        </span>
+<span class="sd">    f_out: ndarray (N_freqs_out,), optional</span>
+<span class="sd">        Output frequency vector. Finer resolution increases computational cost,</span>
+<span class="sd">        but only to a finite limit. Compuatational cost will not increase beyond</span>
+<span class="sd">        f_out = f_in, but large interpolation may result in a large export_ratio.</span>
+<span class="sd">        The default is f_in</span>
+<span class="sd">    bandwidth: float, optional</span>
+<span class="sd">        Local model estimation bandwidth in units of f_in. Larger values yield</span>
+<span class="sd">        better noise filtering but risk underfitting and take longer to run.</span>
+<span class="sd">    transient : boolean, optional</span>
+<span class="sd">        whetheter to include transient estimation in local models. Recommend False</span>
+<span class="sd">        for impact testing or burst random, True otherwise. The default is True.</span>
+<span class="sd">    modelset : iterable of numpy (3,) arrays, optional</span>
+<span class="sd">        Arrays contain [num. order, trans. order, denom. order].</span>
+<span class="sd">        Recommend increasing numerator order for nonlinear structures</span>
+<span class="sd">        and complicated FRFs. The default is [[1,1,0],[1,1,1],[1,1,2]].</span>
+<span class="sd">    export_ratio : f;pat, optional</span>
+<span class="sd">        (centered) proportion of local bands that can be stored and exported.</span>
+<span class="sd">        Larger values risk capturing volatile end behavior but yield faster</span>
+<span class="sd">        computation times. The default is 0.1.</span>
+<span class="sd">    max_parallel: int, optional</span>
+<span class="sd">        for very large systems with high frequency resolution and large bandwidths,</span>
+<span class="sd">        system RAM can be a constraint. The default number of parallel processes</span>
+<span class="sd">        is the number of CPU threads. If this causes freezing, max_parallel can</span>
+<span class="sd">        be set to reduce the allowed number of parallel threads. Also, smaller</span>
+<span class="sd">        pools may initialize faster. The default is 512.</span>
+<span class="sd">    print_time: boolean, optional</span>
+<span class="sd">        print remaining time if more than 10 seconds</span>
+
+<span class="sd">    Returns</span>
+<span class="sd">    -------</span>
+<span class="sd">    f_out: ndarray (N_freqs_out,)</span>
+<span class="sd">        estimated FRM frequency vector</span>
+<span class="sd">    H: ndarray (N_freqs_out, N_out, N_in)</span>
+<span class="sd">        frequency response matrix</span>
+<span class="sd">    model_data: dict</span>
+<span class="sd">        candidate model info and selected model at each frequency in f_out</span>
+<span class="sd">       </span>
+<span class="sd">        </span>
+<span class="sd">    Notes</span>
+<span class="sd">    -------</span>
+<span class="sd">    To improve noise and transient removal, recommend increasing bandwidth</span>
+<span class="sd">    </span>
+<span class="sd">    To reduce computation time, recommend decreasing f_out resolution,</span>
+<span class="sd">    increasing export_ratio, or decreasing bandwidth. For very large systems,</span>
+<span class="sd">    if freezing occurs, reduce max_parallel.</span>
+<span class="sd">    </span>
+<span class="sd">    For more information, see &quot;A practitioner&#39;s guide to local FRF estimation&quot;,</span>
+<span class="sd">    K. Coletti. This function uses the MISO parameterization</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+<span class="w">    </span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    NOTES FOR MAINTAINER</span>
+<span class="sd">    Outline of this function</span>
+<span class="sd">    -------</span>
+<span class="sd">        (1) f_in and f_out are parsed to generate an array of bands at which to</span>
+<span class="sd">            estimate local models (bands) and a list of query points in each</span>
+<span class="sd">            model at which to export solutions (query)</span>
+<span class="sd">        (2) based on the model candidate set and problem size, functional</span>
+<span class="sd">            parameterizations are generated which return linearized coefficient</span>
+<span class="sd">            matrices (for solving) and the FRF matrix (for export)</span>
+<span class="sd">        (3) computation time is estimated, and the LSQ algorithm is selected.</span>
+<span class="sd">            Solving model parameters using interative LLSQ is the majority of</span>
+<span class="sd">            computation time.</span>
+<span class="sd">        (4) in parallel, local models are solved for each model in modelset and</span>
+<span class="sd">            each frequency band. FRFs are exported. CTRL-F &#39;results = Parallel&#39;</span>
+<span class="sd">        (5) the winning model and corresponding FRM is returned in each band</span>
+<span class="sd">        </span>
+<span class="sd">    Notes on variable names</span>
+<span class="sd">    -------</span>
+<span class="sd">    U and Y are input and output data</span>
+<span class="sd">    A,B,D are FRF numerator, transient numerator, and denom., respectively</span>
+<span class="sd">    H is the frequency response matrix.</span>
+<span class="sd">    LR is a function that produces linear coefficients (L) and remainder (R) for the local models</span>
+<span class="sd">    See &quot;A Practitioner’s Guide to Local FRF Estimation&quot;, K. Coletti for more information</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    
+    <span class="c1">#%% Process arguments</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="n">references</span>
+    <span class="n">Y</span> <span class="o">=</span> <span class="n">responses</span>
+    <span class="n">f_in</span> <span class="o">=</span> <span class="n">abscissa</span>
+    <span class="k">del</span> <span class="n">references</span><span class="p">;</span> <span class="k">del</span> <span class="n">responses</span><span class="p">;</span> <span class="k">del</span> <span class="n">abscissa</span>
+    
+    <span class="n">df</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">f_in</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">f_in</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="c1"># frequency step size</span>
+    <span class="k">if</span> <span class="n">bandwidth</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># local model bandwidth</span>
+        <span class="n">bandwidth</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span> <span class="p">[</span><span class="nb">min</span><span class="p">([</span><span class="mi">300</span><span class="o">*</span><span class="n">df</span><span class="p">,</span><span class="mi">40</span><span class="p">])</span> <span class="p">,</span> <span class="nb">max</span><span class="p">([</span><span class="mi">30</span><span class="o">*</span><span class="n">df</span><span class="p">,</span><span class="mi">10</span><span class="p">])]</span> <span class="p">)</span>
+        <span class="c1"># bounded below by the larger of 30 points and 10 Hz,</span>
+        <span class="c1"># above by the smaller of 300 points and 40 Hz</span>
+    <span class="k">if</span> <span class="n">f_out</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="c1"># spacing between center frequencies for estimation</span>
+        <span class="n">f_out</span> <span class="o">=</span> <span class="n">f_in</span>
+    
+    <span class="c1"># Default parameters, hidden from user</span>
+    <span class="n">NitSK</span> <span class="o">=</span> <span class="mi">50</span> <span class="c1"># max SK iterations</span>
+    <span class="n">conv_tol</span> <span class="o">=</span> <span class="mf">.005</span> <span class="c1"># SK parameter convergence tolerance</span>
+    
+    <span class="c1">#%% Calculate general helper vars</span>
+    <span class="n">Nfreq</span> <span class="o">=</span> <span class="n">f_in</span><span class="o">.</span><span class="n">size</span> <span class="c1"># number of frequencies</span>
+    <span class="n">Nin</span> <span class="o">=</span> <span class="n">U</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">Nout</span> <span class="o">=</span> <span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">Nmod</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">modelset</span><span class="p">)</span>
+    <span class="n">U</span> <span class="o">=</span> <span class="n">U</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nin</span><span class="p">,</span><span class="n">Nfreq</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># no realizations/frames needed</span>
+    <span class="n">Y</span> <span class="o">=</span> <span class="n">Y</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nfreq</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span>
+    <span class="n">f_in</span> <span class="o">=</span> <span class="n">f_in</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span>
+    <span class="n">f_out</span> <span class="o">=</span> <span class="n">f_out</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span>
+    
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">U</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">Nfreq</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;input arrays are not correctly sized&#39;</span><span class="p">)</span>
+    
+    <span class="c1">#%% Get estimation bands and query points (STEP 1)</span>
+    <span class="c1"># Estimation bands contain the frequency bins used for parameter estimation,</span>
+    <span class="c1"># and query points are the frequencies at which results are exported</span>
+    <span class="n">Nband</span> <span class="o">=</span> <span class="n">__round_odd</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">/</span><span class="n">df</span><span class="p">)</span> <span class="c1"># number of data points in band</span>
+    <span class="n">N_half_band</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">Nband</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># number of data points on either side of center</span>
+    <span class="n">max_from_center</span> <span class="o">=</span> <span class="n">export_ratio</span><span class="o">/</span><span class="mi">2</span><span class="o">*</span><span class="n">bandwidth</span> <span class="c1"># maximum query distance from center point, except at ends</span>
+    
+    <span class="n">bands</span><span class="p">,</span><span class="n">query</span> <span class="o">=</span> <span class="n">__parse_bands</span><span class="p">(</span><span class="n">N_half_band</span><span class="p">,</span><span class="n">max_from_center</span><span class="p">,</span><span class="n">f_in</span><span class="p">,</span><span class="n">f_out</span><span class="p">)</span> <span class="c1"># bands gives indices of f_in, and query gives r values for query. r=Hz/df.</span>
+    <span class="n">Ninds</span> <span class="o">=</span> <span class="n">bands</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>    
+    
+    <span class="c1">#%% Initialize parameterizations (STEP 2)</span>
+    <span class="n">LR</span><span class="p">,</span><span class="n">D</span><span class="p">,</span><span class="n">lsq_err</span><span class="p">,</span><span class="n">H</span> <span class="o">=</span> <span class="p">[[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">Nmod</span><span class="p">,[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">Nmod</span><span class="p">,[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">Nmod</span><span class="p">,[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="n">Nmod</span><span class="p">]</span>
+    <span class="n">Npar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">Nmod</span><span class="p">)</span>
+    
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Nmod</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">transient</span><span class="p">:</span>
+            <span class="n">LR</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">D</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">lsq_err</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">H</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">Npar</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">__parameterize</span><span class="p">(</span><span class="n">Nin</span><span class="p">,</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nband</span><span class="p">,</span><span class="n">modelset</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">LR</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">D</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">lsq_err</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">H</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">Npar</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">__parameterize_notrans</span><span class="p">(</span><span class="n">Nin</span><span class="p">,</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nband</span><span class="p">,</span><span class="n">modelset</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+            
+        <span class="k">if</span> <span class="n">Npar</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">&gt;=</span><span class="n">Nband</span><span class="o">*</span><span class="n">Nout</span><span class="p">:</span> <span class="c1"># remove infeasible models</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;bandwidth too small for &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">modelset</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">+</span> <span class="s1">&#39; model order&#39;</span><span class="p">)</span>
+            <span class="n">modelset</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+            <span class="n">Nmod</span> <span class="o">-=</span> <span class="mi">1</span>
+            <span class="n">LR</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">k</span><span class="p">);</span><span class="n">D</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">k</span><span class="p">);</span><span class="n">lsq_err</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">k</span><span class="p">);</span><span class="n">H</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="n">k</span><span class="p">);</span><span class="n">Npar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">Npar</span><span class="p">,</span><span class="n">k</span><span class="p">)</span>
+            
+    <span class="n">dof</span> <span class="o">=</span> <span class="n">Nband</span><span class="o">*</span><span class="n">Nout</span><span class="o">-</span><span class="n">Npar</span>
+        
+    <span class="c1">#%% Compute (STEP 3/4)</span>
+    <span class="c1"># Parallel function. # joblib can&#39;t parse lists (only arrays), so query is a parameter</span>
+    <span class="k">def</span> <span class="nf">__solve_ind</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">inq</span><span class="p">,</span><span class="n">lstsq</span><span class="p">):</span>
+        <span class="c1"># Initialize. inq are radii of H to output in this band</span>
+        <span class="n">H_multi_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nin</span><span class="p">,</span><span class="n">Nmod</span><span class="p">,</span><span class="n">inq</span><span class="o">.</span><span class="n">size</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span> <span class="c1"># axis (0) output (1) input (2) model (3) frequency</span>
+        <span class="n">mdl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="n">Nmod</span><span class="p">,</span><span class="n">inq</span><span class="o">.</span><span class="n">size</span><span class="p">),</span><span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span><span class="p">)</span>
+        <span class="n">Uk</span> <span class="o">=</span> <span class="n">U</span><span class="p">[:,</span><span class="n">bands</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span>
+        <span class="n">Yk</span> <span class="o">=</span> <span class="n">Y</span><span class="p">[:,</span><span class="n">bands</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span>
+        
+        <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Nmod</span><span class="p">):</span> <span class="c1"># model loop</span>
+            <span class="n">H_multi_model</span><span class="p">[:,:,</span><span class="n">kk</span><span class="p">,:],</span><span class="n">pstar</span> <span class="o">=</span> <span class="n">__solveLRM</span><span class="p">(</span><span class="n">Uk</span><span class="p">,</span><span class="n">Yk</span><span class="p">,</span><span class="n">LR</span><span class="p">[</span><span class="n">kk</span><span class="p">],</span><span class="n">D</span><span class="p">[</span><span class="n">kk</span><span class="p">],</span><span class="n">lsq_err</span><span class="p">[</span><span class="n">kk</span><span class="p">],</span><span class="n">H</span><span class="p">[</span><span class="n">kk</span><span class="p">],</span><span class="n">inq</span><span class="p">,</span><span class="n">conv_tol</span><span class="p">,</span><span class="n">NitSK</span><span class="p">,</span><span class="n">lstsq</span><span class="p">)</span>
+        
+            <span class="c1"># Model selection - MDL (see Pintelon, IEEE, 2021)</span>
+            <span class="n">mdl</span><span class="p">[</span><span class="n">kk</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">pstar</span><span class="o">/</span><span class="n">dof</span><span class="p">[</span><span class="n">kk</span><span class="p">]</span> <span class="o">*</span> <span class="n">mt</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span> <span class="n">mt</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">Nband</span><span class="p">)</span><span class="o">*</span><span class="n">Npar</span><span class="p">[</span><span class="n">kk</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">dof</span><span class="p">[</span><span class="n">kk</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="p">)</span>
+            
+        <span class="k">return</span> <span class="n">H_multi_model</span><span class="p">,</span><span class="n">mdl</span>
+    
+    <span class="c1"># Estimate time, choose algorithm - SEE APPENDIX NOTES 1 AND 2</span>
+    <span class="n">memory</span> <span class="o">=</span> <span class="n">psutil</span><span class="o">.</span><span class="n">virtual_memory</span><span class="p">()</span><span class="o">.</span><span class="n">total</span><span class="o">/</span><span class="mf">1e9</span>
+    <span class="n">n_parallel</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">psutil</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">(),</span><span class="mi">61</span><span class="p">,</span><span class="n">max_parallel</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">memory</span><span class="o">/</span><span class="n">n_parallel</span> <span class="o">&lt;</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">Nin</span><span class="o">*</span><span class="n">Nout</span> <span class="o">&gt;</span> <span class="mi">500</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;Many parallel processes relative to system RAM. For large problems, freezing may occur.&#39;</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">test_lstsq_time</span><span class="p">(</span><span class="n">alg</span><span class="p">):</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
+        <span class="n">__solve_ind</span><span class="p">(</span> <span class="nb">round</span><span class="p">(</span><span class="n">Ninds</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span><span class="n">query</span><span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">Ninds</span><span class="o">/</span><span class="mi">2</span><span class="p">)],</span><span class="n">alg</span> <span class="p">)</span>
+        <span class="k">return</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">start</span>
+    <span class="k">if</span> <span class="n">Nin</span><span class="o">*</span><span class="n">Nout</span> <span class="o">&lt;</span> <span class="mi">500</span><span class="p">:</span>
+        <span class="n">time_numpy</span> <span class="o">=</span> <span class="n">test_lstsq_time</span><span class="p">(</span><span class="n">__lstsq_numpy</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">time_numpy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span> <span class="c1"># numpy is very slow for large problems</span>
+    <span class="n">time_scipy</span> <span class="o">=</span> <span class="n">test_lstsq_time</span><span class="p">(</span><span class="n">__lstsq_scipy</span><span class="p">)</span>
+    
+    <span class="k">if</span> <span class="n">time_numpy</span> <span class="o">&lt;</span> <span class="n">time_scipy</span><span class="p">:</span>
+        <span class="n">lstsq</span> <span class="o">=</span> <span class="n">__lstsq_numpy</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lstsq</span> <span class="o">=</span> <span class="n">__lstsq_scipy</span>
+        
+    <span class="n">time_est</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">time_numpy</span><span class="p">,</span><span class="n">time_scipy</span><span class="p">)</span><span class="o">*</span><span class="n">Ninds</span><span class="o">/</span><span class="n">n_parallel</span>
+    <span class="k">if</span> <span class="n">time_est</span> <span class="o">&gt;</span> <span class="mi">10</span> <span class="ow">and</span> <span class="n">print_time</span><span class="p">:</span> <span class="c1"># parallel initialization takes 5 seconds</span>
+        <span class="nb">print</span><span class="p">(</span> <span class="s1">&#39;Estimated time remaining: parallel init. + &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span> <span class="nb">round</span><span class="p">(</span><span class="mf">1.1</span><span class="o">*</span><span class="n">time_est</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39; seconds&#39;</span> <span class="p">)</span>
+    
+    <span class="c1"># Compute and extract results into arrays</span>
+    <span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_parallel</span><span class="p">)(</span><span class="n">delayed</span><span class="p">(</span><span class="n">__solve_ind</span><span class="p">)(</span><span class="n">k</span><span class="p">,</span><span class="n">query</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">lstsq</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Ninds</span><span class="p">))</span>
+    <span class="n">H_multi_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nin (2) model (3) Nind</span>
+    <span class="n">mdl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># axis (0) model (1) Nind</span>
+            
+    <span class="c1">#%% Select winning models and export (STEP 5)</span>
+    <span class="n">msel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">mdl</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># axis. (0) freq. ind</span>
+    <span class="n">H</span> <span class="o">=</span> <span class="n">H_multi_model</span><span class="p">[:,:,</span><span class="n">msel</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">f_out</span><span class="o">.</span><span class="n">size</span><span class="p">)]</span> <span class="c1"># axis. (0) Nout (1) Nin (2) freq. ind</span>
+    <span class="n">model_data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;info&#39;</span><span class="p">:</span> <span class="s1">&#39;Model format is [H num. order, trans. num. order, denom. order].&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;modelset&#39;</span><span class="p">:</span><span class="n">modelset</span><span class="p">,</span>
+                  <span class="s1">&#39;model_selected&#39;</span><span class="p">:</span><span class="n">msel</span><span class="p">}</span>
+                
+    <span class="c1"># Return results</span>
+    <span class="k">return</span> <span class="n">f_out</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">H</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="n">model_data</span></div>
+  
+<span class="c1">#%% Local model solver</span>
+<span class="k">def</span> <span class="nf">__solveLRM</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">LR</span><span class="p">,</span><span class="n">D</span><span class="p">,</span><span class="n">lsq_err</span><span class="p">,</span><span class="n">H</span><span class="p">,</span><span class="n">query</span><span class="p">,</span><span class="n">conv_tol</span><span class="p">,</span><span class="n">NitSK</span><span class="p">,</span><span class="n">lstsq</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Local model solver. This function occupies most of the FRF local modeling estimation</span>
+<span class="sd">    time. Inputs are functions that accept parameter values and output local model component</span>
+<span class="sd">    matrices used for least-squares solving and model exporting. Uses Sanathanan-Koerner</span>
+<span class="sd">    iterations to solve the nonlinear rational LSTSQ problem iteratively</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># SK iteration preparations</span>
+    <span class="n">L</span><span class="p">,</span><span class="n">R</span> <span class="o">=</span> <span class="n">LR</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">)</span> <span class="c1"># linearized transfer matrix and residual vector. Lin objective = D^{-1}*(L*t-R)</span>
+    <span class="n">Dp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># initial scaling matrix. Axis (0) Nout (1) Nband</span>
+    <span class="n">t0s</span><span class="p">,</span><span class="n">lsq_min</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">)]</span><span class="o">*</span><span class="mi">2</span>
+    
+    <span class="c1"># Snathanan-Koererner Iterations</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NitSK</span><span class="o">+</span><span class="mi">5</span><span class="p">):</span>
+        <span class="c1"># Define adjusted LLSQ/GLSQ problem</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="n">L</span><span class="o">/</span><span class="n">Dp</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># left matrix in LLSQ stacked over r. Axis (0) Nout (1) Nband (2) Npar</span>
+        <span class="n">q</span> <span class="o">=</span> <span class="n">R</span><span class="o">/</span><span class="n">Dp</span> <span class="c1"># residual vector stacked over r. Axis (0) Nout (1) Nband</span>
+        
+        <span class="c1"># Solve and advance. Vast majority of compution time in all of get_frf</span>
+        <span class="n">t0</span> <span class="o">=</span> <span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">q</span><span class="p">)</span> <span class="c1"># axis. (0) Nout (1) Npar</span>
+    
+        <span class="c1"># Convergence</span>
+        <span class="k">if</span> <span class="nb">abs</span><span class="p">((</span><span class="n">t0</span><span class="o">-</span><span class="n">t0s</span><span class="p">)</span><span class="o">/</span><span class="n">t0</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="o">&lt;</span> <span class="n">conv_tol</span><span class="p">:</span>
+            <span class="k">break</span> <span class="c1"># exit if convergence is reached</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">Dp</span> <span class="o">=</span> <span class="n">D</span><span class="p">(</span><span class="n">t0</span><span class="p">)</span> <span class="c1"># store new adjustment matrix</span>
+        <span class="n">t0s</span> <span class="o">=</span> <span class="n">t0</span>
+
+        <span class="c1"># Capture nonconvergence (in my experience, this is very rare)</span>
+        <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="n">NitSK</span><span class="p">:</span>
+            <span class="n">lsq_t0</span> <span class="o">=</span> <span class="n">lsq_err</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">t0</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">lsq_t0</span> <span class="o">&lt;</span> <span class="n">lsq_min</span><span class="p">:</span>
+                <span class="n">lsq_min</span> <span class="o">=</span> <span class="n">lsq_t0</span>
+                <span class="n">t1</span> <span class="o">=</span> <span class="n">t0</span>
+                
+    <span class="n">t0</span><span class="o">=</span><span class="n">t1</span> <span class="k">if</span> <span class="s1">&#39;t1&#39;</span> <span class="ow">in</span> <span class="nb">locals</span><span class="p">()</span> <span class="k">else</span> <span class="n">t0</span>
+    
+    <span class="k">return</span> <span class="n">H</span><span class="p">(</span><span class="n">t0</span><span class="p">,</span><span class="n">query</span><span class="p">),</span><span class="o">-</span><span class="n">lsq_err</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">t0</span><span class="p">)</span> <span class="c1"># Hstar, pstar (log density)</span>
+
+<span class="c1">#%% Parameterizations. See &quot;A Practitioner’s Guide to Local FRF Estimation&quot;, K. Coletti</span>
+<span class="k">def</span> <span class="nf">__parameterize</span><span class="p">(</span><span class="n">Nin</span><span class="p">,</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nband</span><span class="p">,</span><span class="n">order</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate parameterization functions for estimation including transient removal</span>
+<span class="sd">    Parameterizations are passed into __solveLRM to generate local models</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Components of f, because f can be iterated</span>
+    <span class="c1"># Radius and exponent matrices. r is radius, s power, and A/B/D component matrices</span>
+    <span class="n">N_half_band</span> <span class="o">=</span> <span class="p">(</span><span class="n">Nband</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">N_half_band</span><span class="p">,</span><span class="n">N_half_band</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">rsA</span> <span class="o">=</span> <span class="n">r</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># axis. (0) power (1) radius</span>
+    <span class="n">rsB</span> <span class="o">=</span> <span class="n">r</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="n">rsD</span> <span class="o">=</span> <span class="n">r</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    
+    <span class="c1"># Define theta break points</span>
+    <span class="n">bA</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">Nout</span><span class="o">*</span><span class="n">Nin</span><span class="o">*</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span> <span class="c1"># transfer numerator</span>
+    <span class="n">bB</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">bA</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">bA</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">Nout</span><span class="o">*</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># transient numerator</span>
+    <span class="n">bD</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">bB</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">bB</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">Nout</span><span class="o">*</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># denominator (wrap Nout first, then s)</span>
+    <span class="n">Npar</span> <span class="o">=</span> <span class="n">bB</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">Nout</span><span class="o">*</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span>
+    
+    <span class="k">def</span> <span class="nf">lsq_err</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">t</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># transform from SO format to MO</span>
+        
+        <span class="c1"># Build matrices</span>
+        <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">t</span><span class="p">[</span><span class="n">bA</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsA</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nin</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span>
+        <span class="n">B</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">t</span><span class="p">[</span><span class="n">bB</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsB</span><span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nfreq</span>
+        <span class="n">catmat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">Nout</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">t</span><span class="p">[</span><span class="n">bD</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span><span class="mi">0</span> <span class="p">)</span>
+        <span class="n">D</span> <span class="o">=</span>  <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">catmat</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsD</span><span class="p">)</span> <span class="c1"># axis. (1) Nout (2) Nfreq</span>
+        
+        <span class="c1"># Compute sum of squared residuals</span>
+        <span class="n">Ysim</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jkl,kl-&gt;jl&#39;</span><span class="p">,</span><span class="n">A</span><span class="p">,</span><span class="n">U</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span><span class="p">)</span><span class="o">/</span><span class="n">D</span>
+        <span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">Y</span><span class="o">-</span><span class="n">Ysim</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    
+    <span class="k">def</span> <span class="nf">H</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">r_inq</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># transform from SO format to MO</span>
+        
+        <span class="c1"># Radius and exponent matrices</span>
+        <span class="n">rsA_inq</span> <span class="o">=</span> <span class="n">r_inq</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># axis. (0) power (1) radius</span>
+        <span class="n">rsD_inq</span> <span class="o">=</span> <span class="n">r_inq</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        
+        <span class="c1"># Build matrices</span>
+        <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">t</span><span class="p">[</span><span class="n">bA</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsA_inq</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nin</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># axis. (1) Nout (2) Nin (3) Nfreq</span>
+        <span class="n">temp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">Nout</span><span class="p">),</span><span class="n">t</span><span class="p">[</span><span class="n">bD</span><span class="p">])</span> <span class="p">)</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">temp</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsD_inq</span><span class="p">)</span> <span class="c1"># axis. (1) Nout (2) Nfreq</span>
+        
+        <span class="k">return</span> <span class="n">A</span><span class="o">/</span><span class="n">D</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+
+    <span class="c1"># Build linearized (SK) transfer matrix L(U,y)*t + R(U,Y) = Dp^(-1) (D(t)*Y - N(t)*U - M(t))</span>
+    <span class="c1"># Denominator matrix for SK iterations</span>
+    <span class="k">def</span> <span class="nf">D</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span> <span class="s1">&#39;ij,lj-&gt;li&#39;</span><span class="p">,</span><span class="n">r</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="p">,</span> <span class="n">t</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="mi">0</span><span class="p">)]</span> <span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nband</span>
+    
+    <span class="k">def</span> <span class="nf">LR</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>        
+        <span class="c1"># Compute residual</span>
+        <span class="n">R</span> <span class="o">=</span> <span class="o">-</span><span class="n">Y</span> <span class="c1"># axis (0) Nout (1) Nband</span>
+        
+        <span class="c1"># Compute components</span>
+        <span class="n">r1</span> <span class="o">=</span> <span class="n">r</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">A1</span> <span class="o">=</span> <span class="n">Y</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">Nout</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span><span class="o">*</span> <span class="p">(</span><span class="n">r1</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">))[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># D(t)*Y. Axis. (1) r (2) s (3) Nout</span>
+        <span class="n">A2</span> <span class="o">=</span> <span class="p">(</span><span class="n">U</span><span class="o">.</span><span class="n">T</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">r1</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nband</span><span class="p">,</span><span class="n">Nin</span><span class="o">*</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># A(t)*U. Axis. (1) r (2) Nin*s (3) Nout</span>
+        <span class="n">A3</span> <span class="o">=</span> <span class="p">(</span> <span class="n">r1</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="p">)[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># B(t). Axis. (1) r (2) s (3) empty</span>
+        
+        <span class="c1"># Assemble into array</span>
+        <span class="k">if</span> <span class="n">order</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="n">catmat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> <span class="c1"># A2 and A3 don&#39;t depend on Nout</span>
+            <span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="o">-</span><span class="n">A2</span><span class="o">*</span><span class="n">catmat</span><span class="p">,</span><span class="o">-</span><span class="n">A3</span><span class="o">*</span><span class="n">catmat</span><span class="p">,</span><span class="n">A1</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nband (2) parameter</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="o">-</span><span class="n">A2</span><span class="p">,</span><span class="o">-</span><span class="n">A3</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nband (2) parameter</span>
+        
+        <span class="k">return</span> <span class="n">L</span><span class="p">,</span><span class="n">R</span>
+
+    <span class="k">return</span> <span class="n">LR</span><span class="p">,</span><span class="n">D</span><span class="p">,</span><span class="n">lsq_err</span><span class="p">,</span><span class="n">H</span><span class="p">,</span><span class="n">Npar</span>
+<span class="k">def</span> <span class="nf">__parameterize_notrans</span><span class="p">(</span><span class="n">Nin</span><span class="p">,</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nband</span><span class="p">,</span><span class="n">order</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Generate parameterization functions for estimation not including transient removal.</span>
+<span class="sd">    Parameterizations are passed into __solveLRM to generate local models</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Components of f, because f can be iterated</span>
+    <span class="c1"># Radius and exponent matrices. r is radius, s power, and A/B/D component matrices</span>
+    <span class="n">N_half_band</span> <span class="o">=</span> <span class="p">(</span><span class="n">Nband</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
+    <span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">N_half_band</span><span class="p">,</span><span class="n">N_half_band</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">rsA</span> <span class="o">=</span> <span class="n">r</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># axis. (0) power (1) radius</span>
+    <span class="n">rsD</span> <span class="o">=</span> <span class="n">r</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    
+    <span class="c1"># Define theta break points</span>
+    <span class="n">bA</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">Nout</span><span class="o">*</span><span class="n">Nin</span><span class="o">*</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span> <span class="c1"># transfer numerator</span>
+    <span class="n">bD</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">bA</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">bA</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">Nout</span><span class="o">*</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># denominator (wrap Nout first, then s)</span>
+    <span class="n">Npar</span> <span class="o">=</span> <span class="n">bA</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="n">Nout</span><span class="o">*</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span>
+    
+    <span class="k">def</span> <span class="nf">lsq_err</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">,</span><span class="n">t</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># transform from SO format to MO</span>
+        
+        <span class="c1"># Build matrices</span>
+        <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">t</span><span class="p">[</span><span class="n">bA</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsA</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nin</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span>
+        <span class="n">B</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nband</span><span class="p">))</span> <span class="c1"># axis (0) Nout (1) Nfreq</span>
+        <span class="n">catmat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">Nout</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">t</span><span class="p">[</span><span class="n">bD</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span><span class="mi">0</span> <span class="p">)</span>
+        <span class="n">D</span> <span class="o">=</span>  <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">catmat</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsD</span><span class="p">)</span> <span class="c1"># axis. (1) Nout (2) Nfreq</span>
+        
+        <span class="c1"># Compute sum of squared residuals</span>
+        <span class="n">Ysim</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;jkl,kl-&gt;jl&#39;</span><span class="p">,</span><span class="n">A</span><span class="p">,</span><span class="n">U</span><span class="p">)</span> <span class="o">+</span> <span class="n">B</span><span class="p">)</span><span class="o">/</span><span class="n">D</span>
+        <span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">Y</span><span class="o">-</span><span class="n">Ysim</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    
+    <span class="k">def</span> <span class="nf">H</span><span class="p">(</span><span class="n">t</span><span class="p">,</span><span class="n">r_inq</span><span class="p">):</span>
+        <span class="n">t</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># transform from SO format to MO</span>
+        
+        <span class="c1"># Radius and exponent matrices</span>
+        <span class="n">rsA_inq</span> <span class="o">=</span> <span class="n">r_inq</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># axis. (0) power (1) radius</span>
+        <span class="n">rsD_inq</span> <span class="o">=</span> <span class="n">r_inq</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        
+        <span class="c1"># Build matrices</span>
+        <span class="n">A</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">t</span><span class="p">[</span><span class="n">bA</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsA_inq</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="n">Nin</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># axis. (1) Nout (2) Nin (3) Nfreq</span>
+        <span class="n">catmat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">Nout</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">t</span><span class="p">[</span><span class="n">bD</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span><span class="mi">0</span> <span class="p">)</span>
+        <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">catmat</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nout</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">),</span><span class="n">rsD_inq</span><span class="p">)</span> <span class="c1"># axis. (1) Nout (2) Nfreq</span>
+        
+        <span class="k">return</span> <span class="n">A</span><span class="o">/</span><span class="n">D</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+
+    <span class="c1"># Build linearized (SK) transfer matrix L(U,y)*t + R(U,Y) = Dp^(-1) (D(t)*Y - N(t)*U - M(t))</span>
+    <span class="c1"># Denominator matrix for SK iterations</span>
+    <span class="k">def</span> <span class="nf">D</span><span class="p">(</span><span class="n">t</span><span class="p">):</span>
+        <span class="k">return</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span> <span class="s1">&#39;ij,lj-&gt;li&#39;</span><span class="p">,</span><span class="n">r</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="p">,</span> <span class="n">t</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="mi">0</span><span class="p">)]</span> <span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nband</span>
+    
+    <span class="k">def</span> <span class="nf">LR</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="n">Y</span><span class="p">):</span>        
+        <span class="c1"># Compute residual</span>
+        <span class="n">R</span> <span class="o">=</span> <span class="o">-</span><span class="n">Y</span> <span class="c1"># axis (0) Nout (1) Nband</span>
+        
+        <span class="c1"># Compute components</span>
+        <span class="n">r1</span> <span class="o">=</span> <span class="n">r</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="n">A1</span> <span class="o">=</span> <span class="n">Y</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">Nout</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span><span class="o">*</span> <span class="p">(</span><span class="n">r1</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">))[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># D(t)*Y. Axis. (1) r (2) s (3) Nout</span>
+        <span class="n">A2</span> <span class="o">=</span> <span class="p">(</span><span class="n">U</span><span class="o">.</span><span class="n">T</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">r1</span><span class="p">[:,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">Nband</span><span class="p">,</span><span class="n">Nin</span><span class="o">*</span><span class="p">(</span><span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)</span> <span class="c1"># A(t)*U. Axis. (1) r (2) Nin*s (3) Nout</span>
+        
+        <span class="c1"># Assemble into array</span>
+        <span class="k">if</span> <span class="n">order</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
+            <span class="n">catmat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="n">Y</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span> <span class="c1"># A2 and A3 don&#39;t depend on Nout</span>
+            <span class="n">L</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="o">-</span><span class="n">A2</span><span class="o">*</span><span class="n">catmat</span><span class="p">,</span><span class="n">A1</span><span class="p">),</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nband (2) parameter</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">L</span> <span class="o">=</span> <span class="o">-</span><span class="n">A2</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># axis (0) Nout (1) Nband (2) parameter</span>
+        
+        <span class="k">return</span> <span class="n">L</span><span class="p">,</span><span class="n">R</span>
+
+    <span class="k">return</span> <span class="n">LR</span><span class="p">,</span><span class="n">D</span><span class="p">,</span><span class="n">lsq_err</span><span class="p">,</span><span class="n">H</span><span class="p">,</span><span class="n">Npar</span>
+
+<span class="c1">#%% Frequency parser</span>
+<span class="k">def</span> <span class="nf">__parse_bands</span><span class="p">(</span><span class="n">N_half_band</span><span class="p">,</span><span class="n">max_from_center</span><span class="p">,</span><span class="n">f_in</span><span class="p">,</span><span class="n">f_out</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Local modeling is not constrained to the input frequency vector. This function</span>
+<span class="sd">    processes input parameters and parses f_in into bands to model (bands).</span>
+<span class="sd">    In addition, it returns query points (query) at which to export each</span>
+<span class="sd">    local model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">df_out</span> <span class="o">=</span> <span class="n">f_out</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">f_out</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># query point step size</span>
+    <span class="n">df_in</span> <span class="o">=</span> <span class="n">f_in</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">f_in</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># input frequencies steps size</span>
+    <span class="n">inq_per_band</span> <span class="o">=</span> <span class="n">mt</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">max_from_center</span><span class="o">/</span><span class="n">df_out</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span> <span class="c1"># query points per band</span>
+    
+    <span class="c1"># Assign export indices to band numbers</span>
+    <span class="n">n_lo</span> <span class="o">=</span> <span class="p">(</span><span class="n">f_out</span> <span class="o">&lt;=</span> <span class="n">f_in</span><span class="p">[</span><span class="n">N_half_band</span><span class="p">]</span><span class="o">+</span><span class="n">max_from_center</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="n">n_hi</span> <span class="o">=</span> <span class="p">(</span><span class="n">f_out</span> <span class="o">&gt;=</span> <span class="n">f_in</span><span class="p">[</span><span class="o">-</span><span class="n">N_half_band</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">max_from_center</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+    <span class="n">n_mid</span> <span class="o">=</span> <span class="n">f_out</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="n">n_lo</span> <span class="o">-</span> <span class="n">n_hi</span>
+    
+    <span class="c1"># Parsing is different if n_lo and n_hi &gt; 0</span>
+    <span class="n">is_lo</span> <span class="o">=</span> <span class="n">n_lo</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    <span class="n">is_hi</span> <span class="o">=</span> <span class="n">n_hi</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    
+    <span class="c1"># Get band assignment numbers for each entry in f_out</span>
+    <span class="n">temp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">inq_per_band</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">is_lo</span><span class="p">,</span><span class="n">mt</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">n_mid</span><span class="o">/</span><span class="n">inq_per_band</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">temp</span> <span class="o">=</span> <span class="n">temp</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">order</span><span class="o">=</span><span class="s1">&#39;F&#39;</span><span class="p">)[:</span><span class="n">n_mid</span><span class="p">]</span>
+    <span class="n">inq_assign</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_lo</span><span class="p">)</span> <span class="p">,</span> <span class="n">temp</span> <span class="p">,</span> <span class="p">(</span><span class="n">temp</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">n_hi</span><span class="p">)</span> <span class="p">))</span>
+    <span class="n">inq_assign</span> <span class="o">=</span> <span class="n">inq_assign</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
+    
+    <span class="c1"># Get indices of center points and build bands</span>
+    <span class="n">c_inds_hz</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">f_out</span><span class="p">[</span><span class="n">inq_assign</span><span class="o">==</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">is_lo</span><span class="p">,</span><span class="n">inq_assign</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span><span class="o">-</span><span class="n">is_hi</span><span class="p">)])</span> <span class="c1"># ideal center locations of each export</span>
+    <span class="n">c_inds</span> <span class="o">=</span> <span class="p">((</span><span class="n">c_inds_hz</span><span class="o">-</span><span class="n">f_in</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="n">df_in</span><span class="p">)</span><span class="o">.</span><span class="n">round</span><span class="p">()</span> <span class="c1"># actual center locations of each export on f_in</span>
+    <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">c_inds_hz</span> <span class="o">-</span> <span class="n">f_in</span><span class="p">[</span><span class="n">c_inds</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;int32&#39;</span><span class="p">)])</span><span class="o">.</span><span class="n">max</span><span class="p">()</span> <span class="o">&gt;</span> <span class="n">max_from_center</span><span class="p">:</span>
+        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;&quot;&quot;f_in is too sparse for f_out specification, resulting in LM extrapolation </span>
+<span class="s2">                      beyond the export_ratio. Consider using default specification or increasing bandwidth.&quot;&quot;&quot;</span><span class="p">)</span>
+    <span class="n">lo_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">N_half_band</span><span class="p">])</span> <span class="k">if</span> <span class="n">is_lo</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">hi_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">f_in</span><span class="o">.</span><span class="n">size</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="n">N_half_band</span><span class="p">])</span> <span class="k">if</span> <span class="n">is_hi</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">c_inds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span> <span class="n">lo_ind</span><span class="p">,</span><span class="n">c_inds</span><span class="p">,</span><span class="n">hi_ind</span> <span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
+    <span class="n">bands</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="n">N_half_band</span><span class="p">,</span><span class="n">N_half_band</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">c_inds</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="c1"># axis (0) center ind (1) r</span>
+    <span class="n">bands</span> <span class="o">=</span> <span class="n">bands</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="s1">&#39;int32&#39;</span><span class="p">)</span>
+    
+    <span class="c1"># Get normalized extraction radii</span>
+    <span class="n">query</span> <span class="o">=</span> <span class="p">[(</span><span class="n">f_out</span><span class="p">[</span><span class="n">inq_assign</span><span class="o">==</span><span class="n">k</span><span class="p">]</span> <span class="o">-</span> <span class="n">ind</span><span class="p">)</span><span class="o">/</span><span class="n">df_in</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">ind</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">f_in</span><span class="p">[</span><span class="n">c_inds</span><span class="p">])]</span>
+    
+    <span class="k">return</span> <span class="n">bands</span><span class="p">,</span><span class="n">query</span>
+    
+<span class="c1">#%% General functions</span>
+<span class="k">def</span> <span class="nf">__round_odd</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">y</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">y</span><span class="o">%</span><span class="mi">2</span><span class="o">==</span><span class="mi">0</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">__lstsq_scipy</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="n">b</span><span class="p">):</span> <span class="c1"># extension of np.linalg.lstsq to 3-D arrays. Not vectorized.</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">)[[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span> <span class="p">)</span> <span class="c1"># axis. (0) Nout [1] Npar</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">Ak</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">A</span><span class="p">):</span>
+        <span class="n">X</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">Ak</span><span class="p">,</span><span class="n">b</span><span class="p">[</span><span class="n">k</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">X</span>
+<span class="k">def</span> <span class="nf">__lstsq_numpy</span><span class="p">(</span><span class="n">A</span><span class="p">,</span><span class="n">b</span><span class="p">):</span> <span class="c1"># extension of np.linalg.lstsq to 3-D arrays. Not vectorized.</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">A</span><span class="o">.</span><span class="n">shape</span><span class="p">)[[</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span> <span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">complex</span> <span class="p">)</span> <span class="c1"># axis. (0) Nout [1] Np</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">Ak</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">A</span><span class="p">):</span>
+        <span class="n">X</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">Ak</span><span class="p">,</span><span class="n">b</span><span class="p">[</span><span class="n">k</span><span class="p">],</span><span class="n">rcond</span><span class="o">=</span><span class="kc">None</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">X</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">APPENDIX</span>
+<span class="sd">Note 1: Windows OS restricts parallel processes to 61</span>
+
+<span class="sd">Note 2: linalg.lstsq in scipy and numpy vary wildly in relative speed. In some cases,</span>
+<span class="sd">numpy is 10x faster. For other problem sizes, scipy is 20x faster. Both are tested,</span>
+<span class="sd">and the faster algorithm is selected.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+<jinja2.runtime.BlockReference object at 0x7f5a270b5f00>
+<img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
+
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
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\ No newline at end of file
diff --git a/_modules/sdynpy/signal_processing/sdynpy_rotation.html b/_modules/sdynpy/signal_processing/sdynpy_rotation.html
index bb44a45..9c859ee 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_rotation.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_rotation.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_rotation &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_rotation &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/plot_directive.css" />
@@ -216,23 +216,23 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_rotation</h1><div class="hig
 
 
 <div class="viewcode-block" id="lstsq_rigid_transform"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_rotation.lstsq_rigid_transform.html#sdynpy.signal_processing.sdynpy_rotation.lstsq_rigid_transform">[docs]</a><span class="k">def</span> <span class="nf">lstsq_rigid_transform</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot; Computes the Transform between two point sets such that </span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot; Computes the Transform between two point sets such that</span>
 <span class="sd">        y = Rx + t</span>
 
-<span class="sd">        This is a least squares methods as descibed in </span>
+<span class="sd">        This is a least squares methods as descibed in</span>
 <span class="sd">        &quot;Least-Squares Rigid Motion Using SVD&quot;, O. Sorkine-Hornung, M.</span>
 <span class="sd">        Rabinovich, Department of Computer Science, ETH Zurich, Jan 16, 2017</span>
 <span class="sd">        Note found online at https://igl.ethz.ch/projects/ARAP/svd_rot.pdf</span>
 
-<span class="sd">        INPUT </span>
+<span class="sd">        INPUT</span>
 <span class="sd">            x,y = the two point sets as [x,y,z] column vectors</span>
 <span class="sd">                  sizes are (3,n) for n points</span>
 <span class="sd">            w = weighting vector for each point, wi&gt;0</span>
 <span class="sd">                default is uniform weighting of wi=1</span>
 <span class="sd">                size is (1,n) for n points</span>
 
-<span class="sd">        OUTPUT </span>
-<span class="sd">            R,t  = [(3,3),(3,1)] transformation parameters such that </span>
+<span class="sd">        OUTPUT</span>
+<span class="sd">            R,t  = [(3,3),(3,1)] transformation parameters such that</span>
 <span class="sd">            y = Rx + t</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">if</span> <span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -250,16 +250,16 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_rotation</h1><div class="hig
     <span class="n">Y</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">ybar</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
 
     <span class="c1"># Calculate the Covariance</span>
-    <span class="n">Cov</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">W</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">Cov</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">W</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">Y</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
 
     <span class="c1"># Take the SVD of the Covariance matrix</span>
     <span class="n">U</span><span class="p">,</span> <span class="n">S</span><span class="p">,</span> <span class="n">VH</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">svd</span><span class="p">(</span><span class="n">Cov</span><span class="p">)</span>
-    <span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">VH</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># numpy&#39;s SVD gives you back V&#39;, not V like Matlab</span>
-    <span class="n">det</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">V</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">))</span>
-    <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span><span class="n">det</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">D</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">det</span>
+    <span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">VH</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># numpy&#39;s SVD gives you back V&#39;, not V like Matlab</span>
+    <span class="n">det</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">V</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">))</span>
+    <span class="n">D</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">det</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">D</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">det</span>
 
-    <span class="n">R</span> <span class="o">=</span> <span class="n">V</span> <span class="o">@</span> <span class="n">D</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">U</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="n">V</span> <span class="o">@</span> <span class="n">D</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">U</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
     <span class="n">t</span> <span class="o">=</span> <span class="n">ybar</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">R</span> <span class="o">@</span> <span class="n">xbar</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
 
     <span class="k">return</span> <span class="n">R</span><span class="p">,</span> <span class="n">t</span></div>
@@ -320,7 +320,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_rotation</h1><div class="hig
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb8fd720>
+<jinja2.runtime.BlockReference object at 0x7f5a27eb7a90>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_modules/sdynpy/signal_processing/sdynpy_srs.html b/_modules/sdynpy/signal_processing/sdynpy_srs.html
index 7fa0729..81c6c16 100644
--- a/_modules/sdynpy/signal_processing/sdynpy_srs.html
+++ b/_modules/sdynpy/signal_processing/sdynpy_srs.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>sdynpy.signal_processing.sdynpy_srs &mdash; SDynPy 0.11.0 documentation</title>
+  <title>sdynpy.signal_processing.sdynpy_srs &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
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@@ -97,11 +97,12 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">oaconvolve</span>
 <span class="kn">from</span> <span class="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span><span class="p">,</span> <span class="n">NonlinearConstraint</span><span class="p">,</span> <span class="n">nnls</span>
 
-<div class="viewcode-block" id="srs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.srs.html#sdynpy.signal_processing.sdynpy_srs.srs">[docs]</a><span class="k">def</span> <span class="nf">srs</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">damping</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span> <span class="n">spectrum_type</span> <span class="o">=</span> <span class="mi">9</span><span class="p">,</span>
-                <span class="n">b_filter_weights</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">a_filter_weights</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+
+<div class="viewcode-block" id="srs"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.srs.html#sdynpy.signal_processing.sdynpy_srs.srs">[docs]</a><span class="k">def</span> <span class="nf">srs</span><span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">damping</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">spectrum_type</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+        <span class="n">b_filter_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">a_filter_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes shock response spectrum of a signal</span>
-<span class="sd">    </span>
+
 <span class="sd">    Computes the shock response spectrum using a ramp invariant digital filter</span>
 <span class="sd">    simulation of the single degree-of-freedom system.</span>
 
@@ -120,7 +121,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        Fraction of critical damping to use in the SRS calculation (e.g. you</span>
 <span class="sd">        should specify 0.03 to represent 3%, not 3). The default is 0.03.</span>
 <span class="sd">    spectrum_type : int</span>
-<span class="sd">        The type of spectrum desired: </span>
+<span class="sd">        The type of spectrum desired:</span>
 <span class="sd">        If `spectrum_type` &gt; 0 (pos) then the SRS will be a base</span>
 <span class="sd">        acceleration-absolute acceleration model</span>
 <span class="sd">        If `spectrum_type` &lt; 0 (neg) then the SRS will be a base acceleration-relative</span>
@@ -134,10 +135,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">    b_filter_weights : np.ndarray, optional</span>
 <span class="sd">        Optional filter weights with shape (frequencies.size,3).</span>
 <span class="sd">        The default is to automatically compute filter weights using</span>
-<span class="sd">        `sdof_ramp_invariant_filter_weights`. </span>
+<span class="sd">        `sdof_ramp_invariant_filter_weights`.</span>
 <span class="sd">    a_filter_weights : np.ndarray, optional</span>
 <span class="sd">        Optional filter weights with shape (frequencies.size,3).</span>
-<span class="sd">        The default is to automatically compute filter weights using </span>
+<span class="sd">        The default is to automatically compute filter weights using</span>
 <span class="sd">        `sdof_ramp_invariant_filter_weights`.</span>
 
 <span class="sd">    Returns</span>
@@ -176,7 +177,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`damping` must be positive&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">damping</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Warning: Damping is Zero in SRS calculation!&#39;</span><span class="p">)</span>
-    
+
     <span class="c1"># Get the filter coefficients if not specified</span>
     <span class="k">if</span> <span class="n">b_filter_weights</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">a_filter_weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">b_filter_weights</span><span class="p">,</span> <span class="n">a_filter_weights</span> <span class="o">=</span> <span class="n">sdof_ramp_invariant_filter_weights</span><span class="p">(</span>
@@ -184,67 +185,68 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="k">else</span><span class="p">:</span>
         <span class="n">b_filter_weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">b_filter_weights</span><span class="p">))</span>
         <span class="n">a_filter_weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">a_filter_weights</span><span class="p">))</span>
-    
-    <span class="k">if</span> <span class="n">b_filter_weights</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="n">frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">3</span><span class="p">):</span>
+
+    <span class="k">if</span> <span class="n">b_filter_weights</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">(</span><span class="n">frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,</span> <span class="mi">3</span><span class="p">):</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`b_filter_weights` and `a_filter_weights` must have shape (frequencies.size,3)&#39;</span><span class="p">)</span>
-    
-    <span class="n">srs_output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>  <span class="n">signal</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="n">srs_output</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
                           <span class="o">+</span> <span class="p">((</span><span class="mi">9</span><span class="p">,)</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">spectrum_type</span><span class="p">)</span> <span class="o">==</span> <span class="mi">10</span> <span class="k">else</span> <span class="p">())</span>
                           <span class="o">+</span> <span class="p">(</span><span class="n">frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">,))</span>
-    <span class="k">for</span> <span class="n">i_freq</span><span class="p">,(</span><span class="n">frequency</span><span class="p">,</span><span class="n">b</span><span class="p">,</span><span class="n">a</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
-            <span class="n">frequencies</span><span class="p">,</span><span class="n">b_filter_weights</span><span class="p">,</span><span class="n">a_filter_weights</span><span class="p">)):</span>
+    <span class="k">for</span> <span class="n">i_freq</span><span class="p">,</span> <span class="p">(</span><span class="n">frequency</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
+            <span class="n">frequencies</span><span class="p">,</span> <span class="n">b_filter_weights</span><span class="p">,</span> <span class="n">a_filter_weights</span><span class="p">)):</span>
         <span class="c1"># Append enough zeros to get 1/100 of the residual response</span>
-        <span class="n">zeros_to_append</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mf">0.01</span><span class="o">*</span><span class="n">sample_rate</span><span class="o">/</span><span class="n">frequency</span><span class="p">)]))</span>
+        <span class="n">zeros_to_append</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="mf">0.01</span><span class="o">*</span><span class="n">sample_rate</span><span class="o">/</span><span class="n">frequency</span><span class="p">)]))</span>
         <span class="n">signals_extended</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-            <span class="n">signal</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">zeros_to_append</span><span class="p">,))),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        
+            <span class="n">signal</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">zeros_to_append</span><span class="p">,))),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
         <span class="c1"># Filter the signal</span>
-        <span class="n">response</span> <span class="o">=</span> <span class="n">sdof_filter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span><span class="n">a</span><span class="p">,</span><span class="n">signals_extended</span><span class="p">)</span>
-        
+        <span class="n">response</span> <span class="o">=</span> <span class="n">sdof_filter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">signals_extended</span><span class="p">)</span>
+
         <span class="c1"># Now compute the SRS parameters</span>
         <span class="c1"># Primary response</span>
-        <span class="n">primary_maximum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="n">num_samples</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+        <span class="n">primary_maximum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">num_samples</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
         <span class="n">primary_maximum</span><span class="p">[</span><span class="n">primary_maximum</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">primary_minimum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="n">num_samples</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
+        <span class="n">primary_minimum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">num_samples</span><span class="p">],</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
         <span class="n">primary_minimum</span><span class="p">[</span><span class="n">primary_minimum</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">primary_abs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_maximum</span><span class="p">,</span><span class="n">primary_minimum</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-        
-        <span class="c1"># Now compute the residual response. we need time steps and </span>
+        <span class="n">primary_abs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_maximum</span><span class="p">,</span> <span class="n">primary_minimum</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+
+        <span class="c1"># Now compute the residual response. we need time steps and</span>
         <span class="c1"># responses at two points</span>
-        <span class="n">residual_times</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,(</span><span class="n">zeros_to_append</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">]</span>
-        <span class="n">residual_responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">num_samples</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
-                                             <span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">num_samples</span><span class="o">+</span><span class="n">zeros_to_append</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]],</span>
+        <span class="n">residual_times</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">(</span><span class="n">zeros_to_append</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">]</span>
+        <span class="n">residual_responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">num_samples</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span>
+                                             <span class="n">response</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">num_samples</span><span class="o">+</span><span class="n">zeros_to_append</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]],</span>
                                             <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        
+
         <span class="c1"># Compute the peak response after the structure has stopped responding</span>
         <span class="n">peak_residual_responses</span> <span class="o">=</span> <span class="n">sdof_free_decay_peak_response</span><span class="p">(</span>
             <span class="n">residual_responses</span><span class="p">,</span> <span class="n">residual_times</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">damping</span><span class="p">)</span>
-        
+
         <span class="c1"># Pull out the residual peaks</span>
         <span class="n">residual_maximum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">peak_residual_responses</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
         <span class="n">residual_maximum</span><span class="p">[</span><span class="n">residual_maximum</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
         <span class="n">residual_minimum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">peak_residual_responses</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span>
         <span class="n">residual_minimum</span><span class="p">[</span><span class="n">residual_minimum</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">residual_abs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">residual_maximum</span><span class="p">,</span><span class="n">residual_minimum</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-        
+        <span class="n">residual_abs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">residual_maximum</span><span class="p">,</span> <span class="n">residual_minimum</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+
         <span class="n">all_srs_this_frequency_line</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
             <span class="n">primary_maximum</span><span class="p">,</span> <span class="n">primary_minimum</span><span class="p">,</span> <span class="n">primary_abs</span><span class="p">,</span>
             <span class="n">residual_maximum</span><span class="p">,</span> <span class="n">residual_minimum</span><span class="p">,</span> <span class="n">residual_abs</span><span class="p">,</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_maximum</span><span class="p">,</span><span class="n">residual_maximum</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_minimum</span><span class="p">,</span><span class="n">residual_minimum</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_abs</span><span class="p">,</span><span class="n">residual_abs</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)])</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_maximum</span><span class="p">,</span> <span class="n">residual_maximum</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_minimum</span><span class="p">,</span> <span class="n">residual_minimum</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">primary_abs</span><span class="p">,</span> <span class="n">residual_abs</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)])</span>
 
         <span class="k">if</span> <span class="n">abs_type</span> <span class="o">&lt;</span> <span class="mi">10</span><span class="p">:</span>
-            <span class="n">srs_output</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">i_freq</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_srs_this_frequency_line</span><span class="p">[</span><span class="n">abs_type</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">srs_output</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">i_freq</span><span class="p">]</span> <span class="o">=</span> <span class="n">all_srs_this_frequency_line</span><span class="p">[</span><span class="n">abs_type</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">srs_output</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">i_freq</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">all_srs_this_frequency_line</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="n">srs_output</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">i_freq</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">all_srs_this_frequency_line</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">srs_output</span><span class="p">,</span> <span class="n">frequencies</span></div>
-        
-<div class="viewcode-block" id="sdof_ramp_invariant_filter_weights"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sdof_ramp_invariant_filter_weights.html#sdynpy.signal_processing.sdynpy_srs.sdof_ramp_invariant_filter_weights">[docs]</a><span class="k">def</span> <span class="nf">sdof_ramp_invariant_filter_weights</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">damping</span><span class="p">,</span> 
+
+
+<div class="viewcode-block" id="sdof_ramp_invariant_filter_weights"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sdof_ramp_invariant_filter_weights.html#sdynpy.signal_processing.sdynpy_srs.sdof_ramp_invariant_filter_weights">[docs]</a><span class="k">def</span> <span class="nf">sdof_ramp_invariant_filter_weights</span><span class="p">(</span><span class="n">frequencies</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">damping</span><span class="p">,</span>
                                        <span class="n">spectrum_type</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Computes filter weights for SDOF resonators using a ramp-invariant filter.</span>
-<span class="sd">    </span>
+
 <span class="sd">    The weights are used in conjunction with the function</span>
 <span class="sd">    `sdof_filter` and `srs` to calculate the shock response</span>
 <span class="sd">    spectrum of an acceleration time history using a ramp</span>
@@ -273,39 +275,39 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="c1"># Preallocate arrays</span>
     <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">frequencies</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="mi">3</span><span class="p">,))</span>
     <span class="n">b</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">frequencies</span><span class="o">.</span><span class="n">shape</span><span class="o">+</span><span class="p">(</span><span class="mi">3</span><span class="p">,))</span>
-    
+
     <span class="n">normalized_frequencies</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">frequencies</span><span class="o">/</span><span class="n">sample_rate</span>
-    
+
     <span class="n">small_freq_indices</span> <span class="o">=</span> <span class="n">normalized_frequencies</span> <span class="o">&lt;</span> <span class="mf">0.001</span>
-    
+
     <span class="c1"># Small frequencies</span>
     <span class="n">small_freqs</span> <span class="o">=</span> <span class="n">normalized_frequencies</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">]</span>
-    
+
     <span class="c1"># 2*z*w + w*w*(1-2*z*z) where z is damping and w is normalized frequencies</span>
-    <span class="n">a</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">a</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
           <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="n">small_freqs</span>
-        <span class="o">+</span> <span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-        <span class="p">)</span>
+          <span class="o">+</span> <span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="p">)</span>
     <span class="c1"># -2*z*w + 2*z*z*w*w where z is damping and w is normalized frequencies</span>
-    <span class="n">a</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">a</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
           <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="n">small_freqs</span>
-        <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span>
-        <span class="p">)</span>
-    
+          <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span>
+          <span class="p">)</span>
+
     <span class="k">if</span> <span class="n">spectrum_type</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
         <span class="c1"># Absolute Acceleration Model</span>
         <span class="c1"># z*w + (w*w)*( (1/6) - 2*z*z/3 );</span>
-        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
               <span class="n">damping</span><span class="o">*</span><span class="n">small_freqs</span>
-            <span class="o">+</span> <span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span>
-            <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">6</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
-            <span class="p">)</span>
+              <span class="o">+</span> <span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span>
+              <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">6</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
+              <span class="p">)</span>
         <span class="c1"># 2*w*w*(1-z*z)/3</span>
-        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
             <span class="mi">2</span><span class="o">*</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
             <span class="p">)</span>
         <span class="c1"># -z*w + w*w*( (1/6) - 4*z*z/3 );</span>
-        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
             <span class="o">-</span> <span class="n">damping</span><span class="o">*</span><span class="n">small_freqs</span>
             <span class="o">+</span> <span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span>
             <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="mi">6</span> <span class="o">-</span> <span class="mi">4</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
@@ -313,15 +315,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="k">else</span><span class="p">:</span>
         <span class="c1"># Relative Displacement Model</span>
         <span class="c1"># -w*w/6;</span>
-        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">6</span>
+        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">6</span>
         <span class="c1"># -2*w*w/3;</span>
-        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span>
+        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">3</span>
         <span class="c1"># -w*w/6;</span>
-        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">6</span>
-    
+        <span class="n">b</span><span class="p">[</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">small_freqs</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="mi">6</span>
+
     <span class="c1"># Large frequencies</span>
     <span class="n">large_freqs</span> <span class="o">=</span> <span class="n">normalized_frequencies</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">]</span>
-    
+
     <span class="c1"># Define some helper variables</span>
     <span class="n">sq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
     <span class="n">e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">damping</span><span class="o">*</span><span class="n">large_freqs</span><span class="p">)</span>
@@ -329,25 +331,26 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">sp</span> <span class="o">=</span> <span class="n">e</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
     <span class="n">fact</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="n">sp</span><span class="o">/</span><span class="n">sq</span>
     <span class="n">c</span> <span class="o">=</span> <span class="n">e</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">wd</span><span class="p">)</span>
-    
-    <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">c</span><span class="p">)</span>
-    <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="n">e</span><span class="o">**</span><span class="mi">2</span>
-    
+
+    <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">c</span><span class="p">)</span>
+    <span class="n">a</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="n">e</span><span class="o">**</span><span class="mi">2</span>
+
     <span class="k">if</span> <span class="n">spectrum_type</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
         <span class="c1"># Absolute Acceleration Model</span>
         <span class="n">spwd</span> <span class="o">=</span> <span class="n">sp</span><span class="o">/</span><span class="n">wd</span>
-        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="n">spwd</span>
-        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">spwd</span> <span class="o">-</span> <span class="n">c</span><span class="p">)</span>
-        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">e</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">spwd</span>
+        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span><span class="o">-</span><span class="n">spwd</span>
+        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">spwd</span> <span class="o">-</span> <span class="n">c</span><span class="p">)</span>
+        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">e</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">spwd</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="c1"># Relative Displacement Model</span>
-        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="p">(</span><span class="n">c</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">fact</span> <span class="o">+</span> <span class="n">large_freqs</span><span class="p">)</span><span class="o">/</span><span class="n">large_freqs</span>
-        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">c</span><span class="o">*</span><span class="n">large_freqs</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">e</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">fact</span><span class="p">)</span><span class="o">/</span><span class="n">large_freqs</span>
-        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">e</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">large_freqs</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="n">c</span> <span class="o">+</span> <span class="n">fact</span><span class="p">)</span><span class="o">/</span><span class="n">large_freqs</span>
-    
-    <span class="k">return</span> <span class="n">b</span><span class="p">,</span><span class="n">a</span></div>
+        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="p">(</span><span class="n">c</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">fact</span> <span class="o">+</span> <span class="n">large_freqs</span><span class="p">)</span><span class="o">/</span><span class="n">large_freqs</span>
+        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">c</span><span class="o">*</span><span class="n">large_freqs</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">e</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">fact</span><span class="p">)</span><span class="o">/</span><span class="n">large_freqs</span>
+        <span class="n">b</span><span class="p">[</span><span class="o">~</span><span class="n">small_freq_indices</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">e</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">large_freqs</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="p">)</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="n">c</span> <span class="o">+</span> <span class="n">fact</span><span class="p">)</span><span class="o">/</span><span class="n">large_freqs</span>
+
+    <span class="k">return</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span></div>
+
 
-<div class="viewcode-block" id="sdof_filter"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sdof_filter.html#sdynpy.signal_processing.sdynpy_srs.sdof_filter">[docs]</a><span class="k">def</span> <span class="nf">sdof_filter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span><span class="n">a</span><span class="p">,</span><span class="n">signal</span><span class="p">,</span><span class="n">zi</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<div class="viewcode-block" id="sdof_filter"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sdof_filter.html#sdynpy.signal_processing.sdynpy_srs.sdof_filter">[docs]</a><span class="k">def</span> <span class="nf">sdof_filter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">signal</span><span class="p">,</span> <span class="n">zi</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Applies a filter to simulate a single degree of freedom system</span>
 
@@ -370,13 +373,14 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        The filtered signal</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">aa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">return</span> <span class="n">lfilter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span><span class="n">aa</span><span class="p">,</span><span class="n">signal</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span><span class="n">zi</span> <span class="o">=</span> <span class="n">zi</span><span class="p">)</span></div>
+    <span class="n">aa</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">lfilter</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">aa</span><span class="p">,</span> <span class="n">signal</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">zi</span><span class="o">=</span><span class="n">zi</span><span class="p">)</span></div>
+
 
 <div class="viewcode-block" id="sdof_free_decay_peak_response"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sdof_free_decay_peak_response.html#sdynpy.signal_processing.sdynpy_srs.sdof_free_decay_peak_response">[docs]</a><span class="k">def</span> <span class="nf">sdof_free_decay_peak_response</span><span class="p">(</span><span class="n">responses</span><span class="p">,</span> <span class="n">times_at_responses</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">damping</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Calculates peak response of a freely-decaying sdof system.</span>
-<span class="sd">    </span>
+
 <span class="sd">    The residual response is the peak response of the sdof system</span>
 <span class="sd">    as it decays after the input has ended, i.e., the input is zero.</span>
 <span class="sd">    The first two peaks in the decaying response will be the largest.</span>
@@ -423,24 +427,25 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">e</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">wn</span><span class="o">*</span><span class="n">damping</span><span class="o">*</span><span class="n">times_at_responses</span><span class="p">)</span>
     <span class="n">sd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">wdt</span><span class="p">)</span>
     <span class="n">cd</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">wdt</span><span class="p">)</span>
-    <span class="n">c</span> <span class="o">=</span> <span class="n">e</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">sd</span><span class="p">,</span><span class="n">cd</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
-    <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">c</span><span class="p">,</span><span class="n">responses</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span>
-    
+    <span class="n">c</span> <span class="o">=</span> <span class="n">e</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">sd</span><span class="p">,</span> <span class="n">cd</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
+    <span class="n">z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">c</span><span class="p">,</span> <span class="n">responses</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+
     <span class="c1"># the response has the form</span>
     <span class="c1"># a(t) = exp(-zeta wn t)[z[0]sin(wd t) + z[1]cos(wd t)]</span>
     <span class="c1"># the first maximum response will occur at the time tmax</span>
-    <span class="n">tmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mi">1</span><span class="o">/</span><span class="n">wd</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan</span><span class="p">(</span> <span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">])</span> <span class="p">))</span>
+    <span class="n">tmax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="mi">1</span><span class="o">/</span><span class="n">wd</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan</span><span class="p">((</span><span class="n">wd</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">])))</span>
     <span class="n">tmax</span><span class="p">[</span><span class="n">tmax</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmax</span><span class="p">[</span><span class="n">tmax</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">wd</span>
     <span class="n">tmax</span><span class="p">[</span><span class="n">tmax</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">wd</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmax</span><span class="p">[</span><span class="n">tmax</span> <span class="o">&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">wd</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">wd</span>
-       
+
     <span class="c1"># We can now plug it into the equation to find the maximum</span>
     <span class="n">response_peaks</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">response_peaks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">tmax</span><span class="p">)</span><span class="o">*</span><span class="p">(</span> <span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmax</span><span class="p">)</span> <span class="o">+</span> <span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmax</span><span class="p">)))</span>
+    <span class="n">response_peaks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">tmax</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmax</span><span class="p">)</span> <span class="o">+</span> <span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmax</span><span class="p">)))</span>
     <span class="n">tmin</span> <span class="o">=</span> <span class="n">tmax</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="n">wd</span>
-    <span class="n">response_peaks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">tmin</span><span class="p">)</span><span class="o">*</span><span class="p">(</span> <span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmin</span><span class="p">)</span> <span class="o">+</span> <span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmin</span><span class="p">)))</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">response_peaks</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span></div>
+    <span class="n">response_peaks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">damping</span><span class="o">*</span><span class="n">wn</span><span class="o">*</span><span class="n">tmin</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmin</span><span class="p">)</span> <span class="o">+</span> <span class="n">z</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">wd</span><span class="o">*</span><span class="n">tmin</span><span class="p">)))</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">moveaxis</span><span class="p">(</span><span class="n">response_peaks</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span></div>
+
 
-<div class="viewcode-block" id="octspace"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.octspace.html#sdynpy.signal_processing.sdynpy_srs.octspace">[docs]</a><span class="k">def</span> <span class="nf">octspace</span><span class="p">(</span><span class="n">low</span><span class="p">,</span><span class="n">high</span><span class="p">,</span><span class="n">points_per_octave</span><span class="p">):</span>
+<div class="viewcode-block" id="octspace"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.octspace.html#sdynpy.signal_processing.sdynpy_srs.octspace">[docs]</a><span class="k">def</span> <span class="nf">octspace</span><span class="p">(</span><span class="n">low</span><span class="p">,</span> <span class="n">high</span><span class="p">,</span> <span class="n">points_per_octave</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Constructs octave spacing between low and high values</span>
 
@@ -465,24 +470,25 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">points</span> <span class="o">=</span> <span class="mi">2</span><span class="o">**</span><span class="n">log_points</span>
     <span class="k">return</span> <span class="n">points</span></div>
 
-<div class="viewcode-block" id="sum_decayed_sines"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines</span><span class="p">(</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> 
-                      <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_range</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_per_octave</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_decays</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_delays</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">required_srs</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">srs_breakpoints</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">srs_damping</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span> <span class="n">srs_type</span> <span class="o">=</span> <span class="mi">9</span><span class="p">,</span>
-                      <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">compensation_decay</span> <span class="o">=</span> <span class="mf">0.95</span><span class="p">,</span>
+
+<div class="viewcode-block" id="sum_decayed_sines"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines</span><span class="p">(</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
+                      <span class="n">sine_frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_per_octave</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">sine_amplitudes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_decays</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_delays</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">required_srs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">srs_breakpoints</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">srs_damping</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">srs_type</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+                      <span class="n">compensation_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
                       <span class="c1"># Paramters for the iteration</span>
-                      <span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">convergence</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span> 
-                      <span class="n">error_tolerance</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span>
-                      <span class="n">tau</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">num_time_constants</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">decay_resolution</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">scale_factor</span> <span class="o">=</span> <span class="mf">1.02</span><span class="p">,</span> 
-                      <span class="n">acceleration_factor</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
-                      <span class="n">plot_results</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">srs_frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                      <span class="n">verbose</span> <span class="o">=</span> <span class="kc">False</span>
+                      <span class="n">number_of_iterations</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">convergence</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
+                      <span class="n">error_tolerance</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+                      <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_time_constants</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">decay_resolution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">scale_factor</span><span class="o">=</span><span class="mf">1.02</span><span class="p">,</span>
+                      <span class="n">acceleration_factor</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
+                      <span class="n">plot_results</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">srs_frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                      <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
                       <span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generate a Sum of Decayed Sines signal given an SRS.</span>
-<span class="sd">    </span>
+
 <span class="sd">    Note that there are many approaches to do this, with many optional arguments</span>
 <span class="sd">    so please read the documentation carefully to understand which arguments</span>
 <span class="sd">    must be passed to the function.</span>
@@ -498,11 +504,11 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        then the `sine_tone_range` argument must be specified.</span>
 <span class="sd">    sine_tone_range : np.ndarray, optional</span>
 <span class="sd">        A length-2 array containing the minimum and maximum sine tone to</span>
-<span class="sd">        generate.  If this is argument is not specified, then the </span>
+<span class="sd">        generate.  If this is argument is not specified, then the</span>
 <span class="sd">        `sine_frequencies` argument must be specified instead.</span>
 <span class="sd">    sine_tone_per_octave : int, optional</span>
 <span class="sd">        The number of sine tones per octave. If not specified along with</span>
-<span class="sd">        `sine_tone_range`, then a default value of 4 will be used if the </span>
+<span class="sd">        `sine_tone_range`, then a default value of 4 will be used if the</span>
 <span class="sd">        `srs_damping` is &gt;= 0.05.  Otherwise, the formula of</span>
 <span class="sd">        `sine_tone_per_octave = 9 - srs_damping*100` will be used.</span>
 <span class="sd">    sine_amplitudes : np.ndarray, optional</span>
@@ -513,7 +519,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        An array of decay value time constants (often represented by variable</span>
 <span class="sd">        tau).  Tau is the time for the amplitude of motion to decay 63% defined</span>
 <span class="sd">        by the equation `1/(2*np.pi*freq*zeta)` where `freq` is the frequency</span>
-<span class="sd">        of the sine tone and `zeta` is the fraction of critical damping.  </span>
+<span class="sd">        of the sine tone and `zeta` is the fraction of critical damping.</span>
 <span class="sd">        If not specified, then either the `tau` or `num_time_constants`</span>
 <span class="sd">        arguments must be specified instead.</span>
 <span class="sd">    sine_delays : np.ndarray, optional</span>
@@ -525,16 +531,16 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        passed instead.  The default is None.</span>
 <span class="sd">    srs_breakpoints : np.ndarray, optional</span>
 <span class="sd">        A numpy array with shape `(n,2)` where the first column is the</span>
-<span class="sd">        frequencies at which each breakpoint occurs, and the second column is </span>
+<span class="sd">        frequencies at which each breakpoint occurs, and the second column is</span>
 <span class="sd">        the value of the SRS at that breakpoint.  SRS values at the</span>
-<span class="sd">        `sine_frequencies` will be interpolated in a log-log sense from this </span>
+<span class="sd">        `sine_frequencies` will be interpolated in a log-log sense from this</span>
 <span class="sd">        breakpoint array.  If this argument is not specified, then the</span>
 <span class="sd">        `required_srs` must be passed instead.</span>
 <span class="sd">    srs_damping : float, optional</span>
 <span class="sd">        Fraction of critical damping to use in the SRS calculation (e.g. you</span>
 <span class="sd">        should specify 0.03 to represent 3%, not 3). The default is 0.03.</span>
 <span class="sd">    srs_type : int</span>
-<span class="sd">        The type of spectrum desired: </span>
+<span class="sd">        The type of spectrum desired:</span>
 <span class="sd">        If `srs_type` &gt; 0 (pos) then the SRS will be a base</span>
 <span class="sd">        acceleration-absolute acceleration model</span>
 <span class="sd">        If `srs_type` &lt; 0 (neg) then the SRS will be a base acceleration-relative</span>
@@ -561,7 +567,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        Allowable relative error in the SRS. The default is 0.05.</span>
 <span class="sd">    tau : float, optional</span>
 <span class="sd">        If a floating point number is passed, then this will be used for the</span>
-<span class="sd">        `sine_decay` values.  Alternatively, a dictionary can be passed with </span>
+<span class="sd">        `sine_decay` values.  Alternatively, a dictionary can be passed with</span>
 <span class="sd">        the keys containing a length-2 tuple specifying the minimum and maximum</span>
 <span class="sd">        frequency range, and the value specifying the value of `tau` within that</span>
 <span class="sd">        frequency range.  If this latter approach is used, all `sine_frequencies`</span>
@@ -639,7 +645,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="c1"># Create sine tones</span>
         <span class="k">if</span> <span class="n">sine_tone_per_octave</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">sine_tone_per_octave</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="mi">9</span><span class="o">-</span><span class="n">srs_damping</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
-        <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">octspace</span><span class="p">(</span><span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">octspace</span><span class="p">(</span><span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
                                     <span class="n">sine_tone_per_octave</span><span class="p">)</span>
     <span class="c1"># Now set up the SRS</span>
     <span class="k">if</span> <span class="n">required_srs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">srs_breakpoints</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -648,21 +654,21 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`required_srs` can not be specified simultaneously with `srs_breakpoints`&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">required_srs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">required_srs</span> <span class="o">=</span> <span class="n">loginterp</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span>
-                                 <span class="n">srs_breakpoints</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span>
-                                 <span class="n">srs_breakpoints</span><span class="p">[:,</span><span class="mi">1</span><span class="p">])</span>
+                                 <span class="n">srs_breakpoints</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span>
+                                 <span class="n">srs_breakpoints</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>
     <span class="k">if</span> <span class="n">sine_amplitudes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">srs_amplitudes</span> <span class="o">=</span> <span class="n">required_srs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
         <span class="n">srs_amplitudes</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">srs_amplitudes</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
         <span class="n">quality</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">srs_damping</span><span class="p">)</span>
         <span class="n">srs_amplitudes</span> <span class="o">/=</span> <span class="n">quality</span>
         <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="n">srs_amplitudes</span>
-        
+
     <span class="k">if</span> <span class="n">sine_delays</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-    
+
     <span class="k">if</span> <span class="n">compensation_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
-    
+
     <span class="c1"># Set up decay terms</span>
     <span class="n">decay_terms_specified</span> <span class="o">=</span> <span class="mi">0</span>
     <span class="k">if</span> <span class="n">sine_decays</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -675,11 +681,11 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;One of `sine_decays`, `tau`, or `num_time_constants` must be specified&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">decay_terms_specified</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Only one of `sine_decays`, `tau`, or `num_time_constants` can be specified&#39;</span><span class="p">)</span>
-    
+
     <span class="c1"># Now check and see which is defined</span>
     <span class="k">if</span> <span class="n">num_time_constants</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">period</span> <span class="o">=</span> <span class="n">block_size</span> <span class="o">/</span> <span class="n">sample_rate</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">num_time_constants</span><span class="p">,</span><span class="nb">dict</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">num_time_constants</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
             <span class="n">tau</span> <span class="o">=</span> <span class="p">{}</span>
             <span class="k">for</span> <span class="n">freq_range</span><span class="p">,</span> <span class="n">num_time_constant</span> <span class="ow">in</span> <span class="n">num_time_constants</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                 <span class="n">tau</span><span class="p">[</span><span class="n">freq_range</span><span class="p">]</span> <span class="o">=</span> <span class="n">period</span> <span class="o">/</span> <span class="n">num_time_constant</span>
@@ -688,7 +694,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="k">if</span> <span class="n">tau</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">sine_decays</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">freq</span> <span class="ow">in</span> <span class="n">sine_frequencies</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span><span class="nb">dict</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                 <span class="n">this_decay</span> <span class="o">=</span> <span class="kc">None</span>
                 <span class="k">for</span> <span class="n">freq_range</span><span class="p">,</span> <span class="n">this_tau</span> <span class="ow">in</span> <span class="n">tau</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                     <span class="k">if</span> <span class="n">freq_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">freq</span> <span class="o">&lt;=</span> <span class="n">freq_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
@@ -701,23 +707,23 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                 <span class="n">sine_decays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">freq</span><span class="o">*</span><span class="n">tau</span><span class="p">))</span>
         <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sine_decays</span><span class="p">)</span>
     <span class="c1"># Otherwise we just keep the specified sine_decays</span>
-    
+
     <span class="c1"># Now handle the minimum resolution on the decay</span>
     <span class="k">if</span> <span class="n">decay_resolution</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">decay_resolution</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">sine_decays</span><span class="o">/</span><span class="n">decay_resolution</span><span class="p">)</span>
-        
+
     <span class="k">if</span> <span class="n">compensation_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
-        
+
     <span class="c1"># Now we can actually run the generation process</span>
     <span class="p">(</span><span class="n">acceleration_signal</span><span class="p">,</span> <span class="n">used_frequencies</span><span class="p">,</span> <span class="n">used_amplitudes</span><span class="p">,</span> <span class="n">used_decays</span><span class="p">,</span>
      <span class="n">used_delays</span><span class="p">,</span> <span class="n">used_comp_frequency</span><span class="p">,</span> <span class="n">used_comp_amplitude</span><span class="p">,</span> <span class="n">used_comp_decay</span><span class="p">,</span>
      <span class="n">used_comp_delay</span><span class="p">)</span> <span class="o">=</span> <span class="n">_sum_decayed_sines</span><span class="p">(</span>
-         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> 
-         <span class="n">scale_factor</span><span class="o">*</span><span class="n">required_srs</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> 
+         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
+         <span class="n">scale_factor</span><span class="o">*</span><span class="n">required_srs</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span>
          <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">number_of_iterations</span><span class="p">,</span>
          <span class="n">convergence</span><span class="p">,</span> <span class="n">error_tolerance</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
-    
+
     <span class="n">all_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">used_frequencies</span><span class="p">,</span>
                                       <span class="p">[</span><span class="n">used_comp_frequency</span><span class="p">]))</span>
     <span class="n">all_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">used_amplitudes</span><span class="p">,</span>
@@ -730,27 +736,27 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">velocity_signal</span><span class="p">,</span> <span class="n">displacement_signal</span> <span class="o">=</span> <span class="n">sum_decayed_sines_displacement_velocity</span><span class="p">(</span>
         <span class="n">all_frequencies</span><span class="p">,</span> <span class="n">all_amplitudes</span><span class="p">,</span> <span class="n">all_decays</span><span class="p">,</span> <span class="n">all_delays</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span>
         <span class="n">block_size</span><span class="p">,</span> <span class="n">acceleration_factor</span><span class="p">)</span>
-        
+
     <span class="n">return_vals</span> <span class="o">=</span> <span class="p">(</span><span class="n">acceleration_signal</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">,</span>
                    <span class="n">all_frequencies</span><span class="p">,</span> <span class="n">all_amplitudes</span><span class="p">,</span> <span class="n">all_decays</span><span class="p">,</span> <span class="n">all_delays</span><span class="p">)</span>
-    
+
     <span class="c1"># Plot the results</span>
     <span class="k">if</span> <span class="n">plot_results</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
         <span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">acceleration_signal</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Acceleration&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Velocity&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Displacement&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">acceleration_signal</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Acceleration&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Velocity&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Displacement&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
         <span class="c1"># Compute SRS</span>
         <span class="k">if</span> <span class="n">srs_frequencies</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">srs_frequencies</span> <span class="o">=</span> <span class="n">sine_frequencies</span>
-        <span class="n">this_srs</span><span class="p">,</span><span class="n">this_frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span>
+        <span class="n">this_srs</span><span class="p">,</span> <span class="n">this_frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span>
             <span class="n">acceleration_signal</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">srs_frequencies</span><span class="p">,</span>
             <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">srs_breakpoints</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -758,26 +764,26 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
             <span class="n">srs_ordinate</span> <span class="o">=</span> <span class="n">required_srs</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="n">srs_abscissa</span><span class="p">,</span> <span class="n">srs_ordinate</span> <span class="o">=</span> <span class="n">srs_breakpoints</span><span class="o">.</span><span class="n">T</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">srs_abscissa</span><span class="p">,</span> <span class="n">srs_ordinate</span><span class="p">,</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">this_frequencies</span><span class="p">,</span> <span class="n">this_srs</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;SRS (</span><span class="si">{:0.2f}% d</span><span class="s1">amping)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">srs_damping</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">srs_abscissa</span><span class="p">,</span> <span class="n">srs_ordinate</span><span class="p">,</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">this_frequencies</span><span class="p">,</span> <span class="n">this_srs</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;SRS (</span><span class="si">{:0.2f}% d</span><span class="s1">amping)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">srs_damping</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
         <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-        <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span><span class="n">ax</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Reference&#39;</span><span class="p">,</span><span class="s1">&#39;Decayed Sine&#39;</span><span class="p">))</span>
+        <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Reference&#39;</span><span class="p">,</span> <span class="s1">&#39;Decayed Sine&#39;</span><span class="p">))</span>
     <span class="k">return</span> <span class="n">return_vals</span></div>
-    
-
-<span class="k">def</span> <span class="nf">_sum_decayed_sines</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> 
-                      <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
-                      <span class="n">required_srs</span><span class="p">,</span> 
-                      <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span>
-                      <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> 
-                      <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span>
-                      <span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">convergence</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span>
-                      <span class="n">error_tolerance</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+
+
+<span class="k">def</span> <span class="nf">_sum_decayed_sines</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span>
+                       <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
+                       <span class="n">required_srs</span><span class="p">,</span>
+                       <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span>
+                       <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
+                       <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span>
+                       <span class="n">number_of_iterations</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">convergence</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
+                       <span class="n">error_tolerance</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Optimizes the amplitudes of sums of decayed sines</span>
 
@@ -805,7 +811,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        Fraction of critical damping to use in the SRS calculation (e.g. you</span>
 <span class="sd">        should specify 0.03 to represent 3%, not 3). The default is 0.03.</span>
 <span class="sd">    srs_type : int</span>
-<span class="sd">        The type of spectrum desired: </span>
+<span class="sd">        The type of spectrum desired:</span>
 <span class="sd">        If `spectrum_type` &gt; 0 (pos) then the SRS will be a base</span>
 <span class="sd">        acceleration-absolute acceleration model</span>
 <span class="sd">        If `spectrum_type` &lt; 0 (neg) then the SRS will be a base acceleration-relative</span>
@@ -853,11 +859,11 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="c1"># Compute updated sine amplitudes and compensation terms</span>
         <span class="n">this_sine_amplitudes</span><span class="p">,</span> <span class="n">this_compensation_amplitude</span><span class="p">,</span> <span class="n">this_compensation_delay</span> <span class="o">=</span> <span class="p">(</span>
             <span class="n">_sum_decayed_sines_single_iteration</span><span class="p">(</span>
-                <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> 
-                <span class="n">required_srs</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> 
-                <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> 
-                <span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">convergence</span> <span class="o">=</span> <span class="n">convergence</span><span class="p">,</span>
-                <span class="n">error_tolerance</span> <span class="o">=</span> <span class="n">error_tolerance</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span><span class="p">))</span>
+                <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
+                <span class="n">required_srs</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span>
+                <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span>
+                <span class="n">number_of_iterations</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">convergence</span><span class="o">=</span><span class="n">convergence</span><span class="p">,</span>
+                <span class="n">error_tolerance</span><span class="o">=</span><span class="n">error_tolerance</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">))</span>
         <span class="c1"># get the SRS error at each frequency term by first computing the signal</span>
         <span class="p">(</span><span class="n">this_signal</span><span class="p">,</span> <span class="n">this_compensation_frequency</span><span class="p">,</span> <span class="n">this_compensation_amplitude</span><span class="p">,</span>
          <span class="n">this_compensation_decay</span><span class="p">,</span> <span class="n">this_compensation_delay</span><span class="p">)</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction_with_compensation</span><span class="p">(</span>
@@ -871,31 +877,32 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Sine Table after Iterations:&#39;</span><span class="p">)</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;8s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">,  </span><span class="si">{:&gt;10s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-            <span class="s1">&#39;Sine&#39;</span><span class="p">,</span><span class="s1">&#39;Frequency&#39;</span><span class="p">,</span><span class="s1">&#39;Amplitude&#39;</span><span class="p">,</span><span class="s1">&#39;SRS Val&#39;</span><span class="p">,</span><span class="s1">&#39;SRS Req&#39;</span><span class="p">,</span><span class="s1">&#39;Error&#39;</span><span class="p">))</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">freq</span><span class="p">,</span> <span class="n">amp</span><span class="p">,</span> <span class="n">srs_val</span><span class="p">,</span> <span class="n">srs_req</span><span class="p">,</span> <span class="n">srs_err</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
+            <span class="s1">&#39;Sine&#39;</span><span class="p">,</span> <span class="s1">&#39;Frequency&#39;</span><span class="p">,</span> <span class="s1">&#39;Amplitude&#39;</span><span class="p">,</span> <span class="s1">&#39;SRS Val&#39;</span><span class="p">,</span> <span class="s1">&#39;SRS Req&#39;</span><span class="p">,</span> <span class="s1">&#39;Error&#39;</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">freq</span><span class="p">,</span> <span class="n">amp</span><span class="p">,</span> <span class="n">srs_val</span><span class="p">,</span> <span class="n">srs_req</span><span class="p">,</span> <span class="n">srs_err</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
                 <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">this_srs</span><span class="p">,</span> <span class="n">required_srs</span><span class="p">,</span>
                 <span class="n">srs_error</span><span class="p">)):</span>
             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;8s}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">),</span><span class="n">freq</span><span class="p">,</span><span class="n">amp</span><span class="p">,</span><span class="n">srs_val</span><span class="p">,</span><span class="n">srs_req</span><span class="p">,</span><span class="n">srs_err</span><span class="p">))</span>
+                <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">),</span> <span class="n">freq</span><span class="p">,</span> <span class="n">amp</span><span class="p">,</span> <span class="n">srs_val</span><span class="p">,</span> <span class="n">srs_req</span><span class="p">,</span> <span class="n">srs_err</span><span class="p">))</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Compensating Pulse:&#39;</span><span class="p">)</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;10s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">, </span><span class="si">{:&gt;10s}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-            <span class="s1">&#39;Frequency&#39;</span><span class="p">,</span><span class="s1">&#39;Amplitude&#39;</span><span class="p">,</span><span class="s1">&#39;Decay&#39;</span><span class="p">,</span><span class="s1">&#39;Delay&#39;</span><span class="p">))</span>
+            <span class="s1">&#39;Frequency&#39;</span><span class="p">,</span> <span class="s1">&#39;Amplitude&#39;</span><span class="p">,</span> <span class="s1">&#39;Decay&#39;</span><span class="p">,</span> <span class="s1">&#39;Delay&#39;</span><span class="p">))</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">, </span><span class="si">{:&gt;10.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
             <span class="n">this_compensation_frequency</span><span class="p">,</span> <span class="n">this_compensation_amplitude</span><span class="p">,</span>
             <span class="n">this_compensation_decay</span><span class="p">,</span> <span class="n">this_compensation_delay</span><span class="p">))</span>
     <span class="k">return</span> <span class="p">(</span><span class="n">this_signal</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span>
             <span class="n">sine_delays</span><span class="p">,</span> <span class="n">this_compensation_frequency</span><span class="p">,</span> <span class="n">this_compensation_amplitude</span><span class="p">,</span>
             <span class="n">this_compensation_decay</span><span class="p">,</span> <span class="n">this_compensation_delay</span><span class="p">)</span>
-    
-<span class="k">def</span> <span class="nf">_sum_decayed_sines_single_iteration</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> 
+
+
+<span class="k">def</span> <span class="nf">_sum_decayed_sines_single_iteration</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span>
                                         <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
                                         <span class="n">required_srs</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span>
                                         <span class="n">compensation_decay</span><span class="p">,</span>
-                                        <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> 
+                                        <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
                                         <span class="n">damping_srs</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span>
-                                        <span class="n">number_of_iterations</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="n">convergence</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span>
-                                        <span class="n">error_tolerance</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span>
-                                        <span class="n">verbose</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+                                        <span class="n">number_of_iterations</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">convergence</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span>
+                                        <span class="n">error_tolerance</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+                                        <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Iterates on amplitudes of decayed sine waves to match a prescribed SRS</span>
 
@@ -923,7 +930,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
 <span class="sd">        Fraction of critical damping to use in the SRS calculation (e.g. you</span>
 <span class="sd">        should specify 0.03 to represent 3%, not 3). The default is 0.03.</span>
 <span class="sd">    srs_type : int</span>
-<span class="sd">        The type of spectrum desired: </span>
+<span class="sd">        The type of spectrum desired:</span>
 <span class="sd">        If `spectrum_type` &gt; 0 (pos) then the SRS will be a base</span>
 <span class="sd">        acceleration-absolute acceleration model</span>
 <span class="sd">        If `spectrum_type` &lt; 0 (neg) then the SRS will be a base acceleration-relative</span>
@@ -965,15 +972,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="c1"># Reduce the error tolerance so there&#39;s a bit of room for round-off</span>
     <span class="n">error_tolerance</span> <span class="o">=</span> <span class="n">error_tolerance</span><span class="o">*</span><span class="mf">0.9</span>
     <span class="c1"># Get filter weights for SRS calculations</span>
-    <span class="n">b</span><span class="p">,</span><span class="n">a</span> <span class="o">=</span> <span class="n">sdof_ramp_invariant_filter_weights</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> 
-                                             <span class="n">damping_srs</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">sdof_ramp_invariant_filter_weights</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span>
+                                              <span class="n">damping_srs</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
     <span class="c1"># Copy the arrays so we don&#39;t overwrite anything</span>
     <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sine_amplitudes</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sine_decays</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sine_delays</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="c1"># Iterate one frequency at a time</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">frequency</span><span class="p">,</span><span class="n">amplitude</span><span class="p">,</span><span class="n">decay</span><span class="p">,</span><span class="n">delay</span><span class="p">,</span><span class="n">desired_srs</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">frequency</span><span class="p">,</span> <span class="n">amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">desired_srs</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
             <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
             <span class="n">required_srs</span><span class="p">)):</span>
         <span class="c1"># if i == 10:</span>
@@ -982,10 +989,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="n">iteration_count</span> <span class="o">=</span> <span class="mi">1</span>
         <span class="n">increment</span> <span class="o">=</span> <span class="mf">0.1</span>
         <span class="c1"># Get the pulse without this frequency line</span>
-        <span class="n">other_sine_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_frequencies</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span><span class="n">sine_frequencies</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
-        <span class="n">other_sine_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_amplitudes</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span><span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
-        <span class="n">other_sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_decays</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span><span class="n">sine_decays</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
-        <span class="n">other_sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_delays</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span><span class="n">sine_delays</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
+        <span class="n">other_sine_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_frequencies</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">sine_frequencies</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
+        <span class="n">other_sine_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_amplitudes</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
+        <span class="n">other_sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_decays</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">sine_decays</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
+        <span class="n">other_sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_delays</span><span class="p">[:</span><span class="n">i</span><span class="p">],</span> <span class="n">sine_delays</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">:]))</span>
         <span class="n">other_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
             <span class="n">other_sine_frequencies</span><span class="p">,</span> <span class="n">other_sine_amplitudes</span><span class="p">,</span> <span class="n">other_sine_decays</span><span class="p">,</span>
             <span class="n">other_sine_delays</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
@@ -994,14 +1001,16 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
             <span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">amplitude</span>
             <span class="c1"># Find the pulse at this frequency line</span>
             <span class="n">this_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
-                <span class="n">frequency</span><span class="p">,</span><span class="n">amplitude</span><span class="p">,</span><span class="n">decay</span><span class="p">,</span><span class="n">delay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
+                <span class="n">frequency</span><span class="p">,</span> <span class="n">amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
             <span class="c1"># Get the compensating pulse</span>
             <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span> <span class="o">=</span> <span class="n">sum_decayed_sines_compensating_pulse_parameters</span><span class="p">(</span>
                 <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">)</span>
             <span class="c1"># Find the number of samples to shift the waveform</span>
             <span class="n">num_shift</span> <span class="o">=</span> <span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">compensation_delay</span><span class="o">*</span><span class="n">sample_rate</span><span class="p">))</span>
             <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">num_shift</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">block_size</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of samples in the compensation delay (</span><span class="si">{:}</span><span class="s1">) is larger than the block_size (</span><span class="si">{:}</span><span class="s1">).  The entire pulse will consist of part of the compensation pulse.  Plase increase the block_size or compensation frequency.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_shift</span><span class="p">,</span> <span class="n">block_size</span><span class="p">))</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of samples in the compensation delay (</span><span class="si">{:}</span><span class="s1">) is larger than the block_size (</span><span class="si">{:}</span><span class="s1">).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_shift</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
+                                 <span class="o">+</span> <span class="s1">&#39;  The entire pulse will consist of part of the compensation pulse.&#39;</span>
+                                 <span class="o">+</span> <span class="s1">&#39;  Please increase the block_size or compensation frequency.&#39;</span><span class="p">)</span>
             <span class="n">compensation_delay_corrected</span> <span class="o">=</span> <span class="n">compensation_delay</span> <span class="o">+</span> <span class="n">num_shift</span><span class="o">/</span><span class="n">sample_rate</span>
             <span class="c1"># Find compensating time history</span>
             <span class="n">compensation_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
@@ -1011,28 +1020,28 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
             <span class="k">if</span> <span class="n">num_shift</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">composite_pulse</span> <span class="o">=</span> <span class="p">(</span>
                       <span class="n">compensation_pulse</span>
-                    <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-                        <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_shift</span><span class="p">),</span>
-                        <span class="p">(</span><span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">-</span><span class="n">num_shift</span><span class="p">])))</span>
+                      <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
+                          <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_shift</span><span class="p">),</span>
+                          <span class="p">(</span><span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">-</span><span class="n">num_shift</span><span class="p">])))</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">composite_pulse</span> <span class="o">=</span> <span class="p">(</span>
-                      <span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span> <span class="o">+</span> 
-                    <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-                        <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="o">-</span><span class="n">num_shift</span><span class="p">),</span>
-                        <span class="p">(</span><span class="n">compensation_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">+</span><span class="n">num_shift</span><span class="p">])))</span>
+                      <span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span> <span class="o">+</span>
+                      <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
+                          <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="o">-</span><span class="n">num_shift</span><span class="p">),</span>
+                          <span class="p">(</span><span class="n">compensation_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">+</span><span class="n">num_shift</span><span class="p">])))</span>
             <span class="c1"># Find the SRS at the current frequency line</span>
             <span class="n">srs_prediction</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">composite_pulse</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">damping_srs</span><span class="p">,</span>
-                <span class="n">srs_type</span><span class="p">,</span> <span class="n">b</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># only get the SRS and there should only be one value due to one frequency line</span>
+                                 <span class="n">srs_type</span><span class="p">,</span> <span class="n">b</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># only get the SRS and there should only be one value due to one frequency line</span>
             <span class="n">srs_error</span> <span class="o">=</span> <span class="p">(</span><span class="n">srs_prediction</span> <span class="o">-</span> <span class="n">desired_srs</span><span class="p">)</span><span class="o">/</span><span class="n">desired_srs</span>
             <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Iteration at frequency </span><span class="si">{:}</span><span class="s1">, </span><span class="si">{:0.4f}</span><span class="se">\n</span><span class="s1">  Iteration Count: </span><span class="si">{:}</span><span class="s1">, SRS Error: </span><span class="si">{:0.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">frequency</span><span class="p">,</span><span class="n">iteration_count</span><span class="p">,</span><span class="n">srs_error</span><span class="p">))</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Iteration at frequency </span><span class="si">{:}</span><span class="s1">, </span><span class="si">{:0.4f}</span><span class="se">\n</span><span class="s1">  Iteration Count: </span><span class="si">{:}</span><span class="s1">, SRS Error: </span><span class="si">{:0.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">iteration_count</span><span class="p">,</span> <span class="n">srs_error</span><span class="p">))</span>
             <span class="c1"># Now we&#39;re going to compute the same thing again with a perturbed</span>
             <span class="c1"># amplitude, this will allow us to compute the slope change at the</span>
             <span class="c1"># current amplitude</span>
             <span class="n">amplitude_change</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">srs_error</span><span class="p">)</span><span class="o">*</span><span class="n">increment</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">amplitude</span><span class="p">)</span><span class="o">*</span><span class="n">Amax</span>
             <span class="c1"># Now check and see if we need to modify the amplitude</span>
             <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">srs_error</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">error_tolerance</span><span class="p">:</span>
-                <span class="k">if</span> <span class="n">amplitude_change</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># perturb it a bit</span>
+                <span class="k">if</span> <span class="n">amplitude_change</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># perturb it a bit</span>
                     <span class="n">amplitude_change</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">srs_error</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="o">*</span><span class="n">Amax</span><span class="o">*</span><span class="n">increment</span><span class="o">/</span><span class="mi">10</span>
                 <span class="n">new_amplitude</span> <span class="o">=</span> <span class="n">amplitude</span> <span class="o">+</span> <span class="n">amplitude_change</span>
                 <span class="c1"># Can&#39;t allow the sign of the amplitude to change</span>
@@ -1043,14 +1052,16 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                 <span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_amplitude</span>
                 <span class="c1"># Find new component at this frequency</span>
                 <span class="n">this_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
-                    <span class="n">frequency</span><span class="p">,</span><span class="n">new_amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
+                    <span class="n">frequency</span><span class="p">,</span> <span class="n">new_amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
                 <span class="c1"># Get the compensating pulse</span>
                 <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span> <span class="o">=</span> <span class="n">sum_decayed_sines_compensating_pulse_parameters</span><span class="p">(</span>
                     <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">)</span>
                 <span class="c1"># Find the number of samples to shift the waveform</span>
                 <span class="n">num_shift</span> <span class="o">=</span> <span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">compensation_delay</span><span class="o">*</span><span class="n">sample_rate</span><span class="p">))</span>
                 <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">num_shift</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">block_size</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of samples in the compensation delay (</span><span class="si">{:}</span><span class="s1">) is larger than the block_size (</span><span class="si">{:}</span><span class="s1">).  The entire pulse will consist of part of the compensation pulse.  Plase increase the block_size or compensation frequency.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_shift</span><span class="p">,</span> <span class="n">block_size</span><span class="p">))</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of samples in the compensation delay (</span><span class="si">{:}</span><span class="s1">) is larger than the block_size (</span><span class="si">{:}</span><span class="s1">).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_shift</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
+                                     <span class="o">+</span> <span class="s1">&#39;  The entire pulse will consist of part of the compensation pulse.  &#39;</span>
+                                     <span class="o">+</span> <span class="s1">&#39;Please increase the block_size or compensation frequency.&#39;</span><span class="p">)</span>
                 <span class="n">compensation_delay_corrected</span> <span class="o">=</span> <span class="n">compensation_delay</span> <span class="o">+</span> <span class="n">num_shift</span><span class="o">/</span><span class="n">sample_rate</span>
                 <span class="c1"># Find compensating time history</span>
                 <span class="n">compensation_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
@@ -1060,18 +1071,18 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                 <span class="k">if</span> <span class="n">num_shift</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
                     <span class="n">composite_pulse</span> <span class="o">=</span> <span class="p">(</span>
                           <span class="n">compensation_pulse</span>
-                        <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-                            <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_shift</span><span class="p">),</span>
-                            <span class="p">(</span><span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">-</span><span class="n">num_shift</span><span class="p">])))</span>
+                          <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
+                              <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_shift</span><span class="p">),</span>
+                              <span class="p">(</span><span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">-</span><span class="n">num_shift</span><span class="p">])))</span>
                 <span class="k">else</span><span class="p">:</span>
                     <span class="n">composite_pulse</span> <span class="o">=</span> <span class="p">(</span>
-                          <span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span> <span class="o">+</span> 
-                        <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-                            <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="o">-</span><span class="n">num_shift</span><span class="p">),</span>
-                            <span class="p">(</span><span class="n">compensation_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">+</span><span class="n">num_shift</span><span class="p">])))</span>
+                          <span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span> <span class="o">+</span>
+                          <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
+                              <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="o">-</span><span class="n">num_shift</span><span class="p">),</span>
+                              <span class="p">(</span><span class="n">compensation_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">+</span><span class="n">num_shift</span><span class="p">])))</span>
                 <span class="c1"># Find the SRS at the current frequency line</span>
                 <span class="n">srs_perturbed</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">composite_pulse</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">damping_srs</span><span class="p">,</span>
-                    <span class="n">srs_type</span><span class="p">,</span> <span class="n">b</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># only get the SRS and there should only be one value due to one frequency line</span>
+                                    <span class="n">srs_type</span><span class="p">,</span> <span class="n">b</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># only get the SRS and there should only be one value due to one frequency line</span>
                 <span class="c1"># Get slope of correction</span>
                 <span class="n">correction_slope</span> <span class="o">=</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">amplitude</span><span class="p">)</span><span class="o">-</span><span class="nb">abs</span><span class="p">(</span><span class="n">new_amplitude</span><span class="p">))</span><span class="o">/</span><span class="p">(</span><span class="n">srs_prediction</span><span class="o">-</span><span class="n">srs_perturbed</span><span class="p">)</span>
                 <span class="k">if</span> <span class="n">correction_slope</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
@@ -1079,7 +1090,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                     <span class="c1"># direction</span>
                     <span class="n">new_amplitude</span> <span class="o">=</span> <span class="n">convergence</span><span class="o">*</span><span class="n">correction_slope</span><span class="o">*</span><span class="p">(</span><span class="n">desired_srs</span><span class="o">-</span><span class="n">srs_prediction</span><span class="p">)</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">amplitude</span><span class="p">)</span>
                     <span class="c1"># Never let the change be too large</span>
-                    <span class="n">amplitude_check</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">amplitude</span><span class="p">),</span><span class="n">Amax</span><span class="o">/</span><span class="mi">10</span><span class="p">])</span>
+                    <span class="n">amplitude_check</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">amplitude</span><span class="p">),</span> <span class="n">Amax</span><span class="o">/</span><span class="mi">10</span><span class="p">])</span>
                     <span class="k">if</span> <span class="n">new_amplitude</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="o">*</span><span class="n">amplitude_check</span><span class="p">:</span>
                         <span class="n">new_amplitude</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">amplitude_check</span>
                     <span class="c1"># Never let it be less than zero</span>
@@ -1096,14 +1107,17 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                     <span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">amplitude</span>
                     <span class="c1"># Find new component at this frequency</span>
                     <span class="n">this_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
-                        <span class="n">frequency</span><span class="p">,</span><span class="n">amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
+                        <span class="n">frequency</span><span class="p">,</span> <span class="n">amplitude</span><span class="p">,</span> <span class="n">decay</span><span class="p">,</span> <span class="n">delay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
                     <span class="c1"># Get the compensating pulse</span>
                     <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span> <span class="o">=</span> <span class="n">sum_decayed_sines_compensating_pulse_parameters</span><span class="p">(</span>
                         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">)</span>
                     <span class="c1"># Find the number of samples to shift the waveform</span>
                     <span class="n">num_shift</span> <span class="o">=</span> <span class="o">-</span><span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">compensation_delay</span><span class="o">*</span><span class="n">sample_rate</span><span class="p">))</span>
                     <span class="k">if</span> <span class="nb">abs</span><span class="p">(</span><span class="n">num_shift</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">block_size</span><span class="p">:</span>
-                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of samples in the compensation delay (</span><span class="si">{:}</span><span class="s1">) is larger than the block_size (</span><span class="si">{:}</span><span class="s1">).  The entire pulse will consist of part of the compensation pulse.  Plase increase the block_size or compensation frequency.&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_shift</span><span class="p">,</span> <span class="n">block_size</span><span class="p">))</span>
+                        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The number of samples in the compensation delay&#39;</span>
+                                         <span class="o">+</span> <span class="s1">&#39; (</span><span class="si">{:}</span><span class="s1">) is larger than the block_size (</span><span class="si">{:}</span><span class="s1">).&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_shift</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
+                                         <span class="o">+</span> <span class="s1">&#39;  The entire pulse will consist of part of the compensation pulse.  &#39;</span>
+                                         <span class="o">+</span> <span class="s1">&#39;Please increase the block_size or compensation frequency.&#39;</span><span class="p">)</span>
                     <span class="n">compensation_delay_corrected</span> <span class="o">=</span> <span class="n">compensation_delay</span> <span class="o">+</span> <span class="n">num_shift</span><span class="o">/</span><span class="n">sample_rate</span>
                     <span class="c1"># Find compensating time history</span>
                     <span class="n">compensation_pulse</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
@@ -1113,18 +1127,18 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                     <span class="k">if</span> <span class="n">num_shift</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
                         <span class="n">composite_pulse</span> <span class="o">=</span> <span class="p">(</span>
                               <span class="n">compensation_pulse</span>
-                            <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-                                <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_shift</span><span class="p">),</span>
-                                <span class="p">(</span><span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">-</span><span class="n">num_shift</span><span class="p">])))</span>
+                              <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
+                                  <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_shift</span><span class="p">),</span>
+                                  <span class="p">(</span><span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">-</span><span class="n">num_shift</span><span class="p">])))</span>
                     <span class="k">else</span><span class="p">:</span>
                         <span class="n">composite_pulse</span> <span class="o">=</span> <span class="p">(</span>
-                              <span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span> <span class="o">+</span> 
-                            <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
-                                <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="o">-</span><span class="n">num_shift</span><span class="p">),</span>
-                                <span class="p">(</span><span class="n">compensation_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">+</span><span class="n">num_shift</span><span class="p">])))</span>
+                              <span class="n">other_pulse</span> <span class="o">+</span> <span class="n">this_pulse</span> <span class="o">+</span>
+                              <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span>
+                                  <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="o">-</span><span class="n">num_shift</span><span class="p">),</span>
+                                  <span class="p">(</span><span class="n">compensation_pulse</span><span class="p">)[:</span><span class="n">block_size</span><span class="o">+</span><span class="n">num_shift</span><span class="p">])))</span>
                     <span class="c1"># Find the SRS at the current frequency line</span>
                     <span class="n">srs_corrected</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">composite_pulse</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">frequency</span><span class="p">,</span> <span class="n">damping_srs</span><span class="p">,</span>
-                        <span class="n">srs_type</span><span class="p">,</span> <span class="n">b</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># only get the SRS and there should only be one value due to one frequency line</span>
+                                        <span class="n">srs_type</span><span class="p">,</span> <span class="n">b</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">a</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># only get the SRS and there should only be one value due to one frequency line</span>
                     <span class="n">srs_error</span> <span class="o">=</span> <span class="p">(</span><span class="n">srs_corrected</span> <span class="o">-</span> <span class="n">desired_srs</span><span class="p">)</span><span class="o">/</span><span class="n">desired_srs</span>
                     <span class="c1"># If the SRS error is positive and the amplitude has already been reduced to zero, stop trying</span>
                     <span class="k">if</span> <span class="n">srs_error</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">amplitude</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
@@ -1142,12 +1156,13 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Slope of correction was negative, trying a bigger increment&#39;</span><span class="p">)</span>
                 <span class="n">iteration_count</span> <span class="o">+=</span> <span class="mi">1</span>
                 <span class="k">if</span> <span class="n">iteration_count</span> <span class="o">&gt;</span> <span class="n">number_of_iterations</span><span class="p">:</span>
-                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Warning: SRS did not converge for frequency </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:0.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">frequency</span><span class="p">))</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;  Warning: SRS did not converge for frequency </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:0.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">frequency</span><span class="p">))</span>
                     <span class="k">break</span>
     <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span> <span class="o">=</span> <span class="n">sum_decayed_sines_compensating_pulse_parameters</span><span class="p">(</span>
         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span>
 
+
 <div class="viewcode-block" id="sum_decayed_sines_compensating_pulse_parameters"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_compensating_pulse_parameters.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_compensating_pulse_parameters">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines_compensating_pulse_parameters</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
                                                     <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">):</span>
     <span class="n">omegas</span> <span class="o">=</span> <span class="n">sine_frequencies</span><span class="o">*</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span>
@@ -1157,12 +1172,13 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">var0</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">sine_amplitudes</span><span class="o">*</span><span class="n">sine_delays</span><span class="o">/</span><span class="p">(</span><span class="n">omegas</span><span class="o">*</span><span class="p">(</span><span class="n">sine_decays</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">))))</span><span class="o">/</span><span class="n">compensation_amplitude</span>
     <span class="c1"># TODO Verify if this is a bug or not, I think this should be matlab code but graflab code had it as a matrix division.</span>
     <span class="c1"># var1 = (np.sum(2*sine_decays*sine_amplitudes/(omegas**2*(sine_decays**2 + 1)**2)))/compensation_amplitude</span>
-    <span class="n">var1</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="mi">2</span><span class="o">*</span><span class="n">sine_decays</span><span class="o">*</span><span class="n">sine_amplitudes</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span><span class="nd">@np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">((</span><span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">sine_decays</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:])))</span><span class="o">/</span><span class="n">compensation_amplitude</span>
+    <span class="n">var1</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="mi">2</span><span class="o">*</span><span class="n">sine_decays</span><span class="o">*</span><span class="n">sine_amplitudes</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span><span class="nd">@np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">pinv</span><span class="p">((</span><span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="n">sine_decays</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:])))</span><span class="o">/</span><span class="n">compensation_amplitude</span>
     <span class="n">var2</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">compensation_decay</span><span class="o">/</span><span class="p">(</span><span class="n">omega_comp</span><span class="o">*</span><span class="n">omega_comp</span><span class="o">*</span><span class="p">(</span><span class="n">compensation_decay</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
     <span class="n">var3</span> <span class="o">=</span> <span class="n">omega_comp</span><span class="o">*</span><span class="p">(</span><span class="n">compensation_decay</span><span class="o">**</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">compensation_delay</span> <span class="o">=</span> <span class="o">-</span><span class="n">var3</span><span class="o">*</span><span class="p">(</span><span class="n">var2</span> <span class="o">+</span> <span class="n">var1</span> <span class="o">+</span> <span class="n">var0</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span></div>
 
+
 <div class="viewcode-block" id="sum_decayed_sines_reconstruction"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines_reconstruction</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span>
                                      <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span>
                                      <span class="n">block_size</span><span class="p">):</span>
@@ -1197,17 +1213,18 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="n">sine_delays</span> <span class="o">=</span> <span class="n">sine_delays</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sine_delays</span><span class="p">)</span>
     <span class="c1"># Now go through and compute the sine tones</span>
     <span class="n">omegas</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">sine_frequencies</span>
-    <span class="n">this_times</span> <span class="o">=</span> <span class="n">times</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">sine_delays</span>
+    <span class="n">this_times</span> <span class="o">=</span> <span class="n">times</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">-</span> <span class="n">sine_delays</span>
     <span class="n">response</span> <span class="o">=</span> <span class="n">sine_amplitudes</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">sine_decays</span><span class="o">*</span><span class="n">omegas</span><span class="o">*</span><span class="n">this_times</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">omegas</span><span class="o">*</span><span class="n">this_times</span><span class="p">)</span>
     <span class="n">response</span><span class="p">[</span><span class="n">this_times</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">response</span><span class="p">,</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">response</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
+
 
 <div class="viewcode-block" id="sum_decayed_sines_reconstruction_with_compensation"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_compensation.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_compensation">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines_reconstruction_with_compensation</span><span class="p">(</span>
         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span>
         <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span>
-        <span class="n">sample_rate</span><span class="p">,</span><span class="n">block_size</span><span class="p">):</span>
+        <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    </span>
+
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
@@ -1248,20 +1265,21 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_delay</span> <span class="o">=</span> <span class="n">sum_decayed_sines_compensating_pulse_parameters</span><span class="p">(</span>
         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
         <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">)</span>
-    <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_frequencies</span><span class="p">,[</span><span class="n">compensation_frequency</span><span class="p">]))</span>
-    <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_amplitudes</span><span class="p">,[</span><span class="n">compensation_amplitude</span><span class="p">]))</span>
-    <span class="n">sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_delays</span><span class="p">,[</span><span class="n">compensation_delay</span><span class="p">]))</span>
-    <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_decays</span><span class="p">,[</span><span class="n">compensation_decay</span><span class="p">]))</span>
+    <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="p">[</span><span class="n">compensation_frequency</span><span class="p">]))</span>
+    <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_amplitudes</span><span class="p">,</span> <span class="p">[</span><span class="n">compensation_amplitude</span><span class="p">]))</span>
+    <span class="n">sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_delays</span><span class="p">,</span> <span class="p">[</span><span class="n">compensation_delay</span><span class="p">]))</span>
+    <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_decays</span><span class="p">,</span> <span class="p">[</span><span class="n">compensation_decay</span><span class="p">]))</span>
     <span class="n">signal</span> <span class="o">=</span> <span class="n">sum_decayed_sines_reconstruction</span><span class="p">(</span>
         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
         <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
     <span class="k">return</span> <span class="p">(</span><span class="n">signal</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_amplitude</span><span class="p">,</span>
             <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">compensation_delay</span><span class="p">)</span></div>
 
+
 <div class="viewcode-block" id="sum_decayed_sines_displacement_velocity"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_displacement_velocity.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_displacement_velocity">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines_displacement_velocity</span><span class="p">(</span>
         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span>
-        <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span><span class="n">block_size</span><span class="p">,</span>
-        <span class="n">acceleration_factor</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
+        <span class="n">acceleration_factor</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Creates velocity and displacement signals from acceleration sinusoids.</span>
 
@@ -1311,9 +1329,9 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">z</span> <span class="o">=</span> <span class="n">sine_decays</span>
     <span class="n">tau</span> <span class="o">=</span> <span class="n">sine_delays</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span>
-    <span class="n">tmp1</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">tmp2</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">tmp3</span><span class="o">=</span><span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">tmp1</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">tmp2</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">tmp3</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">x1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
     <span class="n">v</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">d</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -1324,28 +1342,30 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="n">zm1</span> <span class="o">=</span> <span class="n">z</span><span class="o">*</span><span class="n">z</span> <span class="o">-</span> <span class="mi">1</span>
     <span class="n">t</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
     <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)):</span>
-        <span class="n">indices</span> <span class="o">=</span> <span class="n">t</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">&gt;</span><span class="mi">0</span>   <span class="c1"># index&#39;s where vel and disp are evaluated</span>
-        <span class="n">Awz1</span><span class="o">=</span><span class="n">A</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">zp1</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+        <span class="n">indices</span> <span class="o">=</span> <span class="n">t</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span>   <span class="c1"># index&#39;s where vel and disp are evaluated</span>
+        <span class="n">Awz1</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">zp1</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
         <span class="n">tmp1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">Awz1</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">zw</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
         <span class="n">tmp2</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
-        <span class="n">v</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">-</span> <span class="n">tmp1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">*</span><span class="n">tmp2</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">+</span><span class="n">Awz1</span><span class="o">*</span><span class="n">x1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
+        <span class="n">v</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">-</span> <span class="n">tmp1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">*</span><span class="n">tmp2</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">+</span> <span class="n">Awz1</span><span class="o">*</span><span class="n">x1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span>
         <span class="n">Awz2</span> <span class="o">=</span> <span class="n">A</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">/</span><span class="p">(</span><span class="n">w2</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">zp1</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">zp1</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
         <span class="n">tmp1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">Awz2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">zw</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
         <span class="n">tmp2</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">zm1</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
         <span class="n">tmp3</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">Awz1</span><span class="o">*</span><span class="p">(</span><span class="n">t</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">-</span><span class="n">tau</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
         <span class="n">tmp4</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">z</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">Awz2</span>
         <span class="n">d</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">d</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">+</span> <span class="n">tmp1</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span><span class="o">*</span><span class="n">tmp2</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">+</span> <span class="n">tmp3</span><span class="p">[</span><span class="n">indices</span><span class="p">]</span> <span class="o">-</span> <span class="n">tmp4</span>
-    <span class="k">return</span> <span class="n">v</span><span class="p">,</span><span class="n">d</span></div>
+    <span class="k">return</span> <span class="n">v</span><span class="p">,</span> <span class="n">d</span></div>
+
+
+<div class="viewcode-block" id="loginterp"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.loginterp.html#sdynpy.signal_processing.sdynpy_srs.loginterp">[docs]</a><span class="k">def</span> <span class="nf">loginterp</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">xp</span><span class="p">,</span> <span class="n">fp</span><span class="p">):</span>
+    <span class="k">return</span> <span class="mi">10</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">xp</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">fp</span><span class="p">))</span></div>
 
-<div class="viewcode-block" id="loginterp"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.loginterp.html#sdynpy.signal_processing.sdynpy_srs.loginterp">[docs]</a><span class="k">def</span> <span class="nf">loginterp</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">xp</span><span class="p">,</span><span class="n">fp</span><span class="p">):</span>
-    <span class="k">return</span> <span class="mi">10</span><span class="o">**</span><span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">x</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">xp</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">fp</span><span class="p">))</span></div>
 
 <div class="viewcode-block" id="optimization_error_function"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.optimization_error_function.html#sdynpy.signal_processing.sdynpy_srs.optimization_error_function">[docs]</a><span class="k">def</span> <span class="nf">optimization_error_function</span><span class="p">(</span>
         <span class="n">amplitude_lin_scales</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
-        <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> 
+        <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
         <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">control_irfs</span><span class="p">,</span> <span class="n">limit_irfs</span><span class="p">,</span>
-        <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_weights</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span> 
-        <span class="n">b</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">frequency_index</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+        <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_weights</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span>
+        <span class="n">b</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">frequency_index</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">frequency_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">frequency_index</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
     <span class="c1"># Apply the scale factors</span>
@@ -1357,30 +1377,30 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">new_sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
         <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">)</span>
     <span class="c1"># Transform the signal to the control degrees of freedom</span>
-    <span class="n">control_responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">oaconvolve</span><span class="p">(</span><span class="n">control_irf</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">)</span> <span class="k">for</span> <span class="n">control_irf</span> <span class="ow">in</span> <span class="n">control_irfs</span><span class="p">])[</span><span class="o">...</span><span class="p">,:</span><span class="n">block_size</span><span class="p">]</span>
+    <span class="n">control_responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">oaconvolve</span><span class="p">(</span><span class="n">control_irf</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">)</span> <span class="k">for</span> <span class="n">control_irf</span> <span class="ow">in</span> <span class="n">control_irfs</span><span class="p">])[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">block_size</span><span class="p">]</span>
     <span class="c1"># Compute SRSs at all frequencies</span>
-    <span class="n">predicted_control_srs</span><span class="p">,</span><span class="n">frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">control_responses</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span> 
-                                            <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> 
-                                            <span class="kc">None</span> <span class="k">if</span> <span class="n">b</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">b</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span>
-                                            <span class="kc">None</span> <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">a</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">])</span>
+    <span class="n">predicted_control_srs</span><span class="p">,</span> <span class="n">frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">control_responses</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span>
+                                             <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span>
+                                             <span class="kc">None</span> <span class="k">if</span> <span class="n">b</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">b</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span>
+                                             <span class="kc">None</span> <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">a</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">])</span>
     <span class="c1"># Compute error</span>
     <span class="n">mean_control_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span>
-            <span class="p">(</span><span class="n">control_weights</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-             <span class="o">*</span><span class="p">((</span><span class="n">predicted_control_srs</span> <span class="o">-</span> <span class="n">control_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frequency_index</span><span class="p">])</span>
-               <span class="o">/</span><span class="n">control_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frequency_index</span><span class="p">])))</span>
+            <span class="p">(</span><span class="n">control_weights</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+             <span class="o">*</span> <span class="p">((</span><span class="n">predicted_control_srs</span> <span class="o">-</span> <span class="n">control_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frequency_index</span><span class="p">])</span>
+                <span class="o">/</span> <span class="n">control_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frequency_index</span><span class="p">])))</span>
     <span class="k">if</span> <span class="n">limit_srs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">limit_responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">oaconvolve</span><span class="p">(</span><span class="n">limit_irf</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">)</span> <span class="k">for</span> <span class="n">limit_irf</span> <span class="ow">in</span> <span class="n">limit_irfs</span><span class="p">])[</span><span class="o">...</span><span class="p">,:</span><span class="n">block_size</span><span class="p">]</span>
-        <span class="n">predicted_limit_srs</span><span class="p">,</span><span class="n">frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">limit_responses</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span> 
-                                              <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> 
-                                              <span class="kc">None</span> <span class="k">if</span> <span class="n">b</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">b</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span> 
-                                              <span class="kc">None</span> <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">a</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">])</span>
+        <span class="n">limit_responses</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">oaconvolve</span><span class="p">(</span><span class="n">limit_irf</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">)</span> <span class="k">for</span> <span class="n">limit_irf</span> <span class="ow">in</span> <span class="n">limit_irfs</span><span class="p">])[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="n">block_size</span><span class="p">]</span>
+        <span class="n">predicted_limit_srs</span><span class="p">,</span> <span class="n">frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span><span class="n">limit_responses</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span>
+                                               <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span>
+                                               <span class="kc">None</span> <span class="k">if</span> <span class="n">b</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">b</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">],</span>
+                                               <span class="kc">None</span> <span class="k">if</span> <span class="n">a</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">a</span><span class="p">[</span><span class="n">frequency_index</span><span class="p">])</span>
         <span class="n">max_limit_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span>
-            <span class="p">((</span><span class="n">predicted_limit_srs</span> <span class="o">-</span> <span class="n">limit_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frequency_index</span><span class="p">])</span>
-             <span class="o">/</span><span class="n">limit_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">frequency_index</span><span class="p">]))</span>
+            <span class="p">((</span><span class="n">predicted_limit_srs</span> <span class="o">-</span> <span class="n">limit_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frequency_index</span><span class="p">])</span>
+             <span class="o">/</span> <span class="n">limit_srs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">frequency_index</span><span class="p">]))</span>
         <span class="k">if</span> <span class="n">max_limit_error</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">mean_control_error</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">srs_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">((</span><span class="n">mean_control_error</span><span class="p">,</span><span class="n">max_limit_error</span><span class="p">))</span>
+            <span class="n">srs_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">((</span><span class="n">mean_control_error</span><span class="p">,</span> <span class="n">max_limit_error</span><span class="p">))</span>
         <span class="k">elif</span> <span class="n">max_limit_error</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">mean_control_error</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">srs_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">((</span><span class="n">mean_control_error</span><span class="p">,</span><span class="n">max_limit_error</span><span class="p">))</span>
+            <span class="n">srs_error</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">((</span><span class="n">mean_control_error</span><span class="p">,</span> <span class="n">max_limit_error</span><span class="p">))</span>
         <span class="k">elif</span> <span class="n">max_limit_error</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">mean_control_error</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">srs_error</span> <span class="o">=</span> <span class="n">max_limit_error</span>
         <span class="k">elif</span> <span class="n">max_limit_error</span> <span class="o">&lt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">mean_control_error</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">:</span>
@@ -1389,39 +1409,41 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="n">limit_responses</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="n">predicted_limit_srs</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="n">srs_error</span> <span class="o">=</span> <span class="n">mean_control_error</span>
-    
+
     <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">srs_error</span><span class="p">),</span> <span class="n">input_signal</span><span class="p">,</span> <span class="n">control_responses</span><span class="p">,</span>
             <span class="n">predicted_control_srs</span><span class="p">,</span> <span class="n">limit_responses</span><span class="p">,</span> <span class="n">predicted_limit_srs</span><span class="p">,</span>
-            <span class="n">new_sine_amplitudes</span><span class="p">,</span> <span class="n">new_compensation_frequency</span><span class="p">,</span> 
+            <span class="n">new_sine_amplitudes</span><span class="p">,</span> <span class="n">new_compensation_frequency</span><span class="p">,</span>
             <span class="n">new_compensation_amplitude</span><span class="p">,</span> <span class="n">new_compensation_decay</span><span class="p">,</span>
             <span class="n">new_compensation_delay</span><span class="p">)</span></div>
 
-<div class="viewcode-block" id="optimization_callback"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.optimization_callback.html#sdynpy.signal_processing.sdynpy_srs.optimization_callback">[docs]</a><span class="k">def</span> <span class="nf">optimization_callback</span><span class="p">(</span><span class="n">intermediate_result</span><span class="p">,</span> <span class="n">rms_error_threshold</span> <span class="o">=</span> <span class="mf">0.02</span><span class="p">,</span>
-                          <span class="n">verbose</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+
+<div class="viewcode-block" id="optimization_callback"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.optimization_callback.html#sdynpy.signal_processing.sdynpy_srs.optimization_callback">[docs]</a><span class="k">def</span> <span class="nf">optimization_callback</span><span class="p">(</span><span class="n">intermediate_result</span><span class="p">,</span> <span class="n">rms_error_threshold</span><span class="o">=</span><span class="mf">0.02</span><span class="p">,</span>
+                          <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Amplitude Scale: </span><span class="si">{:}</span><span class="s1">, error is </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">intermediate_result</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(),</span>
-                                                       <span class="n">intermediate_result</span><span class="o">.</span><span class="n">fun</span><span class="p">))</span>
+                                                          <span class="n">intermediate_result</span><span class="o">.</span><span class="n">fun</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">rms_error_threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">if</span> <span class="n">intermediate_result</span><span class="o">.</span><span class="n">fun</span> <span class="o">&lt;</span> <span class="n">rms_error_threshold</span><span class="p">:</span>
             <span class="k">raise</span> <span class="ne">StopIteration</span></div>
 
-<div class="viewcode-block" id="sum_decayed_sines_minimize"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_minimize.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_minimize">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines_minimize</span><span class="p">(</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span> 
-                               <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_range</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_per_octave</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                               <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_decays</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">sine_delays</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                               <span class="n">control_srs</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">control_breakpoints</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                               <span class="n">srs_damping</span> <span class="o">=</span> <span class="mf">0.05</span><span class="p">,</span> <span class="n">srs_type</span> <span class="o">=</span> <span class="mi">9</span><span class="p">,</span>
-                               <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">compensation_decay</span> <span class="o">=</span> <span class="mf">0.95</span><span class="p">,</span>
+
+<div class="viewcode-block" id="sum_decayed_sines_minimize"><a class="viewcode-back" href="../../../_autosummary/sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_minimize.html#sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_minimize">[docs]</a><span class="k">def</span> <span class="nf">sum_decayed_sines_minimize</span><span class="p">(</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
+                               <span class="n">sine_frequencies</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_range</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_tone_per_octave</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">sine_amplitudes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_decays</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sine_delays</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">control_srs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">control_breakpoints</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">srs_damping</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">srs_type</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span>
+                               <span class="n">compensation_frequency</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
                                <span class="c1"># Parameters for defining decays</span>
-                               <span class="n">tau</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">num_time_constants</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">decay_resolution</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                               <span class="n">scale_factor</span> <span class="o">=</span> <span class="mf">1.02</span><span class="p">,</span> 
-                               <span class="n">acceleration_factor</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+                               <span class="n">tau</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">num_time_constants</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">decay_resolution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">scale_factor</span><span class="o">=</span><span class="mf">1.02</span><span class="p">,</span>
+                               <span class="n">acceleration_factor</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
                                <span class="c1"># Parameters for imposing limits</span>
-                               <span class="n">limit_breakpoints</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">limit_transfer_functions</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                               <span class="n">control_transfer_functions</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">control_weights</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+                               <span class="n">limit_breakpoints</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">limit_transfer_functions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">control_transfer_functions</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">control_weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                <span class="c1"># Parameters for the optimizer</span>
-                               <span class="n">minimize_iterations</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">rms_error_threshold</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-                               <span class="n">optimization_passes</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
-                               <span class="n">plot_results</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="kc">False</span>
+                               <span class="n">minimize_iterations</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">rms_error_threshold</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">optimization_passes</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+                               <span class="n">plot_results</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
                                <span class="p">):</span>
     <span class="c1"># Handle the sine tone frequencies</span>
     <span class="k">if</span> <span class="n">sine_frequencies</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">sine_tone_range</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -1432,7 +1454,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="c1"># Create sine tones</span>
         <span class="k">if</span> <span class="n">sine_tone_per_octave</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">sine_tone_per_octave</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="mi">9</span><span class="o">-</span><span class="n">srs_damping</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
-        <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">octspace</span><span class="p">(</span><span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">sine_frequencies</span> <span class="o">=</span> <span class="n">octspace</span><span class="p">(</span><span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sine_tone_range</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
                                     <span class="n">sine_tone_per_octave</span><span class="p">)</span>
     <span class="c1"># Now set up the SRS</span>
     <span class="k">if</span> <span class="n">control_srs</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">control_breakpoints</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -1440,32 +1462,32 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="k">if</span> <span class="n">control_srs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">control_breakpoints</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;`control_srs` can not be specified simultaneously with `control_breakpoints`&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">control_srs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">frequencies</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">breakpoint_curves</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span>
+        <span class="n">frequencies</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">breakpoint_curves</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span>
         <span class="n">control_srs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">loginterp</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span>
                                           <span class="n">frequencies</span><span class="p">,</span>
                                           <span class="n">breakpoint_curve</span><span class="p">)</span> <span class="k">for</span> <span class="n">breakpoint_curve</span> <span class="ow">in</span> <span class="n">breakpoint_curves</span><span class="p">])</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">control_srs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">control_srs</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">control_transfer_functions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">control_transfer_functions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(((</span><span class="n">control_srs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">block_size</span><span class="o">//</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
-    <span class="n">tf_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">control_transfer_functions</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">)</span>
+        <span class="n">control_transfer_functions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(((</span><span class="n">control_srs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span> <span class="n">block_size</span><span class="o">//</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+    <span class="n">tf_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">rfftfreq</span><span class="p">(</span><span class="n">control_transfer_functions</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">sine_amplitudes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">tf_at_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span><span class="n">tf_frequencies</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">control_transfer_function</span><span class="p">))</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">tf_frequencies</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">control_transfer_function</span><span class="p">))</span>
             <span class="k">for</span> <span class="n">control_transfer_function</span> <span class="ow">in</span> <span class="n">control_transfer_functions</span><span class="p">])</span>
-        <span class="n">srs_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">nnls</span><span class="p">(</span><span class="n">ai</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span><span class="n">bi</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">ai</span><span class="p">,</span><span class="n">bi</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">tf_at_frequencies</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="n">control_srs</span><span class="o">.</span><span class="n">T</span><span class="p">)])</span>
+        <span class="n">srs_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">nnls</span><span class="p">(</span><span class="n">ai</span><span class="p">[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="n">bi</span><span class="p">)[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">ai</span><span class="p">,</span> <span class="n">bi</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">tf_at_frequencies</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">control_srs</span><span class="o">.</span><span class="n">T</span><span class="p">)])</span>
         <span class="n">srs_amplitudes</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">srs_amplitudes</span><span class="o">.</span><span class="n">size</span><span class="p">)</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*=</span> <span class="o">-</span><span class="mi">1</span>
         <span class="n">quality</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">srs_damping</span><span class="p">)</span>
         <span class="n">srs_amplitudes</span> <span class="o">/=</span> <span class="n">quality</span>
         <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="n">srs_amplitudes</span>
-        
+
     <span class="k">if</span> <span class="n">sine_delays</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">sine_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">)</span>
-    
+
     <span class="k">if</span> <span class="n">compensation_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
-    
+
     <span class="c1"># Set up decay terms</span>
     <span class="n">decay_terms_specified</span> <span class="o">=</span> <span class="mi">0</span>
     <span class="k">if</span> <span class="n">sine_decays</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@@ -1478,11 +1500,11 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;One of `sine_decays`, `tau`, or `num_time_constants` must be specified&#39;</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">decay_terms_specified</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Only one of `sine_decays`, `tau`, or `num_time_constants` can be specified&#39;</span><span class="p">)</span>
-    
+
     <span class="c1"># Now check and see which is defined</span>
     <span class="k">if</span> <span class="n">num_time_constants</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">period</span> <span class="o">=</span> <span class="n">block_size</span> <span class="o">/</span> <span class="n">sample_rate</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">num_time_constants</span><span class="p">,</span><span class="nb">dict</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">num_time_constants</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
             <span class="n">tau</span> <span class="o">=</span> <span class="p">{}</span>
             <span class="k">for</span> <span class="n">freq_range</span><span class="p">,</span> <span class="n">num_time_constant</span> <span class="ow">in</span> <span class="n">num_time_constants</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                 <span class="n">tau</span><span class="p">[</span><span class="n">freq_range</span><span class="p">]</span> <span class="o">=</span> <span class="n">period</span> <span class="o">/</span> <span class="n">num_time_constant</span>
@@ -1491,7 +1513,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
     <span class="k">if</span> <span class="n">tau</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">sine_decays</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">freq</span> <span class="ow">in</span> <span class="n">sine_frequencies</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span><span class="nb">dict</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
                 <span class="n">this_decay</span> <span class="o">=</span> <span class="kc">None</span>
                 <span class="k">for</span> <span class="n">freq_range</span><span class="p">,</span> <span class="n">this_tau</span> <span class="ow">in</span> <span class="n">tau</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                     <span class="k">if</span> <span class="n">freq_range</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">freq</span> <span class="o">&lt;=</span> <span class="n">freq_range</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
@@ -1504,80 +1526,84 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                 <span class="n">sine_decays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">freq</span><span class="o">*</span><span class="n">tau</span><span class="p">))</span>
         <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sine_decays</span><span class="p">)</span>
     <span class="c1"># Otherwise we just keep the specified sine_decays</span>
-    
+
     <span class="c1"># Now handle the minimum resolution on the decay</span>
     <span class="k">if</span> <span class="n">decay_resolution</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">sine_decays</span> <span class="o">=</span> <span class="n">decay_resolution</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">sine_decays</span><span class="o">/</span><span class="n">decay_resolution</span><span class="p">)</span>
-        
+
     <span class="k">if</span> <span class="n">compensation_frequency</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">compensation_frequency</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)</span><span class="o">/</span><span class="mi">3</span>
-        
+
     <span class="c1"># Now set up limits and transfer functions</span>
     <span class="k">if</span> <span class="n">control_weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">control_weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">control_srs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
     <span class="k">if</span> <span class="n">limit_breakpoints</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">frequencies</span> <span class="o">=</span> <span class="n">limit_breakpoints</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">breakpoint_curves</span> <span class="o">=</span> <span class="n">limit_breakpoints</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span>
+        <span class="n">frequencies</span> <span class="o">=</span> <span class="n">limit_breakpoints</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+        <span class="n">breakpoint_curves</span> <span class="o">=</span> <span class="n">limit_breakpoints</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">T</span>
         <span class="n">limit_srs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">loginterp</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span>
                                         <span class="n">frequencies</span><span class="p">,</span>
                                         <span class="n">breakpoint_curve</span><span class="p">)</span> <span class="k">for</span> <span class="n">breakpoint_curve</span> <span class="ow">in</span> <span class="n">breakpoint_curves</span><span class="p">])</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">limit_srs</span> <span class="o">=</span> <span class="kc">None</span>
-    
+
     <span class="c1"># Compute impulse responses</span>
     <span class="n">control_irfs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">control_transfer_functions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">limit_transfer_functions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">limit_irfs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fft</span><span class="o">.</span><span class="n">irfft</span><span class="p">(</span><span class="n">limit_transfer_functions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">limit_irfs</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">b</span><span class="p">,</span><span class="n">a</span> <span class="o">=</span> <span class="n">sdof_ramp_invariant_filter_weights</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
-    
+    <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="n">sdof_ramp_invariant_filter_weights</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
+
     <span class="c1"># Normalize control weights</span>
     <span class="n">control_weights</span> <span class="o">=</span> <span class="n">control_weights</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">control_weights</span><span class="p">)</span>
-    
+
     <span class="c1"># Copy things so we don&#39;t overwrite</span>
     <span class="n">sine_amplitudes</span> <span class="o">=</span> <span class="n">sine_amplitudes</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    
+
     <span class="c1"># Now we will iterate over all of the frequencies and compute the new</span>
     <span class="c1"># amplitudes</span>
     <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">optimization_passes</span><span class="p">):</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="p">)):</span>
             <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
                 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Pass </span><span class="si">{:}</span><span class="s1">, Analyzing Frequency </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:0.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">j</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+
             <span class="c1"># We will now go through and optimize the frequency line</span>
-            <span class="n">error_function</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">optimization_error_function</span><span class="p">(</span>
-                <span class="n">x</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
-                <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> 
-                <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">control_irfs</span><span class="p">,</span> <span class="n">limit_irfs</span><span class="p">,</span>
-                <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_weights</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span> 
-                <span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">frequency_index</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
-        
-            <span class="n">callback</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">intermediate_result</span><span class="p">:</span> <span class="n">optimization_callback</span><span class="p">(</span><span class="n">intermediate_result</span><span class="p">,</span> <span class="n">rms_error_threshold</span><span class="p">,</span> 
-                                                       <span class="n">verbose</span><span class="p">)</span>
-            <span class="n">optimization_result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">error_function</span><span class="p">,</span> 
+            <span class="k">def</span> <span class="nf">error_function</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+                <span class="k">return</span> <span class="n">optimization_error_function</span><span class="p">(</span>
+                    <span class="n">x</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
+                    <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
+                    <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">control_irfs</span><span class="p">,</span> <span class="n">limit_irfs</span><span class="p">,</span>
+                    <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_weights</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span>
+                    <span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">frequency_index</span><span class="o">=</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="k">def</span> <span class="nf">callback</span><span class="p">(</span><span class="n">intermediate_result</span><span class="p">):</span>
+                <span class="k">return</span> <span class="n">optimization_callback</span><span class="p">(</span>
+                    <span class="n">intermediate_result</span><span class="p">,</span> <span class="n">rms_error_threshold</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
+
+            <span class="n">optimization_result</span> <span class="o">=</span> <span class="n">minimize</span><span class="p">(</span><span class="n">error_function</span><span class="p">,</span>
                                            <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span>
-                                           <span class="n">method</span> <span class="o">=</span> <span class="s1">&#39;Powell&#39;</span><span class="p">,</span>
-                                            <span class="n">bounds</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)],</span>
+                                           <span class="n">method</span><span class="o">=</span><span class="s1">&#39;Powell&#39;</span><span class="p">,</span>
+                                           <span class="n">bounds</span><span class="o">=</span><span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)],</span>
                                            <span class="n">callback</span><span class="o">=</span><span class="n">callback</span><span class="p">,</span>
-                                           <span class="n">options</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span><span class="n">minimize_iterations</span><span class="p">})</span>
-        
+                                           <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;maxiter&#39;</span><span class="p">:</span> <span class="n">minimize_iterations</span><span class="p">})</span>
+
             <span class="c1"># Populate the amplitudes with the updated result</span>
             <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Initial Amplitude: </span><span class="si">{:}</span><span class="s1">, Updated Amplitude: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">optimization_result</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()))</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Initial Amplitude: </span><span class="si">{:}</span><span class="s1">, Updated Amplitude: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*</span><span class="n">optimization_result</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()))</span>
             <span class="n">sine_amplitudes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*=</span> <span class="n">optimization_result</span><span class="o">.</span><span class="n">x</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
-        
+
     <span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">,</span> <span class="n">control_responses</span><span class="p">,</span> <span class="n">predicted_control_srs</span><span class="p">,</span>
      <span class="n">limit_responses</span><span class="p">,</span> <span class="n">predicted_limit_srs</span><span class="p">,</span>
-     <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span> 
+     <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">compensation_frequency</span><span class="p">,</span>
      <span class="n">compensation_amplitude</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span>
      <span class="n">compensation_delay</span><span class="p">)</span> <span class="o">=</span> <span class="n">optimization_error_function</span><span class="p">(</span>
          <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">sine_frequencies</span><span class="o">.</span><span class="n">size</span><span class="p">),</span>
          <span class="n">sample_rate</span><span class="p">,</span> <span class="n">block_size</span><span class="p">,</span>
-         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span> 
+         <span class="n">sine_frequencies</span><span class="p">,</span> <span class="n">sine_amplitudes</span><span class="p">,</span> <span class="n">sine_decays</span><span class="p">,</span> <span class="n">sine_delays</span><span class="p">,</span>
          <span class="n">compensation_frequency</span><span class="p">,</span> <span class="n">compensation_decay</span><span class="p">,</span> <span class="n">control_irfs</span><span class="p">,</span> <span class="n">limit_irfs</span><span class="p">,</span>
-         <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_weights</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span> 
+         <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_weights</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span>
          <span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
-    
+
     <span class="n">all_frequencies</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_frequencies</span><span class="p">,</span>
                                       <span class="p">[</span><span class="n">compensation_frequency</span><span class="p">]))</span>
     <span class="n">all_amplitudes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_amplitudes</span><span class="p">,</span>
@@ -1586,54 +1612,54 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                                  <span class="p">[</span><span class="n">compensation_decay</span><span class="p">]))</span>
     <span class="n">all_delays</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">sine_delays</span><span class="p">,</span>
                                  <span class="p">[</span><span class="n">compensation_delay</span><span class="p">]))</span>
-    
+
     <span class="c1"># Now compute displacement and velocity</span>
     <span class="n">velocity_signal</span><span class="p">,</span> <span class="n">displacement_signal</span> <span class="o">=</span> <span class="n">sum_decayed_sines_displacement_velocity</span><span class="p">(</span>
         <span class="n">all_frequencies</span><span class="p">,</span> <span class="n">all_amplitudes</span><span class="p">,</span> <span class="n">all_decays</span><span class="p">,</span> <span class="n">all_delays</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span>
         <span class="n">block_size</span><span class="p">,</span> <span class="n">acceleration_factor</span><span class="p">)</span>
-    
+
     <span class="n">return_vals</span> <span class="o">=</span> <span class="p">(</span><span class="n">input_signal</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">,</span>
                    <span class="n">control_responses</span><span class="p">,</span> <span class="n">predicted_control_srs</span><span class="p">,</span>
-                   <span class="n">limit_responses</span><span class="p">,</span> <span class="n">predicted_limit_srs</span><span class="p">,</span> 
+                   <span class="n">limit_responses</span><span class="p">,</span> <span class="n">predicted_limit_srs</span><span class="p">,</span>
                    <span class="n">all_frequencies</span><span class="p">,</span> <span class="n">all_amplitudes</span><span class="p">,</span> <span class="n">all_decays</span><span class="p">,</span> <span class="n">all_delays</span><span class="p">,</span>
                    <span class="p">)</span>
-    
+
     <span class="k">if</span> <span class="n">plot_results</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
         <span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">block_size</span><span class="p">)</span><span class="o">/</span><span class="n">sample_rate</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Acceleration&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Velocity&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Displacement&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">input_signal</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Acceleration&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">velocity_signal</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Velocity&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">displacement_signal</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Displacement&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
         <span class="c1"># Compute SRS</span>
-        <span class="n">this_srs</span><span class="p">,</span><span class="n">this_frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span>
+        <span class="n">this_srs</span><span class="p">,</span> <span class="n">this_frequencies</span> <span class="o">=</span> <span class="n">srs</span><span class="p">(</span>
             <span class="n">input_signal</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">sample_rate</span><span class="p">,</span> <span class="n">sine_frequencies</span><span class="p">,</span>
             <span class="n">srs_damping</span><span class="p">,</span> <span class="n">srs_type</span><span class="p">)</span>
         <span class="k">if</span> <span class="n">control_breakpoints</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">srs_abscissa</span> <span class="o">=</span> <span class="n">sine_frequencies</span>
             <span class="n">srs_ordinate</span> <span class="o">=</span> <span class="n">control_srs</span><span class="o">.</span><span class="n">T</span>
         <span class="k">else</span><span class="p">:</span>
-            <span class="n">srs_abscissa</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">srs_ordinate</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span><span class="mi">1</span><span class="p">:]</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">srs_abscissa</span><span class="p">,</span> <span class="n">srs_ordinate</span><span class="p">,</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">this_frequencies</span><span class="p">,</span> <span class="n">this_srs</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;SRS (</span><span class="si">{:0.2f}% d</span><span class="s1">amping)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">srs_damping</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Reference&#39;</span><span class="p">,</span><span class="s1">&#39;Decayed Sine&#39;</span><span class="p">))</span>
+            <span class="n">srs_abscissa</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">srs_ordinate</span> <span class="o">=</span> <span class="n">control_breakpoints</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">srs_abscissa</span><span class="p">,</span> <span class="n">srs_ordinate</span><span class="p">,</span> <span class="s1">&#39;k--&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">this_frequencies</span><span class="p">,</span> <span class="n">this_srs</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;SRS (</span><span class="si">{:0.2f}% d</span><span class="s1">amping)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">srs_damping</span><span class="o">*</span><span class="mi">100</span><span class="p">))</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Reference&#39;</span><span class="p">,</span> <span class="s1">&#39;Decayed Sine&#39;</span><span class="p">))</span>
         <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-        <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span><span class="n">ax</span><span class="p">)</span>
+        <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
         <span class="c1"># Now plot all control channels</span>
-        <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">predicted</span><span class="p">,</span> <span class="n">control</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">predicted_control_srs</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_responses</span><span class="p">)):</span>
-            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">predicted</span><span class="p">,</span> <span class="n">control</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">predicted_control_srs</span><span class="p">,</span> <span class="n">control_srs</span><span class="p">,</span> <span class="n">control_responses</span><span class="p">)):</span>
+            <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Control Signal </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
-            <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span><span class="n">response</span><span class="p">)</span>
+            <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Acceleration&#39;</span><span class="p">)</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Control SRS </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
@@ -1643,15 +1669,15 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
             <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
             <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Desired&#39;</span><span class="p">,</span><span class="s1">&#39;Achieved&#39;</span><span class="p">))</span>
+            <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Desired&#39;</span><span class="p">,</span> <span class="s1">&#39;Achieved&#39;</span><span class="p">))</span>
             <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-            <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span><span class="n">ax</span><span class="p">)</span>
+            <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
         <span class="c1"># Now plot all limit channels</span>
         <span class="k">if</span> <span class="n">limit_srs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">predicted</span><span class="p">,</span><span class="n">limit</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">predicted_limit_srs</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span> <span class="n">limit_responses</span><span class="p">)):</span>
-                <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">predicted</span><span class="p">,</span> <span class="n">limit</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">predicted_limit_srs</span><span class="p">,</span> <span class="n">limit_srs</span><span class="p">,</span> <span class="n">limit_responses</span><span class="p">)):</span>
+                <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
                 <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Limit Signal </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
-                <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span><span class="n">response</span><span class="p">)</span>
+                <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">times</span><span class="p">,</span> <span class="n">response</span><span class="p">)</span>
                 <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Acceleration&#39;</span><span class="p">)</span>
                 <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Time (s)&#39;</span><span class="p">)</span>
                 <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Limit SRS </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
@@ -1661,10 +1687,10 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
                 <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
                 <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
                 <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-                <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Desired&#39;</span><span class="p">,</span><span class="s1">&#39;Achieved&#39;</span><span class="p">))</span>
+                <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="s1">&#39;Desired&#39;</span><span class="p">,</span> <span class="s1">&#39;Achieved&#39;</span><span class="p">))</span>
                 <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-                <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span><span class="n">ax</span><span class="p">)</span>
-                
+                <span class="n">return_vals</span> <span class="o">+=</span> <span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+
     <span class="k">return</span> <span class="n">return_vals</span></div>
 </pre></div>
 
@@ -1675,7 +1701,7 @@ <h1>Source code for sdynpy.signal_processing.sdynpy_srs</h1><div class="highligh
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eb8b6590>
+<jinja2.runtime.BlockReference object at 0x7f5a27369c60>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.rst.txt
index 829ede9..29bdd1f 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.rst.txt
@@ -20,6 +20,7 @@ sdynpy.core.sdynpy\_coordinate.CoordinateArray
       ~CoordinateArray.from_matlab_cellstr
       ~CoordinateArray.from_nodelist
       ~CoordinateArray.local_direction
+      ~CoordinateArray.offset_node_ids
       ~CoordinateArray.sign
       ~CoordinateArray.string_array
    
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.rst.txt
index e27ba9f..40a9a99 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.rst.txt
@@ -15,6 +15,7 @@ sdynpy.core.sdynpy\_data.CoherenceArray
 
    .. autosummary::
    
+      ~CoherenceArray.from_time_data
    
    
 
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.rst.txt
index 7b20a52..2e207f1 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.rst.txt
@@ -15,6 +15,7 @@ sdynpy.core.sdynpy\_data.MultipleCoherenceArray
 
    .. autosummary::
    
+      ~MultipleCoherenceArray.from_time_data
    
    
 
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.rst.txt
index 5cca62b..d60a912 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_data.NDDataArray.rst.txt
@@ -22,12 +22,15 @@ sdynpy.core.sdynpy\_data.NDDataArray
       ~NDDataArray.extract_elements_by_abscissa
       ~NDDataArray.from_uff
       ~NDDataArray.from_unv
+      ~NDDataArray.get_drive_points
+      ~NDDataArray.get_reciprocal_data
       ~NDDataArray.interpolate
       ~NDDataArray.join
       ~NDDataArray.load
       ~NDDataArray.max
       ~NDDataArray.min
       ~NDDataArray.plot
+      ~NDDataArray.plot_image
       ~NDDataArray.reshape_to_matrix
       ~NDDataArray.save
       ~NDDataArray.to_imat_struct
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.rst.txt
index 2dd27e6..f4bf8f2 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.rst.txt
@@ -16,10 +16,12 @@ sdynpy.core.sdynpy\_data.PowerSpectralDensityArray
    .. autosummary::
    
       ~PowerSpectralDensityArray.angle
+      ~PowerSpectralDensityArray.bandwidth_average
       ~PowerSpectralDensityArray.coherence
       ~PowerSpectralDensityArray.compare_asds
       ~PowerSpectralDensityArray.error_summary
       ~PowerSpectralDensityArray.eye
+      ~PowerSpectralDensityArray.from_time_data
       ~PowerSpectralDensityArray.generate_time_history
       ~PowerSpectralDensityArray.get_asd
       ~PowerSpectralDensityArray.mimo_forward
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.rst.txt
index 6a667fb..99e35d7 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.rst.txt
@@ -32,6 +32,7 @@ sdynpy.core.sdynpy\_data.TimeHistoryArray
       ~TimeHistoryArray.rms
       ~TimeHistoryArray.shift_signal
       ~TimeHistoryArray.sine_signal
+      ~TimeHistoryArray.sine_sweep_signal
       ~TimeHistoryArray.split_into_frames
       ~TimeHistoryArray.srs
       ~TimeHistoryArray.stft
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.rst.txt
index 76de765..4c93ae7 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.rst.txt
@@ -27,6 +27,7 @@ sdynpy.core.sdynpy\_data.TransferFunctionArray
       ~TransferFunctionArray.plot
       ~TransferFunctionArray.plot_cond_num
       ~TransferFunctionArray.plot_singular_values
+      ~TransferFunctionArray.plot_with_coherence
       ~TransferFunctionArray.substructure_by_constraint_matrix
       ~TransferFunctionArray.substructure_by_coordinate
       ~TransferFunctionArray.svd
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.rst.txt
index 8743c23..85d1915 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.Geometry.rst.txt
@@ -21,6 +21,7 @@ sdynpy.core.sdynpy\_geometry.Geometry
       ~Geometry.camera_visualization
       ~Geometry.compress_ids
       ~Geometry.copy
+      ~Geometry.from_excel_template
       ~Geometry.from_exodus
       ~Geometry.from_imat_struct
       ~Geometry.from_uff
@@ -40,6 +41,7 @@ sdynpy.core.sdynpy\_geometry.Geometry
       ~Geometry.reduce
       ~Geometry.rigid_body_shapes
       ~Geometry.save
+      ~Geometry.write_excel_template
       ~Geometry.write_to_unv
    
    
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.rst.txt
index 0d5cd73..d078c96 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.rst.txt
@@ -22,6 +22,7 @@ sdynpy.core.sdynpy\_geometry.ShapePlotter
       ~ShapePlotter.next_shape
       ~ShapePlotter.play_animation
       ~ShapePlotter.prev_shape
+      ~ShapePlotter.reset_shape
       ~ShapePlotter.save_animation
       ~ShapePlotter.save_animation_all_shapes
       ~ShapePlotter.save_animation_from_action
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.rst.txt
new file mode 100644
index 0000000..e9d4dca
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.core.sdynpy\_geometry.from\_excel\_template
+==================================================
+
+.. currentmodule:: sdynpy.core.sdynpy_geometry
+
+.. autofunction:: from_excel_template
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.rst.txt
index ce06ef4..583db50 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.rst.txt
@@ -16,6 +16,7 @@ sdynpy.core.sdynpy\_geometry
    
       coordinate_system_array
       element_array
+      from_excel_template
       from_exodus
       from_imat_struct
       from_uff
@@ -27,6 +28,7 @@ sdynpy.core.sdynpy\_geometry
       node_array
       split_list
       traceline_array
+      write_excel_template
    
    
 
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.rst.txt
new file mode 100644
index 0000000..5606192
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.core.sdynpy\_geometry.write\_excel\_template
+===================================================
+
+.. currentmodule:: sdynpy.core.sdynpy_geometry
+
+.. autofunction:: write_excel_template
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.rst.txt
index b8d171a..ea55c71 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.rst.txt
@@ -18,6 +18,7 @@ sdynpy.core.sdynpy\_shape.ShapeArray
       ~ShapeArray.complex_data_dtype
       ~ShapeArray.compute_frf
       ~ShapeArray.concatenate_dofs
+      ~ShapeArray.edit_comments
       ~ShapeArray.expand
       ~ShapeArray.from_exodus
       ~ShapeArray.from_imat_struct
@@ -25,6 +26,7 @@ sdynpy.core.sdynpy\_shape.ShapeArray
       ~ShapeArray.from_unv
       ~ShapeArray.is_complex
       ~ShapeArray.mode_table
+      ~ShapeArray.normalize
       ~ShapeArray.optimize_degrees_of_freedom
       ~ShapeArray.overlay_shapes
       ~ShapeArray.plot_frequency
@@ -35,6 +37,7 @@ sdynpy.core.sdynpy\_shape.ShapeArray
       ~ShapeArray.shape_alignment
       ~ShapeArray.system
       ~ShapeArray.time_integrate
+      ~ShapeArray.to_complex
       ~ShapeArray.to_real
       ~ShapeArray.transform_coordinate_system
       ~ShapeArray.write_to_unv
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.rst.txt
new file mode 100644
index 0000000..b6dac17
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.rst.txt
@@ -0,0 +1,31 @@
+sdynpy.core.sdynpy\_shape.ShapeCommentTable
+===========================================
+
+.. currentmodule:: sdynpy.core.sdynpy_shape
+
+.. autoclass:: ShapeCommentTable
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ShapeCommentTable.__init__
+      ~ShapeCommentTable.accept
+      ~ShapeCommentTable.update_mode
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+   
+   
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_shape.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_shape.rst.txt
index 23562a3..98d80d5 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_shape.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_shape.rst.txt
@@ -38,6 +38,7 @@ sdynpy.core.sdynpy\_shape
       :template: custom-class-template.rst
    
       ShapeArray
+      ShapeCommentTable
    
    
 
diff --git a/_sources/_autosummary/sdynpy.core.sdynpy_system.System.rst.txt b/_sources/_autosummary/sdynpy.core.sdynpy_system.System.rst.txt
index ada2209..55ad212 100644
--- a/_sources/_autosummary/sdynpy.core.sdynpy_system.System.rst.txt
+++ b/_sources/_autosummary/sdynpy.core.sdynpy_system.System.rst.txt
@@ -30,6 +30,7 @@ sdynpy.core.sdynpy\_system.System
       ~System.reduce_craig_bampton
       ~System.reduce_dynamic
       ~System.reduce_guyan
+      ~System.remove_transformation
       ~System.save
       ~System.set_proportional_damping
       ~System.simulate_test
diff --git a/_sources/_autosummary/sdynpy.doc.sdynpy_latex.figure.rst.txt b/_sources/_autosummary/sdynpy.doc.sdynpy_latex.figure.rst.txt
new file mode 100644
index 0000000..d965d13
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.doc.sdynpy_latex.figure.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.doc.sdynpy\_latex.figure
+===============================
+
+.. currentmodule:: sdynpy.doc.sdynpy_latex
+
+.. autofunction:: figure
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.doc.sdynpy_latex.rst.txt b/_sources/_autosummary/sdynpy.doc.sdynpy_latex.rst.txt
index fff8de3..4954006 100644
--- a/_sources/_autosummary/sdynpy.doc.sdynpy_latex.rst.txt
+++ b/_sources/_autosummary/sdynpy.doc.sdynpy_latex.rst.txt
@@ -15,6 +15,8 @@ sdynpy.doc.sdynpy\_latex
       :toctree:
    
       create_latex_summary
+      figure
+      table
    
    
 
diff --git a/_sources/_autosummary/sdynpy.doc.sdynpy_latex.table.rst.txt b/_sources/_autosummary/sdynpy.doc.sdynpy_latex.table.rst.txt
new file mode 100644
index 0000000..af24f4b
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.doc.sdynpy_latex.table.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.doc.sdynpy\_latex.table
+==============================
+
+.. currentmodule:: sdynpy.doc.sdynpy_latex
+
+.. autofunction:: table
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.rst.txt b/_sources/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.rst.txt
index 270f7d2..d42d3da 100644
--- a/_sources/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.rst.txt
+++ b/_sources/_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.rst.txt
@@ -27,22 +27,18 @@ sdynpy.fileio.sdynpy\_uff\_datasets.sdynpy\_uff\_dataset\_55.Sdynpy\_UFF\_Datase
 
    .. autosummary::
    
-      ~Sdynpy_UFF_Dataset_55.buckling_eigenvalue
       ~Sdynpy_UFF_Dataset_55.dataset_number
+      ~Sdynpy_UFF_Dataset_55.eigenvalue
       ~Sdynpy_UFF_Dataset_55.frequency
       ~Sdynpy_UFF_Dataset_55.frequency_step_number
       ~Sdynpy_UFF_Dataset_55.id_number
-      ~Sdynpy_UFF_Dataset_55.imaginary_eigenvalue
-      ~Sdynpy_UFF_Dataset_55.imaginary_modal_a
-      ~Sdynpy_UFF_Dataset_55.imaginary_modal_b
       ~Sdynpy_UFF_Dataset_55.load_case_number
+      ~Sdynpy_UFF_Dataset_55.modal_a
+      ~Sdynpy_UFF_Dataset_55.modal_b
       ~Sdynpy_UFF_Dataset_55.modal_hysteretic_damping
       ~Sdynpy_UFF_Dataset_55.modal_mass
       ~Sdynpy_UFF_Dataset_55.modal_viscous_damping
       ~Sdynpy_UFF_Dataset_55.mode_number
-      ~Sdynpy_UFF_Dataset_55.real_eigenvalue
-      ~Sdynpy_UFF_Dataset_55.real_modal_a
-      ~Sdynpy_UFF_Dataset_55.real_modal_b
       ~Sdynpy_UFF_Dataset_55.time
       ~Sdynpy_UFF_Dataset_55.time_step_number
    
diff --git a/_sources/_autosummary/sdynpy.modal.rst.txt b/_sources/_autosummary/sdynpy.modal.rst.txt
index 5d10b42..f7b9a0a 100644
--- a/_sources/_autosummary/sdynpy.modal.rst.txt
+++ b/_sources/_autosummary/sdynpy.modal.rst.txt
@@ -29,6 +29,7 @@ sdynpy.modal
    :recursive:
 
    sdynpy.modal.sdynpy_ccmif
+   sdynpy.modal.sdynpy_modal_test
    sdynpy.modal.sdynpy_modeshape
    sdynpy.modal.sdynpy_polypy
    sdynpy.modal.sdynpy_signal_processing_gui
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.rst.txt
new file mode 100644
index 0000000..2572609
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.rst.txt
@@ -0,0 +1,67 @@
+sdynpy.modal.sdynpy\_modal\_test.ModalTest
+==========================================
+
+.. currentmodule:: sdynpy.modal.sdynpy_modal_test
+
+.. autoclass:: ModalTest
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   
+   .. automethod:: __init__
+
+   
+   .. rubric:: Methods
+
+   .. autosummary::
+   
+      ~ModalTest.__init__
+      ~ModalTest.compute_resynthesized_frfs
+      ~ModalTest.compute_spectral_quantities
+      ~ModalTest.compute_spectral_quantities_SignalProcessingGUI
+      ~ModalTest.create_documentation_latex
+      ~ModalTest.create_documentation_pptx
+      ~ModalTest.create_documentation_word
+      ~ModalTest.define_spectral_processing_parameters
+      ~ModalTest.edit_mode_comments
+      ~ModalTest.fit_modes_PolyPy
+      ~ModalTest.fit_modes_SMAC
+      ~ModalTest.plot_cmif
+      ~ModalTest.plot_coherence_image
+      ~ModalTest.plot_deflection_shapes
+      ~ModalTest.plot_drive_point_frf_coherence
+      ~ModalTest.plot_drive_point_frfs
+      ~ModalTest.plot_figures_for_documentation
+      ~ModalTest.plot_mac
+      ~ModalTest.plot_mmif
+      ~ModalTest.plot_modeshape
+      ~ModalTest.plot_nmif
+      ~ModalTest.plot_psmif
+      ~ModalTest.plot_qmif
+      ~ModalTest.plot_reciprocal_frfs
+      ~ModalTest.plot_reference_autospectra
+      ~ModalTest.retrieve_modes_PolyPy
+      ~ModalTest.retrieve_modes_SMAC
+      ~ModalTest.retrieve_spectral_quantities_SignalProcessingGUI
+      ~ModalTest.set_autopower_spectra
+      ~ModalTest.set_channel_table
+      ~ModalTest.set_coherence
+      ~ModalTest.set_fit_modes
+      ~ModalTest.set_frfs
+      ~ModalTest.set_geometry
+      ~ModalTest.set_resynthesized_frfs
+      ~ModalTest.set_rigid_body_shapes
+      ~ModalTest.set_time_histories
+      ~ModalTest.set_units
+   
+   
+
+   
+   
+   .. rubric:: Attributes
+
+   .. autosummary::
+   
+      ~ModalTest.response_indices
+   
+   
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.rst.txt
new file mode 100644
index 0000000..ea37310
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.modal.sdynpy\_modal\_test.read\_modal\_fit\_data
+=======================================================
+
+.. currentmodule:: sdynpy.modal.sdynpy_modal_test
+
+.. autofunction:: read_modal_fit_data
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.rst.txt
new file mode 100644
index 0000000..9c5c86f
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modal_test.rst.txt
@@ -0,0 +1,38 @@
+sdynpy.modal.sdynpy\_modal\_test
+================================
+
+.. automodule:: sdynpy.modal.sdynpy_modal_test
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      read_modal_fit_data
+   
+   
+
+   
+   
+   .. rubric:: Classes
+
+   .. autosummary::
+      :toctree:
+      :template: custom-class-template.rst
+   
+      ModalTest
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.rst.txt
new file mode 100644
index 0000000..f79b59e
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.modal.sdynpy\_modeshape.generate\_kernel\_complex
+========================================================
+
+.. currentmodule:: sdynpy.modal.sdynpy_modeshape
+
+.. autofunction:: generate_kernel_complex
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.rst.txt
new file mode 100644
index 0000000..6ca6d4c
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.modal.sdynpy\_modeshape.generate\_kernel\_real
+=====================================================
+
+.. currentmodule:: sdynpy.modal.sdynpy_modeshape
+
+.. autofunction:: generate_kernel_real
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.rst.txt
index e817671..aafd7a3 100644
--- a/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.rst.txt
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.rst.txt
@@ -17,6 +17,9 @@ sdynpy.modal.sdynpy\_modeshape
       compute_residues
       compute_shapes
       compute_shapes_multireference
+      generate_kernel_complex
+      generate_kernel_real
+      stack_and_lstsq
    
    
 
diff --git a/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.rst.txt b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.rst.txt
new file mode 100644
index 0000000..e4dc42a
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.modal.sdynpy\_modeshape.stack\_and\_lstsq
+================================================
+
+.. currentmodule:: sdynpy.modal.sdynpy_modeshape
+
+.. autofunction:: stack_and_lstsq
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.signal_processing.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.rst.txt
index cb01b6a..4d2754b 100644
--- a/_sources/_autosummary/sdynpy.signal_processing.rst.txt
+++ b/_sources/_autosummary/sdynpy.signal_processing.rst.txt
@@ -38,6 +38,7 @@ sdynpy.signal\_processing
    sdynpy.signal_processing.sdynpy_geometry_fitting
    sdynpy.signal_processing.sdynpy_harmonic
    sdynpy.signal_processing.sdynpy_integration
+   sdynpy.signal_processing.sdynpy_lrm
    sdynpy.signal_processing.sdynpy_rotation
    sdynpy.signal_processing.sdynpy_srs
 
diff --git a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.rst.txt
new file mode 100644
index 0000000..cb11479
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.signal\_processing.sdynpy\_cpsd.nth\_octave\_freqs
+=========================================================
+
+.. currentmodule:: sdynpy.signal_processing.sdynpy_cpsd
+
+.. autofunction:: nth_octave_freqs
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rst.txt
index bd2325e..9f2defe 100644
--- a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rst.txt
+++ b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_cpsd.rst.txt
@@ -23,6 +23,7 @@ sdynpy.signal\_processing.sdynpy\_cpsd
       dB_pow
       db2scale
       match_coherence_phase
+      nth_octave_freqs
       plot_asds
       plot_cpsd_error
       rms
diff --git a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.rst.txt
index 914c631..986db02 100644
--- a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.rst.txt
+++ b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.rst.txt
@@ -21,6 +21,7 @@ sdynpy.signal\_processing.sdynpy\_generator
       ramp_envelope
       random
       sine
+      sine_sweep
    
    
 
diff --git a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.rst.txt
new file mode 100644
index 0000000..dbedc3a
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.signal\_processing.sdynpy\_generator.sine\_sweep
+=======================================================
+
+.. currentmodule:: sdynpy.signal_processing.sdynpy_generator
+
+.. autofunction:: sine_sweep
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.rst.txt
new file mode 100644
index 0000000..2ad6d77
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.rst.txt
@@ -0,0 +1,6 @@
+sdynpy.signal\_processing.sdynpy\_lrm.frf\_local\_model
+=======================================================
+
+.. currentmodule:: sdynpy.signal_processing.sdynpy_lrm
+
+.. autofunction:: frf_local_model
\ No newline at end of file
diff --git a/_sources/_autosummary/sdynpy.signal_processing.sdynpy_lrm.rst.txt b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_lrm.rst.txt
new file mode 100644
index 0000000..7c33f26
--- /dev/null
+++ b/_sources/_autosummary/sdynpy.signal_processing.sdynpy_lrm.rst.txt
@@ -0,0 +1,30 @@
+sdynpy.signal\_processing.sdynpy\_lrm
+=====================================
+
+.. automodule:: sdynpy.signal_processing.sdynpy_lrm
+
+   
+   
+   
+
+   
+   
+   .. rubric:: Functions
+
+   .. autosummary::
+      :toctree:
+   
+      frf_local_model
+   
+   
+
+   
+   
+   
+
+   
+   
+   
+
+
+
diff --git a/_sources/example_problems/airplane_modal_test.rst.txt b/_sources/example_problems/airplane_modal_test.rst.txt
index b45f3ee..41cb8f9 100644
--- a/_sources/example_problems/airplane_modal_test.rst.txt
+++ b/_sources/example_problems/airplane_modal_test.rst.txt
@@ -849,7 +849,7 @@ function wants as its arguments.  The first argument is ``sensors_to_keep``,
 which is the number we just defined.  The second is the ``shape_matrix``.
 Reading the documentation, this ``shape_matrix`` should have its first dimension
 corresponding to each sensor (here a sensor could be a channel or group of
-channels for a triaxial accelerometer) and its last dimension be correspond to
+channels for a triaxial accelerometer) and its last dimension correspond to
 each target mode.  We will therefore want to set up a matrix with shape 
 (``candidate_node_ids.size`` x 3 x ``target_shapes.size``).  This way, the
 target shape dimension is last, and the group of channels corresponding to each
@@ -1726,29 +1726,29 @@ look through all the functions to ensure they look right.
   :width: 600
   :alt: Drive points used for a MIMO modal test.
   
-Fitting Modes using PolyMax
----------------------------
+Fitting Modes using PolyPy
+--------------------------
 
 Now that we have frequency response functions created, we can fit modes to them.
 SDynPy has two mode fitters implemented,
-:py:class:`PolyMax<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>` and 
+:py:class:`PolyPy<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>` and 
 :py:class:`SMAC<sdynpy.modal.sdynpy_smac.SMAC_GUI>`.  Both curve fitters can
 be used via graphical user interface or via Python commands if it is desirable
 to automate the curve fitting.  This example will use the
-:py:class:`sdpy.PolyMax_GUI<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>` approach.
+:py:class:`sdpy.PolyPy_GUI<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>` approach.
 
-Running PolyMax
+Running PolyPy
 ~~~~~~~~~~~~~~~
 
-We open the PolyMax GUI by initializing the
-:py:class:`sdpy.PolyMax_GUI<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>` class
+We open the PolyPy GUI by initializing the
+:py:class:`sdpy.PolyPy_GUI<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>` class
 with our frequency response function dataset ``frf_sampled``
 
 .. code-block:: python
 
     # Now that we have FRFs we can go fit modes.  We will first look at using
-    # PolyMax
-    pm = sdpy.PolyMax_GUI(frf_sampled)
+    # PolyPy
+    pm = sdpy.PolyPy_GUI(frf_sampled)
     
 The initial screen shows mode indicator functions, as well as options for
 computing the initial stabilization diagram.  We can see from the shown
@@ -1757,25 +1757,25 @@ modes.  We can drag the frequency region on the figure to select the frequency
 range of interest, set the polynomial orders, and press the button to compute
 the stabilization curve.
 
-.. image:: figures/airplane_polymax_stabilization.png
+.. image:: figures/airplane_polypy_stabilization.png
   :width: 600
-  :alt: Setting up parameters to compute the stabilization plot in PolyMax
+  :alt: Setting up parameters to compute the stabilization plot in PolyPy
 
 Once the stabilization plot is computed, stable poles can be selected by
 clicking on them in the stabilization plot.  Once all poles are selected,
 shapes can be computed.
 
-.. image:: figures/airplane_polymax_stabilization_selection.png
+.. image:: figures/airplane_polypy_stabilization_selection.png
   :width: 600
-  :alt: Selecting poles on the stabilization plot in PolyMax
+  :alt: Selecting poles on the stabilization plot in PolyPy
 
-The final tab of the PolyMax implementation allows you to see how well the
+The final tab of the PolyPy implementation allows you to see how well the
 modes fit to the measured frequency response data.  On this page, modes can be
 saved to a file.
 
-.. image:: figures/airplane_polymax_stabilization_resynthesis.png
+.. image:: figures/airplane_polypy_stabilization_resynthesis.png
   :width: 600
-  :alt: Visualizing the modal fits in PolyMax
+  :alt: Visualizing the modal fits in PolyPy
 
 Comparing Test and Finite Element Modes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -1789,15 +1789,15 @@ function.
 
 .. code-block:: python
 
-    # In the PolyMax GUI we saved the shapes to disk, so we will now load them.
-    test_shapes_polymax = sdpy.shape.load('shapes_polymax.npy')
+    # In the PolyPy GUI we saved the shapes to disk, so we will now load them.
+    test_shapes_polypy = sdpy.shape.load('shapes_polypy.npy')
     
     # Let's compare the shapes to the finite element model shapes
-    mac = sdpy.shape.mac(test_shapes,test_shapes_polymax)
+    mac = sdpy.shape.mac(test_shapes,test_shapes_polypy)
     sdpy.correlation.matrix_plot(
         mac,text_size=6)
     shape_correspondences = np.where(mac > 0.9)
-    shape_1 = test_shapes_polymax[shape_correspondences[1]]
+    shape_1 = test_shapes_polypy[shape_correspondences[1]]
     shape_2 = test_shapes[shape_correspondences[0]]
     print(sdpy.shape.shape_comparison_table(shape_1, shape_2,
                                             percent_error_format='{:0.4f}%'))
@@ -1901,14 +1901,14 @@ and global finite element geometry are handled automatically by SDynPy.
 
     # Perform the expansion using the finite element shapes in the bandwidth
     expansion_basis = shapes_global[shapes_global.frequency < shape_bandwidth]
-    expanded_shapes = test_shapes_polymax.expand(test_geometry,geometry_global,
+    expanded_shapes = test_shapes_polypy.expand(test_geometry,geometry_global,
                                                  expansion_basis)
     # We can then plot the expanded shapes on the original finite element geometry
     geometry_global.plot_shape(expanded_shapes,plot_options)
     
     # Or overlay the geometries and shapes
     expansion_comparison_geometry,expansion_comparison_shapes = sdpy.shape.overlay_shapes(
-        (test_geometry,geometry_global),(test_shapes_polymax,expanded_shapes),[1,7])
+        (test_geometry,geometry_global),(test_shapes_polypy,expanded_shapes),[1,7])
     expansion_comparison_geometry.plot_shape(expansion_comparison_shapes,plot_options,
                                              deformed_opacity=0.5,undeformed_opacity=0)
                                              
diff --git a/_sources/example_problems/rattlesnake_demonstration.rst.txt b/_sources/example_problems/rattlesnake_demonstration.rst.txt
index d037f6d..32933f8 100644
--- a/_sources/example_problems/rattlesnake_demonstration.rst.txt
+++ b/_sources/example_problems/rattlesnake_demonstration.rst.txt
@@ -445,7 +445,7 @@ Fitting Modes to the FRFs
 -------------------------
 
 Now that we have data and geometry, we can perform experimental modal analysis.
-We will fit modes both with SMAC and PolyMax.  First we will do SMAC, so we can
+We will fit modes both with SMAC and PolyPy.  First we will do SMAC, so we can
 call :py:class:`sdpy.SMAC_GUI<sdynpy.modal.sdynpy_smac.SMAC_GUI>` in the
 IPython console to load the GUI.
 
@@ -508,39 +508,39 @@ The MAC can also be plotted to visualize the independence of the shapes.
 Shapes are saved to the file :code:`'rattlesnake_test_shapes_smac.npy'` so they
 can be loaded into the script without re-running SMAC.
 
-Similarly, PolyMax can be run by calling the :py:class:`sdpy.PolyMax_GUI<sdynpy.modal.sdynpy_polymax.PolyMax_GUI>`
+Similarly, PolyPy can be run by calling the :py:class:`sdpy.PolyPy_GUI<sdynpy.modal.sdynpy_polypy.PolyPy_GUI>`
 class from the IPython console.
 
 .. code-block:: console
 
-    In [13]: sdynpy.PolyMax_GUI(frfs)
-    Out[13]: <sdynpy.modal.sdynpy_polymax.PolyMax_GUI at 0x14fff4c5d30>
+    In [13]: sdynpy.PolyPy_GUI(frfs)
+    Out[13]: <sdynpy.modal.sdynpy_polypy.PolyPy_GUI at 0x14fff4c5d30>
     
-The first page of the SDynPy PolyMax implementation allows users to select the
+The first page of the SDynPy PolyPy implementation allows users to select the
 frequency range of the analysis.  Also selected is the number of poles to use
 in the polynomial fitter.
 
-.. image:: figures/sdynpy_demo_polymax_setup.png
+.. image:: figures/sdynpy_demo_polypy_setup.png
   :width: 600
-  :alt: Initial setup for PolyMax frequency range
+  :alt: Initial setup for PolyPy frequency range
   
 Once the poles are solved, a stability plot is presented.  The user can click
 on individual icons on the stability plot to select given poles to use in the
 resynthesis.
 
-.. image:: figures/sdynpy_demo_polymax_stability_plot.png
+.. image:: figures/sdynpy_demo_polypy_stability_plot.png
   :width: 600
-  :alt: PolyMax stability plot
+  :alt: PolyPy stability plot
   
 With the poles selected, shapes can be computed.  Resynthesized FRFs are plotted
 for the user to investigate.
 
-.. image:: figures/sdynpy_demo_polymax_resynthesize.png
+.. image:: figures/sdynpy_demo_polypy_resynthesize.png
   :width: 600
-  :alt: PolyMax resynthesis
+  :alt: PolyPy resynthesis
   
-Shapes are saved to the file :code:`'rattlesnake_test_shapes_polymax.npy'` so they
-can be loaded into the script without re-running PolyMax.
+Shapes are saved to the file :code:`'rattlesnake_test_shapes_polypy.npy'` so they
+can be loaded into the script without re-running PolyPy.
 
 Analyzing Modal Parameters
 --------------------------
@@ -553,7 +553,7 @@ shape files into our script for analysis using the
 
     # Load the shape files from our curve fitting into the script
     test_shapes_smac = sdpy.shape.load('rattlesnake_test_shapes_smac.npy')
-    test_shapes_polymax = sdpy.shape.load('rattlesnake_test_shapes_smac.npy')
+    test_shapes_polypy = sdpy.shape.load('rattlesnake_test_shapes_smac.npy')
     
 We also want to extract finite element shapes from the Exodus file using the
 :py:func:`sdpy.shape.from_exodus<sdynpy.core.sdynpy_shape.ShapeArray.from_exodus>`
@@ -584,7 +584,7 @@ finite element shapes reduced to the test degrees of freedom.
     response_dofs = test_shapes_smac[0].coordinate
     # Can extract a shape matrix by indexing the shape arrays with a CoordinateArray
     test_shape_matrix_smac = test_shapes_smac[response_dofs].T
-    test_shape_matrix_polymax = test_shapes_smac[response_dofs].T
+    test_shape_matrix_polypy = test_shapes_smac[response_dofs].T
     test_shape_matrix_fem = fem_shapes[response_dofs].T
     # Create an actual ShapeArray object
     fem_shapes_reduced = sdpy.shape_array(response_dofs,
@@ -601,11 +601,11 @@ function that provides a nice visualization of the Modal Assurance Criterion mat
 .. code-block:: python
 
     # Look at correlation between shapes from different tests/analyses
-    mac_smac_polymax = sdpy.correlation.mac(test_shape_matrix_smac,test_shape_matrix_polymax)
+    mac_smac_polypy = sdpy.correlation.mac(test_shape_matrix_smac,test_shape_matrix_polypy)
     mac_smac_fem = sdpy.correlation.mac(test_shape_matrix_smac,test_shape_matrix_fem)
-    mac_polymax_fem = sdpy.correlation.mac(test_shape_matrix_polymax,test_shape_matrix_fem)
+    mac_polypy_fem = sdpy.correlation.mac(test_shape_matrix_polypy,test_shape_matrix_fem)
     # Plot the mac matrix between the 
-    sdpy.correlation.matrix_plot(mac_polymax_fem)
+    sdpy.correlation.matrix_plot(mac_polypy_fem)
 
 .. image:: figures/sdynpy_demo_mac_test_fem.png
   :width: 600
@@ -625,11 +625,11 @@ of the first mode to a :code:`.gif` file.
 
     # Compare shapes by overlaying them
     # Find the best match in the FEM to each of the test shapes
-    fem_matches = np.argmax(mac_polymax_fem,axis=1)
+    fem_matches = np.argmax(mac_polypy_fem,axis=1)
     # Overlay the shapes and geometry
     combined_geometry,combined_shapes = sdpy.shape.overlay_shapes(
         (test_geometry,test_geometry),
-        (test_shapes_polymax,fem_shapes_reduced[fem_matches]),
+        (test_shapes_polypy,fem_shapes_reduced[fem_matches]),
         color_override=[1,11])
     # Plot the shapes to set up the animation
     geometry_kwargs = {'view_up':[0,1,0],'line_width':5,'node_size':8}
@@ -679,7 +679,7 @@ slide that does not have a subtitle placeholder on it.
                                   title='BARC Modal with Rattlesnake',
                                   subtitle='A SDynPy Demonstration',
                                   geometry = test_geometry,
-                                  shapes = test_shapes_polymax,
+                                  shapes = test_shapes_polypy,
                                   frfs=frfs,
                                   geometry_plot_kwargs = geometry_kwargs,
                                   content_slide_layout_index=2,
@@ -724,7 +724,7 @@ to allow animation of the shapes inside the exported PDF.
     sdpy.doc.create_latex_summary(
         figure_basename='figures/rattlesnake_test.png', 
         geometry = test_geometry, 
-        shapes = test_shapes_polymax, 
+        shapes = test_shapes_polypy, 
         frfs = frfs,
         output_file = 'rattlesnake_test_memo_content.tex',
         geometry_plot_kwargs = geometry_kwargs)
@@ -798,7 +798,7 @@ shapes should be included in the expansion.
 
 .. code-block:: python
 
-    q = np.linalg.lstsq(test_shape_matrix_fem[:,:14],test_shape_matrix_polymax)[0]
+    q = np.linalg.lstsq(test_shape_matrix_fem[:,:14],test_shape_matrix_polypy)[0]
 
 We then want to multiply this coefficient by the full finite element shapes to
 estimate what the test shapes would look like in the full finite element space.
@@ -824,7 +824,7 @@ us to fill that information in with the test frequencies.
     # Linearly combine the existing shapes into new shapes
     fexo_repack = fexo.repack(q)
     # Fill in the abscissa data (frequencies in this case)
-    fexo_repack.time = test_shapes_polymax.frequency
+    fexo_repack.time = test_shapes_polypy.frequency
     
 If we want to plot the new shapes in SDynPy, we could load the shapes back into
 a :py:class:`sdpy.ShapeArray<sdynpy.core.sdynpy_shape.ShapeArray>` object, and
@@ -834,11 +834,11 @@ even combine with the finite element shapes for comparison.
 
     # Plot expanded shapes against finite element shapes
     # Load shapes into sdynpy ShapeArray object
-    expanded_shapes_polymax = sdpy.shape.from_exodus(fexo_repack)
+    expanded_shapes_polypy = sdpy.shape.from_exodus(fexo_repack)
     # combine the shapes
     combined_expanded_geometry,combined_expanded_shapes = sdpy.shape.overlay_shapes(
         (fem_geometry,fem_geometry),
-        (expanded_shapes_polymax,fem_shapes[fem_matches]),color_override=[1,11])
+        (expanded_shapes_polypy,fem_shapes[fem_matches]),color_override=[1,11])
     # Plot the shapes overlaid
     plotter = combined_expanded_geometry.plot_shape(
         combined_expanded_shapes,{'view_up':[0,1,0],'node_size':0},
@@ -855,11 +855,11 @@ to visualize in external software.
 .. code-block:: python
 
     # Create an ExodusInMemory object from sdynpy objects
-    fexo_out = sdpy.ExodusInMemory.from_sdynpy(test_geometry,test_shapes_polymax)
+    fexo_out = sdpy.ExodusInMemory.from_sdynpy(test_geometry,test_shapes_polypy)
     # Write it to a file
     fexo_out.write_to_file('rattlesnake_test_output.exo',clobber=True)
     # Repeat with the expanded data
-    fexo_out = sdpy.ExodusInMemory.from_sdynpy(fem_geometry,expanded_shapes_polymax)
+    fexo_out = sdpy.ExodusInMemory.from_sdynpy(fem_geometry,expanded_shapes_polypy)
     fexo_out.write_to_file('rattlesnake_test_output_expanded.exo',clobber=True)
 
 The Exodus files can then be loaded into an external software such as Paraview
diff --git a/_sources/modal_tutorials.rst.txt b/_sources/modal_tutorials.rst.txt
index 49c2d85..820fdec 100644
--- a/_sources/modal_tutorials.rst.txt
+++ b/_sources/modal_tutorials.rst.txt
@@ -9,4 +9,5 @@ Modal Tutorials
    modal_tutorials/Modal_02_Basics_of_Vibrations/Modal_02_Basics_of_Vibrations
    modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations
    modal_tutorials/Modal_04_Modal_Analysis/Modal_04_Modal_Analysis
-   modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis
\ No newline at end of file
+   modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis
+   modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes
\ No newline at end of file
diff --git a/_sources/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb.txt b/_sources/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb.txt
new file mode 100644
index 0000000..5af974c
--- /dev/null
+++ b/_sources/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb.txt
@@ -0,0 +1,3842 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cc4417ad-c73f-4426-8e76-c7a69ae33dd5",
+   "metadata": {},
+   "source": [
+    "# Modal Tutorial 06: Complex Modes\n",
+    "Complex modes can be unintuitive and difficult to understand compared to real normal modes.  George Fox Lang wrote an article in Sound and Vibration magazine called \"Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints.\"  He states:\n",
+    "\n",
+    "> Complex Modes is one of those topics that every vibration practitioner understands -- just not fully.\n",
+    "\n",
+    "The author of this document agrees with Lang; in fact, his own lack of knowledge of complex modes is what caused him to write this document, to teach himself what is going on!\n",
+    "\n",
+    "To better understand complex modes, we will start from the differential equations of motion and develop the theory of complex modes, discussing:\n",
+    "\n",
+    "  - Real Modes and their Limitations\n",
+    "  - The state space transformation to derive the complex mode eigenvalue problem\n",
+    "  - Computing natural frequency, damping, and complex mode shapes\n",
+    "  - Computing FRFs from the modal parameters\n",
+    "\n",
+    "This document will largely follow the equations from the following sources:\n",
+    "\n",
+    "  - [An engineering interpretation of the complex modal mass in\n",
+    "structural dynamics by Lopez and Brinckner](https://doi.org/10.1016/j.ymssp.2023.110621)\n",
+    "  - [Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints by Lang](http://www.sandv.com/downloads/1211lang.pdf)\n",
+    "  - [The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation? by Peeters, et al.](https://content.iospress.com/articles/shock-and-vibration/sav00272)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fee8375-b72c-474f-bdac-a2ddd842070c",
+   "metadata": {},
+   "source": [
+    "## The Second-Order, Linear Differential System of Equations\n",
+    "Most vibration engineers are familiar with the standard system of second-order, linear differential equations.\n",
+    "\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "where $\\mathbf{M}$ is the system mass matrix, $\\mathbf{C}$ is the system damping matrix, $\\mathbf{K}$ is the system stiffness matrix, $\\mathbf{F}$ is the forcing applied to the system, and $\\mathbf{x}$ is the system displacement response.  $\\dot{\\mathbf{x}}$ and $\\ddot{\\mathbf{x}}$ represent the first and second derivatives of the response, velocity and acceleration, respectively.\n",
+    "\n",
+    "For a standard linear, time-invariant (LTI) system, the mass, stiffness matrices are not functions of time (i.e. the mass of the system doesn't change).  However, the forces and responses are generally a function of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24f927e0-5131-4f23-91a1-7c42a9ec8a37",
+   "metadata": {},
+   "source": [
+    "## Real Modes -- Damping is Hard\n",
+    "\n",
+    "Many times, when we are just starting out, we will simply ignore the damping in our part because it is \"hard\" or it doesn't fit with our elegant mathematical tools.\n",
+    "\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "To try to understand the free vibration response of the this system, we can make the following assumptions:\n",
+    "\n",
+    "  - $\\mathbf{F} = \\mathbf{0}$ -- There is no force on the system.\n",
+    "  - $\\mathbf{x} = \\mathbf{\\phi}\\cos(\\omega t)$ -- The response of the system looks like some shape $\\mathbf{\\phi}$ that will vibrate over time (represented mathematically by multiplying by a cosine function), and the vibration will have a frequency of $\\omega$ radians per second.\n",
+    "\n",
+    "Using the chain rule for differentiation, it follows that $\\ddot{\\mathbf{x}} = -\\omega^2\\mathbf{\\phi}\\cos(\\omega t)$\n",
+    "\n",
+    "We can substitute these equations into the previous equation to arrive at an equation in the form of the well-known [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem).\n",
+    "\n",
+    "First we make the substitutions.\n",
+    "\n",
+    "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}\\cos(\\omega t)) + \\mathbf{K}\\mathbf{\\phi}\\cos(\\omega t) = \\mathbf{0}$$\n",
+    "\n",
+    "We can then recognize that the term $\\cos(\\omega t))$ is in general nonzero so can be cancelled.\n",
+    "\n",
+    "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}) + \\mathbf{K}\\mathbf{\\phi} = \\mathbf{0}$$\n",
+    "\n",
+    "We can then add $\\omega^2\\mathbf{M}\\mathbf{\\phi}$ to both sides of the equation.\n",
+    "\n",
+    "$$\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "\n",
+    "This is now the same form of the [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem), which is $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae951b07-3074-4634-8c80-ab60435a9630",
+   "metadata": {},
+   "source": [
+    "Here it can help to further rearrange the equation by premultiplying by the inverse of the mass matrix $\\mathbf{M}^{-1}$.  Note that this is generally acceptable to do, because the mass matrix should generally be positive definite and therefore invertable (said another way, every degree of freedom in the system has mass associated with it).\n",
+    "\n",
+    "$$\\mathbf{M}^{-1}\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{\\phi}$$\n",
+    "\n",
+    "### Geometric Interpretation of the Eigenvalues and Eigenvectors\n",
+    "\n",
+    "We can gain intuition here by thinking about what the eigenvalue problem means *geometrically*.  We have a vector $\\mathbf{\\phi}$ that represents some shape.  Looking at the right side of the equation, if we multiply that vector by a scalar $\\omega^2$, it will simply scale the length of the vector, but not change its direction in which it's pointing.  In the context of a vibration shape, this means that it will scale the shape larger or smaller, but it won't change the shape of vibration.  You get a bigger or smaller vibration with the same shape.\n",
+    "\n",
+    "Now because of the equality, the left-hand side of the equation must be equivalent to the right-hand side of the equation, which means that the left-hand side must also be equivalent to simply scaling the original vector $\\mathbf{\\phi}$. However, we know from linear algebra that multiplying a vector by a matrix will in general change the length of the vector *and* where the vector is pointing.  In the context of our vibration shape $\\mathbf{\\phi}$, this means that multiplying a vibration shape by a matrix will in general change not only the size of the vibration shape, but the shape itself.\n",
+    "\n",
+    "Let's look at a brief example to illustrate this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e58c8729-2539-49d2-a7c7-5913080653e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scalar:\n",
+      "0.5507979025745755\n",
+      "Matrix:\n",
+      "[[0.70814782 0.29090474]\n",
+      " [0.51082761 0.89294695]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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4YKb7lS9fXt7e3vr+++/T1E+ZMiVLxylYsGC6Rulajhw54uq2lqTY2FjNnj1btWrVuu5zGsLDw1WiRAm99dZbWrt2bZZ/SUvVvXt3JSUlqV+/fjp58mSa5x94e3urU6dOWrBgQZqZxVJdPSVpp06ddOrUKb333nvp9kvtnQgICJCUvsEuU6aMatWqpVmzZqXZtm3bNq1YsSLdl/+NmDFjRpopP6dOnarLly8rPDw8zX5du3aVw+HQM888o3379mXpesbHx2vdunUZbvvmm28kXRkekZXr5O3tLYfDkebX2wMHDmTpSere3t7peoM+++wzHT58+LqvBWAN2qb0aJtuvm3KSIsWLTR69Gi999571/w3zGpbkpr0ZedzlZkXX3xRBw8e1KxZszRhwgRVqFBBERERSkhIuOn3Rubo6bGxiIiI6+4TFBSkzp07a/LkyXI4HKpUqZKWLl3q6nK9nrp162rVqlWaMGGCypYtq4oVK7qeK5CRKlWqqHfv3tq4caNKlSqlmTNn6vjx41n6Zd/Hx0ePPfaY3nvvPXl7e6tr165ZijFVs2bNVK5cOS1evFj+/v6uaTpTvfnmm/ruu+/UsGFD9enTR9WrV9eZM2f066+/atWqVa4pmnv06KHZs2dr0KBB2rBhg+655x7FxcVp1apVevrpp9W+fXv5+/urevXq+uSTT1SlShUVK1ZMNWrUUI0aNTRu3DiFh4erUaNG6t27ty5evKjJkycrKCgozfMSblRiYqLuu+8+Pfroo9q1a5emTJmiu+++Ww899FCa/YKDg9WmTRt99tlnKlKkSJYe2hYfH6/GjRvrrrvuUps2bRQSEqJz585p0aJF+uGHH9ShQwfVrl07y9epbdu2mjBhgtq0aaNu3brpxIkTev/991W5cmX99ttv14ylXbt2GjVqlHr16qXGjRtr69atmjt3rsLCwm784gFwO9qmtGibbr5tyoiXl5deeeWV6+6X1bakUqVKKlKkiKZNm6bChQurYMGCatiwYbbvbfr22281ZcoUDR8+3DWFdmRkpJo3b65XX3013zzkO1+yYso45LyrpwW9ln9OC2oY5hSWnTp1MgICAoyiRYsa/fr1M7Zt25alaUF37txpNG3a1PD39zckuaYIzWxa0LZt2xrLly83atasaTidTuO2224zPvvsszTvmdlUpYZhGBs2bDAkGffff//1L0oGhgwZYkgyHn300Qy3Hz9+3BgwYIAREhJi+Pj4GKVLlzbuu+8+Y8aMGWn2i4+PN15++WWjYsWKrv0eeeQRY+/eva59/ve//xl169Y1fH19000RumrVKqNJkyaGv7+/ERgYaDz44IPG9u3b0xwj9XqfPHkyS+eWes3Xrl1r9O3b1yhatKhRqFAho3v37mmmIL3ap59+akgy+vbtm6VjJCUlGR988IHRoUMHo3z58obT6TQCAgKM2rVrG+PGjTMSEhKyfZ0++ugj49Zbb3V9HiIjIzP8rGU0ZfXgwYONMmXKGP7+/kaTJk2MdevWpZseNPXz9M/PGQD3o23KGtqmtLLbNhlG2imrM5PZlNVZaUsMwzAWL15sVK9e3ShQoECaz2GzZs2M22+/PcNjXv0+sbGxRvny5Y06deoYSUlJafZ77rnnDC8vL2PdunVZPmdkj8MwrnO3MJBDKlSooBo1amjp0qU3/B5btmxRrVq1NHv27GzP5oL0Fi9erA4dOuj777/XPffcY3U4AJDraJvyHtomuAP39CBf+eCDD1SoUKF03f+4MR988IHCwsJ09913Wx0KAORbtE05i7YJ7sA9PcgXvvzyS23fvl0zZszQwIEDb3oWGU83f/58/fbbb/rqq6/0zjvv5OisNADgKWibchZtE9yJ4W3INTczhKBChQo6fvy4WrdurTlz5qhw4cJuiNBzOBwOFSpUSF26dNG0adNu6GF2AGAHtE15B20T3MmtSc/UqVM1depU19N2b7/9dg0bNizd9IQAAAAA4C5uTXq+/PJLeXt769Zbb5VhGJo1a5bGjRunzZs3Z/nBXQAAAABwM3J9eFuxYsU0btw49e7dOzcPCwAAAMBD5dpgyeTkZH322WeKi4tTo0aNMtwnISEhzdNoU1JSdObMGRUvXpyb2QAgFxmGofPnz6ts2bLy8vLsiT5pmwAgb7iptsndDwL67bffjIIFCxre3t5GUFCQ8dVXX2W6b+oDr1hYWFhY8sZy6NAhdzcTeR5tEwsLC0veWm6kbXL78LbExEQdPHhQMTEx+vzzz/Xhhx9q7dq1ql69erp9//lrWkxMjEJDQ3Xo0CEFBga6M0wAwFViY2MVEhKic+fOKSgoyOpwLEXbBAB5w820Tbl+T0/Lli1VqVIlTZ8+/br7xsbGKigoSDExMTQsAJCL+P7NHNcGAKxxM9+/uT5QOyUlJc0vZgAAAADgTm6dyOCll15SeHi4QkNDdf78ec2bN09r1qzR8uXL3XlYAAAAAHBxa9Jz4sQJ9ejRQ0ePHlVQUJBq1qyp5cuXq1WrVu48LAAAAAC4uDXp+eijj9z59gAAAMgFycnJSkpKsjoM2JyPj4+8vb3d8t659pweAAAA5C+GYejYsWM6d+6c1aHAQxQpUkSlS5fO8eegkfQAAAAgQ6kJT8mSJRUQEMADeeE2hmEoPj5eJ06ckCSVKVMmR9+fpAcAAADpJCcnuxKe4sWLWx0OPIC/v78kc16AkiVL5uhQt1yfshoAAAB5X+o9PAEBARZHAk+S+nnL6XvISHoAAACQKYa0ITe56/NG0gMAAADA1kh6AAAAgKscOHBADodD0dHRWX5NVFSUihQpYnkcyBhJDwAAALLF4ci95UYdOnRITz75pMqWLStfX1+VL19ezzzzjE6fPn3d14aEhOjo0aOqUaNGlo/XpUsX/fHHHzce8A2444471L9//wy3zZkzR06nU6dOnbqpY7gjmbMCSQ8AAABsZd++fapXr552796t//73v9qzZ4+mTZum1atXq1GjRjpz5kymr01MTJS3t7dKly6tAgWyPtGxv7+/SpYsmRPhZ1nv3r01f/58Xbx4Md22yMhIPfTQQypRokSuxpSZ5ORkpaSkWHZ8kh4AAADYyoABA+Tr66sVK1aoWbNmCg0NVXh4uFatWqXDhw/r5Zdfdu1boUIFjR49Wj169FBgYKD69u2b4bCyJUuW6NZbb5Wfn59atGihWbNmyeFwuB7c+s8ekREjRqhWrVqaM2eOKlSooKCgID322GM6f/68a59ly5bp7rvvVpEiRVS8eHG1a9dOe/fuzfJ5Pv7447p48aIWLFiQpn7//v1as2aNevfuLUlavHix6tSpIz8/P4WFhWnkyJG6fPmya/9z586pX79+KlWqlPz8/FSjRg0tXbpUa9asUa9evRQTEyOHwyGHw6ERI0ZIks6ePasePXqoaNGiCggIUHh4uHbv3u16z9TrsWTJElWvXl1Op1MHDx7M8rnlNJIeAAAA2MaZM2e0fPlyPf30067nvqQqXbq0unfvrk8++USGYbjqx48frzvvvFObN2/Wq6++mu499+/fr0ceeUQdOnTQli1b1K9fvzSJU2b27t2rRYsWaenSpVq6dKnWrl2rN99807U9Li5OgwYN0i+//KLVq1fLy8tLDz/8cJZ7REqUKKH27dtr5syZaeqjoqJUrlw53X///frhhx/Uo0cPPfPMM9q+fbumT5+uqKgovf7665KklJQUhYeH66efftLHH3+s7du3680335S3t7caN26sSZMmKTAwUEePHtXRo0f1/PPPS5J69uypX375RUuWLNG6detkGIYeeOCBNFNNx8fH66233tKHH36o33//Pdd7wq7Gw0kBAABgG7t375ZhGKpWrVqG26tVq6azZ8/q5MmTrj/C7733Xg0ePNi1z4EDB9K8Zvr06apatarGjRsnSapataq2bdvmShwyk5KSoqioKBUuXFiS9MQTT2j16tWu13Xq1CnN/jNnzlRwcLC2b9+e5fuJevfurfDwcO3fv18VK1aUYRiaNWuWIiIi5OXlpZEjR2ro0KGKiIiQJIWFhWn06NF64YUXNHz4cK1atUobNmzQjh07VKVKFdc+qYKCguRwOFS6dGlX3e7du7VkyRL99NNPaty4sSRp7ty5CgkJ0aJFi9S5c2dJ5rN2pkyZojvvvDNL5+JO9PQAAADAdq7uybmeevXqXXP7rl27VL9+/TR1DRo0uO77VqhQwZXwSFKZMmV04sQJ1/ru3bvVtWtXhYWFKTAwUBUqVJCkbA0Da9WqlcqVK6fIyEhJ0urVq3Xw4EH16tVLkrRlyxaNGjVKhQoVci19+vTR0aNHFR8fr+joaJUrV86V8GTFjh07VKBAATVs2NBVV7x4cVWtWlU7duxw1fn6+qpmzZpZfl93IukBAACAbVSuXFkOhyPNH99X27Fjh4oWLarg4GBXXcGCBd0Si4+PT5p1h8ORZujagw8+qDNnzuiDDz7Q+vXrtX79eknmZApZ5eXlpZ49e2rWrFlKSUlRZGSkWrRo4eqtuXDhgkaOHKno6GjXsnXrVu3evVt+fn7phgDmJH9//zzzcFuSHgAAANhG8eLF1apVK02ZMiXdrGbHjh3T3Llz1aVLl2z9MV61alX98ssvaeo2btx4U3GePn1au3bt0iuvvKL77rvPNezuRvTq1UuHDh3SwoUL9cUXX7gmMJCkOnXqaNeuXapcuXK6xcvLSzVr1tRff/2V6XTbvr6+Sk5OTlNXrVo1Xb582ZWkXX0+1atXv6FzcDeSHgAAANjKe++9p4SEBLVu3Vrff/+9Dh06pGXLlqlVq1a65ZZbrnsvzj/169dPO3fu1Isvvqg//vhDn376qaKioiTphnsyihYtquLFi2vGjBnas2ePvv32Ww0aNOiG3qtixYq699571bdvXzmdTnXs2NG1bdiwYZo9e7ZGjhyp33//XTt27ND8+fP1yiuvSJKaNWumpk2bqlOnTlq5cqX279+vb775RsuWLZNkDtG7cOGCVq9erVOnTik+Pl633nqr2rdvrz59+ujHH3/Uli1b9Pjjj+uWW25R+/btb+gc3I2kBwAAALZy66236pdfflFYWJgeffRRVapUSX379lWLFi20bt06FStWLFvvV7FiRX3++edauHChatasqalTp7pmb3M6nTcUo5eXl+bPn69NmzapRo0aeu6551wTJdyI3r176+zZs+rWrZv8/Pxc9a1bt9bSpUu1YsUK1a9fX3fddZcmTpyo8uXLu/ZZsGCB6tevr65du6p69ep64YUXXL07jRs3Vv/+/dWlSxcFBwdr7NixksznANWtW1ft2rVTo0aNZBiGvv7663RD+vIKh5Gdu7xyWWxsrIKCghQTE6PAwECrwwEAj8H3b+a4NvAUly5dcs0IdvUf0TC9/vrrmjZtmg4dOmR1KLZyrc/dzXz/MmU1AAAAcB1TpkxR/fr1Vbx4cf30008aN26cBg4caHVYyCKSHgAAAOA6du/erddee01nzpxRaGioBg8erJdeesnqsJBFJD0AAADAdUycOFETJ060OgzcICYyAAAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAIBsWrNmjRwOh86dOydJioqKUpEiRdLsM2PGDIWEhMjLy0uTJk3SiBEjVKtWrZs67oEDB+RwOBQdHX1T7+NpSHoAAACQPQ5H7i03oGfPnnI4HOrfv3+6bQMGDJDD4VDPnj1v8iKk1aVLF/3xxx+u9djYWA0cOFAvvviiDh8+rL59++r555/X6tWrc/S4/3THHXdkeN6SNGfOHDmdTp06deqmjpFRgpfXkfQAAADAdkJCQjR//nxdvHjRVXfp0iXNmzdPoaGhOX48f39/lSxZ0rV+8OBBJSUlqW3btipTpowCAgJUqFAhFS9ePMePfbXevXunO+9UkZGReuihh1SiRAm3xpBVycnJSklJyZVjkfQAAADAdurUqaOQkBAtXLjQVbdw4UKFhoaqdu3aafZNSEjQv//9b5UsWVJ+fn66++67tXHjxjT7fP3116pSpYr8/f3VokULHThwIM32q3s/oqKidMcdd0iSwsLC5HA4dODAgQyHt3344YeqVq2a/Pz8dNttt2nKlClptm/YsEG1a9eWn5+f6tWrp82bN1/zvB9//HFdvHhRCxYsSFO/f/9+rVmzRr1795YkLV68WHXq1JGfn5/CwsI0cuRIXb582bX/uXPn1K9fP5UqVUp+fn6qUaOGli5dqjVr1qhXr16KiYmRw+GQw+HQiBEjJElnz55Vjx49VLRoUQUEBCg8PFy7d+9Od42WLFmi6tWry+l06uDBg9c8n5xC0gMAAABbevLJJxUZGelanzlzpnr16pVuvxdeeEELFizQrFmz9Ouvv6py5cpq3bq1zpw5I0k6dOiQOnbsqAcffFDR0dH617/+paFDh2Z63C5dumjVqlWSzKTl6NGjCgkJSbff3LlzNWzYML3++uvasWOH3njjDb366quaNWuWJOnChQtq166dqlevrk2bNmnEiBF6/vnnr3nOJUqUUPv27TVz5sw09VFRUSpXrpzuv/9+/fDDD+rRo4eeeeYZbd++XdOnT1dUVJRef/11SVJKSorCw8P1008/6eOPP9b27dv15ptvytvbW40bN9akSZMUGBioo0eP6ujRo66YevbsqV9++UVLlizRunXrZBiGHnjgASUlJbniiI+P11tvvaUPP/xQv//+e5reMbcy8rCYmBhDkhETE2N1KADgUfj+zRzXBp7i4sWLxvbt242LFy+m3yjl3nIDIiIijPbt2xsnTpwwnE6nceDAAePAgQOGn5+fcfLkSaN9+/ZGRESEYRiGceHCBcPHx8eYO3eu6/WJiYlG2bJljbFjxxqGYRgvvfSSUb169TTHePHFFw1JxtmzZw3DMIzIyEgjKCjItX3z5s2GJGP//v2uuuHDhxt33nmna71SpUrGvHnz0rzv6NGjjUaNGhmGYRjTp083ihcvnubfYOrUqYYkY/PmzZme/7JlywyHw2Hs27fPMAzDSElJMcqXL2+88sorhmEYxn333We88cYbaV4zZ84co0yZMoZhGMby5csNLy8vY9euXRm+/z/P1TAM448//jAkGT/99JOr7tSpU4a/v7/x6aeful4nyYiOjs409mt97m7m+7dA7qRWAAAAQO4KDg5W27ZtFRUVJcMw1LZt23T3s+zdu1dJSUlq0qSJq87Hx0cNGjTQjh07JEk7duxQw4YN07yuUaNGNxVbXFyc9u7dq969e6tPnz6u+suXLysoKMh13Jo1a8rPzy9bx23VqpXKlSunyMhIjRo1SqtXr9bBgwddvVxbtmzRTz/95OrZkcz7ay5duqT4+HhFR0erXLlyqlKlSpbPZ8eOHSpQoECa61S8eHFVrVrVdR0lydfXVzVr1szy++YUkh4AAADY1pNPPqmBAwdKkt5//32Lo7niwoULkqQPPvggXULl7e19U+/t5eWlnj17atasWRoxYoQiIyPVokULhYWFuY49cuRIdezYMd1r/fz85O/vf1PHvxZ/f385bnBWvpvBPT0AAACwrTZt2igxMVFJSUlq3bp1uu2VKlWSr6+vfvrpJ1ddUlKSNm7cqOrVq0uSqlWrpg0bNqR53c8//3xTcZUqVUply5bVvn37VLly5TRLxYoVXcf97bffdOnSpWwft1evXjp06JAWLlyoL774wjWBgWRO8rBr1650x61cubK8vLxUs2ZN/fXXX2mm4L6ar6+vkpOT09RVq1ZNly9f1vr16111p0+f1q5du1zX0UokPQAAALAtb29v7dixQ9u3b8+wB6VgwYJ66qmnNGTIEC1btkzbt29Xnz59FB8f70oU+vfvr927d2vIkCHatWuX5s2bp6ioqJuObeTIkRozZozeffdd/fHHH9q6dasiIyM1YcIESVK3bt3kcDjUp08fbd++XV9//bXGjx+fpfeuWLGi7r33XvXt21dOpzNNr86wYcM0e/ZsjRw5Ur///rt27Nih+fPn65VXXpEkNWvWTE2bNlWnTp20cuVK7d+/X998842WLVsmSapQoYIuXLig1atX69SpU4qPj9ett96q9u3bq0+fPvrxxx+1ZcsWPf7447rlllvUvn37m75WN4ukBwAAALYWGBiowMDATLe/+eab6tSpk5544gnVqVNHe/bs0fLly1W0aFFJUmhoqBYsWKBFixbpzjvv1LRp0/TGG2/cdFz/+te/9OGHHyoyMlJ33HGHmjVrpqioKFdPT6FChfTll19q69atql27tl5++WW99dZbWX7/3r176+zZs+rWrVua+4Jat26tpUuXasWKFapfv77uuusuTZw4UeXLl3fts2DBAtWvX19du3ZV9erV9cILL7h6dxo3bqz+/furS5cuCg4O1tixYyWZzwGqW7eu2rVrp0aNGskwDH399dfy8fG56Wt1sxyGYRhWB5GZ2NhYBQUFKSYm5pofVABAzuL7N3NcG3iKS5cuaf/+/apYsWKaP5g9yqVLkqeeu0Wu9bm7me9fenoAAACAf4qNlY4ftzoK5BCSHgAAAOBq589Le/bQy2MjJD0AAABAqgsXpN27pZQUKSDA6miQQ0h6AAAAAEmKj7+S8EiSG59Xg9xF0gMAAABcvCj98YeU+vwZX1+pQAFrY0KOIekBAABAplJSez3s7NIlM+G5fPlKHUPbLOGuzxvpKwAAANLx9fWVl5eXjhw5ouDgYPn6+srhcFgdVs5LTJT275eSktLW+/iYyRByhWEYSkxM1MmTJ+Xl5SVfX98cfX+SHgAAAKTj5eWlihUr6ujRozpy5IjV4bjH5cvmtNRX9/CkcjjMe3yQqwICAhQaGiovr5wdkEbSAwAAgAz5+voqNDRUly9fVnLqvS52ceqU9MQTZi9PRlaskEJDczcmD+ft7a0CBQq4pUeRpAcAAACZcjgc8vHxkY+Pj9Wh5Kxjx6R//csc3jZwYNpencKFpcqVpRzubYB1+JcEAACA56lXT+re3UxsUhOesmXN/9asScJjM/xrAgAAwDMlJUkjR5plX1/pxx+lpk2lWrUsDQs5j+FtAAAA8EyRkVfu6enbV6pYUZo7V9qwwdq4kONIegAAAOB5Ll2SRo82y35+0n/+Y5bLlTMX2Ipbh7eNGTNG9evXV+HChVWyZEl16NBBu3btcuchAQAAgOv74APpr7/M8oABUpky1sYDt3Jr0rN27VoNGDBAP//8s1auXKmkpCTdf//9iouLc+dhAQAAgMzFx0uvv26WCxaUXnzR2njgdm4d3rZs2bI061FRUSpZsqQ2bdqkpk2buvPQAAAAQMamTDEfSipJzzwjBQdbGw/cLlfv6YmJiZEkFStWLMPtCQkJSkhIcK3HxsbmSlwAAGSGtgmwmfPnpTffNMuBgdLgwdbGg1yRa1NWp6Sk6Nlnn1WTJk1Uo0aNDPcZM2aMgoKCXEtISEhuhQfgJhw5Il2+bHUUgHvQNgH508GDmWx4913p9GmzPHiwlMmP8bAXh2EYRm4c6KmnntI333yjH3/8UeUymREjo1/TQkJCFBMTo8DAwNwIE0A2nT8vdeworVxpdSTISbGxsQoKCuL7V7RNQH4UHS1NnSpNn/6PDefOmdNSnztnJjv795u9PcgXbqZtypXhbQMHDtTSpUv1/fffZ5rwSJLT6ZTT6cyNkADkgKQk6ZFHJEb7wM5om4D85eBB6YEHpN69M9g4YYKZ8EjSCy+Q8HgQtw5vMwxDAwcO1BdffKFvv/1WFStWdOfhAOQiw5D695dWrJAqVbI6GgAAzHzmgQeko0czaJtOnZImTjTLJUtKAwfmdniwkFt7egYMGKB58+Zp8eLFKly4sI4dOyZJCgoKkr+/vzsPDcDNRo+WZs40y5UrWxsLAAAJCeZw699/N9fTtU3jxkkXLpjll14yp6qGx3BrT8/UqVMVExOj5s2bq0yZMq7lk08+cedhAbhZVJQ0fPiVdXp6AABWMgxzONt3312pS9M2HTsmTZ5slm+5xRyqAI/i1p6eXJojAUAuWrFC6tMnbR09PQAAK738sjR37pX1gACpdOmrdhgzRrp48crOfn65Gh+sl2tTVgPI/7ZsMScu+Of01PT0AACsMn26mdNcrVIlyeH4e+XQIWnaNLNcvnwmMxzA7kh6AGTJoUPmzaHnz6etL1hQKlXKmpgAAJ7tq6+kp59OX59mBMLrr0uJiWZ52DDJ1zdXYkPeQtID4LpSZ8M5ciT9tjS/pgEAkEt++UV69FEpJSX9NtcIhP37pY8+MsuVK0s9euRafMhbSHoAXJNhSG+9ZT7DrUmT9NsZ2gYAyG3nz0uvvSY1aCBVq5Z+u6ttGjXqypjsESOkArnyiErkQSQ9AK7J4TDHSq9da04FmsrHx/wvkxgAAHJb4cLSokXmbG3ly1+pT81pKleW9Mcf0uzZZkX16tJjj+V2mMhDSHoAZElSkjRpklkuVMhsaAoWpKcHAGCdrVulZcvM8r33SlOnmuVKlWT27KSOfRs1SvL2tiJE5BH08QHIkk8+MSczkKR+/cyhbnPmSEFB1sYFAPBc48dfKb/wgtS6tflw0pCYbdL8+eaGWrWkhx+2JD7kHSQ9AK7LMMwHWUvm0IFnnjHLDz+c8Q2kAAC4219/SfPmmeWaNaX77zfLb78teXUebjZekjR6tOTF4CZPxycAwHWtXCn99ptZ7tpVCgm5so12BABghXfeuTJHwfPPX5lJ1Cv6V2nhQnOlYUOpbVtrAkSewp8rAK4rtZdHMhsWAACsFBNjPpRUksqV+8ccBcOGXSmPHs1zFSCJpAfAdfz6q7RqlVlu3docQgAAgJWmT7/ysOxnn70yo6jWrTOfWCpJ99wjtWxpRXjIg0h6AFzT1TeJDhliXRwAAEhSYqI5tE2SAgOlPn2u2nh1L89rr9HLAxeSHgCZ+vNP6dNPzXLt2uZ0oAAAWGnePOnIEbPcv7+Z+EgyHyiXOjShVSupaVNL4kPeRNIDIFMTJ0rJyWZ5yBB+MAMAWMswroxA8PG5MpuoDEN69dUrO44eneuxIW8j6QGQobNnpQ8/NMvly0udO1sbDwAA33xjPodHkh5/XCpb9u8NK1dKP/xgltu1M2dtA65C0gMgQ1OnSnFxZnnQIPP5PAAAWCnD2UT/2cszalSuxoT8gaQHQDqXLknvvmuWixaVnnzS2ngAAPjlF2nNGrPctq1UvfrfG5YulTZsMMudOpk3oQL/QNIDIJ2PP5aOHzfLTz8tFSpkbTwAAFzdy+OaTTQl5Uovj8MhjRyZ63EhfyDpAZBGSsqVm0SdTun//s/aeAAA2LdP+vxzs1y//lUTsy1cKG3ZYpa7dpVuv92S+JD3kfQASOPLL6Vdu8xyjx5SqVLWxgMAwIQJ5o9y0lWziSYnX3kuj5eXNHy4ZfEh7yPpAZBG6vABh0MaPNjaWAAAOHVKmjnTLIeFSR07/r1h/nxpxw6zHBEhValiSXzIH0h6ALisWyf99JNZfughqWpVa+MBAGDKFOniRbM8aJDk7S3p8mVpxAiz0sfnSo8PkAmSHgAuGd4kCgCARS5elN57zywXLy716vX3htmzpT17zHLv3lKFClaEh3yEpAeAJOmPP6RFi8xy48ZSkyaWhgMAgGbNkk6eNMsDB0oBAZISE688i8fplF5+2bL4kH+Q9ACQZN4kahhmmV4eAIDVkpOlt982y35+0oABf2/46CPpzz/Ncv/+UrlylsSH/IWkB4BOnJCiosxylSrm/TwAAFhp0aIrI9h69ZKCg2WOd3vtNbMyIEAaOtSq8JDPkPQA0HvvSQkJZnnwYHPmTwAArGIYaWcTHTTo7w3TpklHjpjlgQOl0qUtiQ/5D3/aAB4uLk56/32zXLKk+WweAACs9OOP0vr1ZrljR6lyZUkXLkhvvmlWFi4svfCCZfEh/yHpATzczJnSmTNm+f/+zxw3DQCAlTKcTfS998zx2JL07LPmdG5AFpH0AB7s8mVzAgPJHBr91FPWxgMAwI4d0pdfmuV77pEaNpQUEyONHWtWFily1Xg3IGtIegAPtmCBdOCAWe7dmx/NAADWS52xTbqql2fSJOns2SuVRYrkclTI70h6AA919U2i3t78aAYAsN7Ro9KcOWa5WjWpbVuZY7BThyWUKCH9+9+WxYf8i6QH8FBr1kibNpnlzp15mDUAwHqTJ5vPHpWk55//ezbR8eOl2FizcuhQqVAhy+JD/kXSA3ioDG8SBQDAIufPS1OmmOUyZaTu3WVOXPDuu2Zl6dLcfIobRtIDeKCtW6VvvjHL994r1aljbTwAAHz4oTlfgWSOYHM6Jb31lvlsBUl6+WVz1h3gBpD0AB5o/PgrZXp5AABWS0qSJk40y4UKSf37y3wIaWrXT0iI1KePZfEh/yPpATzMX39J8+aZ5TvukFq3tjYeAAA+/VQ6dMgs9+nz9+Rsb7whXbpkVr766t9dP8CNIekBPMw775jP55HMm0QdDmvjAQB4tn/OJvrss5L+/FOaMcOsDAuTeva0KDrYRQGrAwCQe2JipOnTzfItt0iPPWZtPAAArFolbdlilh97TAoNldTnNXPMmyQNHy75+FgWH+yBnh7Ag8yYYc6OI5m/pPn6WhoOAADpZxPds0eKjDQrbrvt72ncgJtDTw/gIRITzaFtkhQYKPXta208AABER0srV5rl+++X7rxTUo9RUnKyWTlihDnmDbhJ9PQAHuK//5UOHzbL/fubiQ8AAFZK18uzY4f08cdmxR13mE/PBnIAPT2AB7j6JlEfH+mZZ6yNBwCAP/+UPvnELNeqJd13n6THRpiNliSNGiV58fs8cgafJMADfPON9PvvZrl7d6lsWWvjAQBg0qQro9iGDJEcv20x566WpLp1pfbtLYsN9kNPD+ABrh4+8Pzz1sUBAIAknT0rffCBWQ4N/XsU2yPDruwwejTPVECOoqcHsLlffpHWrDHLDzwg3X67peEAAKBp06S4OLP83HOST/RGackSs6JxY6lNG+uCgy3R0wPYXLqbRAEAsFBCgvTuu2a5SBHpX/+S9MirV3aglwduQE8PYGP79kmff26W69WTmjWzNh4AAD7+WDp2zCw/9ZRUKPpHaflys6JFC+nee60LDrZFTw9gYxMnSikpZvmFF/jhDABgrZSUKyMQfH2lf/9bUtd/9PIAbkBPD2BTp09LM2ea5bAwqWNHa+MBAGDpUmnXLrPco4dUevu3V248bdNGatLEsthgb/T0ADY1ZYoUH2+WBw3igdYAAOtdfZ/p4EGG1PuVKxX08sCN6OkBbOjiRem9d1PUTl+qeHGpVy+rIwIAeLqff5Z+/NEsP/SQdNuBZdK6dWZF+/bmzaeAm9DTA9jQrFnS86deVJwKqs6ABxUQYHVEAABPl2Y20ecN6dmrenlGjcr9gOBR3NrT8/333+vBBx9U2bJl5XA4tGjRInceDoDMp1sff/U9DdF4HfSppIEDrY4IAODpdu+WvvjCLN91l9Tk5CLp11/NikcflWrWtCw2eAa3Jj1xcXG688479f7777vzMACusuHlxXr11L8lSbe1razgYIsDAgB4vAkTJMMwy0MGp8gxfJi54uUljRhhWVzwHG4d3hYeHq7w8HB3HgLAVYyf16v2uK7yktmyPPJiJYsjAgB4uhMnpKgos1y5stQh8VNp2zazont3qVo1y2KD52AiA8Au9uzR5Tbt5JdyUZIUX6CwwhrSzQMAsNZ770mXLpnl55+9LK9RI8wVb29p+HCLooKnyVMTGSQkJCghIcG1Hhsba2E0QD5y8qQUHi6fmFOuKiOsEk8jBXIAbRNw4+LipNS7HIKDpV6+c688qOfJJ6VKjEhA7shTPT1jxoxRUFCQawkJCbE6JCDvu3jRnPtzz5401QVrVrYoIMBeaJuAGxcZKZ05Y5afeTpJvmNGmiu+vtIrr2T+QiCH5amk56WXXlJMTIxrOXTokNUhAXlbcrI5Hvrnn9Nv49czIEfQNgE35vJlcwIDSQoIkJ4JjJT27zcr+vaVQkOtCw4eJ08Nb3M6nXI6nVaHAeQPhiENGnRlDtB/IukBcgRtE3BjFi68KsfpcUmFJo42V/z8pP/8x7rA4JHc2tNz4cIFRUdHKzo6WpK0f/9+RUdH6+DBg+48LOAZTpwwp8H54APtC22efntlhrcBAKxhGFceRurlJb1S6gPpr7/MigEDpDJlrAsOHslhGKmzpue8NWvWqEWLFunqIyIiFJU6d+E1xMbGKigoSDExMQoMDHRDhED+d/7oBV2+JVRFjbM66VVSJYIdchw/Lv35J0MHcMP4/s0c1wa4vjVrpNQ/AR/vGK85P4VJx49LBQua3T88RA434Ga+f906vK158+ZyY04FQNKmpz9Sc+OsJOn3ls+q+bCmUni4VK6cxZEBADxVai+PJL1RboqZ8EjSM8+Q8MASeeqeHgDZk3Txsip9OVGSdEEFVWtaf6liUWnBAnM8AQAAuWzbNunrr81y+N3nFTL3TXMlMFAaPNi6wODR+KsIyMc2DPlMIcl/SpI21e6jIhWLmhtatbIwKgCAJxs//kp5Uti70unT5srgwVKxYtYEBY9H0gPkU0aKoeIzx0qSLstbld971tqAAAAe7/Bhad48s9yo2jnduuTvDKhYMenZZy2LCyDpAfKpzeNX67aL0ZKk9RW66JbG5a0NCADg8d55R0pKMstTb50gx7lz5soLL5jD2wCLkPQA+VTK2Ct3iZZ4c4iFkQAAIMXGStOnm+UapU+p5rfmPacqWVIaONC6wACR9AD50s5Ptqje6RWSpF+LtVTVLrWsDQgA4PFmzDATH0n68LZxcly4YK689JI5VTVgIZIeIB86NfTKXaLGkBcsjAQAACkxUZo0ySxXKnhMDdZPNlduuUXq39+yuIBUJD1APnP450NqeGC+JGmnfy3VeaGlxREBADzd/PnmJAaSFFl1jBwXL5orL78s+flZFxjwN5IeIJ/ZPWCSfHRZknS65/NyeDksjggA4MkM48rDSCt4H9Ld26aZK+XLS717WxcYcBWSHiAfifnznOr+OkOSdNg7RA3GP2pxRAAAT7dsmflAUkn6qNLrciQmmivDhkm+vtYFBlyFpAfIRzb3m6bCMm8M3dPuOfkE+FgcEQDA07l6ebRfLfZ9ZK5Uriz16GFdUMA/kPQA+URCbIKqrXxHkhSjINWd+i+LIwIAeLpNm6TvvjPLM24ZJcdlc/i1Ro6UChSwLjDgH0h6gHxi/TNzVSrlmCRpc6OnVKhMYYsjAgB4utRenlv1h1oenW2uVK8udeliXVBABkh6gHwg5XKKys4zp6lOkK+qTfm3xREBADzd/v3SZ5+Z5XeLjpAjJcVcGTVK8va2LjAgAyQ9QD6wcdTXqpy4Q5K0ocoTKlWrjMURAQA83cSJUkqKdLu2qfU581EKqlVLevhhS+MCMkLSA+QDznfGucpl3x5sYSQAAEinT0sf/T1nwbiA4XIYhrkyerTkxZ+XyHv4VAJ53LaP1qtW7PeSpPWlHlSldtUsjggA4OmmTJHi46Xa+lXh8QvNyoYNpbZtrQ0MyARJD5DHnR9+pZfH75UhFkYCAIB08aI0ebJZHuMz7MqG0aMlBw/MRt5E0gPkYX+u3qOGh81f0LYVaqiaT99tcUQAAE83e7Z08qR0l9apddJXZuU990gtW1obGHANJD1AHvbnMxPkJXOc9IX+Q+Tw4hc0AIB1kpOlt982y695XdXL89pr9PIgTyPpAfKoUztOqv7vkZKkAz6VVf/1DtYGBADweEuWSLt3S021VvelrDIrW7WSmja1NjDgOkh6gDxqa//35a9LkqSDnQbJ25dnHgAArGU+jNTQaL16pXL0aKvCAbKMpAfIg+JPxavmD+9Jkk46glX//Z7WBgQA8Hg//SStWye10ko11Q9mZbt25qxtQB5H0gPkQRsHRKm4cVqS9HvzgfIv5m9xRAAATzd2rJSul2fUKKvCAbKFpAfIY5ITk1VhoXmXaLz8dcfUpy2OCADg6XbuNO/naaelaqgNZmWnTlLt2tYGBmQRSQ+Qx2wYulDlL++TJG2840kVr1rC4ogAAJ7u7bclh1Ku9PI4HNLIkdYGBWQDSQ+QhxgphgJnmA8jTZaXKr47yOKIAACe7tgx89k8HbVQtbTFrOzaVbr9dmsDA7KBpAfIQ7ZM/l63x22UJG0o10mhzcMsjggA4OkmT5YuJyZrlP5+Lo+XlzR8uLVBAdlE0gPkIYlvjHOVg14bYmEkAABIFy5IU6dKj2m+qmuHWRkRIVWpYm1gQDaR9AB5xJ4l29XgxFeSpM1Fmqt6RH2LIwIAeLqPPpJiz17WCI0wK3x8pGHDLI0JuBEkPUAecXTweFf58rP08gAArJWUJE2YIPXQbN2qPWZl795ShQqWxgXcCJIeIA84uumIGu75WJK023m76r0abnFEAABP99ln0tGDiRqmv5/F43RKL79sbVDADSLpAfKAXU+/I18lSZKOP/68HF4OiyMCAHgyw5DGjZN66yNV0J9mZf/+Urly1gYG3KACVgcAeLrYv2JVe8M0SdJRr7JqMKmbxREBADzd6tXSzuiLWqrXzIqAAGnoUGuDAm4CPT2AxX596gMFKVaStKv1M/It5GtxRAAATzdunNRf03SLjpgVAwdKpUtbGxRwE+jpASyUFJ+kKt9MkiTFqrBqT+tnbUAAAI+3ZYv004oLmq03zYrChaUXXrA2KOAm0dMDWGj9oPkqm/yXJGlzvb4KCg2yOCIAgKcbP14aqPdUSifMimeflYoXtzQm4GbR0wNYxEgxVHKW+TDSJBVQlSnPWhsQAMDjHTwoff3fGO3WWEmSUaSIHIMGWRwVcPPo6QEssmnMClW5tFWStD6sm8rUZ0YcAIC1Jk2SBiZPUjGdlSQ5hgyRihSxNCYgJ9DTA1jE6+2xrnLJsc9bGAkAANK5c9LnM85oqyZIkowSJeT497+tDQrIIfT0ABbYMfdX1Tn7rSRpY4k2qtLpDosjAgB4umnTpP5x410zijqGDpUKFbI4KiBn0NMDWODsf8a5ygWGDrEwEgAApIQE6eMJJ/Sz3pUkpZQqLa+nnrI4KiDn0NMD5LK/fjygBgc/kyTt8K+jWs+1sDgiAICnmztX6nXyLRVSnCTJ65WXzQeSAjZBTw+Qy/YOnKhySpYknf3XEDm8HBZHBADwZCkp0qwxR7RMUyRJl8uGqECfPhZHBeQsenqAXHRmzxnV2/KhJOlQgQpqMPYRiyMCAHi6r7+WOu95Q/66JEkqMOJVyem0OCogZ5H0ALloS/+pKqh4SdK+9oNUwI/OVgCAtWaP/lN9NUOSlBgSJvXsaW1AgBuQ9AC55NK5S6rxrXmD6BlHMdWb8qTFEQEAPN369VKrDa/JV0mSJN/Xhks+PhZHBeQ8kh4gl2wYOFvBxglJ0m9NnlbBkgUtjggA4OnmDN+jXoqUJMWH3iZ1725xRIB7kPQAuSDlcopCPn1bknRJTt0+daDFEQEAPN2ePVKD5aNU4O/JdfzfGiF5e1sbFOAmJD1ALtj46hJVTPpDkrThtggF1yhlcUQAAE8379UdelwfS5LOhd4hx6OdLY4IcB+SHiAX+L9vPow0RQ6FTBpscTQAAE938qRU/dMR8pIhSSo8cZTkxZ+FsC8+3YCbbZ3+P9U8/z9J0oYy7VWxdRWLIwIAeLrPX92iR1I+lSSdCK0r74fbWxwR4F4kPYCbxY0c5yoXHD7EwkgAAJDi46UKM4e51gMnjZYcPCgb9pYrSc/777+vChUqyM/PTw0bNtSGDRty47CA5fZ9s0sNji6WJP1WuInu6NfY4ogAAJ7u65EbFZ60RJJ0MKSx/Dq0sTgiwP3cnvR88sknGjRokIYPH65ff/1Vd955p1q3bq0TJ064+9CA5f4a9LZrvPTFgfTyAACslZwslZz8qms96B16eeAZ3J70TJgwQX369FGvXr1UvXp1TZs2TQEBAZo5c6a7Dw1Y6uS242qwc7YkaZ9vVdUf9aDFEQEAPN3aN35U04vLJUm7bmmhoIfvtTgiIHe4NelJTEzUpk2b1LJlyysH9PJSy5YttW7dOnceGrDc7/0ny08JkqS/ugyWVwFuoQMAWMcwpMBxV3p5Ck0YbWE0QO4q4M43P3XqlJKTk1WqVNpnkpQqVUo7d+5Mt39CQoISEhJc67Gxse4MD3Ar3zo1tHfjbSp8+awavPuE1eEAuEG0TbCLywnJOlevlc6tidb+knep9qNNrA4JyDV56qfnMWPGKCgoyLWEhIRYHRJwwxq/+5gqxv2uiyt/kl8RP6vDAXCDaJtgFz5+3mr57X/kc2i/yi6ZZnU4QK5ya9JTokQJeXt76/jx42nqjx8/rtKlS6fb/6WXXlJMTIxrOXTokDvDA9zOq4CXyt9byeowANwE2ibYTcFbiqhUg/JWhwHkKrcmPb6+vqpbt65Wr17tqktJSdHq1avVqFGjdPs7nU4FBgamWQAAsBJtEwDkf269p0eSBg0apIiICNWrV08NGjTQpEmTFBcXp169ern70AAAAADg/qSnS5cuOnnypIYNG6Zjx46pVq1aWrZsWbrJDQAAAADAHdye9EjSwIEDNXDgwNw4FAAAAACkkadmbwMAAACAnEbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGskPQAAAABsjaQHAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANgaSQ8AAAAAWyPpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArZH0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGskPQAAAABsjaQHAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANgaSQ8AAAAAWyPpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArZH0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGtuS3pef/11NW7cWAEBASpSpIi7DgMAAAAA1+S2pCcxMVGdO3fWU0895a5DAAAAAMB1FXDXG48cOVKSFBUV5a5DAAAAAMB1cU8PAAAAAFtzW0/PjUhISFBCQoJrPTY21sJoAACgbQIAO8hWT8/QoUPlcDiuuezcufOGgxkzZoyCgoJcS0hIyA2/FwAAOYG2CQDyP4dhGEZWdz558qROnz59zX3CwsLk6+vrWo+KitKzzz6rc+fOXff9M/o1LSQkRDExMQoMDMxqmACAmxQbG6ugoCC+f0XbBAB5xc20Tdka3hYcHKzg4OBsHSA7nE6nnE6n294fAIDsom0CgPzPbff0HDx4UGfOnNHBgweVnJys6OhoSVLlypVVqFAhdx0WAAAAANJwW9IzbNgwzZo1y7Veu3ZtSdJ3332n5s2bu+uwAAAAAJCG26asjoqKkmEY6RYSHgAAAAC5ief0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGskPQAAAABsjaQHAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANgaSQ8AAAAAWyPpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArZH0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGskPQAAAABsjaQHAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANgaSQ8AAAAAWyPpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArZH0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGtuS3oOHDig3r17q2LFivL391elSpU0fPhwJSYmuuuQAAAAAJBOAXe98c6dO5WSkqLp06ercuXK2rZtm/r06aO4uDiNHz/eXYcFAAAAgDTclvS0adNGbdq0ca2HhYVp165dmjp1KkkPAAAAgFzjtqQnIzExMSpWrFim2xMSEpSQkOBaj42NzY2wAADIFG0TAOR/uTaRwZ49ezR58mT169cv033GjBmjoKAg1xISEpJb4QEAkCHaJgDI/7Kd9AwdOlQOh+Oay86dO9O85vDhw2rTpo06d+6sPn36ZPreL730kmJiYlzLoUOHsn9GAADkINomAMj/sj28bfDgwerZs+c19wkLC3OVjxw5ohYtWqhx48aaMWPGNV/ndDrldDqzGxIAAG5D2wQA+V+2k57g4GAFBwdnad/Dhw+rRYsWqlu3riIjI+XlxWOBAAAAAOQut01kcPjwYTVv3lzly5fX+PHjdfLkSde20qVLu+uwAAAAAJCG25KelStXas+ePdqzZ4/KlSuXZpthGO46LAAAAACk4bbxZj179pRhGBkuAAAAAJBbuMkGAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANgaSQ8AAAAAWyPpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArZH0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGskPQAAAABsjaQHAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANgaSQ8AAAAAWyPpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArZH0AAAAALA1kh4AAAAAtkbSAwAAAMDWSHoAAAAA2BpJDwAAAABbI+kBAAAAYGskPQAAAABsjaQHAAAAgK2R9AAAAACwNZIeAAAAALZG0gMAAADA1kh6AAAAANiaW5Oehx56SKGhofLz81OZMmX0xBNP6MiRI+48JAAAAACk4dakp0WLFvr000+1a9cuLViwQHv37tUjjzzizkMCAAAAQBoF3Pnmzz33nKtcvnx5DR06VB06dFBSUpJ8fHzceWgAAAAAkOTmpOdqZ86c0dy5c9W4ceNME56EhAQlJCS41mNiYiRJsbGxuRIjAMCU+r1rGIbFkViPtgkA8oabapsMN3vhhReMgIAAQ5Jx1113GadOncp03+HDhxuSWFhYWFjyyLJ37153NxN5Hm0TCwsLS95abqRtchhG9lKloUOH6q233rrmPjt27NBtt90mSTp16pTOnDmjP//8UyNHjlRQUJCWLl0qh8OR7nX//DXt3LlzKl++vA4ePKigoKDshJnvxMbGKiQkRIcOHVJgYKDV4biVp5yrp5ynxLnaUUxMjEJDQ3X27FkVKVLE6nAsRdtk/8+7p5ynxLnalaec6820Tdke3jZ48GD17NnzmvuEhYW5yiVKlFCJEiVUpUoVVatWTSEhIfr555/VqFGjdK9zOp1yOp3p6oOCgmz9D3i1wMBAztVmPOU8Jc7Vjry8eLIBbZPnfN495TwlztWuPOVcb6RtynbSExwcrODg4GwfSJJSUlIkKc0vZgAAAADgTm6byGD9+vXauHGj7r77bhUtWlR79+7Vq6++qkqVKmXYywMAAAAA7uC2cQsBAQFauHCh7rvvPlWtWlW9e/dWzZo1tXbt2gyHCWTE6XRq+PDhWd4/P+Nc7cdTzlPiXO3IU87zRnjStfGUc/WU85Q4V7vylHO9mfPM9kQGAAAAAJCfcIcqAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACAreWrpOehhx5SaGio/Pz8VKZMGT3xxBM6cuSI1WHlqAMHDqh3796qWLGi/P39ValSJQ0fPlyJiYlWh+YWr7/+uho3bqyAgADbPfX9/fffV4UKFeTn56eGDRtqw4YNVoeU477//ns9+OCDKlu2rBwOhxYtWmR1SG4xZswY1a9fX4ULF1bJkiXVoUMH7dq1y+qw3GLq1KmqWbOm6wF3jRo10jfffGN1WHmWJ7RLEm2TndA22QdtU/bapnyV9LRo0UKffvqpdu3apQULFmjv3r165JFHrA4rR+3cuVMpKSmaPn26fv/9d02cOFHTpk3Tf/7zH6tDc4vExER17txZTz31lNWh5KhPPvlEgwYN0vDhw/Xrr7/qzjvvVOvWrXXixAmrQ8tRcXFxuvPOO/X+++9bHYpbrV27VgMGDNDPP/+slStXKikpSffff7/i4uKsDi3HlStXTm+++aY2bdqkX375Rffee6/at2+v33//3erQ8iRPaJck2ia7oG2yF9qmbLZNRj62ePFiw+FwGImJiVaH4lZjx441KlasaHUYbhUZGWkEBQVZHUaOadCggTFgwADXenJyslG2bFljzJgxFkblXpKML774wuowcsWJEycMScbatWutDiVXFC1a1Pjwww+tDiNf8JR2yTBom/Ij2iZ7o226tnzV03O1M2fOaO7cuWrcuLF8fHysDsetYmJiVKxYMavDQBYlJiZq06ZNatmypavOy8tLLVu21Lp16yyMDDklJiZGkmz//2VycrLmz5+vuLg4NWrUyOpw8jxPapck2qb8hrbJ/mibri3fJT0vvviiChYsqOLFi+vgwYNavHix1SG51Z49ezR58mT169fP6lCQRadOnVJycrJKlSqVpr5UqVI6duyYRVEhp6SkpOjZZ59VkyZNVKNGDavDcYutW7eqUKFCcjqd6t+/v7744gtVr17d6rDyLE9rlyTapvyItsneaJuuz/KkZ+jQoXI4HNdcdu7c6dp/yJAh2rx5s1asWCFvb2/16NFDhmFYeAZZk93zlKTDhw+rTZs26ty5s/r06WNR5Nl3I+cK5BcDBgzQtm3bNH/+fKtDcZuqVasqOjpa69ev11NPPaWIiAht377d6rByjae0SxJtE20T7IK26fochsXfzCdPntTp06evuU9YWJh8fX3T1f/1118KCQnR//73vzw/9CK753nkyBE1b95cd911l6KiouTlZXl+mmU38m8aFRWlZ599VufOnXNzdO6XmJiogIAAff755+rQoYOrPiIiQufOnbPtr8AOh0NffPFFmnO2m4EDB2rx4sX6/vvvVbFiRavDyTUtW7ZUpUqVNH36dKtDyRWe0i5JtE3/RNtkP7RN9pXdtqmAm+O5ruDgYAUHB9/Qa1NSUiRJCQkJORmSW2TnPA8fPqwWLVqobt26ioyMzFeNinRz/6Z24Ovrq7p162r16tWuL9mUlBStXr1aAwcOtDY43BDDMPR///d/+uKLL7RmzRqPalQk8/ObH75nc4qntEsSbZMnoW2yH9qm7LVNlic9WbV+/Xpt3LhRd999t4oWLaq9e/fq1VdfVaVKlfLFr2lZdfjwYTVv3lzly5fX+PHjdfLkSde20qVLWxiZexw8eFBnzpzRwYMHlZycrOjoaElS5cqVVahQIWuDuwmDBg1SRESE6tWrpwYNGmjSpEmKi4tTr169rA4tR124cEF79uxxre/fv1/R0dEqVqyYQkNDLYwsZw0YMEDz5s3T4sWLVbhwYdf496CgIPn7+1scXc566aWXFB4ertDQUJ0/f17z5s3TmjVrtHz5cqtDy3M8pV2SaJtom/IX2ibapgy5ZQ45N/jtt9+MFi1aGMWKFTOcTqdRoUIFo3///sZff/1ldWg5KjIy0pCU4WJHERERGZ7rd999Z3VoN23y5MlGaGio4evrazRo0MD4+eefrQ4px3333XcZ/vtFRERYHVqOyuz/ycjISKtDy3FPPvmkUb58ecPX19cIDg427rvvPmPFihVWh5UneUq7ZBi0TbRN+QttU6TVoeW4nGibLL+nBwAAAADcKX8NyAUAAACAbCLpAQAAAGBrJD0AAAAAbI2kBwAAAICtkfQAAAAAsDWSHgAAAAC2RtIDAAAAwNZIegAAAADYGkkPAAAAAFsj6QEAAABgayQ9AAAAAGyNpAcAAACArf0/C3VNdU7E98sAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set random seed so we can get repeatable results for the documentation.\n",
+    "# Remove this line to allow the matrices and vectors to be random.\n",
+    "np.random.seed(3)\n",
+    "\n",
+    "# Create a vector to test\n",
+    "vector = np.array([[1],\n",
+    "                   [2]])\n",
+    "\n",
+    "# Now create a random scalar\n",
+    "scalar = np.random.random()\n",
+    "print('Scalar:')\n",
+    "print(scalar)\n",
+    "\n",
+    "# Now create a random matrix\n",
+    "matrix = np.random.random((2,2))\n",
+    "print('Matrix:')\n",
+    "print(matrix)\n",
+    "\n",
+    "# Now multiply the vector by the scalar, and multiply the vector by the matrix\n",
+    "vector_scalar = scalar*vector\n",
+    "vector_matrix = matrix@vector # Matrix multiplication is @ in numpy.\n",
+    "\n",
+    "# Now plot them\n",
+    "fig, ax = plt.subplots(1,2,figsize=(10,5), sharex=True,sharey=True)\n",
+    "ax[0].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[0].quiver(*vector_scalar, color='red', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[0].set_title('Multiply Vector by Scalar')\n",
+    "ax[1].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[1].quiver(*vector_matrix, color='red', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[1].legend(['Original Vector','Modified Vector'])\n",
+    "ax[1].set_title('Multiply Vector by Matrix')\n",
+    "ax[0].set_ylim([-3,3])\n",
+    "ax[0].set_xlim([-3,3]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c7ea4b-d78c-45ca-b5f9-29ecf27aa4ed",
+   "metadata": {},
+   "source": [
+    "Clearly, multiplying by a scalar has resulted in a vector with the same direction and a different length, whereas multiplying by a matrix has changed not only the length, but also the direction of the vector.\n",
+    "\n",
+    "The resulting direction of the vector will not only be dependent on the matrix that it is multiplied by, but also the original vector itself.  Multiplying different vectors by the same matrix will generally result in a different final direction of the resulting vectors.  We can investigate this by multiplying many different vectors by the matrix from the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "45bc6338-9cbb-46cd-a241-093ef02801bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we make a 20x20 grid of 2x1 vectors that we can multiply by our matrix\n",
+    "vectors = np.mgrid[-3:3:20j,-3:3:20j][np.newaxis].transpose()\n",
+    "\n",
+    "scaled_vectors = matrix @ vectors\n",
+    "\n",
+    "# Reshape for easier plotting\n",
+    "vectors_for_plotting = vectors.reshape(-1,2).transpose()\n",
+    "scaled_vectors_for_plotting = scaled_vectors.reshape(-1,2).transpose()\n",
+    "fig, ax = plt.subplots(figsize=(10,10))\n",
+    "ax.quiver(*vectors_for_plotting, *vectors_for_plotting, color='blue', angles='xy', scale_units='xy', scale=10)\n",
+    "ax.quiver(*vectors_for_plotting, *scaled_vectors_for_plotting, color='red', angles='xy', scale_units='xy', scale=10)\n",
+    "ax.legend(['Original Vector','Multiplied Vector']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4844ae-ad85-46cd-b741-2321b59604c9",
+   "metadata": {},
+   "source": [
+    "Clearly, we can see that in general, vectors in different directions will be modified by the matrix differently.  However, looking closely at the above plot, we can see that there are vectors in particular directions that maintain their original direction after multiplication by the matrix (a change in length is acceptable).  And if you recall, we are looking for exactly such a vector that when multiplied by a matrix retains its direction.  Therefore, in order to  solve the eigenvalue equation listed previously, we need to find these special directions.  We can't just use any shape $\\mathbf{\\phi}$.  We have to find particular shapes $\\mathbf{\\phi}$ that when multiplied by the system matrices $\\mathbf{M}^{-1}\\mathbf{K}$ do not change shape but perhaps get bigger or smaller.  Mathematically, we call these vectors the *eigenvectors* where the prefix *eigen* is German for *particular*.  In structural dynamics, we call these particular shapes the *mode shapes*, and they have many special properties.\n",
+    "\n",
+    "Similarly, we call the scalar quantity $\\omega^2$ that gets multiplied by the eigenvector the *eigenvalue*.  It is the particular value that goes along with the particular vector.  We will generally have as many eigenvector/eigenvalue pairs as we have degrees of freedom in the system.\n",
+    "\n",
+    "We note that there are linear algebra tools available to solve for the eigenvalues and eigenvectors of a given matrix.  For example, in Numpy, we can use `np.linalg.eig`.  We can then plot the eigenvector directions on the above figure to show that they indeed align with the directions where the vectors are scaled but the direction does not change.  Note also that there are two degrees of freedom in this problem, so we find two eigenvectors, or two directions where the direction of the vectors do not change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "92c994f7-31b6-4014-ade5-8ce28df278a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenvalues, eigenvectors = np.linalg.eig(matrix)\n",
+    "# Plot the positive and negative eigenvectors so it looks like a line through the origin\n",
+    "# Also reshape them so they are easier to plot\n",
+    "# Multiply by 4 so they take up the whole plot\n",
+    "eigenvectors_to_plot = 4*np.array((eigenvectors,-eigenvectors)).transpose(1,0,2)\n",
+    "ax.plot(*eigenvectors_to_plot,'k--')\n",
+    "ax.set_ylim([-3,3])\n",
+    "ax.set_xlim([-3,3])\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4b233b-ab96-49cf-afe7-21d8ac40ac72",
+   "metadata": {},
+   "source": [
+    "We see that vectors that are along the directions represented by the eigenvectors do not change direction, only change scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83279b35-f120-4a31-88f8-3d6087c90379",
+   "metadata": {},
+   "source": [
+    "Without approximation or loss of generality, we can take *all* of the eigenvectors or mode shapes of a system as column vectors and *stack* them side-by-side to form a matrix.  This matrix forms a coordinate transformation: in the same way we can transform between a local and global coordinate system, we can also transform between *physical* and *modal* degrees of freedom.  Physical degrees of freedom are generally represented by some physical quantity (e.g., Point 1 moves 2.5 centimeters in the vertical direction).  Modal degrees of freedom are generally represented by some *modal degree of freedom* or *modal quantity*.  Motions are then represented by how much of each mode shape is present in the motion (e.g., the system moves with a shape that looks like mode shape 1 scaled up by a factor of 2, plus mode shape 2 scaled down by a factor of 1/2).  We can represent this mathematically by saying physical motions $\\mathbf{x}$ are represented by a linear combination of shapes $\\mathbf{\\phi}$.  We call the coefficient matrix $\\mathbf{q}$ the *modal coefficients* or *modal degrees of freedom*, because they describe how much of each mode shape in $\\mathbf{\\phi}$ is present in $\\mathbf{x}$.\n",
+    "$$\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a4baa0-4a8d-44fc-84a8-14e0885710e3",
+   "metadata": {},
+   "source": [
+    "We can substitute this transformation into our original undamped equations of motion to get equations of motion in terms of the modal degrees of freedom $\\mathbf{q}$.\n",
+    "$$\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{F}$$\n",
+    "\n",
+    "We note that the mode shapes $\\mathbf{\\phi}$ are invariant in time, (they are a function of the matrices $\\mathbf{M}$ and $\\mathbf{K}$, which are also invariant in time).  Therefore taking the derivative of the physical response $\\mathbf{x}$ is simply\n",
+    "$$\\ddot{\\mathbf{x}} = \\mathbf{\\phi}\\ddot{\\mathbf{q}}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c218db3-a489-4015-861e-492ef63345eb",
+   "metadata": {},
+   "source": [
+    "While the transformed equation above is technically valid, we generally do not like this form, because it breaks the symmetry of the mass and stiffness matrices.  For all real structures, the mass, stiffness, and damping matrices should be symmetric.  This is a function of Netwton's third law of equal and opposite reactions: if there is a force developing at degree of freedom 1 due to motion of degree of freedom 2, an equal and opposite force will develop on degree of freedom 2 due to degree of freedom 1 pushing back on it.  Currently, however, in our transformed equations of motion, the new effective mass matrix $\\mathbf{M}\\mathbf{\\phi}$ is not symmetric, nor is the equivalent effective stiffness matrix.  We therefore premultiply the equations of motion by $\\mathbf{\\phi}^T$.\n",
+    "\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "In this final format, the effective mass and stiffness matrices $\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$ and $\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$ are symmetric.  We will often make a substitution\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "$$\\tilde{\\mathbf{K}}=\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$$\n",
+    "where $\\tilde{\\mathbf{M}}$ and $\\tilde{\\mathbf{K}}$ are referred to as the *modal mass* and *modal stiffness* matrices.  The modal system of equations is then\n",
+    "\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "The symmetry of the system matrices is not the only interesting property when we transform into modal coordinates.  It turns out that the mode shapes $\\mathbf{\\phi}$ *diagonalize* the mass and stiffness matrices.  The modal mass and stiffness matrices, formed by pre- and post-multiplying the mass and stiffness matrices by the mode shapes, are diagonal.  This physically means that there is no coupling between modal degrees of freedom; the response of mode 1 does not depend at all on the response of mode 2.  They can be treated as entirely separate equations.  Compare this to a physical mass and stiffness matrix, where there would be coupling between the degrees of freedom.\n",
+    "\n",
+    "We can think about this concept physically.  In the physical domain, if I pluck the end of a beam, all of the degrees of freedom on the beam will eventually vibrate.  This is because the physical degrees of freedom are all connected to one another through the mass and stiffness of a beam.  In the modal domain, however, if I pluck \"mode 1\" (an admittedly hard thing to accomplish in practice, as you would need to deform the beam into its first mode shape and then let it go instantaneously), none of the other modes will be excited at all, because there is no coupling between them.  Note that this still means that the entire beam will vibrate, because the mode shape of the beam contains all the beam degrees of freedom, so if the mode shape vibrates, the whole beam vibrates.  In fact because the modal representation described above is exact, plucking the end of a beam will result in exactly the same beam motion regardless of whether it is represented by modal or physical degrees of freedom.  The modal transformation is simply a different way to represent a dynamic system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0db175db-4400-49c1-915c-f81bbede2855",
+   "metadata": {},
+   "source": [
+    "### An Infinite Number of Mode Shapes\n",
+    "One last concept to describe before we move on is the concept of mode shape scaling.  From the previous figure, we saw that each eigenvector of a matrix mostly represented a particular direction, rather than a specific vector.  Any vector in that direction would be only scaled by the matrix instead of having its direction modified.  Therefore, eigenvectors are only really unique up to a scale factor.  An eigenvector times a scalar is still a valid eigenvector of a matrix, so you could argue there are an infinite number of eigenvectors of any matrix.\n",
+    "\n",
+    "Rather than allowing for any arbitrary scaling of eigenvectors, we will often scale them to some preferred normalization.  One might, for example, scale an eigenvector such that it has a length of 1, i.e. make it a unit vector.  Another might, for example, scale a specific degree of freedom to 1.  The most common scaling on an eigenvector, particularly in the context of structural dynamics where an eigenvector represents a mode shape, is to scale the eigenvectors such that the modal mass matrix $\\tilde{\\mathbf{M}}$ is equal to the identity matrix.  This is known as *mass normalization*.  We have already described how the modal mass matrix is diagonal.  To mass-normalize a mode shape, we only need to scale the mode shape such that the diagonal entries obtained when computing the modal mass matrix end up being 1.  To do this for the $r$th mode, we can simply compute the modal mass matrix with a single unscaled mode shape $\\tilde{\\mathbf{\\phi}}_r$, then divide the mode shapes by the square-root of the diagonal entry of the modal mass matrix corresponding to that mode shape.\n",
+    "\n",
+    "$$\\mathbf{\\phi}_r = \\frac{\\tilde{\\mathbf{\\phi}}_r}{\\sqrt{{\\tilde{\\mathbf{\\phi}}_r}^T\\mathbf{M}\\tilde{\\mathbf{\\phi}}_r}}$$\n",
+    "\n",
+    "When we compute the modal stiffness using mass normalized modes, the entries on the diagonal of the modal stiffness matrix will be equal to the natural frequency squared.  For example, for the $r$th mode shape:\n",
+    "\n",
+    "$${\\mathbf{\\phi}_r}^T\\mathbf{K}\\mathbf{\\phi}_r = {\\omega_r}^2$$\n",
+    "\n",
+    "### Units with Scaled Mode Shapes\n",
+    "Note that when we mass-normalize the mode shapes, the mode shapes will have a unit of $\\frac{1}{\\sqrt{m}}$.  The mass matrix has dimensions of $m$ where $m$ is the unit of mass, and to equal the dimensionless identity matrix when the modal mass matrix is assembled, the mode shape units from pre- and post-multiplying must cancel out the units of the mass matrix.  The modal coefficient $\\mathbf{q}$ must then have units of ${l}{\\sqrt{m}}$ where $l$ is a length dimension, and one must recall that taking a derivative over time adds a $\\frac{1}{t}$ where $t$ is the unit representing time.\n",
+    "\n",
+    "As an example, we will assume we have a meter/kilogram/newton/second unit system.  The modal transformation equation must have consistent units.\n",
+    "$$ \\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$\n",
+    "$$ \\left[m\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right]$$\n",
+    "\n",
+    "Similarly, the equations of motion must have consistent units.\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{N}{m}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[N\\right]$$\n",
+    "\n",
+    "We can substitute $\\left[N\\right] = \\left[\\frac{kg{\\,}m}{s^2}\\right]$\n",
+    "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{m{\\,}s^2}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{s^2}\\right]$$\n",
+    "\n",
+    "Then with a good deal of cancelling units\n",
+    "$$\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{m\\sqrt{kg}}{s^2}\\right] = \\left[\\frac{m\\sqrt{kg}}{s^2}\\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd03536b-95a6-405b-8249-153b7887f208",
+   "metadata": {},
+   "source": [
+    "### Adding Damping\n",
+    "Eventually, we will need to consider the damping values that we simply disregarded previously.  If we bring back our original damped system of equations, and transform to modal space, we end up with the following equations of motion:\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "Here the matrix $\\tilde{\\mathbf{C}} = \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$ is referred to as the modal damping matrix.\n",
+    "\n",
+    "The important consideration that we must make to determine if the mode shapes that we have computed are still valid mode shapes is to understand if the modal damping matrix is also diagonalized by the mode shape matrix.  If it is, then our mode shapes are still valid: they represent independent, uncoupled degrees of freedom in our system.  However if the modal damping matrix is not diagonal, that means there is coupling between the degrees of freedom, and the degrees of freedom represented by the equation are not actually modal degrees of freedom.\n",
+    "\n",
+    "The mode shapes can diagonalize the damping matrix, but only in a very limited case.  Since the mode shapes only decouple the mass and stiffness matrices from which they were computed, the damping matrix will only be decoupled completely if the physical damping matrix is a linear combination of the physical mass and stiffness matrices.\n",
+    "\n",
+    "$$\\mathbf{C} = \\alpha\\mathbf{M} + \\beta\\mathbf{K}$$\n",
+    "\n",
+    "This is known as *proportional damping*, where the damping is proportional to the mass and stiffness of the system.  Transforming to modal coordinates will diagonalize this matrix.  If we do have proportional damping, and we use mass normalized modes for the modal transformation, the modal damping matrix will be diagonal, and the entry corresponding to the $r$th mode will be equal to\n",
+    "\n",
+    "$$\\tilde{\\mathbf{C}}_{rr} = {\\mathbf{\\phi}_r}^T\\mathbf{C}\\mathbf{\\phi}_r = 2\\zeta_r\\omega_r = \\alpha+\\beta\\omega_r^2$$\n",
+    "\n",
+    "where $\\omega_r$ is the natural frequency and $\\zeta_r$ is the fraction of critical damping for mode $r$.\n",
+    "\n",
+    "We only get a diagonal modal damping matrix in this case.  For any other case, we will still have off-diagonal terms.  Luckily for us, these diagonal terms are often small, so using the real mode shapes derived from the mass and stiffness of the system is not a terrible approximation.  However, there are cases where the approximation is not good, and in those cases, we need to turn to a different toolset to help us analyze the system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3590f2c-04ea-443e-903d-cb05fdeabd13",
+   "metadata": {},
+   "source": [
+    "## Complex Modes -- The General Solution\n",
+    "A more general solution to decoupling damped vibration problems requires us to rearange our equations of motion.  Rather than treating the system of equations as $N$ equations with a second order derivative, we will instead treat the system of equations as $2N$ first-order equations using a simple introducton of a new variable $\\mathbf{v} = \\dot{\\mathbf{x}}$ to represent velocity.  In this way, we can eliminate the double-derivative in the acceleration term ($\\ddot{\\mathbf{x}} = \\dot{\\mathbf{v}}$)\n",
+    "\n",
+    "$$\\mathbf{M}\\dot{\\mathbf{v}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "However, we now need to add some relationship between $v$ and $x$.  We could simply write $\\mathbf{v} = \\dot{\\mathbf{x}}$ as we showed above.  However, it will be useful to keep our matrices symmetric, so we will construct the equations in a slightly more complex way.\n",
+    "\n",
+    "$$\\mathbf{M}\\dot{\\mathbf{x}} - \\mathbf{M}\\mathbf{v} = \\mathbf{0}$$\n",
+    "\n",
+    "We can combine these two sets of equations into one set of equations of the form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "Here we note that our coefficient matrices are both symmetric, the first term contains all of the derivatives, the second term contains no derivatives, and the right-hand side of the equation contains our input forces."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e95a290-9795-4fa3-8b31-d71776e2db66",
+   "metadata": {},
+   "source": [
+    "We can perform a similar operation as previous to try to understand the free response of the system.  We will make the following assumptions.\n",
+    "\n",
+    "- $\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$ -- The force into the system is zero.\n",
+    "- $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$ -- The response is a complex sinusoid.\n",
+    "\n",
+    "In this case, the first assumption is essentially identical to the first assumption in the real mode shape case.  The second assumption is essentially a complex generalization of the second assumption made in the real mode shape case.  In this generalization, instead of a \"real\" shape where all responses are either in-phase or 180$^\\circ$ out-of-phase, the shape can now be complex, meaning there can be any phasing between different values in the shape.  The exponential $e$ now represents an oscillatory portion and decaying or growing portion of the response ($\\lambda$ is in general a complex number.  The imaginary portion of it will result in sinusoidal motion of the form $e^{j\\theta} = \\cos{\\theta} + j\\sin{\\theta}$ where $j = \\sqrt{-1}$.  The real part will result in exponential decay or exponential growth of the form $e^{x}$).  The term $\\lambda$ appears on the first row of the second assumption due to the chain rule when differentiating $\\mathbf{v} = \\dot{x}$.\n",
+    "\n",
+    "Using the chain rule for differentiation, we can also say $\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} = \\lambda\\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e824ad9-e3a4-4638-8b43-ace3c75bc707",
+   "metadata": {},
+   "source": [
+    "We can then apply these assumptions to our equations of motion:\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can again recognize that the term $e^{\\lambda t}$ is in general nonzero and therefore can be cancelled.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can then subtract $\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$ from both sides of the equation and we end up with\n",
+    "\n",
+    "$$ \\lambda \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = - \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b05928-6886-4d5a-b714-081a5d10abfd",
+   "metadata": {},
+   "source": [
+    "While a little more complex, this equation is also a [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9aa2bf5c-7063-4df0-97eb-4d0ad1cfb50e",
+   "metadata": {},
+   "source": [
+    "Transforming the eigenvalues and eigenvectors into natural frequencies and mode shapes is not as straightforward in the present case with complex modes.  In general, we will solve for $2N$ complex eigenvalues; however, due to the fact that the matrices are real, the eigenvalues will be complex conjugates of one another.  We will often hear these values called *poles* because they are zeros of the denominator polynomials in the frequency response function formulation that we will discuss later.\n",
+    "\n",
+    "The eigenvalues $\\lambda$ are often expressed as a function of the critical damping ratio $\\zeta$ and the natural frequency $\\omega$.  For the $r$th mode,\n",
+    "\n",
+    "$$\\lambda_r = -\\zeta_r \\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$$\n",
+    "\n",
+    "The real part $-\\zeta_r \\omega_r$ drives the exponential decay of the sinusoid.  The imaginary part drives the oscillatory behavior.  Because of this, the term $\\omega_r\\sqrt{1-\\zeta_r}$ is often referred to as the *damped natural frequency* $\\omega_d$\n",
+    "\n",
+    "$$\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$$\n",
+    "\n",
+    "To recover the natural frequency $\\omega_r$, we can simply take the magnitude of the eigenvalue.\n",
+    "\n",
+    "$$\\left|\\lambda_r\\right| = \\sqrt{\\Re{(\\lambda_r)}^2 + \\Im{(\\lambda_r)}^2} = \\sqrt{(\\zeta_r^2 \\omega_r^2) + (\\omega_r^2 (1-\\zeta_r^2))} = \\sqrt{\\omega_r^2 + \\omega_r^2\\zeta_r^2 - \\omega_r^2\\zeta_r^2} = \\omega_r$$\n",
+    "\n",
+    "To recover the damping ratio, we can divide the negative real part of the eigenvalue by the magnitude of the eigenvalue.\n",
+    "\n",
+    "$$- \\frac{\\Re{(\\lambda_r)}}{\\left|\\lambda_r\\right|} = \\frac{\\zeta_r\\omega_r}{\\omega_r} = \\zeta_r$$ \n",
+    "\n",
+    "Note that for complex modes, the damping is part of the solution to the free vibration problem and appears in the solution, whereas in the real mode case where damping was ignored, it was not.\n",
+    "\n",
+    "We will also solve for $2N$ eigenvectors.  These will also appear in complex conjugate pairs.  They will be of the form\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$\n",
+    "\n",
+    "We can extract the mode shape from the eigenvector simply by selecting the bottom partition of the vector.\n",
+    "\n",
+    "The equations to compute the modal mass and modal damping matrices are the same as those used in the case of real modes.  However, in general, the modal mass and modal damping matrices are not diagonal and have complex elements.\n",
+    "\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "$$\\tilde{\\mathbf{C}}=\\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$$\n",
+    "\n",
+    "Recall that the eigenvalue problem we solved for was with respect to the generalized form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.  Therefore the eigenvectors will diagonalize these matrices.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{A}}$$\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{B}}$$\n",
+    "\n",
+    "We often refer to these matrices $\\tilde{\\mathbf{A}}$ and $\\tilde{\\mathbf{B}}$ as the *Modal A* and *Modal B* matrices.\n",
+    "\n",
+    "Modal mass is defined in different ways for complex modes.  Some references will treat the diagonal entries in Modal A $\\tilde{\\mathbf{A}}_{rr}$ directly as the modal mass values.  Other references define the modal mass for the $r$th mode $m_r$ as:\n",
+    "\n",
+    "$$\\tilde{\\mathbf{A}}_{rr} = 2\\lambda_r m_r + 2\\zeta_r\\omega_r m_r = 2j\\omega_{dr} m_r$$\n",
+    "$$m_r = \\frac{\\tilde{\\mathbf{A}}_{rr}}{2j\\omega_{dr}}$$\n",
+    "\n",
+    "It doesn't really matter which definition is used as long as the definition is consistently used when reconstructing responses from modal parameters.  This document will proceed with the modal mass defined as the diagonal terms of $\\tilde{\\mathbf{A}}$.\n",
+    "\n",
+    "We can also see that if we premultiply the eigenvalue equation by our eigenvectors, we get a relationship between the Modal A and Modal B matrices.\n",
+    "\n",
+    "$$\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f2a6ce4-2692-4839-8826-5ef45900ff85",
+   "metadata": {},
+   "source": [
+    "## An Example Problem\n",
+    "Before we get too far, let's illustrate some of these concepts with a small example problem.  We will generate a 3-degree-of-freedom problem that we will simulate.  We will explore undamped, proportionally damped, and generally damped cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6eb06f7-2c41-44fc-bf7b-d160b7bd4303",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.linalg as la\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "def pretty_print_table(df):\n",
+    "    return display( HTML( df.to_html().replace(\"\\\\n\",\"<br>\") ) )\n",
+    "\n",
+    "# Set up physical matrices\n",
+    "K = np.array([[ 6000, -2000,     0],\n",
+    "              [-2000,  4000, -2000],\n",
+    "              [    0, -2000,  6000]])\n",
+    "M = np.array([[2, 0, 0],\n",
+    "              [0, 3, 0],\n",
+    "              [0, 0, 2]])\n",
+    "alpha = 1.36/2\n",
+    "beta = 0.00154865/2\n",
+    "C_proportional = alpha*M+beta*K\n",
+    "\n",
+    "C_general = np.array([[14, -2, 0],\n",
+    "                      [-2, 10, -2],\n",
+    "                      [0, -2, 10]])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "457c045b-1adb-4923-a5b9-a64cd6294ba4",
+   "metadata": {},
+   "source": [
+    "For the complex mode cases, let's construct the $\\mathbf{A}$ and $\\mathbf{B}$ matrices.  The $\\mathbf{A}$ matrix will depend on damping, the $\\mathbf{B}$ matrix will not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0065867a-cd5c-4548-8cf2-dd6580fbf2ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For convenience, construct a matrix of zeros\n",
+    "Z = np.zeros(M.shape)\n",
+    "# Assemble State Space Matrices\n",
+    "A_undamped = np.block([[Z, M],\n",
+    "                       [M, Z]])\n",
+    "A_proportional = np.block([[Z, M],\n",
+    "                           [M, C_proportional]])\n",
+    "A_general = np.block([[Z, M],\n",
+    "                      [M, C_general]])\n",
+    "B = np.block([[-M, Z],\n",
+    "              [ Z, K]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6031364-f521-4438-a584-335ff3e83900",
+   "metadata": {},
+   "source": [
+    "Now let's compute the eigenvalues and eigenvectors.  For the 2nd-order eigenvalue using the mass and stiffness matrices directly, we can use the `eigh` function which assumes Hermetian, positive definite matrices.  For the complex mode cases, we will have to use the more general `eig` function.\n",
+    "\n",
+    "When using an eigenvalue solver, be wary of the order that matrices occur in the function call.  For example, in the [scipy.linalg.eig documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html), it states the following:\n",
+    "\n",
+    "> **scipy.linalg.eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False)**\n",
+    ">\n",
+    "> Solve an ordinary or generalized eigenvalue problem of a square matrix.\n",
+    "> \n",
+    "> Find eigenvalues w and right or left eigenvectors of a general matrix:\n",
+    ">\n",
+    "> ```python\n",
+    ">  a   vr[:,i] = w[i]        b   vr[:,i]\n",
+    ">  a.H vl[:,i] = w[i].conj() b.H vl[:,i]\n",
+    ">   ```\n",
+    ">\n",
+    "> where `.H` is the Hermitian conjugation.\n",
+    "\n",
+    "SciPy uses the definition $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$ for its eigenvalue problem, and our equation we have defined as $- \\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\lambda \\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$. Note that what SciPy calls `a` in its function call is what we have defined as $-\\mathbf{B}$, and what SciPy calls `b` is what we have defined as $\\mathbf{A}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "954836de-ba9e-4019-8e02-5e730de1fdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute Eigenvalues and Eigenvectors\n",
+    "lam_2ndOrder,E_2ndOrder = la.eigh(K,M)\n",
+    "lam_undamped, E_undamped = la.eig(-B,A_undamped)\n",
+    "lam_proportional, E_proportional = la.eig(-B,A_proportional)\n",
+    "lam_general, E_general = la.eig(-B,A_general)\n",
+    "\n",
+    "# Since the half the complex eigenvalues and eigenvectors are complex conjugates of one another,\n",
+    "# we will only keep those corresponding to the eigenvalues with imaginary part > 0\n",
+    "keep_undamped = lam_undamped.imag > 0\n",
+    "keep_proportional = lam_proportional.imag > 0\n",
+    "keep_general = lam_general.imag > 0\n",
+    "\n",
+    "# Now reduce to just those we want to keep\n",
+    "lam_undamped = lam_undamped[keep_undamped]\n",
+    "E_undamped = E_undamped[:,keep_undamped]\n",
+    "lam_proportional = lam_proportional[keep_proportional]\n",
+    "E_proportional = E_proportional[:,keep_proportional]\n",
+    "lam_general = lam_general[keep_general]\n",
+    "E_general = E_general[:,keep_general]\n",
+    "\n",
+    "# Let's also sort the eigenvalues so they are all ascending\n",
+    "for evals,evects in zip([lam_2ndOrder, lam_undamped, lam_proportional, lam_general],\n",
+    "                        [E_2ndOrder, E_undamped, E_proportional, E_general]):\n",
+    "    isort = np.argsort(np.abs(evals))\n",
+    "    evals[...] = evals[isort]\n",
+    "    evects[...] = evects[:,isort]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c785b0f3-4103-44b9-a3f6-99c149557c88",
+   "metadata": {},
+   "source": [
+    "Now we can extract the natural frequencies and damping ratios from the eigenvalues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "99f8881b-af4f-4ac1-9131-a90cd1fb0566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute natural frequencies\n",
+    "omega_2ndOrder = np.sqrt(lam_2ndOrder)\n",
+    "omega_undamped = np.abs(lam_undamped)\n",
+    "omega_proportional = np.abs(lam_proportional)\n",
+    "omega_general = np.abs(lam_general)\n",
+    "\n",
+    "# Compute damping ratios\n",
+    "zeta_undamped = -np.real(lam_undamped)/np.abs(lam_undamped)\n",
+    "zeta_proportional = -np.real(lam_proportional)/np.abs(lam_proportional)\n",
+    "zeta_general = -np.real(lam_general)/np.abs(lam_general)\n",
+    "\n",
+    "# Compute damped natural frequencies\n",
+    "omega_d_undamped = omega_undamped*np.sqrt(1-zeta_undamped**2)\n",
+    "omega_d_proportional = omega_proportional*np.sqrt(1-zeta_proportional**2)\n",
+    "omega_d_general = omega_general*np.sqrt(1-zeta_general**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5ea9635-f9d9-49fd-b4a2-88f8540f2f24",
+   "metadata": {},
+   "source": [
+    "Let's plot these datasets in a table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8643d599-fbb1-42d8-991f-50e46bc40f3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Undamped</th>\n",
+       "      <th>Proportional Damping</th>\n",
+       "      <th>General Damping</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mass Matrix</th>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stiffness Matrix</th>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Damping Matrix</th>\n",
+       "      <td>[[0. 0. 0.]<br> [0. 0. 0.]<br> [0. 0. 0.]]</td>\n",
+       "      <td>[[ 6.00595 -1.54865  0.     ]<br> [-1.54865  5.1373  -1.54865]<br> [ 0.      -1.54865  6.00595]]</td>\n",
+       "      <td>[[ 7. -1.  0.]<br> [-1.  5. -1.]<br> [ 0. -1.  5.]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poles</th>\n",
+       "      <td>[[0.+27.2518998j ]<br> [0.+54.77225575j]<br> [0.+59.92217695j]]</td>\n",
+       "      <td>[[-0.62753244+27.24467371j]<br> [-1.5014875 +54.75167153j]<br> [-1.73017173+59.89719356j]]</td>\n",
+       "      <td>[[-0.73753202+27.24242803j]<br> [-1.50037636+54.76028673j]<br> [-1.59542495+59.89042233j]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Natural Frequencies</th>\n",
+       "      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>\n",
+       "      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>\n",
+       "      <td>[[27.25240977]<br> [54.78083727]<br> [59.91166888]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Damping Ratios</th>\n",
+       "      <td>[[-0.]<br> [-0.]<br> [-0.]]</td>\n",
+       "      <td>[[0.02302711]<br> [0.02741329]<br> [0.02887365]]</td>\n",
+       "      <td>[[0.027063  ]<br> [0.02738871]<br> [0.02662962]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Undamped','Proportional Damping','General Damping'],\n",
+    "                  index=['Mass Matrix','Stiffness Matrix','Damping Matrix','Poles','Natural Frequencies','Damping Ratios'],\n",
+    "                  dtype=object)\n",
+    "df.at['Mass Matrix', 'Undamped'] = str(M)\n",
+    "df.at['Mass Matrix', 'Proportional Damping'] = str(M)\n",
+    "df.at['Mass Matrix', 'General Damping'] = str(M)\n",
+    "df.at['Stiffness Matrix', 'Undamped'] = str(K)\n",
+    "df.at['Stiffness Matrix', 'Proportional Damping'] = str(K)\n",
+    "df.at['Stiffness Matrix', 'General Damping'] = str(K)\n",
+    "df.at['Damping Matrix', 'Undamped'] = str(np.zeros(C_proportional.shape))\n",
+    "df.at['Damping Matrix', 'Proportional Damping'] = str(C_proportional)\n",
+    "df.at['Damping Matrix', 'General Damping'] = str(C_general)\n",
+    "df.at['Poles', 'Undamped'] = str(lam_undamped[:,np.newaxis])\n",
+    "df.at['Poles', 'Proportional Damping'] = str(lam_proportional[:,np.newaxis])\n",
+    "df.at['Poles', 'General Damping'] = str(lam_general[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'Undamped'] = str(omega_undamped[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'Proportional Damping'] = str(omega_proportional[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'General Damping'] = str(omega_general[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'Undamped'] = str(zeta_undamped[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'Proportional Damping'] = str(zeta_proportional[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'General Damping'] = str(zeta_general[:,np.newaxis])\n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c1b1019-07d2-47a9-b59d-834e6fb3e08a",
+   "metadata": {},
+   "source": [
+    "We will now look at the eigenvectors.  To start out, we will normalize the eigenvectors **to a value that is NOT equal to unity modal mass or unity Modal A**.  This is useful while we are learning, because if we have unity modal mass, it allows us to be sloppy and simply drop or ignore effects of the modal mass.  This could result in significant errors down the road if we assume that the formulae we developed for the unity modal mass case also apply to the non-unity modal mass case.  It is therefore better to develop and check all the formulae with non-unity modal mass to ensure that we understand its effects and have accounted for it successfully.  We will deliberately normalize to a value not equal to 1, because many eigenvector solvers will automatically normalize for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aa9e0cb3-b626-4b88-9a55-b67e71cfab75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize Eigenvectors using einsum to pull out the m_ii terms from the modal normalization matrix\n",
+    "# We will multiply by 2 to ensure non-unity modal mass.\n",
+    "E_2ndOrder = 2*E_2ndOrder/np.sqrt(np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder))\n",
+    "E_undamped = 2*E_undamped/np.sqrt(np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped))\n",
+    "E_proportional = 2*E_proportional/np.sqrt(np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional))\n",
+    "E_general = 2*E_general/np.sqrt(np.einsum('ji,jk,ki->i',E_general,A_general,E_general))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8a0830-55f4-48b3-bfee-8d3fff1c6ec1",
+   "metadata": {},
+   "source": [
+    "Now that we have the eigenvectors normalized to a non-unity modal mass, we will compute the Modal Mass and Modal A matrices to extract the modal mass.\n",
+    "\n",
+    "We will use `np.einsum` in the same way we used previously but here we will describe it a bit better.  Numpy's `einsum` function implements a generalized version of Einstein Summation Notation.  While we can represent matrix operations like multiplication simply by using symbols for the matrices\n",
+    "$$\\mathbf{C} = \\mathbf{A}\\mathbf{B}$$\n",
+    "we can also represent those operations with the indices\n",
+    "$$C_{ik} = A_{ij}B_{jk}$$\n",
+    "In the previous equation, we have the index $i$ representing the rows of $\\mathbf{A}$, and the index $j$ representing the columns.  Similarly for $\\mathbf{B}$, we have the indices $j$ representing the rows and $k$ representing the columns.  When we do matrix multiplication, we sum over the inner dimension, e.g., the $j$ indices, and that index drops out.  We are left with rows $i$ and columns $k$ of our output matrix $\\mathbf{C}$.  This operation would be represented in Numpy's `einsum` function as\n",
+    "```python\n",
+    "C = np.einsum('ij,jk->ik',A,B)\n",
+    "```\n",
+    "The first argument `'ij,jk->ik'` is a string that gives the index operations to be performed.  We see that there is an arrow `->` that separates input indices and output indices, and the different input indices are separated by a comma, and there is only one set of output indices.  The remaining arguments are the actual matrices corresponding to the input indices passed in the first argument.  Therefore `ij` will be associated with `A`, and `jk` will be associated with `B`.  The output indices on the right side of the arrow `ik` will be associated with the function's output `C`.  We note that any index not listed in the output indices will be summed over and drop out.\n",
+    "\n",
+    "This notation is very useful for writing complex matrix expressions in a compact way.  For example, if instead of $\\mathbf{C} = \\mathbf{A}\\mathbf{B}$ we wanted to do $\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$, we can simply swap the indices on $\\mathbf{A}$, which now means we are summing across the rows of $\\mathbf{A}$, which is equivalent to matrix multiplication by a transpose.\n",
+    "$$\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$$\n",
+    "$$C_{ik} = A_{ji}B_{jk}$$\n",
+    "\n",
+    "We can therefore represent a more complex product like\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "as\n",
+    "$$\\tilde{{M}}_{il}={\\phi}_{ji}{M}_{jk}{\\phi}_{kl}$$\n",
+    "In the previous equation, we can see that the $j$ index corresponds to the rows of the mass matrix and the rows of the mode shape matrix.  Because we sum over $j$, this is equivalent to multiplying the matrix $\\mathbf{M}$ by $\\mathbf{\\phi}^T$.  We can indeed improve upon this if, for example, we wanted a 1D array of modal mass values, rather than a 2D matrix that we will then need to extract the diagonals from.  We can replace the $l$ index with another $i$ index and have the output specify just a single $i$, and we will get a 1D array of modal mass values.  As an aside, this is a syntax that is not acceptable in true Einstein Summation Notation, where a repeated index is automatically summed over.  In Numpy's generalized version, it subverts some of these very strict rules to give more flexibility to the user.\n",
+    "$$\\tilde{{M}}_{i}={\\phi}_{ji}{M}_{jk}{\\phi}_{ki}$$\n",
+    "\n",
+    "We can then perform these operations in code to solve for the modal mass and modal A quantities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0e7c1966-278c-4e88-b4cd-bcb8246b282b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mm_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder)\n",
+    "ma_undamped = np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped)\n",
+    "ma_proportional = np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional)\n",
+    "ma_general = np.einsum('ji,jk,ki->i',E_general,A_general,E_general)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7184e6a9-807d-473a-a1d2-5a12854ea046",
+   "metadata": {},
+   "source": [
+    "The last thing we will do is to pull out the mode shapes from the eigenvectors.  For the 2nd Order case, the mode shapes are the eigenvectors.  But for the complex case, the mode shapes are the lower half of the eigenvector matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "806b85b5-ef45-441e-bd98-d0cb087350b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = E_2ndOrder\n",
+    "psi_undamped = E_undamped[E_undamped.shape[0]//2:,:]\n",
+    "psi_proportional = E_proportional[E_proportional.shape[0]//2:,:]\n",
+    "psi_general = E_general[E_general.shape[0]//2:,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f52d5276-14d0-4ae8-a0f0-924b20ecddb3",
+   "metadata": {},
+   "source": [
+    "We should be able to check that the top half of the eigenvector matrix is the bottom half multiplied by the eigenvalue.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2533c3db-1a36-4fd3-aca6-770889ed201e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,\n",
+       "        -3.16635239-3.71568777j],\n",
+       "       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n",
+       "         2.00036437+2.07501753j],\n",
+       "       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n",
+       "        -3.52944456-3.37788878j]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "E_general[:E_general.shape[0]//2,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "87a69d80-b352-43e3-9c7f-55f1fe6c272b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,\n",
+       "        -3.16635239-3.71568777j],\n",
+       "       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n",
+       "         2.00036437+2.07501753j],\n",
+       "       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n",
+       "        -3.52944456-3.37788878j]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lam_general*psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6db7704-a5c2-4394-8d5d-422e23ae82d6",
+   "metadata": {},
+   "source": [
+    "One final thing to note is that in general, the mode shapes extracted from a complex analysis will not diagonalize the orignal mass, stiffness, or damping matrices, as these were not the matrices the eigensolution was performed on.  There will be off-diagonal terms, and they will be complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3e036cbb-b641-4aac-ac0b-2769f764020d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-6.13061679e-08-7.34176433e-02j, -1.43810774e-04+9.83528782e-06j,\n",
+       "         1.15410477e-04+2.13846966e-06j],\n",
+       "       [-1.43810774e-04+9.83528782e-06j, -1.24812005e-07-3.65342888e-02j,\n",
+       "        -1.36140173e-04+1.88235958e-06j],\n",
+       "       [ 1.15410477e-04+2.13846966e-06j, -1.36140173e-04+1.88235958e-06j,\n",
+       "         1.86118173e-07-3.33825843e-02j]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@M@psi_general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6ce545ce-0d31-4d53-a589-4bd891eea2b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.00014982-0.10829239j,  0.00048468+0.01181488j,\n",
+       "         0.00045558-0.01005105j],\n",
+       "       [ 0.00048468+0.01181488j, -0.00125664-0.1096167j ,\n",
+       "        -0.00020565+0.01561439j],\n",
+       "       [ 0.00045558-0.01005105j, -0.00020565+0.01561439j,\n",
+       "         0.00140645-0.10654111j]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@C_general@psi_general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bed4c0f0-266e-4434-a7e4-65002b1bfeed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-6.49635076e-05-5.45227499e+01j,  2.15177460e-01-2.97517095e-03j,\n",
+       "        -1.87975974e-01-1.36004490e-02j],\n",
+       "       [ 2.15177460e-01-2.97517095e-03j, -1.51087546e-03-1.09568400e+02j,\n",
+       "         4.46495302e-01+1.79584080e-02j],\n",
+       "       [-1.87975974e-01-1.36004490e-02j,  4.46495302e-01+1.79584080e-02j,\n",
+       "         1.57583896e-03-1.19907950e+02j]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@K@psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb5b9b23-80f7-4940-bd63-759731824312",
+   "metadata": {},
+   "source": [
+    "## Frequency Response Functions and Modal Parameters\n",
+    "Now that we have familiarized ourselves with the modal parameters for real and complex modes, let's discuss the relationship to the frequency response function.  This is of utmost importance, because we eventually would like to fit the modal parameters from the frequency response functions, as well as reconstruct frequency response functions from the modal parameters.\n",
+    "\n",
+    "### Real Modes\n",
+    "\n",
+    "Starting with the modal system of equations, and assuming proportional damping, we have\n",
+    "\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "We can also substitute in the diagonal values for the modal parameters.  *Note the inclusion of the modal mass* $m_r$ *in all terms, because we have not assumed mass normalized modes in this case*.  We will represent the generalized modal force $\\mathbf{\\phi}^T\\mathbf{F}$ as $\\mathbf{Q}$.\n",
+    "$$\\left[m_r\\right]\\ddot{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]\\dot{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "\n",
+    "We can then use the *Laplace Transform* to make this differential equation into an algebraic one.\n",
+    "$$\\left[m_r\\right]s^2{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]s{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "We can collect terms of $\\mathbf{q}$ to create an equation of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{b}$.\n",
+    "$$\\left[m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "\n",
+    "Because the equation is diagonal, inverting the coefficient matrix is trival.\n",
+    "$${\\mathbf{q}} = \\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{Q}$$\n",
+    "\n",
+    "This is now a representation of a *modal* transfer function.  If we apply a set of *modal* forces $\\mathbf{Q}$, we get out a set of *modal* responses $\\mathbf{q}$.  To transform to physical space, we need to transform our modal quantities to physical quantities.  We know the substitution for modal forces $\\mathbf{Q} = \\mathbf{\\phi}^T\\mathbf{F}$, and we know our equation for physical response from modal responses $\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$.  Therefore we can make the subsitution for modal force, and pre-multiply the entire equation by the mode shape matrix.\n",
+    "\n",
+    "$$\\mathbf{x} = \\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}\\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "If instead of the entire Laplace domain we only consider sinusoidal responses, we can substitute $s=j\\omega$ to get the familiar form for the frequency response function.\n",
+    "\n",
+    "$$\\mathbf{H} = \\mathbf{\\phi}\\left[\\frac{1}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T$$\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$\n",
+    "\n",
+    "To pull out a specific entry in the FRF matrix, we can index the mode shapes.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96ab4e96-9288-4414-bf16-82a4534ad509",
+   "metadata": {},
+   "source": [
+    "### Complex Modes\n",
+    "The derivation for the frequency response function in terms of complex modes is similar.  Recall that our physical vector for the complex state space formulation includes velocities and displacements.  Similarly our eigenvectors consisted of pairs of shapes, the shape and its complex conjugate.  Therefore, in the case of complex modes, the projection from modal space to physical space is\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "Applying the transformation to the complex state space equation\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "\n",
+    "Gives\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "\n",
+    "And then pre-multiplying by the eigenvector matrix transpose completes the transformation into modal space.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "$$= \\begin{bmatrix}\\lambda \\mathbf{\\psi}^T & \\mathbf{\\psi}^T \\\\ \\lambda^* {\\mathbf{\\psi}^*}^T & {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$\n",
+    "\n",
+    "Transforming into the modal matrices\n",
+    "$$\\tilde{\\mathbf{A}}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\tilde{\\mathbf{B}}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "We recognize that the diagonal terms of $\\tilde{\\mathbf{A}}$ are simply what we've defined as the modal mass, and the diagonal terms of $\\tilde{\\mathbf{B}}$ are simply the negative modal mass times the eigenvector $\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & \\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e520d365-73f4-45dc-a5f0-24158348da43",
+   "metadata": {},
+   "source": [
+    "Once again, transforming to the Laplace Domain,\n",
+    "\n",
+    "$$\\begin{bmatrix}s\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & s\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "$$\\begin{bmatrix}(s-\\lambda)\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & (s-\\lambda^*)\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "Once again, because this matrix is diagonal, it is easy to invert.\n",
+    "\n",
+    "$$\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "This is the modal transfer function.\n",
+    "\n",
+    "To compute the transfer functions between the force $\\mathbf{F}$ and the response $\\mathbf{x}$ (we ignore $\\mathbf{v}$ here), we can use the identities $\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$, which we defined previously, and the last row of $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$, which is $\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$. \n",
+    "\n",
+    "$$\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4afe2217-3136-4cef-a027-a4be65bc7982",
+   "metadata": {},
+   "source": [
+    "The transfer function in the $s$-domain is then\n",
+    "\n",
+    "$$\\mathbf{H} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}$$\n",
+    "\n",
+    "Because it is diagonal, we can take it out of matrix form and put it into summation form, which reveals the usual partial fraction form of the transfer function.\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "We can extract a specific transfer function $\\mathbf{H}_{ij}$\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_{ir}\\mathbf{\\psi}_{jr}}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_{ir}^*\\mathbf{\\psi}_{jr}^*}{m_r(s-\\lambda_r^*)}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17543413-bd30-4a68-80b1-d1b7f117cf34",
+   "metadata": {},
+   "source": [
+    "To translate into more intuitive variables, we can make the following substitutions.\n",
+    "\n",
+    "- $\\lambda_r = -\\sigma_r + j\\omega_{dr} = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$  - Substitute the poles for the natural frequency and critical damping ratio.\n",
+    "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I$ - Split the mode shapes into real and imaginary parts.\n",
+    "\n",
+    "From these, it follows that the complex conjugate versions simply have the sign flipped on their imaginary components.\n",
+    "\n",
+    "- $\\lambda_r^* = -\\sigma_r - j\\omega_{dr} = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$\n",
+    "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I$\n",
+    "\n",
+    "Substituting these into the previous equation, we get\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left(\\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R + j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r + j\\omega_{dr}\\right))} + \\frac{\\left(\\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R - j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r - j\\omega_{dr}\\right))}\\right)$$\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s + \\sigma_r - j\\omega_{dr}\\right)} + \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)}\\right)$$\n",
+    "\n",
+    "If we put the two terms over a common denominator, we get\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "  \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n",
+    "  \\left(s+ \\sigma_r + j\\omega_{dr}\\right)\n",
+    "+ \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n",
+    "  \\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n",
+    "{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)\\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n",
+    "\\right)$$\n",
+    "\n",
+    "We can expand and reduce this equation\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s+\\sigma_r\\right)^2 + \\omega_{dr}^2\\right)\n",
+    "}\n",
+    "\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97b4be2f-c392-4c79-950e-00d0e32a038f",
+   "metadata": {},
+   "source": [
+    "### What are Real Modes in the Complex Mode Framework?\n",
+    "\n",
+    "One might naively believe that a real mode is simply a complex mode where the imaginary part is zero.  However, it isn't clear whether or not that should be true, because the eigenvalue problem was posed differently.  What is clear, however, is that real modes should certainly be some subset of complex modes; in other words, we should be able to represent a real mode as a complex mode.  In this section, we will investigate that relationship.\n",
+    "\n",
+    "Starting with the previous equation, we can further expand the denominator with natural frequency and damping ratio, where $\\sigma_r = \\zeta_r\\omega_r$ and $\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s+-\\zeta_r\\omega_r\\right)^2 + \\left(\\omega_r\\sqrt{1-\\zeta_r^2}\\right)^2\\right)\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s^2+2s\\zeta_r\\omega_r + \\omega_r^2\\zeta_r^2\\right) + \\left(\\omega_r^2 - \\omega_r^2\\zeta_r^2\\right)\\right)\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_rs^2+2\\zeta_r\\omega_rm_rs + m_r\\omega_r^2\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "The denominator in the previous equation is identical to that from the real modes equation.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}$$\n",
+    "\n",
+    "The numerators should also be equivalent.\n",
+    "\n",
+    "$$2\\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\\right) = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "We note that there is no terms with $s$ on the real modes side of the equation, so any term containing $s$ on the complex modes side of the equation must be zero in the case of real modes.\n",
+    "\n",
+    "$$\\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I = 0$$\n",
+    "\n",
+    "This then leaves the remainder of the equation\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "We must find a solution for these two equations simultaneously.  Since the modeshape at location $i$ and the mode shape at location $j$ are generally independent of one another, we can treat them as independent.  In other words, $\\psi_{ir}$ cannot be a function $\\psi_{jr}$.  This means for the first equation, there are two possibilities; $\\psi_{ir}^I = -\\psi_{ir}^R$ or $\\psi_{ir}^I = \\psi_{ir}^R$.\n",
+    "\n",
+    "To figure out which, we will look at the drive point transfer function where $i = j$, as the relationship should hold for that point as much as any other point.\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R \\right)\\omega_{dr} = \\phi_{ir}^2$$\n",
+    "$$\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "\n",
+    "The since term $\\phi_{ir}^2$ cannot be negative and $\\omega_{dr}$ is also non-negative, the term $\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R$ must be negative.  This will be the case if $\\psi_{ir}^I = -\\psi_{ir}^R$, but will not be the case if $\\psi_{ir}^I = \\psi_{ir}^R$.\n",
+    "\n",
+    "Substituting that expression,\n",
+    "\n",
+    "$$-{\\psi_{ir}^R}^2 + -{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "$$-2{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "$${\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{4\\omega_{dr}}$$\n",
+    "$${\\psi_{ir}^R} = \\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}} = -\\psi_{ir}^I$$\n",
+    "\n",
+    "To verify that this is the general expression, let's plug it into the original equation.\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}} + \n",
+    "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-2\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$\\phi_{ir}\\phi_{jr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "Therefore for a real mode represented in a complex mode, we should expect the imaginary part to be equal in magnitude to the real part, with phase angle of -45$^\\circ$ instead of zero degrees, which may have been expected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b59fa20c-70e7-44b3-ab9b-b584f3b15400",
+   "metadata": {},
+   "source": [
+    "### Back to our Example Problem\n",
+    "\n",
+    "Let's return to our example problem to compute frequency response functions from the matrices as well as from modal parameters.  We will start with computing the frequency response functions from dynamic stiffness matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ae0359c0-8d92-45f2-ab35-1815657a7f89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get frequency lines\n",
+    "omegas = np.linspace(0,np.max(omega_2ndOrder)*1.5,1000)\n",
+    "# Set up omega for broadcasting the FRF matrix, the first dimension will be frequency line.\n",
+    "# The second two dimensions will be i,j degrees of freedom.\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Construct Dynamic Stiffness\n",
+    "Z_undamped = M*(1j*omegas_bc)**2 + K\n",
+    "Z_proportional = M*(1j*omegas_bc)**2 + C_proportional*1j*omegas_bc + K\n",
+    "Z_general = M*(1j*omegas_bc)**2 + C_general*1j*omegas_bc + K\n",
+    "# Invert to get frequency response functions\n",
+    "H_undamped = np.linalg.inv(Z_undamped)\n",
+    "H_proportional = np.linalg.inv(Z_proportional)\n",
+    "H_general = np.linalg.inv(Z_general)\n",
+    "# Plot the FRFs\n",
+    "fig,axes = plt.subplots(H_undamped.shape[1]*2,H_undamped.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_u,f_p,f_g in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                          H_undamped.reshape(omegas.size,-1).T,\n",
+    "                          H_proportional.reshape(omegas.size,-1).T,\n",
+    "                          H_general.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_u)*180/np.pi)\n",
+    "    ax[0].plot(omegas,np.angle(f_p)*180/np.pi)\n",
+    "    ax[0].plot(omegas,np.angle(f_g)*180/np.pi)\n",
+    "    ax[1].plot(omegas,np.abs(f_u))\n",
+    "    ax[1].plot(omegas,np.abs(f_p))\n",
+    "    ax[1].plot(omegas,np.abs(f_g))\n",
+    "    ax[1].set_yscale('log')\n",
+    "ax[1].legend(['Undamped','Proportional Damping','General Damping'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8819a537-c842-495b-b0c3-484a69f959e8",
+   "metadata": {},
+   "source": [
+    "Now let's reconstruct the frequency response functions from modal parameters and see if they match those constructed from the dynamic stiffness matrices.\n",
+    "\n",
+    "For the second-order case,\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "25f2ed27-2e2d-4d85-9092-8da2c49e1a95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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wtMiNEQlRN3lKvdC5YShr9WYAmBU7tgNr0XXpjPEkVW54bdy4kYSEhBOOJyQksHHjRsAxHTEtreZb2UVFRfzwww+88MIL9O/fn6ZNm/L444/TtGlT3nnnHQzD4NVXX+WRRx5h1KhRtG/fnk8//ZTU1FR+/vnnGo9XCCGEZzMwPDKrIUCTqAC2W9o6yz3UbazYk+XGiISo/f67gbInjXh1jA9jvVGe4K6xbTcpRyXpjiepcsOrZcuWPPfcc5SWljqPWa1WnnvuOVq2dEx5OHjwIDExMa6LspJsNht2u/2EtWV+fn4sWbKEPXv2kJ6eztChQ53PhYSE0KNHD5YtW1bT4QohhPAynrCW4xhFUQhu1MW5n1c3dRsrkw+7OSoh6h5PWfsZE2zhoKWps9xK2c+W1Fw3RiT+q8rJNd566y0uvvhiGjRoQPv2jrSVGzduxG63M3v2bACSk5O54447XBtpJQQFBdGrVy+eeuopWrVqRUxMDF999RXLli2jadOmpKc7Npj8b6MwJibG+dx/lZSUUFJS4izn5soPsBCiaqQe8V7H9257Us/2MV0bR7FmZ3MGaBuIUbI5uGcr0NHdYQkXkzrEcxw/Cu4oew5FUTDXa4P9gIKmGLRW9zEvLZcR7eq5OzRRpsojXr1792bPnj08+eSTtG/fnvbt2/Pkk0+yZ88eevbsCcD111/Pfffd5/JgK+Ozzz7DMAzq16+PxWLh9ddf5+qrr0ZVq/xRAXj22WcJCQlxPuLj410csRCitpN6pPbwtKZX98RwVujlKaTjctZyKFfWLNc2Uod4Nk+qF5rWjybZiAOgmXKA7QePuDkicbyzao0EBQVx2223MX36dKZPn87//d//ERQU5OrYzkqTJk1YtGgR+fn5pKSksHLlSqxWK40bNyY2NhaAjIyMCq/JyMhwPvdfDz74IDk5Oc5HSkpKtX8GIUTtIvWI9zo+g5knjni1qhfMJlMbZ7m7so2Ve2WdV20jdYjnOD7TqYNn1Qut6gWzxXDkYrAoNgpTt7o5InG8s2p4ffbZZ/Tt25e4uDj27dsHwCuvvMIvv/zi0uDORUBAAPXq1ePo0aPMmzePUaNGkZiYSGxsLAsWLHCel5uby4oVK+jVq9dJr2OxWAgODq7wEEKIqpB6pPbwlLUcx2iqgiWhKyWGGYDu6jZWSYKNWkfqEM/mSdVC67hgtujlSfCiC3ZwtKD0NK8QNanKDa933nmHyZMnM2LECI4ePYrdbgcgLCyMV1991dXxVdm8efOYO3cue/bs4Y8//mDQoEG0bNmScePGoSgKd999N08//TS//vorGzdu5IYbbiAuLo5LLrnE3aELIYTwMBXXeHmmjomxJJVlMktQD7Frt+xNKUR18tSshgCNIwPYoTZyllur+9iaJmsCPUWVG15vvPEGM2fO5OGHH8ZkKs/N0bVrV2c6eXfKyclhwoQJtGzZkhtuuIG+ffsyb948zGZHb+D999/PnXfeyfjx4+nWrRv5+fnMnTv3hEyIQgghxH951i2WQ4/EcOdGygDhR1aTXSg93ELUFMWDagaTpmKPKp9+3FrZxxZpeHmMKje89uzZQ6dOnU44brFYKCgocElQ52L06NHs3r2bkpIS0tLSePPNNwkJCXE+rygKTz75JOnp6RQXF/Pnn3/SvHlzN0YshBDCUxkVxrk85+bqeO0ahLBOKd9IubuyjZUy3VCIavHffbw8UVz9BA4ZoYBjxGvzwRz3BiScqtzwSkxMJCkp6YTjc+fOpVWrVie+QAghhPBiFW6yPLDtZTFp2Ot3p9TQABikJfHX1owzvEoI4QoGnrXGC6BNXDCb9EYAhCoFZB7Y4d6AhFOV9/GaPHkyEyZMoLi4GMMwWLlyJV999RXPPvss77//fnXEKIQQQriHZ3dsO/Vt3YhlB9swQNtAA+Uw+7csx663R1M97I5QCC9neNyqrhO1rR/CYiORwSQBEHx0MwUlNgIsVb7tFy5W5X+BW265BT8/Px555BEKCwu55ppriIuL47XXXmPMmDHVEaMQQgjhdo7U8p55y3Vemxjem9eVAdoGAHqULmPNvivpnhju5siEqN08sRnWql4wM4zGznI7JZnNqblSH3iAs0onf+2117Jz507y8/NJT0/nwIED3Hzzza6OTQghhHArLxnwIiEigD0R/Z3lS9Sl/LB6nxsjEqJ2Mgzw9JrB16xRGNnOWW6r7GGjrPPyCGfV8DrG39+f6OhoV8UihBBCeJzydPKKx63lOF6fTu1YbHfcbCWoh8jc8Ae5xVY3RyVE7eap9UK9BolkGo7kcu3UPWw6kO3egARQyamGnTp1qvSmkWvXrj2ngIQQQghPYRie3bN9vCu7NODJP4fQX3Ns7TKGuXy54nJuG9DEzZEJUXs4phx7fr3QrkEoGzckMlhLIlQp4FDKDuDErOSiZlVqxOuSSy5h1KhRjBo1iuHDh7N7924sFgsDBw5k4MCB+Pr6snv3boYPH17d8QohhBA1qsKIl5tjOZ3oYF+MFiPIKEsjfZ62hr///oM8GfUSoto4shp6Xs3Qtn4IG41EZzk0e5PUBR6gUiNejz32mPP7W265hUmTJvHUU0+dcE5KSoproxNCCCHcyIsGvAAYP6glb26/hKfMHwMwyfYJL83twxOXtDv9C4UQlWIYnphO40St6gXzDs2c5a7KdlbvO8qgFrJEyJ2qvMbru+++44Ybbjjh+HXXXccPP/zgkqCEEEIIT3HsJssT9+v5rw7xoRxpfhUpehQAvbUt2Fd9wALZ10uIauGpI+G+Zo2SuG7YDUd0PdWtLE8+4uaoRJUbXn5+fixduvSE40uXLsXX19clQQkhhBCewMsGvAC474IOPG7c4ixPNX3KZ199xpp9R90YlRC1gzfVCe2bJrDZaARAK3U/m3ftdWs84iz28br77ru5/fbbWbt2Ld27dwdgxYoVfPjhhzz66KMuD1AIIYRwp4prvDyxb7uixMgA+gy/io/mrmWcaR4+ip03eZFJ79sZdfl1jOpY390hCuHVjk+u4alZDQF6No5g+eLWtFf3ABCUvpK84qEE+ZrdHFndVeURrylTpvDJJ5+wZs0aJk2axKRJk1i7di0fffQRU6ZMqY4YhRBCCLfwtjVex4zr04jNbe9ngd2RxSxQKeY99Tn2fv8It3+yjF2H8t0coRDeyZvqhM4Nw1hNG2e5v7KOxTsOuzEiUeURL4DRo0czevRoV8cihBBCeBxv2cfreIqi8MwVnZlQ8BS2vY8zXFuNSdG5y/Qj+3Yv4fXXLqOg2cVc0b0p/ZpHYjFp7g5ZCK/x33TynjoS7uejYW/Un6KUV/FTShmqreWZzalc0L6eu0OrsyrV8DIMwyNTZQohhBDVyfCqFR0V+ZhU3r6xD4/++BJbkt7gTtNPmBSdBPUQL6szOLLnC37e1ZfP1W74NelDj6YxdIgPpVW9YHzN0hAT4qS8rEoY2Caef/a14zxtDVFKDpnb/8Vq74RZq/KkN+EClWp4tWnThqlTp3LZZZfh4+NzyvN27tzJ9OnTSUhIkGmHQgghagVvymr4X2ZN5bkrOzG7xdNc/1N37rB9Rj9tEwARSh43m37nZn4nN9mPtbuas9howts0oTi8JYGRCTSKDiIxMoAGoX5EB/sSE2wh0GKSzlhRpx3/0+/pyeWHto5h+uwunKetAaC3bSUr91xNn6aRbo6sbqpUw+uNN97ggQce4I477mDYsGF07dqVuLg4fH19OXr0KFu2bGHJkiVs3ryZiRMncvvtt1d33EIIIUS186b1HKdzYfs4+jW7mZmLBzFz6W9cbvzB+epKLIoNgGCliIHaegay3vGCXCjOMbNnVyx7jVi2GpH8bYSSYYSRa4rAHhiLEhCJyT+UYH9fgn1NhPiZCfYzE+JnJsBiws9Hw9+sOb76aPiaNfx9TPiZNXzNqjTehFc62Si4J/8o1wvxIzW6P/asmWiKwUXqMt5clyINLzepVMNryJAhrF69miVLlvDNN9/wxRdfsG/fPoqKioiMjKRTp07ccMMNXHvttYSFhVV3zEIIIUSNUZRjN1oefHdVCSF+Zv43vAX5A5vwa9IV3LJqC+FpixmkrqOvuolIJbfC+b6KlVZKCq1IOfFihWUPINfwI5cAcowAco0AcgigAF+yDB8K8aUIH4oNC4VYKMKHIsNCERZ0zYKh+aCrPqCawWQGzQyqD2g+KCbHV9Xk+N5kMmHWVEyqgqaqaCrlX5WTHFNVNEWhTVwwQ1vHVP//YFFnlNcJnj/iBdC/Y2uW/NmOAdoG4tVM0jf9TdGo9vj5yJTimlal5Bp9+/alb9++1RVLjXrrrbd48cUXSU9Pp0OHDrzxxhvO9PhCCCHEyXn+TdaZBFpMXNOjIdf0aMjh/EEs3pHJszsPk5Gyg9CjG2mrJNNESSNRSaOhkoGPYj/t9YKVIoIpooFyltnS9LKH7fSn2Q0FKyZsaNhRyx6O721o6Ibi+HqsjMoRI4jZHd6RhpeoVp5eK4zqWJ/n5vdlgLYBgPPti/hj61Vc3CHOzZHVPWeV1dDbffPNN0yePJkZM2bQo0cPXn31VYYPH8727duJjo52d3hCCCE8kDf0bFdVZKCFyzo34LLODYCO5BVfyubUXPYcLmD14QL2ZuZSmLkXNT+dwNLDxChHiVaOEqMcJYx8QpQCgikgWCkghIIzNtLOhaYYaFgB68lPOMk/zyEjlDlq7ft3E+5jGP/dx8vzxYb4kt9oOPkHPiRQKeYCbTn3rt4tDS83qJMNr+nTp3Prrbcybtw4AGbMmMFvv/3Ghx9+6JKkIMt2H2H6z//ip5aiKCqoGqgmVFXDUDQU1YSiaaBoKKqGqmll0yQUVFVBU46fKqGgHntOUcqmWBw7z/HVeUxR0FROeZ6PpmIxqfiaNSwmFUvZPHuLqeJXWTgthPvd89VqjqQmo6gm0Eyg+TjqEc2EovmgqCZMJhVT2dQrk1r2VVMwayrase9Vx/dmzTEVy6yV1Q+aillVyl9fdtykqphNKr4mFT8frWw9juNxrKzV4RvZ2l41Bvma6dk4gp6NI4472gOAYqudQ7klZOQVk5FbTEqhlc1FVnKKrOQUWskpLKW4MB+9+ChKaQFGaRGqrRDFVoSPXowvJfgrJfhRii8l+CmlmLFjxuZ8+Ci2imVsmLBjLjtuKRvv0tDLH8p/ymXPm9ApxYSpjv68bjqYw9SvFhOolqBo5dM4Fc2EYvJBPa4O8XHWA8fqiLKvFb53nGPWVMeUT02peK6qOusfX7PjXsPvuHrj2L1Hrby/8IKPNLJLU+bu784V2mKClSL89sznUF43ooN83R1anVLnGl6lpaWsWbOGBx980HlMVVWGDh3KsmXLTji/pKSEkpISZzk3N/eEc/4rt9jK6KPvcaVpcaVi0g0FOyq6c+pE+eP4Y7qhYvvvseOmVJzpPCsmCjGTZfhQgplifCgxyr6WlYsNH/KVAGyWUHS/MPALxxIYRlxYIAkRASRGBtA6LpiYYPlFFaKyzqYeyTqcwad5t572HJvh+P0/6cNwfLWW3YYe/5zVcByzlpWPfV/kfI2JYnyca3OKMVOEhSLDh2Is2FQLdpMvhuaH4RuE4heGOSCcYH9fQvzNhPr5EB5gJi7Uj/phftQP9SPI13zO/x/dwTC8q2e7OvmaNRpG+NMwwr/Kr7XZdYptOoWlNopLdQqtNoqtOja7Tqldx2o3sNp0rGXlAruB1V5WtunYdMfzpXbH97puYNMN7McehoHdXvZVr/gYEBdcDf83atbZ1CF5xTZGZn/BLabfT3mO9bh6wlE/mBzfH1cXHKs3SjGdcPzY60owOa9lQ3PUH0bZmj4sznKJ4oOu+aGb/dBN/mD2Q/ELxewfSnCAL6F+PoT6mwn1NxMRYCEu1Jf6oX5EBlpQPaQB/d96wFtGwoe3iWXizwO4Ase96UXKEmavT+OmvolujqxuqXMNr8OHD2O324mJqTjfOyYmhm3btp1w/rPPPssTTzxRpffQdQNV0St9vqoYqNiBM0zRqMnfbR0ocDzsmQqpyZHsNWLYZ8Twu9GYgyFdaNq8LaO7N6RNXEi1h5NVUMq2tFwOZheRmV9CZl4J+cU2im06xaU2bNZiFMBQVBRFRVE0FNUxwufvoxFgMRFgcWTUCvDRCA+0EBnoQ1SghaggCyF+5trZCyc8wtnUI9hPMZ3qOCbF0at/0qlXNfHjbMdZT3AYcg1/so0Asgkkywgm1YhgjRHJQSOSLEsDTPXa0KxBDJ0bhtKnaaTXNMaOn1YktcTZMWkqgZpjRoWourOpQ2y6jukM9xVmxY4ZO37/faK6f9CP3fKUAPmOQzmGP9lGINkEkmMEkEkIS4xIUo0IMpRoSoMbElyvGa3rh9K2fgg9EiM8JjmEp26gfLwAi4mw1oNI2/oW9ZQsBqrruXHtFml41TCpAc/gwQcfZPLkyc5ybm4u8fHxp32N3TBI0ptiweqc+qBWmAahoykVj6ll0yLUk51fNo2i4vkGprKxLA0dUxUaelWlKQbxSibxZNKPTcACKJzJzrX1+XLVYF5rfBWPXNLlrHpBT0XXDRbtyGTuxjQO7FhDvcJttFT2U185TGMlmyiyCVSK8MWKL6Woysn7oksMMwVYKMSXAsOXgrKvWQSzxQjhsBFCphFKlhqGLTQRv6hGJEYF0yQ6kI7xoTSNCvSYXjbhvc6mHinSNX6190LDcWNkOv6hHF/WMZVNxzr2nLks5YCJ8u+rc+3NMcFKIcFKIQ3JPPFJA+wHFZIPxLHq3+b8j87YGw3k6j4tGNQi2mN/z4wK33tmjKL2O5s6xGY32Gw0Yra9h7MOMR+rK5Rj9YrNWU+UH7c5z3W+rgbqjxClkBClkAQOnfyEQijYZWHrzgQ26I35TOmImtiPS7o24bw2MTW2IbBhGP9Z4+U99cKozg35ZVNvbjPNxqzYScz4g92ZA2gSFeju0OqMs2p47d69m48++ojdu3fz2muvER0dze+//07Dhg1p06aNq2N0qcjISDRNIyMjo8LxjIwMYmNjTzjfYrFgsViq9B6dGoZhv/J+rIZBse5oRPx3+oNulE+PODZlovw8yp/XK553wmuc54Gh28seNgy7HcOwo+iOr+g2DLsVxVoEthKwFaPaS1BsxZiNUiyUYlGs+FNCCAWEKXmEKAWEkUeEkkuCkkGIUljhczZTD/KY+hn7983lwTfuYsL1V9O7ybnvC7Fw+yFm/PoPg3J+ZJK2jPrKETjLjnGLYsWClXDyz9yDlw8leSb2745hlxHH93pTtppb4Z/QlSFtGzKsdQxhAafeQFyIUzmbemTihT05WtiZYrtBgW5g1XXsuoHVbmArm25lsxvY9GPfO6Zr2fWyY3ZHHWG1l73OpmPXbSi6Dd1WWlYn2MBeiqHbQbc6Rtl0O9hLUWzFqPYizPZiRweHc21OKX5KieMrJQQqRYRSQKiST2hZsoVQ8k/aGaQpBs2UgzRTD3INC8lJeYevvhjMB1HX8tAVfWhbv/pHz89GhREv77nHErXI2dQhbeKCKbjqHmx2gyJ7xXrCWla22h11hfVYnWHXsZad5zjuqDtsdju63YZhL0UvqzeO3Vc46g2b46vdiqJby+4vCrEYjnrCTylb11dWfxxb5xeglBBMAaFKAaHkEVqWpOVUnakBSgldlR10VXdwE3PJ3/cqPyb349qAy7j54sEMb3PifVxN8JZ6oU+TCGZYBnGbfTYAl2hL+WXdQSaf18LNkdUdVW54LVq0iBEjRtCnTx8WL17MtGnTiI6OZv369XzwwQd8//331RGny/j4+NClSxcWLFjAJZdcAoCu6yxYsICJEye65D3qh/pRv2N9l1yrJtjsOiU2nWKrnSKrnZwiK9mFVo4WlnKw0Mr6/BL2HS7gyOF0lMzttLFtZpCWRDd1BwAN1Uw+NJ7g1k+shN9xCy1jz24+vWEYPPPbFvRlb/GJ6Vt8TaeeanXUcExFKMaHYsyU4INuqKhlI4kqBho6Ptjwp5gApYiAsoXdp2NRbI4bQw4yQlsFQP5eX/5K7sRDP/fC0mYktwxo7rE3iKL26N88yt0hAGDXDYqtdmf94Phep8hqp7DUTn6xjfSiUrYVWsvqjlKOFpRSkpsJ2fvxL0qjvpJJM+UgbdW9NFdSnKNvIUoht5lmc0XWYh5+51YuvPIWLvKwLFvHb6DsTT3bQkQH+3Jhe/f9PhmGo6PoWL1RVOqoQ4qsdopLHfVHQamNA8U2NheWkl1oJbvISk5BCdaCo9jz0jHnHSSWw9RXMmmspNFG2UtDtXxEPVAp5gbTH1xV/Devf3kp/3SZyOOj2mGqxtEvg4r9uN609tOkqbTs0JNtq+NpqabQVd3BC2vXYAxrLsstakiVG15Tpkzh6aefZvLkyQQFBTmPDx48mDfffNOlwVWXyZMnM3bsWLp27Ur37t159dVXKSgocGY5rGtMmiOrUUDZ3PsGp9kD266fz9a0XOZvTuflFYu51/oe3dQdWBQrr/Iyt3/RmC/uvvisKr3n5myl0fJHuda8wHmsxDCxTG/Dv3prdqqJ2MKa4BMSS1hwECF+ZkfmJJMjW5KiOEYKdaPsq25QatMpKLVTUGKjoNROUXEJ1uJ8jPxDKAWZ+JceIVLJoZ6SRaKSRqKSTqKSjkUpb/QFKsVcrC3jYm0ZB7Z/yjubL6a0w/U8eEFbwmUETNRymqqUrZE8u5npxVY7qdlF7MjIY15qLtP3pmPa/w/DleVcqC7DotiIVHJ5W5vO/d8W4GuezDAP23NJqfC93JwIURmKouBjUvAxqYT4nd20FbtucDi/hANHi9h9KJ8PUnNITjlAYNpyBitruEBbgb9SgkWxcp/5W2av28+U0mm8eFWXGm1IeFOtcGnnBvyyog8t1a8B6Jy3iC1pF9TIen1xFg2vjRs38uWXX55wPDo6msOHz3LzxBp21VVXkZmZydSpU0lPT6djx47MnTv3hIQb4kSaqtC2fght64eQ178x93/TloJdDzBQW0+4ks9lRz/iuzWdubp7wypdd+H2QxT/O8PZ6NINhU/s5zEv/FqGdmvHFc2jaBIV6PI01iU2O4fzS0nLLiL5cAG/HC5gz6Ec8lM206BwM93UbQxR1xGqFADQQDnMNPOHrN/0N7ftvIdHx46iXQOprIQ4FV+zRuOoQBpHBXJ+23pAC/JL+jBnYxrXL1zJzblvM1xbjaYYPGuayY3fNqTdPTcTG+IZmVMNr+rPFqJ20VSFmGBfYoJ96ZIQBsQDbckpHMJf2zO4dclm+mV8yq3ab2iKwYXactI3vcRnCc9xQ69G1RLTf/fx8jbt6ofwTFB/KHY0vAZr61iw9ZA0vGpIlRteoaGhpKWlkZhYMQvKunXrqF/fe6bXTZw40WVTC+uqIF8zr17bnbFv3E/n7FsJVgq5VPuHsUuTGNMtvtK9TbpuMGPWEj4xlTfoJ9sn0PXC8XzVo2G19lpZTJpjamioH10bhR/3TA/Sc4pZlnyYhzYexLpzAdcwl0HaegA6qMm8X/oAd76bwYSbbqZ7YvjJ30AIcYJAi4nRXeMZ1TGOabOakbHmSW4w/YGPYudR/R2mz+vNC6M7uTtMp2M3WQbes5ZDiNosxN/MpZ0acEnH+ny/pjW3/9KaN9WX8VHs3GL6nevn9mJku9uIDKzaurizYaB41TQ9RVFo2boju1bH0VRNpYuyg9e37IIhzdwdWp1Q5flgY8aM4YEHHiA9PR1FUdB1naVLl/K///2PG264oTpiFB7MYtL4vxHd+cI+BAAfxU6bw3PZnpFX6WvM35LO0Ozv8C2b3veJbRidL7iV63omuLUyiw3x5dJODXj7hh68+OD/2DzoA643nmSn7uhgCFYKeUd9kVc/+ZLdmflui1MIb2UxaTxxSTtWt7qfDbqjM6+VmkLh+p/IyC12c3QOhvd2bAtR6ymKwpVd4xl99S08a7vGeXyC8RWf/ru3Wt7TwPDqES+Aoa1i+FPvDDiSHkWmLeZQnmfUubVdlRtezzzzDC1btiQ+Pp78/Hxat25N//796d27N4888kh1xCg83IDmUSz2H+4sD1STWLzjJOmkT+Hn1clcpS0EoNgw83v49VzXI8HlcZ6LUH8fJg5uxkv3/h9P13uDBXZHb7y/UsJ04yUe/XoJNnv1pfQXorZSFIVHL+7Aa8bVzmOj1b/4ce1BN0ZVUfmIl6zwEsITDW0dQ067cezW6wHQU93KihVLsOs100Dytnqhe2I4y7SuzvIgLYl/dnjHciFvV+WGl4+PDzNnziQ5OZnZs2fz+eefs23bNj777DM0zTM2shM1S1UVElt2IEV3ZGHrpm5nxfYDlXptXrEVfddCgpUiAH7Te3DFgC4eu6dPTLAvM28dxHeNp7FCbwlArHKUyw+9wcfV1LsmRG0XFWQhot15zjqkr7qJfzfucHNUDt7dry1E3TF+YDM+tZ/nLPcuWUJSylHXv5HhfQ2t//Ixqfg17k2+4VhL21PdwopkaXjVhLPOtxkfH8/IkSO5/PLLKSgo4OjRavjhFl6jb7Mo/tUde7hZFBvW1I0YlZijs2pvFn1JcpbnGz04r41nJznxMam8cm1PXg68jxzDsWn05doSFvw1n7ziU6fAF0Kc2kWdGjBX7waAqhgEpS8n10N+n47dZHnbWg4h6pKWscHsDB/kLA9W17Jg6yk2Y3YhR71Q7W/jct2bRLNGbw5AjJLNgeTNbo6obqhyw+vuu+/mgw8+AMButzNgwAA6d+5MfHw8f//9t6vjE16ibVwIW4zy6YFxpXs4lHf6PbMA1uw7Spey/cDshkJxXG+Cfc9yt+Qa5Oej8cBVg5luu9J5bJztW75ZleLGqITwXl0TwllJO2e5p7KZ1Xuz3BiRg6zxEsJ7dGjTis26416knbqXzXsqN/umKmpLldCjcTjL9dbOcsPctaTlFLkxorqhyg2v77//ng4dOgAwa9YskpOT2bZtG/fccw8PP/ywywMU3qFBmB97tUbOcktlP9vSz5xgY/OeNFoq+wHYYcTTunGD6grR5bokhHOo+RjSDEdGwyHqWv5Ytga9huaUC1Gb+PlolMR1c5bbqHvZmlb5JD3VydsX0gtRV3RNCGOd3tRZVtI3Vsvf5OPrBMNQvHL1Z8vYYDaa2zrLPdStrEh2f2dXbVflhtfhw4eJjY0FYM6cOYwePZrmzZtz0003sXHjRpcHKLyDqioY0eU9J82VA+ysRGZDPXM7muKowNbrjekYH1pdIVaLcf1b8qVtMODIDNQ7dw6rPKCXXghvlBBXjwNGJADNlANsT8t1c0RQe/q3haj92tUPYaPR2FluYtvF3iMFLn2P2jIKrqkKAY26UWg4Uu53V7exYo/cv1S3Kje8YmJi2LJlC3a7nblz5zJs2DAACgsLJblGHRcZHUu2EQBAfeUwB7NPP2RdYrPjV5TmLO83okmI8K/WGF2tW6MwVoVdgG44ertGqiv4fVO6m6MSwjs1jw1iux4PQLBSRFb6HjdHdExZVkPDO9dyCFFXRAf7kupXvh9VK3U/2ysx+6aqKox4OQ54pa6No50jhPWVI6Ts8YykRrVZlRte48aNY/To0bRt2xZFURg6dCgAK1asoGXLli4PUHiP+qF+pJb1VtdTjpB29PS9TOk5xcQpR5zlNCOCuFC/ao3R1RRFoVfHdqw2HAtUm6kH2b5xVaUSiwghKmoRE8ROo3y6sU/WTrdP3ZVfZSG8ixbRxPl9vHKIlKOFLr2+UYtGwTsnhDnvXwBCs9aRU+QZSY1qqyo3vB5//HHef/99xo8fz9KlS7FYHEOUmqYxZcoUlwcovEdcqB+pZeudfBQ7hUdPP/KTml2x4ZVtjvGKxBr/NaJdLHPt3Z3l9oXLq7SBtBDCISHCn/1GtLMcbWRyuODMSXpqigFeuZZDiLokMjKKLCMQgAZKJilZ1ZswwluzGgK0rR/MBqWFs9xF2cG6/ZKlvDqZzuZFV1xxxQnHxo4de87BCO8WF+rHvrIRLwAl5/QZ/lKzi4hTyveNsAfHVVts1alZdCBbA3tAyWcA9FY38++uI7SMDXZzZEJ4l8hACxlEOMtxymFSs4uJDvJ1W0xGLdizR4i6JD7MnxQjmnAln3pkkZqV49Lr16ZRcItJwxrbBf2QgqoYdFF38Me+owxsEX3mF4uzclYNr4KCAhYtWsT+/fspLS2t8NykSZNcEpjwPvVDfVlmlN80BRanU1Rqx8/n5Gv/UrOL6Fc24qUbCuYw78loeDxFUYhv2o70TWHEKkfppm7ny11p3NQ30d2hCeFVNFWhNDAOyga54pQs0rKLPCbpjjf3bAtRV8SH+5FiRNGBZFTFoPTIfqCPS9+j4hov7x4Hb5UYz46MBrRUUmit7OOVPWlAizO+TpydKje81q1bx8iRIyksLKSgoIDw8HAOHz6Mv78/0dHR0vCqw2JD/Jyp1QFilKMcyismISLgpOen5hQ5pxoeIpSYMO8dIerTLIqlG9pwubYEf6WE4j0rsdl7YtLOeo9yIeokNSQeyvY8rccRtuUUuzUeA0PSyQvhReLD/VljRDnLWt5Bl16/ttUGXRLCWPtvM1qqKZgUHePgamz2/nL/Uk2q/H/1nnvu4aKLLuLo0aP4+fmxfPly9u3bR5cuXXjppZeqI0bhJQItJnJN5SNe0crR026inHE0l2glG3Ak1qjvZYk1jtercQTL9DbOcgfbRjanekIqbCG8S2h4BHmGoy6opxwh9QzZUWuSt/dsC1EXRAVaOGyEOMtBtqMUltpc+h7/zWqoePFQeOeEMFbr5Qk22ti3V2ofVnF2qtzwSkpK4t5770VVVTRNo6SkhPj4eF544QUeeuih6ohReBF7YKzz+2glm4zcU/dWW4+W90IdNCKIC3XfOo5zFR3sy8HQLs5yN3UbK2U/DCGqLDbEl/SykfNY5SgZOe5teDnWeNW2Pm4haq/IoIoNr0glh8N5pad5RdXUtqzFkYEWMkI6OMtd1e2s2ScJNqpLlRteZrMZVXW8LDo6mv379wMQEhJCSsrpkymI2k8JOq7hxVEO5Z58xMswDLTcA85yqhFJvRDvHfECaNS4FQfL1rh1UXeyOvmQmyMSwvtEB/lyyAgFwF8pIS83263xHM/Rs+3uKIQQpxPgo5GrhTrLkUoOmfmuzY56fDVQG0bCYxu15rDhWO7RWd3Jmr1HzvAKcbaq3PDq1KkTq1atAmDAgAFMnTqVL774grvvvpu2bdu6PEDhXYKCwygyfACIUbJPOdUwt8hGuK28YZLq5VMNAXo0jmCl7tjLzl8poWDvarfvQSSEt4kKspBJeW+1kefeDckdKeSFEN5CURRsfuVrvCLJ5bALG161bMALgC6NwllTNt0wRCnkyN5Nbo6o9qpyw+uZZ56hXr16AEybNo2wsDBuv/12MjMzee+991weYFWsXbuWYcOGERoaSkREBOPHjyc/P7/COfv37+eCCy5wJgO57777sNlcO/e3LosJ8SPDCAPK1nidYqrhweyiipsnE0FMsPdONQTonhjOCr2Vs9zGukn28xKiiqKDLBwqq0MAtALPGTl2ZDWUZpgQHi+oPB16pJLj0oYX/Hf6sffXCV0bhbFGb+YsxxdsIM3N07xrqyo3vLp27cqgQYMAx1TDuXPnkpuby5o1a+jQocMZXl19UlNTGTp0KE2bNmXFihXMnTuXzZs3c+ONNzrPsdvtXHDBBZSWlvLvv//yySef8PHHHzN16lS3xV3bRAdZOEQo4Og1OZp78gQTaTkV9/Aq9quHj8m7M+jEhfqxP6ijs9xD3SrrvISoouggC5nHrc8IsB6hqNTuxohkjZcQ3sYSFIndcDSIXL3G62S8vT+maVQgW8ytneUuyg5Z51VNvPtO9zizZ8/GbDbz1ltv0aJFC7p168aMGTP44Ycf2LVrFwDz589ny5YtfP7553Ts2JERI0bw1FNP8dZbb52wH5k4O9HBFuf6DAB7btpJz3NsnlzeKDFC4qs7tBpRL7EdmWXzpLuq21l5knVe2YWlzN2UxrzN6RRb3XtDKYSniQqqWIdEK9lkniY7anU7flqRUQt6toWoCyKC/MkiCKj+Ea/a0C2jqgq+8Z0pMRy7THVRpeFVXarc8MrIyOD6668nLi4Ok8mEpmkVHu5SUlKCj4+PM/EHgJ+fY83QkiVLAFi2bBnt2rUjJibGec7w4cPJzc1l8+bNp7xubm5uhYc4NcfC+PJpQkpexknPO5hd7BzxKjbMBIXFnPQ8b9OjSfk6r2CliOw96ypkQFqzL4vbX/qIgm9uJeur27jhxa/ZdSj/VJcTtYTUI5UXaDGRo5VvSxGlZHMoz717eR1/kyVNL+EOUodUTeRxKeUjyeFwNdch3p9eAzo0imGj0RiAxmo6O/fsdW9AtVSVG1433ngja9eu5dFHH+X777/nxx9/rPBwl8GDB5Oens6LL75IaWkpR48eZcqUKQCkpTlGXdLT0ys0ugBnOT395Au4n332WUJCQpyP+PjaMTJTXWL+M+LlX3r4pKM6qUcLnWu8DhqRxIV5d2KNY3r8Z51X8+KNJB8uAOBQXjEvffwN79sf5XLtH642LeTtkik89MlcSm26u0IWNUDqkcpTFAU9sHx9RvRpkvTUBKNW9GcLbyd1SNVEBvqQWXYvYlFsFOW5LkufY4uJ48q1oNEFjo2Uj1/n5Z+xxuX7n4mzaHgtWbKEL774gttvv51LLrmEUaNGVXi42pQpU1AU5bSPbdu20aZNGz755BNefvll/P39iY2NJTExkZiYmAqjYFX14IMPkpOT43xIyvzTizouFTRAjHL0pNOEco5mEqg4eqDSjHDivDyj4TENw/3Z7dfeWe6hbmNFsmNK5avzt/OQfQYBSvn/j0gll9G5n/DNavm5qs2kHqkaJbC8gywK9041hP+MctWOeyzhZaQOqZqoIF8OH5cdVc/PrNb38/Y1XgAd4kNJMlo4y52VHaxPyXFjRLWTqaoviI+Pr9HN4+69994KCTJOpnFjx9DoNddcwzXXXENGRgYBAQEoisL06dOdz8fGxrJy5coKr83IyHA+dzIWiwWLxXKOn6LuCPY1kXXcNKHosmlC8eH+FU/MqbiHV21peCmKQmTjTuRs9ydEKaSXupn7t6TQJSGMorVf0868F4AMI5QQCvBVrFyq/sM1S9ZwXY+GkjGtlpJ6pGoCQiIoOWTGoliJUrJZ4c6phjLgJTyA1CFVExnoQ9JxSXrUAtc1vAyMWplwJ8BiIi+6E5Qt7eqmbuOfPUfo1STi9C8UVVLloaBXX32VKVOmsHfv3moI50RRUVG0bNnytA8fH58Kr4mJiSEwMJBvvvkGX19fhg0bBkCvXr3YuHEjhw6VJzz4448/CA4OpnXr1ohzpygKekB5b3W0kn3CJso2u465oDzpRioRxHn55snH69s8mgV6Z8CR2VHbNZ8pXy/nf6ZvnOfcY72Dd+0XAqApBl2OziUpJdsd4QrhcaKCfJ17ebk7uYaDUfZfpVas5RCitosKKl/jBRBYjdlRa8tUQ4BmjZuwS48DoJOyi5Ubt7k5otqnUg2vsLAwwsPDCQ8PZ8yYMfz99980adKEoKAg5/FjD3d68803Wbt2LTt27OCtt95i4sSJPPvss4SGhgJw3nnn0bp1a66//nrWr1/PvHnzeOSRR5gwYYL0JLmQGlQ+ehjN0RPWZ2TklVCP8t6nVCOCuFDv3sPreOe3jWU2/Z3l69X5DDj8JfXL1rQttHeg66BL+TfofOc5l2uL+WGNTB0RAiA6uHzKcoSSx5HcArfFUvv6tYWo/aKCLM41XuDazIaOSV+1s2Y4r3Usv+vdAVAVg8aH/2LPYffVv7VRpaYavvrqq9UchmusXLmSxx57jPz8fFq2bMm7777L9ddf73xe0zRmz57N7bffTq9evQgICGDs2LE8+eSTboy69vE/bppQtJLN8v9sorz/SCENlfJRx0NqLOEBPv+9jNcK8jUT1mYoe7d8QCM1g17aFnqxBQCrofGO70181L8xJlVh+aJW9FS30kRNI3njMmwXt8Wk1ZpdHoQ4K/+9aSrNOXl21JpyrD/bsYGyW0MRQlSCIztqqLMcVZak54RlDy5gUDvWeAF0TwzndUs/7rT/DMCF2nJ+TUrlrqHNTv9CUWmVaniNHTu2uuNwiU8//fSM5yQkJDBnzpwaiKbuig7245ARSrySSbRy4ojX/qyCCg0ve2ijWre2aeLQljy/6VreUadXOP6O/SKuGTmMAIuJizvEMfOvXvRUtwLQr/QflidfTt9mke4IWQiPERVk4cBxDS81330Nr5pc0yyEcA1FUbD7R0PZ7UcULhzxovbm2NFUhSZtu7N7XT2aqGn0VLfy6op/uWNQE8zSKewSlf6/qOs6zz//PH369KFbt25MmTKFoqKi6oxNeKnoYAsZOPbyClfyycrNq/D8viOFNFQcN1KlhoZfZO1Li5sYGUDvC8byqPVGCgwLpYbG+7YRHO5yL6M6OuZPN4oMYG/0UGyG49fwIm0Zs5IOujNsITxC9H9GvMzFh7Dr7m8A1eYbLiFqGzWofFuKSCWn2taK1ra1n1d1S+BL+xBneUTRLOZtPvmWS6LqKt3wmjZtGg899BCBgYHUr1+f1157jQkTJlRnbMJLRf8npbwtp+Iv7L4j5SNeKUY0DSOCajK8GnN9r0acd+Mj3NdkNhMazSbs0pd44pJ2FUb3BnRqyVK9LQANlMMc3LxY9vQSdV5UkIVDhJaXyeZIgXsSbDgaW+5v9AkhqsYnKNLZsRnlwiQ9tX0UvF2DELbXu5hCw5H7YLS2iM/mL8dml3sTV6h0w+vTTz/l7bffZt68efz888/MmjWLL774Al2XfwhRUXRQxU2UyU2rUFHlpO9x7mW1z4ghIcL1c649Rb9mUbx9Q3dmjuvN5V0anDCl8oL2cczSeznLg21L+Gdn9e43IoSniwiwcLhCwyvnhOyo7iBrvITwHpHB/hwhGIAYJZuMXNdtS3F8Z0xtrBfG9G/HV/bBAPgppYzK/pQvV+53c1S1Q6UbXvv372fkyJHO8tChQ1EUhdTU1GoJTHivhAh/9hrlmQ0bWPeQXlbhFZXaCcza5Hxui5FAq3rBNR6jp6gf6sehuKGUGI7llhdoy/lt/YEzvEqI2k1TFay+Uc5ytHKUTBetz6gqw5ARLyG8Uf1QPw4YjnokWskm/XCWS65bF2qDkW3r8WfkdeQZjq1+rjEt5K8537NXMhyes0o3vGw2G76+FVN+m81mrFary4MS3q1huD/J5qbOchtlL5sO5gKwJS2XNsoe53ObjURax9XdhhfA4I7NWKx3ABy9cke2LKLYWj37jQjhLYygivsBZnrKiFctWsshRG2WEOHPPqO8HrEd2XOas6um4ohX7Vv7qaoKEy/sySu2K5zHnlHf4oFP/iCnSO77z0WlshqCY07rjTfeWGG/q+LiYm677TYCAgKcx3788UfXRii8jqIoqLFt0dMUVMWgnZrM/JRshrWOYXnyEboqO5zn5oW3xd+n0j+GtdLI9vWYNqcXw7Q1AAzTl7Bw29WMaFfPzZEJ4T6WkFjsRxU0xSBOOcKmo4VuiaM23lQJURckhAewwChPsOFfsJ9Sm46P6dyy89XyJV5OfZpG8nuXW/l33Rp6a1uIU7J4NOcx7njfj9fHDSQiUPa/PRuV/ukbO3Ys0dHRhISEOB/XXXcdcXFxFY4JAdCiYRzbjQYAtFX2sm7LdgCWb95FN9WxE/oePYbExi3dFqOniA7yJbfhUIoMx15mI7SVzFonc6lF3ZYQFeKcstxMOciOtGz3BkTt2q9HiNquYYQ/+/TyEa+GZHDARR04yn9KtW1LnGMeurANr4Xcx0EjAoC26l6mZk7mtjd/Ys2+o26OzjtVeqjho48+qs44RC0zqGU0f/7bhVZqStnu5wv4YU1nEtLmYjI7ErIs0DsztE3sGa5UN5zXqQkLDnTiQm0FEUoevtt/YndmG5pEBbo7NCHcomW9YLYaDWlCGhbFSkHadqBHjcdhGIas8RLCC4X4mTni2xDKcsC1UvezJS2Xxuf8d7Xu1Af+PiZeuul8Jr+dw9vWR4lQ8mihHuCDont48r0b+KXbdUwc0ozoIN8zX0wAVRjxEqIquiaEsdSnr7N8m2kWb34/lwmmX5zHFvgMpmfjcHeE53FGtq3H98pwZ/lO7UdenbfVjREJ4V6t6gWxTW/oLIflbiOn0N1rC2SFlxDexKd+e6yGBkB7JZkNB3Jcct3avsbrePHh/kwbfwW3W54nWXd0lgcrhbxknsFla8fy0Auv8uAPG9joov+3tZ00vES1MGkqvfsMZKHdkTSivnKEhZZ7qac4sgotsHeiV59BWEyaO8P0GCH+Zlr3voBl9tYAJKoZ1N/6Pn9uyXBzZEK4R9PoQDar5Ul6eqpbWJZ8uMbjkH28hPBereOj2Go4OnCaKKls33vuWYPryhqv4zWNDuLNiZfzaNRr/GAv71TvqO7mfe1Zblk/ml/fmcLVL3zLM3O2sjz5iCQJOwVpeIlqc1PfRN7xv40jRsUNkjONEN4Nmsgt/RLdFJlnurVfY97QrsNuOPrO7jH9wIff/sDuzHw3RyZEzbOYNNSGvSgxzAD01zaycOsht8ZkoMgiLyG8SKeGYazRmwOgKgaBB5dwtKD0nK97fC1QG/fxOpnoYF8+ueM80ge/xq22B9iqxzufa6Km8bD5S74qvJVLl49m3Yd3MemJZ7n+jd95ctYWvludQlJKNgUlNjd+As9Qt9PJiWoVaDEx7aaLuP0Dg2uLPqeFksJmoxHfBN7A8zeNqPPZDP8rLMCHSy+8mA9+Xsp4029YFCuvGc8xYYYvj4y7jPYNQt0dohA1qmeLeFbsb0l/bSMNlMOkbVpIwcVtCLDUYN1h1O5pRELUZj0bR/C50plxzAPgPHUlczenc3X3hmd45anVwQEvJ5OmMmFQUw52uoc3/xxJ3rofuUb9g97aFuc5rdT9tFL3A7PgCCRnxrLdiGeR0YD39QbkBCSihicQHh5J/VA/6of5US/El8hACxGBPoQH+NTq2VBy5yuqVbOYID669yrmbOzPb1mFJEYG8Gm7eviaa+8v1bm4oksDJm6ZyIpdu+mhbiNKyeF928M8NGMfjQdcy/j+jQmsyZtOIdxoZPt6vDSvH/21jQDcoP/MzH9GcvfQ5m6Jp7av5RCitvHz0TA3HUB28uuEKgWMUFdw/eI1XNmlASbt7Cd9VVzjVXuzGp5K/VA/nr2iI2nDWvDtqut5bcVyuhYuZpi2ho7q7grnNlbTaUw6I1jlOGAFMiA33Z+DRiQHjEj2GxGsM4I5QjBZRhCF5nB0/wiUgEhM/qH4+foS5Gsi0GIi0GIm0NdEkMVEoK+JAIsJX5OKr1nDYlbxNWn4mjV8zSoWk4bFpKKqnvPvI3dwotoFWExc2TX+zCcKFEXhxTHduXXGY9x/+GE6qMkEK4W8aXqVBYsXMX7paFp3HcglnerTJi64zlX2om6pH+pHUdMLSd/7FbHKUYZq6/jx769Z1XQi3RrVTGIeA4O63ccthHe7qkcTvto5mNtNs/BR7IzOeZ8Zi9oycXCzs7peXVzjdSr1Qvy4a2gz7hzclI0HL+LPrRm8tGU7wZlr6KTsoIu6gzbKPizKiYmRgpVCgpX9tOIU2+cUlT2AIsOHfPzIM/zIx498w488/MnHj8OGL8X4UIyZEqPsKz6UYKbY8KEYH+yaBUOzoGu+oJrBZAbV5PheczxUzex8TtV80DQNs6Zi1hRMmsr9w1sQHXzu2Rul4SWEh/H3MfH2rcOY9IkvYw4+wwjN0Us0RFvHENaxeWUCvyzrzbN+3Ylu0oEuiZG0qhdE85gggnzNbo5eCNe66/x2vPDmGKab3wHgBe0t7vrAh20XXM013Rui1WBPZl1ZyyFEbTKgeRTvhl/JNTkLCFEKuVxbwiML3uJ98yRu7pt4lh2Yxkm+q7tUVaFDfCgd4kPhvBbkFY9gfUoO/+w7yrsHsyjM2I1/zg6acJDGajpxHKa+cph6yhF8lDMn4fBTSvGjlCjlHDMn2sselUiQazcUbJiwovG8bQwFg545t/cuIw0vITxQiJ+ZmbcM5PU/45i35DPu174kriwjZBt1H23UfWD7iiPbgli7pRnLjHg+1uPJCWyCEtbQOXc6LtSPyLI502EBPoT7+xDiZ/aoYXchTqdVvWAie9/A7OXruFBbTqBSzEz1Ob6evYKr/x5N325dGdwymtb1gqvl59owJKuhEN5MVRXuu7w/T793HS+a3wPgafNHfDLvANetv5XrB7ZnSKsYzJWcemhIfXBGQb5m+jaLpG+zyLIjPSi22knOLGDfkQK2ZBfxR3YRqVkFFGWnouSmYSo+Qhi5hJNLhJJLhJJHBDkEKkUEUkSQUkQQhQRShKZU/7+BphhoWLFgRUPH5KK/L17T8Jo2bRq//fYbSUlJ+Pj4kJ2dfcI5q1atYsqUKaxZswZFUejevTsvvPACHTp0cJ6zYcMGJkyYwKpVq4iKiuLOO+/k/vvvr8FPIkTl+JhU/nd+S7a0f4Cn/7iA4B0/MEZbWGH+dISSxzBtLcNY6zhQSoW50weNCI4YIewmiCwjiKMEkU0QVp9QdJ9AFEsQiiUQ1TcIX4svARbHHGp/H8ccaYtJxWJS8SmbJ+1TVraYNXw0FYtZdZ5j1lQ0VXF+NamO4XmTqjjLMjVSnI37z2/JhIxH8dkzlfO0NaiKwTWmvxhTtJBVi1ow568OvGBugxLTliYN6pEY6U+DMH/iwx2dD65K5ONY4yU/w0J4my4JYSwf+n+8syCN202zABhr+oNLM5fyw9f9uMnUj5BmPenRJJq2ccG0qhd82rXoFWoB+btWKb5mjdZxwbSOCz7p87pukFts5XB+KVkFpRzJL+FgQSn5JTbyi23kl9jILbaSX2TFWlyAXpILxbkYpQWotmIUezGqvQRfrPhSiq9SiqXse8tx35uxY8KOSbE5vzdjx4wNEzbMStnzZcdN2MgygvExuSYRvNc0vEpLS7nyyivp1asXH3zwwQnP5+fnc/7553PxxRfz9ttvY7PZeOyxxxg+fDgpKSmYzWZyc3M577zzGDp0KDNmzGDjxo3cdNNNhIaGMn78eDd8KiHOrHVcMG+P7c2uQ+34ce2tPLt2NW0KltNL3UJ3dSshSuEJrznj3GkDKCl7lCkxTBTiSwG+5Bt+FGOmFDMlRtnXskeh4fhaislxzPChFBNWTNjQnA87KjajYllXTOiqhqKYMFQNQzVjKCYUVUPXTCiqGVQNFA1UjQIthA9uGUCIv0yhrMtMmsqbY3vz+C/TWbnmPe42/UCgUoyqGPRQttFD3QaAnq6wLy2afUYsKUYUy40oUo0I8k1h2PwiUAKjsARGEhroR1iAD8G+JoJ8zQT7mQj2NTu/D/I1E+xrIsDHVDbiJYTwdncMbMLrtqk88nckj5g+x1exEqwUMs40j3HMI3tHAGu2NWex0Yh3jUQKghtjimhEg8gwEiIcnTn1QnzJK5aU6NVBVRVC/X0I9fc562sYhkGJTafEqlNsszu/FlvtFFt1Smx2bHYDq13Hpju+FtkN8nQdq93AZnd8teo6tmNl3SDGrrssm67XNLyeeOIJAD7++OOTPr9t2zaysrJ48skniY93JHJ47LHHaN++Pfv27aNp06Z88cUXlJaW8uGHH+Lj40ObNm1ISkpi+vTp0vASHq9pdBD3n98SY3gLdmdewvLkIzyafISs1F34Hd1Oc/aTqKZTn8PEVWHu9DEWxYaFfMLIr7k7Tb3s6ynmXN9nHY9u9K+hYIQnM2sq0y7ryL/tn2T8byPpcGgWV2iLaKKmOc9RFYNEJYNETrLxeFlHg35YIZsAjhpB5OFPruFPLv5kGP7swp9cI4Bc/Mkz/MlT/MnV/TFbHL9HssZLCO+lKAp3DWvO3w0f4oafe3N53hdcrP2Ln+LY1ytUKXCupQagCPQUhbSUcPbrMaQYUewgjHQjgh7q8RsxS6XgKRRFKctoqBGCZ3bYek3D60xatGhBREQEH3zwAQ899BB2u50PPviAVq1a0ahRIwCWLVtG//798fEpb00PHz6c559/nqNHjxIWFnbCdUtKSigpKR8WyMlxLOzLzc2t3g8kxGlE+8LFrcO4uHUY0JQS2zCSM/M5kFXE6pwiUnOKST9aQHHuIYyCLLTiLPxsuYQpeYSRT4hSgD/FBCgl+FOEv1JCAMXHHSvGFytqDcyjPp2iUiv5+Xlo9pNXoMd+Dw0PTzMl9YjrtI324Z0b+5GU0o73N93Eru0baZi3jlbKXlqpKTRVDuJ3kgxa5QxM5BFFHlFnerOyH6vcsn+6fN1OcUG+/LvVMt5Qj0gd4jqd6/ny9q3nMWdjO25cuZWYjL/pq26ih7qVMKXgP2cbBHGYNhymzfGHdcgtG/gqtRbLv0MdV6U6xPAyH330kRESEnLS5zZu3Gg0adLEUFXVUFXVaNGihbF3717n88OGDTPGjx9f4TWbN282AGPLli0nveZjjz12LJewPOQhDw99pKSkuKyOqQ5Sj8hDHp7/8OR6ROoQecjD8x+VqUMUw3BfF8+UKVN4/vnnT3vO1q1badmypbP88ccfc/fdd5+QXKOoqIiBAwfSsmVLJk6ciN1u56WXXmLbtm2sWrUKPz8/zjvvPBITE3n33Xedr9uyZQtt2rRhy5YttGrV6oT3/28vk67rZGVlERERccZEAbm5ucTHx5OSkkJw8MkXE3oiibtmSdxnzzAM8vLyiIuLQ1Vds/C1OpxtPeIJ/4/PhrfGDd4bu8R99ryhHpF7EYm7ukncZ68qdYhbpxree++93Hjjjac9p3HjxpW61pdffsnevXtZtmyZ80N/+eWXhIWF8csvvzBmzBhiY2PJyKg49/9YOTY29qTXtVgsWCyWCsdCQ0MrFdMxwcHBXvVDfIzEXbMk7rMTEhLitveurHOtR9z9//hseWvc4L2xS9xnx9PrEbkXkbhrisR9dipbh7i14RUVFUVU1Bln2VdKYWEhqqpW6Pk5VtZ1xwr+Xr168fDDD2O1WjGbHWtG/vjjD1q0aHHS9V1CCCGEEEII4QqeOaZ+Evv37ycpKYn9+/djt9tJSkoiKSmJ/Px8AIYNG8bRo0eZMGECW7duZfPmzYwbNw6TycSgQYMAuOaaa/Dx8eHmm29m8+bNfPPNN7z22mtMnjzZnR9NCCGEEEIIUct5TVbDqVOn8sknnzjLnTp1AmDhwoXOtV2zZs3iiSeeoFevXqiqSqdOnZg7dy716tUDHMOA8+fPZ8KECXTp0oXIyEimTp1abankLRYLjz322AnTAzydxF2zJG5xKt76/9hb4wbvjV3iFqfirf+PJe6aJXHXDLcm1xBCCCGEEEKIusBrphoKIYQQQgghhLeShpcQQgghhBBCVDNpeAkhhBBCCCFENZOGlxBCCCGEEEJUM2l4CSGEEEIIIUQ1k4aXEEIIIYQQQlQzaXgJIYQQQgghRDWThpcQQgghhBBCVDNpeAkhhBBCCCFENZOGlxBCCCGEEEJUM2l4CSGEEEIIIUQ1k4aXEEIIIYQQQlQzaXgJIYQQQgghRDWThpcQQgghhBBCVDOTqy9YUlKCxWJx9WU9hq7rpKamEhQUhKIo7g5HiDrNMAzy8vKIi4tDVb2nH0nqESE8hzfWI1KHCOE5qlKHnHPD6/fff+frr7/mn3/+ISUlBV3XCQgIoFOnTpx33nmMGzeOuLi4c30bj5Gamkp8fLy7wxBCHCclJYUGDRq4O4xKk3pECM/jTfWI1CFCeJ7K1CGKYRjG2Vz8p59+4oEHHiAvL4+RI0fSvXt34uLi8PPzIysri02bNvHPP/+wbNkybrzxRp566imioqLO6oN4kpycHEJDQ0lJSSE4ONjd4QhRp+Xm5hIfH092djYhISHuDqfSpB4RwnN4Yz0idYgQnqMqdchZj3i98MILvPLKK4wYMeKkw2qjR48G4ODBg7zxxht8/vnn3HPPPWf7dh7j2JB+cHCwVHZCeAhvm2oj9YgQnseb6hGpQ4TwPJWpQ8664bVs2bJKnVe/fn2ee+65s30bIYQQQgghhPB63rGKVAghhBBCCCG8mEuyGk6ePPmkxxVFwdfXl6ZNmzJq1CjCw8Nd8XZCCCGEEEII4VVc0vBat24da9euxW6306JFCwB27NiBpmm0bNmSt99+m3vvvZclS5bQunVrV7ylEEIIIYQQQngNl0w1HDVqFEOHDiU1NZU1a9awZs0aDhw4wLBhw7j66qs5ePAg/fv3rxXJNYQQQgghhBCiqlzS8HrxxRd56qmnKmTWCQkJ4fHHH+eFF17A39+fqVOnsmbNGle8nRBCCCGEEEJ4FZc0vHJycjh06NAJxzMzM8nNzQUgNDSU0tJSV7ydEEIIIYQQQngVl001vOmmm/jpp584cOAABw4c4KeffuLmm2/mkksuAWDlypU0b97cFW8nhBBCCCGEEF7FJck13n33Xe655x7GjBmDzWZzXNhkYuzYsbzyyisAtGzZkvfff98VbyeEEEIIIYQQXsUlDa/AwEBmzpzJK6+8QnJyMgCNGzcmMDDQeU7Hjh1d8VZCCCGEEEII4XVcuoFyeno6aWlpNGvWjMDAQAzDcOXlhRBCCCGEEMIruaThdeTIEYYMGULz5s0ZOXIkaWlpANx8883ce++9rngLIYQQQgghhPBaLml43XPPPZjNZvbv34+/v7/z+FVXXcXcuXNd8RZCCCHqiEO5xTw7ZytTftjAzow8d4cjhBBCuIRL1njNnz+fefPm0aBBgwrHmzVrxr59+1zxFkIIIeoAu24w6b3fmJr7BOFKLvetv4vnJ99GXKifu0MTQgghzolLRrwKCgoqjHQdk5WVhcViccVbCCGEqAM2Hcyh+9E5tFb3Easc5T4+4auV+90dlhBCCHHOXNLw6tevH59++qmzrCgKuq7zwgsvMGjQIFe8hRBCiDpg/YFsOqk7neX26h7Wbt7mxoiEEEII13DJVMMXXniBIUOGsHr1akpLS7n//vvZvHkzWVlZLF261BVvIYQQog5I2neUC9XdFY4FHN5AsfVCfM2am6ISQgghzp1LRrzatm3Ljh076Nu3L6NGjaKgoIDLLruMdevW0aRJE1e8hRBCiDrg0P5thCv5FY61Yg/b0yXJhhBCCO/mkhEvgJCQEB5++GFXXU4IIUQdk1NoJTx7I/hUPN5W3cum1Bw6xIe6JS4hhBDCFc664bVhw4ZKn9u+ffuzfRshhBB1hGN9164TjrdV9/D6wRw3RCSEEEK4zlk3vDp27IiiKBiGgaIozuOGYQBUOGa3288hRCGEEHVBUko2/Y5b37VFT6C1uo96ShYHDuwHpBNPCCGE9zrrNV579uwhOTmZPXv28MMPP5CYmMjbb79NUlISSUlJvP322zRp0oQffvjBlfEKIYSopTbtP0RrZS8AyXosS/S2zud8Dm2k1Ka7KTIhhBDi3J31iFdCQoLz+yuvvJLXX3+dkSNHOo+1b9+e+Ph4Hn30US655JJzClIIIUTtZhgGhSkbsCg2AJKMpmzSGzmfb2Eks/NQHm3iQtwUoRBCCHFuXJLVcOPGjSQmJp5wPDExkS1btrjiLYTwCiU2O/uOFFBUKtNrhaiKA0eLaFyy1VlO0puw2WjkLLdT97D5YK4bIhNCCCFcwyUNr1atWvHss89SWlrqPFZaWsqzzz5Lq1atXPEWwsvUxSlBvyQd5ManZ/DF9Hu5bdobfLZ8n7tDEsJrrEvJpuNx67v2WFrSonUn8g1fANoqe9koCTaEEEJ4MZekk58xYwYXXXQRDRo0cGYw3LBhA4qiMGvWLFe8hfAS+44U8MhnfxCQuR5T8yE8N6YngRaX7Vrgsf7cksGy76bzlfl9MDuOPT5rN/ODHua8NrHuDU4IL7BmbxY3KTsAKDHM+MV3oE2DMLbsTKC7sp14NZM9KQeAtqe/kBBCAL9vTOP3jQdpXT+MG3ol4O9T++9Fjldis7MzI5/oYAvRQb7uDkeUcclPYffu3UlOTuaLL75g27ZtAFx11VVcc801BAQEuOIthBcwDIMpn/7F69l3EmXOZdXu2bw87yMeu7h23ygVW+2898sCPjN9XOH4Q6YvuO7n7gxscS0+JpcMLgtRa+3ek0yCegiA9UZjOjSKoV39EDbpiXRXtwPge2gtxdZh+Jo1d4YqhPBwczelsfrrp3nO9B1Lt7bh8YyXeGF0F3eHVWPmbU7n4+9/pod1Bfv0GMwdr+TxSzrUucanJ3LZv0BAQADjx4931eWEF9qdWUCrw/OIMjvWYXRTd/Dq+qUYF7WpsL1AbfPh0j2MLfwIi2arcNxHsXNJ0Y/8tnEQl3Zq4KbohPB8+SU2gjLXOEeL1+jN6ZIQRqt6wXxjNOcm5gLQ2dhKUko2PRtHuDFaIYSn+3LRBt43fYWPYmeYtpZZ638kc0RbooIs7g6t2q3bf5Sfv3qXz7VX0EyOLZ6Wb/qbidnPMeOm/tIR7GZn/X9/+fLllT63sLCQzZs3n+1buVR2djZdu3alY8eOtG3blpkzZ7o7pFpj2e7D9FIr/js3Lt7CgaNFboqo+mXmlbB84Wwu0FY6ykYwPYvfIM/wA+By7R9+WLLRnSEK4fGS9mfTuWyaIcA6owUdGoQS4mcmO6qb83gPdSsrkrPcEaIQwktY7Tpa2jp8lPIkV8PU1SzakenGqGqGYRi89PMyntdmoCmG83hPdSvXpTzGA9+tQdeN01xBVLezbnhdf/31DB8+nO+++46CgoKTnrNlyxYeeughmjRpwpo1a846SFcKCgpi8eLFJCUlsWLFCp555hmOHDni7rBqhRW7D9FD3VrhWAc1uVYviH9l/lb+Z3zsLL9su4rRg3vwrX0gABbFSkL6fPYcPvnviF03mLspnWfnbOWLFfvIK7bWQNRCeJZlyYfprm5zlgtju+Ln45hO2KJJU3bpcQC0V5JZn3zQLTEKIbzDrkP5tDKSKxzroOxm44Fs9wRUg/7ekUmPQ98QrBQCsFlPILesI3iwlkS3zdN4cd62011CVLOzbnht2bKFCy64gEceeYTQ0FDatGnDsGHDuOiii+jbty+RkZF07tyZPXv2MH/+fG644QZXxn3WNE3D398fgJKSEgzDwDCk9X+udN0ge/cqgpWKo1uJStopGx3ebmtaLiVrv6K9ugeALXoCtvbXMGlIMxb4DHKeN0pbyi9JJ94sFpXaGf/+QtK+nsTo5ZcSPvtmbn75CzbV4oaqECeTtG0nHVTHjdJmPYE2TRs5n+vROJyVeksAzIodNWUZhaW2k11GCCHYdDCHNmV/l49pqGaSnHLATRHVnG/+3ckN2h8AlBoa40snc4f+P0oMx8qia0wL8V36Ai/8vrVOZp/2BGfd8DKbzUyaNInt27ezbNkybr31Vtq2bUv9+vUZOHAg7777LqmpqXz11Ve0a9eu0tddvHgxF110EXFxcSiKws8//3zCOW+99RaNGjXC19eXHj16sHLlyirFnp2dTYcOHWjQoAH33XcfkZGRVXq9ONG29Dzalm444XhjJY3kzNrX8DIMgxdnreU+0zfOYy8YN3DfiNaYNJVm7XuzU68PQHd1O8vXJp3QwH/ilyRuSnmEcaZ5NFHTGKGt4v3SKTz1wbekZBXW6OcRwl0O5RUTnbHUWV6kd2Bg82hnuWdiBP8YHZzlAcZq/tl5uEZjFEJ4j00Hc2ir7D3huCljPVZ77W1sZOaVYNo1j1DFcc/1m96TOy8bzM3X3cD99tud591l+pHW/97FuGdnMv6TFTwzZyvfrkohq6D0VJcWLuSS5Bpdu3ala9eurrgUBQUFdOjQgZtuuonLLrvshOe/+eYbJk+ezIwZM+jRowevvvoqw4cPZ/v27URHO/5Yd+zYEZvtxB7R+fPnExcXR2hoKOvXrycjI4PLLruMK664gpiYGJfEX1f9u/swvY9b33XQiKC+coQwJZ/MQ6lAh1O/2AvN35JBh/2fEGs6CsAf9i50GjCKmGBHytZRnRvw86o+3Kd+C0DnnD9Zf+A8OsaHArAzIw896Wv6mCuuiQtWinjZ/hz3f1aPjyeMlEWwotZbtD2TQVqSs7xC68ytjcKc5RB/M0XxAyhJexOLYmWotpbpm9MZLts0CCFOYs+Bg84Mqcdrpe9mR0YebeJC3BBV9fsl6SCXqYuc5VnKIN7oEEeAxcShUeN56uejPGr+HIALtRVcaF9BSbKJlN3R7DAaMGn2RVx35WjObyt1a3XyuLu6ESNG8PTTT3PppZee9Pnp06dz6623Mm7cOFq3bs2MGTPw9/fnww8/dJ6TlJTEpk2bTnjExcVVuFZMTAwdOnTgn3/+OWU8JSUl5ObmVniIE63Ymepco3HQiGChvaPzOeXILjdFVT2KSu188MsCbtMce9RZDY2ZvuMY37+x85xO8aGsDh7iLF+qLeHHNSnO8tt/beMO7Rdn+ebSe0nSmwDQQDnMtYdf482/dlb3RxE1ROqRU5u/fi+D1CQAcg0//Jv0wqxV/NPUr20jluptAKinZJG6bTl2WSAu6hCpQyrHrhuo6eUJrf6xl29n005NrrVT+Q3DYMHKDQxQ1wOO+7CwNkMIKNtH9apuDWl00f1Msk0i2yjf5smi2GiqpjJSW8knTGXWV++wco8kMKpOHtfwOp3S0lLWrFnD0KFDncdUVWXo0KEsW7asUtfIyMggLy8PgJycHBYvXkyLFi1Oef6zzz5LSEiI8xEfH39uH6IWKrXpWPcux1dxJIZYam9LslHeyI0u3V+rhrDf/GsHtxW9h6Xs835gH8m1Fwx2JgMAUBSFXl06s6JsbUoz9SC71/9DqU13rHnb9CON1AwAltjb0LDX5TwT9DBHjUAALtBWsm/xZ2yoA4uB6wKpR07uSH4J5uQ/CSpbGzrP3o3h7U78fzOsVQx/6uV78PQpXcq/u2W6oag7pA6pnOTMfJrpu53lWXov8g3HTJR26h42HKidDa/Nqbm0y5rrzGT4g70fl3dtWOGc63smcOP4yUyK+YSp1rH8Zu/ONj3euf5LUwyeN83gpe/+kvVf1cirGl6HDx/GbrefMC0wJiaG9PT0Sl1j37599OvXjw4dOtCvXz/uvPPO065Be/DBB8nJyXE+UlJSTnluXZWUkk1XvXx911K9DclGPWe5kZJeaxJsbEnNZe+SbxhcNjUqzQhnWYObuLhD3AnnXtapAT/Y+znLw6wLWbj9EO/8tYMJ2s/O4zOVK7hrSDMevXooU+03OY8/oX3ItK8XUmy1I7yb1CMnN2dTOhep5eu75ip9GNb6xGnfDSP82RM5CKvh6Ny4TPuH71ftq7E4hXA3qUMqZ1NqDu2OS6yxQW/CJiMRcMwm2Z9SO+uNH9akcLlWPntrqf8weiaeuN9h54ZhfHrHMMbd8ywFoz7kl97fc030T/xi7w1AoFLMJbmf89vG1BqLva6pc1tYd+/enaSkpEqfb7FYsFhq/4Z752LupnSuUsu3C8iM6oVqK4CytlZiWcOrS0LYKa7gHYqtdp746i/e1t53HnvGdj2PXNrtpBtEN4zwJ6P++RRnfIyvYmWU9i9jfltHi9x/aWpyVGor9Ja06jWSUH8fQv19aDzgemYvXsmF2nJClQL+L+dVnv+9GY9d3PaE6wvvIfXIiQzDYO7KzXxYNs0w0wgmoGX51Jj/GtatLX/P68gwbQ2xylEKt84np6gDIX7mGoxaCPeQOqRyNhzI4XrF0fAqMcykmePZoDemZ9lWNz6HNlBiG4HFpJ3uMl7FatfZkbSUFqoja+NqvTk9unZDVU+8LzkmMTKAxEjHlEO73oLr3ixg8JF1BClFXKYtYdyiJC7pWP+k9zbi3Lh8xKu4uNjVl3SKjIxE0zQyMjIqHM/IyCA2VhYDuoOuG2xdv8L5C79Kb07Xti3xj2pMaVnvtCOlfL47w3SJ5+Zs5bbs6UQojqmq8+1daND3aprFBJ3yNSO7NWeO3gOAMCWfQTk/c3dZwg2A94xLuaVforM8cXBTvoi4k0zDsfh3sJaEseJd/tpW8WdeCG+XlJJN24xfnVN2f7X34aJODU95/iWd6vODMdBZvoIFfL+m9qeHFkJU3u79B2msOmZAbTESGN2jMRv18r+xrY3d7Ej3/vuR4/29PZOhpQuc5R/s/bi0c4NKv15TFW4c0omv7Y5tcHwVK20y57A1Lc/lsQoXNbx0Xeepp56ifv36BAYGkpzs2I/l0Ucf5YMPPnDFWwDg4+NDly5dWLCg/AdM13UWLFhAr169XPY+ovJW7s2iZ3F5Fp3f7D25qH09GkUHs99wTBlKVNLZm+ndv8A/rTuAZeWbDNIcC1cPGaF8GD6Zu4c1P+3rRnWszzc+lzvLU8xfO/8oLNdb0aTnxUQGlvdimjWVJ64ewIN6eerXB01f8t63v3Aot/o6NYSoaZ8u3c31pj+c5fn+FzKoRdQpzw8P8EFrMZxDRigAw9Q1zF+8tFanhxZCVJ7NrqOmJznL6/XG9G4aydHQ8hkjHdTdJNWytdM/r9nLxdq/gGOU70Dc+c7RrMoa2iqGP/0vcJYv1v7l1/Uy3bA6uKTh9fTTT/Pxxx/zwgsv4OPj4zzetm1b3n///dO88kT5+fkkJSU5pwPu2bOHpKQk9u/fD8DkyZOZOXMmn3zyCVu3buX222+noKCAcePGueKjiCr6YtluRmuOhpduKGwPH0yzmCASIwPYU7bOy1exknvIe+dVr0/JZs6Pn/KA6WvnsYf1/+OpawaccbqCr1njvEGD+N7ev8Jxq6HxqjqWCYOanfCa5jFBDLjgGt63jQAcWYem2abzv8//kQWvolbYd6QA2+ZfaKA4EmQstHdgYO9emLTT/0m6sV8zPrKdD4CqGIwq/IFZcnMghAB2ZebT6rjEGhv0xrSvH0JUw5YcMRwzU7qr21mx+8RU897qSH4J+vb5zpk4f+hdOL/rqRPGnYqmKnTs1IUNZaOD7dU9rFu3+oT9R8W5c0nD69NPP+W9997j2muvRdPKb0Q7dOjAtm3bqnSt1atX06lTJzp16gQ4GlqdOnVi6tSpAFx11VW89NJLTJ06lY4dO5KUlMTcuXNlHy43SM0ugq2zqKc4Uo8u0DszuJtjv67EyACSjfLpn6ajyehemP557+ECpn3yEy+rr6OWZQt6xXo5gy+67rRTDI83tlcCc+LvZbHdkcSlyPDhf7bbuOnKSwnxP/n6lOt6NGRts0ls0hsB0ERNY2zaNB758cSNmIXwNm/8uZ1J6g/O8mdcwJhuZ87S1q1ROJviLifP8APgcu0fvl6wos6PeuUVW8kttro7DCHcakNKDu3V8oZXRmBrIgIt9GoSyXK9FQDBSiHZu1d75f3IyXy9KoUxynxn+WcGcGG7E5N9VcbFHeKYZS+fPdalYDGbDsq2Ba7mkobXwYMHadq06QnHdV3Haq3aH4OBAwdiGMYJj48//th5zsSJE9m3bx8lJSWsWLGCHj16nOtHEGfhzQXb+T/1V2f5K2UEo8tunhofN+IFUF9PJd3LpsqlZhdx33u/8Ib1cYLL0l3PsXcnp9vdXN391GtR/sukqbx1Yz9W9H2f60M+4q6G3zN63GTOO80GsIqi8MyVXXnC9z7nnhtDtHU03fASMxYln9sHE8KNdh3Kx77hO5qpBwHHutCmPS8iLMDnDK90uGFQB76wO/bIsyhWrsz5mK9X7q+2eD1Zek4xt3+0lClPPskjTz7GLTP/dnSICVEHrd57hM6qY//LfMOXkHhHY6tXkwiWle0DCNC6ZD07Dnn38gdwTK38599/GaA5skrv06MJaTv8lB26Z9K6XjCbQgY6y0O0tfy5VdaXu5pLGl6tW7c+6SbE33//vXPkStQu29PzKFr7DW3VvQBs0hvRoPP5zgxjUUEW0rT6zvMbK2lelVL+wNFC7np3Fi8XTyVGyQZgg57Idw0e4uGLqp5h0M9H477hLfnsnst47+YB9GkaecbXhPr78PiNF3G3Phmb4fhVHW/6jSN/vMxny7136qaouwzD4Nlf1nCvqTzBzFtcxf8NaFLpawxpGc0/0deSY/gDjlGv3/6YT05h3Rrxycwr4c53fua+PTfxls/rvO7zFo+l3MJdM37laC3aN1GIyjqwe5Pz7/VqvQVdEx1rRuPD/UkO7Ow8r6+6iSU7vX8fwD+3HmJ40Wxn+TP7MG7oXfm69L8URaFN63Zs0x0d6J2UXazesv2c4xQVuaThNXXqVCZOnMjzzz+Pruv8+OOP3HrrrUybNs05RVDUHja7zpPf/sODpi+dx6Yb1zBhcPl6JUVR0CPKK4BEJY1kL2l47czIY/Lb3/NK4YM0VDMB2KHX57mIp3ltbF/MZ1iH4kpt4kIYc9V1PGa70XnsEfMXbJ/1Ct+tln1chHeZuymdjvs+dK7tWmxvR5s+FxIRWPk02aqqcOeFPXjDdqmjrBjcb3uXabM3VkvMnsgwDO774l+eK3zcmawHIF7N5NHCZ3nmt01ujE6Imncwu4hGeWud5eV6K3o2Lt/HKr5pB1KNcAB6qFv5Z6N3zxwxDIMvF23kcm0x4FjCsC32YjrEh57TdYe0imGB7hgwURWDuEOLScuRUXRXcskd5KhRo5g1axZ//vknAQEBTJ06la1btzJr1iyGDRvmircQHuSledsZk/ka0WU9SwvsnWjT7xJign0rnBcaFe/cMT5RSWdPpuc3vFbuyWLqjM95u/Rh583hHj2GR4Of5o2bhxHsW/N7Bp3fNpbE8+/kZesVzmNPmz8i6afpfLR0z2leKYTnOJxfwsc/z2G85uihLTU03vQdzx0DT5ymfiY9G0eQ2vw6duuO6cyd1V0Er3+fhdtrz6L50/lmVQoDDrxDEzUNgP16FAcNx01me3UPlvWfsj4l240RClGzlu8+Qk91i7O8yac9LWPL12Gf1zaWefZugCNhVcjBhWTmldR4nK6ybPcR2qd+41wG8ZO9D5f1Off9Prs2CmO5qbuzPFRdy4KtdaNerSku67rv168ff/zxB4cOHaKwsJAlS5Zw3nnnuerywkN8uyoFlr7KRdpyALKNAN4NvpMJg068eUqMCiS5bJ1XvHKIfZlHazTWqvp8+T4+ff81PtAfI1JxLCjdoifwYMiLvD5+ZJV65V3tln6NMQ96gHdsFzmPTTN/yNE5T/Pi3K2ScEN4NMMweOS7NTxufQWLYgNgpv0Cxl407JQbJp/JI6M68bhyO7rh2ODzf6ZvmfHd7Fq/7cKh3GJmzfmFsZpjQX2R4cMN1incVTrBec4E0y+8t7Bqia2E8GaLtqfTR3WM9OYbvgQnVtxAuE/TSBap5fkAzldXMm9z+gnX8RYz/ljPLaY5ANgMlZ/9r+TC9meXVON4Zk0lokUvMo1gAPqpG1m0RWbXuFLNzZkSXu+71Sms+vkNppiPS6tuu5WHxgzB13xiWvUmUeUJNjTFoDRjR43FWhXFVjsP/bCerNlTedP8Kv6Koxdspd6CZ6JfZMbtI04YzXOHO4c0I6f3w8w4rvE12fw99ZY8zKQvVlFQYnNjdEKc2gdL9tBt9+u0Uh1/wLfq8WxofBsj2539xvdxoX6MvOBSPrYPBxzbVjxT+gL/++yfWp3l8Mlfkpiqz3BmWZ1uu4LHb7yYIcMv4Q97FwDqKVn4b/+BXYdq10axQpxMqU0nZ/sSZ0r1f/R29GlRMdO1r1kjqEV/Dpc1KIaoa5m70jun5C7ddZi2B74hVHHMIvpF78OoIX3xMbnmln5w6zgW2h3TDf2VEow9iykslfsLVznrf6WwsDDCw8Mr9RDeza4bvDR3G1t/ep4Xze86j79kvZK+o26m4ynmFDeLDmK7Xr57ekjebvI9rHGwPT2PG9+YzbCkO5lk+tl5/Ed7X95PeJn3xg8h1L9y2daqm6IoPDCiJfYhj/O09Vrn8etMC7hux53c/NZv7Dvi+dM5Rd2yaEcm2+a+y82m3wEoMUw8br6baaO7oijKGV59emO6xbM8cQJb9ATAse3CtenP8djPG2rlKPDcTek03jaTFuoBwJHwJ6vdzQxsEc11PRvyiXap89z/02bz/qJd7gpViBqzPPkIfewrneX59q4Ma3XiFkOXdG7Ij/Z+gGO6YYuMOWw6mFNjcbqCza7zxq9Luc3kyChtNxS+8b2KK7o0OMMrK29A8ygWGl2c5YHGav6pBclIPMVZN7xeffVVXnnlFV555RUeeeQRAIYPH87jjz/O448/zvDhjl7IRx991DWRCrdIzsxn3LsLabz0XqaaP3Me/8g2HGufyadNq944KoBdlO/N01xNYWeGZ6RwNQyDT/7dy0tvvcEbORMZpK0HHJXYU9Zr2dHrJd4Z1xd/n7ObBlVdFEVhwqCmNB01hXusd1BiOOLroW7jlZy7efj1D/h53UE3RymEw6aDOcz88iummd53HnvSdgP/d+VFRLpg6q6iKDw/pieP+U5xZjkcrq2m+bppvDyvdmXjyimy8tHPvzPR9BPgmF70jOkOHr6oPQBBvmY69BrGCr0lAE3VVLLWz/bqdSxCVMb8zWmcp64GHL8Xh+MGEn2SWSoDmkfxl99wZ3mMtpAvvCxD8NerUhiV9ZFzbde39oGMGtofi+nEWUdnK8TPTHHD/pQYjjXtQ7R1/Lk5zWXXr+vOuuE1duxY52Pp0qU8+eSTfPXVV0yaNIlJkybx1Vdf8eSTT7Jo0SJXxitqSG6xlenzt/Ps66/zTPqtXKYtcT73hu0SDvV5gikjWp32Gr5mjcLQ5s5yC+UAOzyg4bXncAG3zFyEPud+ZmrPE1W2nivTCOEW/SE6jH6EKSNboann1htfncZ0b8jFN9zDTcoTpBthgGN60SdMZd8Pj3D3l6vIKapb6bWFZ9mRkce0D77mLeNZ57quz2xDiRl8B4Nbum7D+7AAHx4bewGT9UnObRduNM3HtOR5Xl+ws9aMfD3160buL30LH8UOwHv2C7n64gsIP27/s7G9G/GhXj4V+UblN9l6QtRqpTadPRv/pZHq2G9qpd6SXm1PnrDHpKl0797L2TnRTD3IkaRfyfCSdaGH8oqZM28OV2l/A5Br+PFL+DjGdKv8vqKV1a9NI5bojmQd9ZQs0rb+i60WT+GuSS6ZEDpv3jzOP//8E46ff/75/Pnnn654C1FDMvNKeH3BTm5/7j26/HMLM7Xnndn9CgwLk2yTCBzxOPef37JS04RC6jWh0HD0bDdXUtie7r41ByU2O6/9uZNnX3uFJw7ezDjTPOdzC+ydmBjyJg9OvJ2LO5z7AtWaMKhFNNMmjuOu4Fedf0g0xeAu04+M3XYbt07/irmb0mvNjafwHltSc3l05ne8aX+KEKUQgCX2Nqxs+QB3Dq56FsMzaVs/hCuvupEHbOOdx+42/YjPwieYNnuL1/8O/LDmAA02vkGXss1hk/VYkhLHn1BXRQf5EtL+Ame2x97aFlYtW0ix1V7jMQtRE/7adojzSsvvM2fpvRjZtt4pz7+me0M+1i90lu9QfuTdv3dXa4yuYBgGj/24lsfsbzrXd75uu4xJo/pUSyfxeW1imKt3c5b7WJexck+Wy9+nLnJJwysiIoJffvnlhOO//PILERERJ3mF8CRWu87f2w9x15ereOz55+j09418wUPO3dDBcdM0zvIK199yD+P6JFZ6bUaz2BB2GI6NlBOUQ+xNy6yWz3Am/+46zPXTf6L5ott5T3vR2ZgsNsw8ar2Rpd3e4pM7L6B5TNAZruRZGkUG8MHEC/m29Vu8ZL3S2ePfSd3FZ6X3sPWrh7j9k385mC37cIiasXTXYZ599yPetT7iXOy+Wm/O+/Wn8eJVXc55XdepnN+2Hl1HTeBJ6/XOY7eZZtFkxcNM+mKF1yaf2Zaey+xfvmaSVj7F8DHlDh67/ORr5G7u35QP7SOc5dHWX/hh7YEai1eImvTzqt2M0v4FHBk+U+uPoFFkwCnPjw3xJazzKLbqjlGijupuUlf96PHro79fc4C2O99xru/crCdwqPWN9G4SWS3v1yDMn9TogdjLssaer65k7iaZbugKLlnA8sQTT3DLLbfw999/06OHI13nihUrmDt3LjNnznTFWwgXyym0siz5MAu2ZJC2ZSn9bUuZoi2jnqlij0aKHsV0+5WE97yOj85rUeXUzy1igtihx9NRTUZVDKyHtgEDXPhJTu9wfgnPzt5IyMaP+dD0HYFa+ZSCJfY2vO57O3dcM5yBLaJrLCZXC7SYeHlMV2a1epIbfurE0/rrNFbTsSg27jH/wO7kf3nk5ZvpMGAU4/s39rh1a6J2MAyDL1bsZ9nsj5ipvYmv4pjqul5vzGsxzzBjXL+TZj91pau7N+RT+/94eLaZp0wfoSoGV5sW0mRHKuPefIjnbhhK46jAao3BldJyipj6wc+8q7xyXBbDK7ly9JXUD/U76WtaxAaRkXgpWSnfEq7kc6G6nGsXreLqbg0rpNcWwtulZBXiu3sOoSZHo2mu3o0LurU84+tuH9iU59ZcxlvqqwA8qHzKtF8G895Nfaoz3LO2OTWH33/5gve1WYBjD8QnzZN455KO1fq+vdu3YOVfreilbSFRzWDnplXoF7eVeuQcueQO7MYbb6RVq1a8/vrr/PjjjwC0atWKJUuWOBtiwn0MwyA9t5j1KTkkpWSzaeduQjOW01vZxF3aBsfoz39+Evbp0bxnv5DsFlcx6bw2tIg9u5Gg5rFBfGmUZ9uJLkwmq6C0wrqE6qDrBt+sTmHWnNk8pL9LW/Ne53OZRjDP2K4jvOd1fHheCwLPch8hT3NRhzi6NrqJR75tS9d9M7lFm4NZsdNETeMjnubPRb9x4/IbuXz4YK7oEu/Ra9iEdykosfHoj+totvk13jLNch5fbG/HB/Ue562bBpz1fl1VdUOvRvxouZd7fgzgBe0dLIqN7up2Xsu9hwdfv51BI0Zzfc8Ej795OJRbzF0z5/JSyZOEqY4p2n/bO5DdecIZp0OPHdCKzz4Zxl2mnzArdgbn/szC7f0YcpJMb0J4q4+W7OFmdbaz/LMyhLfan3qa4THx4f7E9hzNspXz6aVtIUE9ROvk95m1PpGLPGypwdGCUh7/9HfeU99wdr68YruScVdeUO33UcPbxPLZn93opTk2pu5WtJSkA5fSuWFYtb5vbeeyv4Q9evTgiy++cNXlxFnKL7GRnJnP7sx8dh8qYEfqEQoObCKheBvtlGRGqbuZou4H84mvtRoaf+sd+ZrziGg3nLF9G9MmLuSc4kkI9ydZLV/42VxNYUdGHj0bV98U1G3puUz7YQVD097lc+1PVLV8fccXtiH8FjOehy7rRdv65/bZPFG9ED/ev6U/v65vynWzBnK/dYZzXchQbR0Drev5+pdBXLPoeq4d1oML2tWTBpg4Jyv3ZPHydwuYnP8SPUzlm/b+aO/LwhaP8u5V3ap9pOu/LuvcgIjAuxj3ZT1eNl6inpJFPSWLj7VpfDJnJTdtmMiDl3Q56w6l6rb/SCH3vj+bFwoeoaHqmJ69VW/IB7GPMvPitmd8fd+mkbwdfim35czCoti4RlvApEWbpOElao2cIivJq+fSTt0LwEa9EY27Dq90R+pdw5pza9KtdLPdi0nRmaj9zNifOtMx/mbiw/2rMfLKKyixMfHDhTxZOM3Z+fKHvTMFXSdw/mnWsblK0+hAdoT1h/xPADhfW8Uvm9Kl4XWOXNLw2r9//2mfb9jQ9RlX6iK7bpBTZCUjt5i0nCJSs4tJzS4iLaeYrKwj6Ef2EFq4hyZqKk2UVEYqadyppGJRrCdtaIFjT51/9TbM0XuQFjuEQZ1a8HLn+i7bu8qkqVjDW0LZVhnHMhtWR8OrsNTGa3/uIPXfr3hJ+5QYU7bzua16Q6ap4xl+4cV81r1hrW5sKIrCqI71Gdh8LC/M7cZXa77kXtO31FOyMCk615kWcEXeYr76bjDXzL+Kq4f25ML29TBpsp+6qLzcYisvz92KddXHvG/6kiDVsY7Qamg8Z7sarfcEXh3hvuygA5pH0ejOG7n74wbcmf08fbXNAIw1/cGwtDW8+ObVBHW9mruGNifCBantXWXxjkze+fJ7XjNeIE51TP0+YETyWNDjvDduQKUasYqicFn/zvz8c1+uMv1NsFJIYspPbDzQlXYNal+Hk6h7Plq6h3HGz87y+/YL+V/fxpV+fbCvmWsuGs5b3y/mLtNPmBSdZ/XXuP+zxrx/2/AaG6E/lWKrnUmfLuXuzEdopTrusffoMXwSM4UPLzpz54urdGnXjqSlTeio7qa1uo8n16/BGFG55Gri5Fzyk9WoUaPT/iPY7XUro1JBiY1DeSXYdR27DjZdx64b2HQD+3GPUptOYamdglIbhSU2Cq12Cksc5YISG9kFJZTkH8UoPIxamIW59Cih5BFNNnHKEeKUI3Qt+xpcljmMM7SX7IbCFiOBpXo7ViptIb4XvVvFc3e7eqdcM3CuouolkJPtT4hSSAs1hfnprk8pv2hHJm/+8CcTCt9hoGm983ihYeEV2+UcanMT0y9qR3TQiXt71FYh/mamXdaBbb0TmTr7Aprt+YzbTbMIUorwVayMM83jmvwFfPPDIK6ZP5qL+nfn8i4NZA2YOC2bXeerVSnMnj+fe2wf0NO81flcmhHOfdzDdVdfWSM9smeSEBHAR3dewPNzEpm/6n0eNH2Fn1JKnJLFK6a3WLt2Hv9bM4Ym3Udy64AmxJxk75+aUlhq48W52yhZ8SEfmz51dJjhyGA4JfBpXr11ZJU6xC7uGMcNc0dxle1vAG4xzeGpP69hxo29qyN8IWrM4fwS1i2ezd1lCcAOGJHorUdVeaTq4g5xTN46gTVbN9FF3UlDNZPJRx7n7i9CeHtsb8xu6ozMLbZy50eLGZ/6KN20HQAcNoJ5wPIor18/AB9TzcV1fttYfl3cnY6qI/Njj/y/WL1vJN0ahddYDLWNS+6w1q1bV6FstVpZt24d06dPZ9q0aa54C6+yYNshfvrmQ1ooKWjojodix4TdWTZhxwcbfkoJIRQTSwkBSjH+lOBPMQFKMaHkY1KO2zfhFKNWp2IzVPYasWw0EtmoN2anuRmm+h1onRBLn6aR3NgwrEamADWPDWb7lni6K9upp2RxIDUNaOeSax/OL2HarA1EbvqQT03f46eVOp/7w96F9wJv485LB9G/eZRL3s8btYwNZuYt/flnZytunX0xg458xfXan/grJVgUGzeY/uCawgXMmdOD8fMuom2PIYztnUC9kOppiAvvZNcNftuYxhd/rODS7I/5SltUYRrv17aBzIq9g2lj+p42q1hN8/cx8cQl7VnSZipjv+3G/xV/wBDN8Ters7qLj3iadau+5okVo/BpPYKrejSmZ+PwGuvRtdl1flx3kO/mL2JS0Tv0M29yPrdab86LIY/w5vjhVe40spg0+vfpz4K/OjFEW0d95QixO79i7f5WMlVIeLU3F+zkLsqXtrxiu4KJQ06/r+jJKIrCk5d25Ob99/Nm4X1EK9l0U3eQvWcqd372NK9e26PGp0nvP1LIfZ8s4MHsqXTUkgHIM/y4U32YZ269hNiQmu0cahMXzFOhQ9Hzv0ZVDC7VlvDe2gPS8DoHLml4dejQ4YRjXbt2JS4ujhdffJHLLrvMFW/jNUyqwoXaMi4/btPh6lJimEg3wkk1IjlIJLv1OJKpR1FwE3yjm5AQHUrb+iFc3yCUhHB/tywobxEbyHY9nu7qdseBzK0YxrBzvrGZszGNT3/8hUftb9PGXL5JaKoRzlP2G2nWfwyfDWpa4xWnp+rXLIo+d13I/C3duPmPVfQ/8g03aPMJUEowKToXa8u4mGWsWdaMZ5aMRGl9EWN6JNKzcYTHJyIQ1afYaueXpIP8uHAFI3K/5RNtIb6m8s259+nRPGHcQp/zR/Np70YeO423b7NIOt93NTP+7s5X//zIA8pnNFMPAo7tF95WXyZt+0d8u2UgrwYNp1P7jpzfNpYODUKqpRGWU2jluzUpzP93NaPyvuZL7W/MWvnskE9tw1jR/H98cFXXs04ANLZ3I27952qG6I6G5kTTz/xvzmV89H8DZaqQ8EqbU3PIXvUVnU27ANiuN0Bpf9VZr9cM8jXz3E0jufvtI8zUHyNAKWGYtgbL7vu5debjvHx93xqbKfPH/7N33+FNlW8Dx7/nJGm6F12UllIotOy9NzJcCC5UHIAoiigqKoIL98CFA8WJe/1UXK+CigKCLIFC2atAaWkpdO8k57x/pAQKiLSkTdLen+vK1T7nnCR3C3n63OdZW7OY/9XXvKS/SBP1KAB5uh+3KzOZcePVJETU/ZxURVEY0K0TK/9oQ1/DFpqpWRzYtIzyS9piNkrbqiZqdUxRYmIia9eurc23cEsGVUHTa9YVXKabKMabEt2bXPzJ1QPIIcD+VQ8glwCO6IGU+TRGD2qCX3AUjUP8iA72pkmwD5dG+BPXyNetPhCJUYH8occ6yrGWfWQWlNW4R6Wo3Mpj328ifOObfGz8BpNqb6xousKHtuH82eQWHrm8JwkRnrNsdF1RVYXz20Uxou3F/L6tB5N++4ce2V9zreF3wpQCALqqu+iqvkLmzo/4attAXgm4gAE9unFlt5gGNVSzoduTXcRnq/azY90SRtsW8om6ApPxeGJQoPvyqvVS0ltdx6yLOxLXyH16uf6Nr5eRacMTOdj9Tl7+dTjWlAVMVr8jSU0DoLGSw53Gb7mz9FtSVjZj4fIezPbpSXjzznRvHka3ZiG0CPev0RAkTdNJPVrMqr1HWbL5AGrqUq5Q/uBzdT0G4/Gew3S9EY9qN9Fj+NW83v/s90w8HX+zkfMGD+PHX3sx0rCKMKWAdmmf8teudg16FIDwTDZN56lvVvKK4SPHsRe1sTwy/L+XkD+T5uH+3DP+Kqa8W8gb6gv4KuUMMKQQnXkHt78ynalXjaRfy9rZLwsgp7iCp37cRKOUd/nA+BVeir2ezdRDuNP4CLMmXkGb6MBae///MqpTNHN+70ffynmyw6xL+HP75W4xnNwTOSXxKigoqFLWdZ1Dhw7x6KOP0rJlS2e8hUcxKPYE4DetKzZUdMWIrhpAMaCrRlCN6IoBVBO6ly+K2R/F5IvB2x8fsxe+XgZ8vYyE+HoR6mcixM+LJF8vQvy8CPXzIsTXq07H+J6r6CBv0oxxjnIrJY0dmYU1Sry2ZxbwyIe/cG/xi/Qw7XAc36Y15Qn1VkaNGskHXWOlh+Y/KIrCsDaRDG19IX/v6ckDy3YQvGcBNxoWOhqhUUouU43fcXvJ9yz/ox2P/X4eWqvzGdU1nkGJ4dKTWA9lFZTxc8oh/kjeRWzGQq41/E5bdT+c8E9drJv51DaUPxqNZeolvWptA8/aFBPiy4tXdeXA0Na8tfRKDm/4P8bwO0PU9Rgql2xur+6zr5hm/Yq8HX6s3ZbEAq0Ve5RYykMTCYyMJyrIh8hAM438zJhNKiaDikFR7PN2K2wcLSrnYG4pmdk5aJkbaWnZQXd1J3PUTfgZy6vEVKj78K71QjbFXsfDl/dw2n5j1/WK44Zl13FBxRqMisYtxp+46YeL6H33KJfNYRGiJuavSOXirHmEG+1tzoW27rQZdCUxIee+CmHXuFCmTLyJSfO9mas/Q5BSQoKawQeW+3nxg39Y0H4C95zflmgnzoUvqbDywd/7+HvJIqZr79DBlOo4t0ZL5MXAGcyeMMLlN7ViQnzJjhlOWeb7eCsWRhpW8uC6fZJ41ZCi67r+35edmaqqp9yV03Wd2NhYvvjiC3r37n2ub+E2CgoKCAoKIj8/n8DA09+BsC+koWFUVVQFGdIB3PD6L3x05GoAVmmt2TT0UyYNaFGt11i4+RBffPUpLykvE6rYl1a16QpzbaPYmXQbj4zqKD0y52BPdhHzl+8lff0iruEXhqgbqs4xBI7qAfyfrRe/GgfQpO1ARnVuQs/mjVw2vOxsPo/uyF3i1nWdbYcKWbYrm5VbUwk9uJiL1JUMUDc57roek6/78oFtBKvCr2TceV0Y3iaq3tzgyC2u4NsN6fy6ch1d8xZxvmEtHdTUMz6nVPciSw8hixBy9ADKMVGhm7Ch4qeU4UcZoUohMcphwpWCf32dQ3oon1uH8E/EpUw6vwcDW4U7/W/Gl2sPYP3+Lq41LgZgga0vR4a9zs0Dzn4VuPrMXT6P1eGJMZ+Lzen5vP7mK8wzvghAke7NjX5z+XjapU4d4bM5PZ/HP/iBx8pnO1YTBPvN3Ve1MYR2GcX1fZqRFFXz33laTgmfrTnAxjVLGW/5kuGGdY5zmq7wju1CtrS+i2eu6OLy1RWP+WLNAXx/nMQlhpUA3Ga5m8dmzCQ8wH1WhHWl6nwenZJ4LV26tEpZVVXCw8NJSEjAaHSP/zTO0tAqO2eZ+e0m7to4kkgljxzdnyfb/MRLV3U+6+e/vWwPBxa9xqPGDx3JQJoWzgPKHVw66nIu7dxEElwnySup4Nv16fy6egNdcn7hasOfjr2ETpSmhfO91oflPoNp17EnF7RvTOfY4DptjHvq59FVcVttGjuyCtlwII/1+46Stesf2petY4C6ia7qTsyK9ZTnJGst+NR2HjnNLub6Aa1rJTFwF7qus+lgPou2ZLJh0yYS8/+it7qVHup2Qipv9jjDUT2AxbYuLKY75tbnc23v5vSIr70FPWyaznWv/cwbOZMcP8cN2iyev/c2l67k6C48sR7xxJhrqqjcyo2vfMdbxXc6/v/OtEzkkhsfpHcL529Nk1NcwX2frWTg/te4zvC7Y+NigO1aLF/aBrEj4ny6tW5Jv5bhtI0OPGOCVFxuJSU9n7WpOazespPGWUu4xvAHXdTdVa7bqTXhMXUyIy8cxVXdY92qns0vtTDt6Rd5z/AMAMts7UkZ8gFTBie4ODL3UOeJ17Jly+jTp88pSZbVauXvv/9mwIAB5/oWbqMhVXbONH9FKi0WXs8AQwoA14V8zCd3XvKfz9N1nVd+34m2ZDbTTF87ji+xdeSdiAd59tr+brPZYX2j6zrrD+Tx5ep9HEn5jUtZzDB1Hd6K5ZRrt2lN+cXWgzU+fUlo243z20XTs3lorQ9l8tTPY13EnV9iYefhQnZmFbIrq4i96Vmoh9bTxraDzupuuqi7aKScfmuHQ3ooP9t6sthrMO27D+Ca7k3daqXCuqDrOqlHilmdmsOaPdlkp6YQVrSDRDWNRCWNWCWbSCX3+FYe/yJTDyFND2ePFs1GvQUHfFrTqHlnhraNZlBiOIHe1VyutobWH8jl67ee4GnTe4B9UYJXWrzD3Bt6u1UDzxU8sR7xxJhrwqbpTP7gb27Zdydd1V0A/GzrwaberzLjwuqvZHi2NE3n87UH+OWXH5iuvXdKD7hNV0jRm7NCa8tWrRkF/s1Rgxpj8g1GNRixWi1YS/IgP52Akv20V/bSVd1JF2WXYzjzMVl6MK9aL6OozdU8MLKj294Mmf7VBqZsHkOcehiAMeY3+Pz+sW67oFJdqvPEy2AwcOjQISIiIqocP3r0KBEREfVqH6+GUtk529+7j7D1g9u5yfgLAONtD/LeY/f95wd27p+70Rc/zu3G7x3H3rJexJ4O9/LEZR3dahGR+qywzMJPmw6xaN1OQtN+Y7RhBX3Vzaf8AQH7Jo+LtB4sN/YisnUfhreLpl9CWK0MmfDUz+PZxm3TdCw2jQqbhsWqYbHZy8UVVvJKLOSVWMgvrSC3xEJmfhkZuUWU5mSgFhwktDydVupBWioHaaUcJEY5UuXO7ckOaOH8oXXmD0M/wlv356JOTeiXEO5R80lr27FkdntmIQdzSzhcUE5efh5acQ66rQJsFaDZwMsXxRyAwSeQyJAgYkN9aB7mR8fYYJdu03D//9YzNmUiHVX7MtWvW0fR5PKnubRzjMticgeeWI94Ysw18eSPW2i35l5GG/4G7IvP3Bf2Bh/eNrxO5igeLixj7uJdZP7zPbeoC07ppTodm66c9m/jybZpsXxqG0pW88u58/wOtGvi3pubJ6fl8cu8Gcw0fQ7APOvFJF7/MoMTI/7jmfVfdT6PTmkJ6bp+2jtmR48exc+vYd0lFafXKiqABSesbNhcO8CBnBLiz3AX/fvkdA7//iqPmY4nXU9YriV8+D08N6B5g79LW5cCvE1c06Mp1/RoSkZeX37YmMG89VtodeQ3Rhn+pvMJf4zi1SxuVX/kVn4kc2sIv6d0YRqdsTTtT++kpgxKDCchwl/+/f7DJS/9StSRvzFhw4QVk2LFCysmrPhRRrBSRDBFhCrFNFcKiSKXKCUH07G5Wf+x126e7sdaLZG/tPbsDuhJ88T2DEyM5O2WYbJwyr8I8jXRvVmox+5hM/2Ctty27TY+tt2Pl2JjsuEHrv++J72bT6zz/YGE+C9vLd1DwOoXGG20J10lupl71PuZPXZAnS0MExHgzWOj25M5uCUfrryC2etWMqh0MYPUZMdCVCc7U9K1R2vMb1o3/jL2pGXXwYzrFUfLyLpfJr4mOsYE8Vz4xVTk2ldeHGNYwgMrd0niVU3nlHgd259LURTGjx+P2Xx8kp3NZmPTpk306dPn3CIU9UKYv5lMc3OoXKvh2MqG/5Z4bU7P5/++ns+bxuPLxj5sGU+rkdO4vlfcaZ8j6kZ0sA+3DmzBrQNbsDNrMD8kZzA7JYVWucsYoa6lp7rN8YcnSsnlOuNirmMx5elzWJ3Wmi8WdWSrX0+aJ3VicFIkvVs0cpsJxO4kWCniba+XnfJaRbo3u/UmbNGasV5rSVZQe8KatqFrfCNuTAhrcMMIG6pG/mbGX3oxr36xmntN/8Og6Dyuvc6DX7XjnYkD6s1iKcLzvbc8ldxfZzPD9C1gX3TiHtsUpo2/kqaN6n56QVSQN/efn4RteCIr94zk213ZbNmxg4AjySRwkObqIUIorJyDpmPFSIluJkNvRIYexg7iKA7vSELzBIa1ieTGZqEeN5pAURRG9u7ALz/1ZJThb0KVIvx2/cD+o51cvvKiJzmn1k5QkL1bVNd1AgIC8PE5PoTCy8uLXr16cfPNN59bhKLeMEa2hkP27xPVg/yVVcj57aJOua6kwsrjn/3G24Y3HA34162jiDv/Tkm63EyryADuHZEIIxLZffh8Fm3JZG7KDqKzljBCXUt/NcWxYINZsTLAkGKf51fxCQeSw1myvhP36B0obdKbTi3j6JsQRqfYYI/7g1QrjP/RZXWSXN3f8Uc+XW9Euh5GKk0oD21FcFRzWkYF0bpxIDObBhPmLytRNVQXtG/Mr5tvYeO2dXRU95KgZjB8/0vM/TOaO85reNu/CPei6zrv/LWXrEUv8rDpC8fxJ6zXcd5lE+kR79reZoOq0K9lmH1frwtbU2YZyd7sYlKPFJNeUsHWUguapmM0qPh6GYgK8qZdsA+3RfjXi5EEozpFc+vP5zMKey/kTYb/491l1/HEpe1dHJnnOKfEa/78+QA0a9aMe++9V4YVijOKaxzO/vQI4tTDtFLSeO9Q/mmvm/3zVqYWvESwoRiw79VxpNu9zOoXX5fhimpKiPAnISKBKYMTSM8bwq9bMrklZR/mg3/Rn2QGGTYSoxxxXN9UzeYG9Tdu4DdsWQqbMluwYmlb3lQ7YGzWix4J0fRJaETrqMAGeSfeZvTjWcvVWDBSgRELRiy6/WuZYqLCFILmE4LiE4zBL5TgAH9ign2IDvahebAP/YJ9iGvkK3s1iVPMGtWRSXvu5APLdHyVcq4yLmH6H2+zIu5++iZ43r5son7QNJ0nftpC2JrneNj0g+P4s5araXL+NK7o6n5zEb1NBtpEB7p0g+O65Gc20rrHMNav/ogu6m5aq2lkrP8/coYnEupXvZuFDZVTxvfMmjXLGS8j6rnEqAB26rHEcRg/pZy8zD1AtyrXbDtUQP7az+lnsu+QfkgP5aPwe/hwZDuZE+RBmgT7MKFvPBP6xlNU3p8Vu48wd3sW+7atp13pGgapG+mubnfsFWVQdDoruyvnin1P2X4Ta1MTeW3hUPpdciPXNcCezmfG9KTC1h0vg4rJqGAy2Dfo9TKoeBlVWUlK1FiwrxfTxo7kwfd28LLpDQAeN85n/OetiL/jOqduEivE2Sgos3D/l2sZuvtpLjf+5Tj+ouUKgodP56b+suecuxjfN56n/r6YLuocAMbxE5+supyp0mN+VmqceHXp0oXFixcTEhJC586dz9goXr9+fU3fRtQjrSIDWKnHMAz7ZoE+uTspt9ocKxPqus7sH9bxjPFzx3Me1ibxVB1OpBXO5282MqJtFCPaRqHrHdiRdTl/bs/m7W378D64gl7KZvqqm2mlpjue461Y6G/YzJ9aZ3o1d/4+LZ7AFfMYRMPRq3kj1g+byKe/7+Ba42K8FQvPW57j7vcjeW/KhfjLvEtRR7YdKmDWRwt5oPgZOhnsK25qusKj1huIGXEnkwa0cHGE4kTRwT54t7+EfVs/p5maxQBDCm+u+JNJA5rXi+GUta3GNeuoUaMci2mMHj3aWfGIeqxVpD8fasdXNmxJGnuzi2nd2N5F/8/+XJIOfEGUKReA32xdaT/wijOufCg8i6IoJEUFkhQVyORBLSguH8ia1By+2n2Erbt2EZ69ir7qFvoYthCjHGG7TxdahMu/vxC14dYBLZi89x7a79tLBzWVWDWb+/MeY9onIbwxvi9GueElapGm6Xy0ch/LF37JPPU1QlX75sjluolpttsZfuXNjOrUxMVRitOZOKAl76ZcyJOqfcrRWMs3fL5mGBP6ypSQ/1LjxOvE4YUy1FCcjQBvE7l+zaFy/92W6kF2ZhU6Eq/3/tzG48aFgP1u17ve4/lggAwvqM/8zEYGJ0UwOCkCaMPRovNZufcoc3cdYf/uzUQ1ay1DTIWoJaqqMPvqHtw892FeKbqXxkoOXdTdjNz3JI//8DyPjW4vnz9RKw4cLeHhr1YyLH0u7xoXHz+uhXO/4V7uuP4K+sh8Q7fVJjqQzPjLyE77hnClgIvU1Yz980+u7t4UHy/p9ToTp44lqKio4PDhw2iaVuV406ZNnfk2woN5RSViPaBiVDRaKQf5Jct+h2v34SKCdi8gwpQHwC9ad0YM7C8f4Aamkb+ZiztEc3GHaKAD5db6s/m6EO4oyNfE8zeez92vH+E97WH8lHJGGlZxeN3TvOT7KPeMSHJ1iKIeKa2w8dayPaQs/ZYnlXeJNWY7zv1u68z7ETN44fqBNJF5hm5v8rB2vPX2SB4yfYqq6Fxf/jmfrh4k8/H+g1PGEezcuZP+/fvj4+NDXFwc8fHxxMfH06xZM+LjpdtRHNc8KpT9eiQALZQMdmXmAfD1uoOMNRy/6/WZYTRXdY893UuIBuTY/D8hRO2Ja+THPeOuZJptKjbd3sM10fgLxr+e5c0le1wcnagPbJrO98npjHvhC9ouvYX3DM8Qq9qTrhLdzMOW8Szv+irzJw+TpMtDdI0LZV/81WTr9q2lLjKs4bc/F1NSYXVxZO7NKYnXhAkTUFWVn376iXXr1rF+/XrWr1/Phg0bZGENUUXLyAB26vYlYb0VC8VZe7BpOsnrV9FRtU+q3aTF067HENlUVwgh6kj3ZqFcfOWNzLQe33vzTuMCcn97ng9WpLowMuHJNE3nx40ZXPvSAnK+vpuPy6cyzHC8XbhaS2K8+WUumPAwj47uIDfbPMyU4e15w3qJozzB8iUf/r3fhRG5P6e0bJOTk1m3bh1JSTIkQZxZq8gAluoxXMBaAPzyd7J052EGlC52/G/81tafa7q4334dQghRn43sGE1x+R08+n0Zj5o+AuAB0+c8/TPMs97HrQNldTlxdorKrXy7/iA/Lf+H8/P/xweGxXgbLY7zWXowz1rHEtB9LO9f0FpW0fRQnZuG8Gbzq8g88BNRSi7nG9Zy7ZKfuar7LbKv179wSo9XmzZtOHLkyH9fKBq8lhH+7NKOJ1UJpDNvyV5GqPZEzKqr7AwfTmJUgKtCFEKIBuvqHk2JvWAaz1vGOI49YPqc8t+e5MVF29F13YXRCXem6zpbMvJ59PvN3Pb0XEJ/nsRnRTdzo3Eh3oo96SrRzcy1XsL9jd9n0u0zeXx0e0m6PNztI9rzmvVSR3ma9iGv/r7ThRG5N6ckXs899xzTp09nyZIlHD16lIKCgioPIY7xMxvJ8z9+1zRRTePw/i20UA8BsE5vRZ8O0nMqhBCuMrFfPL7nTa+SfN1p/JbAvx7j8R+3oGmSfInj0nJKeHPJHq55aQHfzp3JmHVj+Uh5hIsNqzEq9sXWSnUv3rJexISgd0m45nnm3zLEsaKx8GwdYoIpaTeWXZp96f+u6i6Orv2S3YeLXByZe3LKbYahQ4cCcN5551U5rus6iqJgs8nKZOI476hWWPYbMCk2WioHGaImO84ttnVmdFKk64ITQgjBlCEtedc0k8cWmpll+hiAm40/8781RUzNf5AXru4um6U2UBabRnJaHou3HWbT1i00y1nB+eoaJqmbMZiqJuXZeiCf2oayKnQ044b14PO2UaiqbFFQ39x7QTse3Xod7/AcANPVz3nq/85n3oS+Lo7M/Tgl8frzzz+d8TKigWgRFcq+fVG0VNJprhxiuOEfx7kU317MbCzDDIUQwtVu6t+cz83Tuf97b54xvouq6FxpXEb0zqNMnDeLVyYMIszf7OowRS3LK6lgS0YBa/flsHFPBsrBNXTWtzBS3cAMdT+YTn3OBi2Bj2zDKWs1kuv7tuLOFo1kT7h6rEmwD636XsqyFT8zwJBCrJpNs90fsWxnKwa0Cnd1eG7FKYnXwIEDnfEyooGwr2zYhJakY1as9FK2AfaNE+Nbd5HKWQgh3MQ1PZri63U3U//nxwvGuXgrFvoathCRfTeTX3+EpydcRMtIuVnm6aw2jUP5ZaTllnAwp5R9R4vZdSiH0oxthBfvoo26n0HqDqYo+zAZTj+K6YAWzgKtHyt9z6NLl+5M69GU2FDfOv5JhKtMHtySiWvH09d6LwZFZ6pxAeMXnEePaZdL7/gJnJJ4bdq06bTHFUXB29ubpk2bYjbLXTFh1yrSnz/0GGBNleN/aJ0ZIsMMhRDCrYzq1IQw/zu46eMw5uizCVMKaKmm82bpfdwzN50xV4zlog6NXR2mOMne7CK+T86gwqZhsWr2rzaNcqtGSZmV0uI8tOKjqKU5eJUdIZpsYpXDxCrZjFSyaKFk4KXY4AyL023UmvOHrTP/mLsT264Po7vEcEezUBlO2AD5m41cOmI4n/4wlBuMv+GrlHNz0Tze+LMj04Ynujo8t+GUxKtTp05n7KUwmUxcddVVvPXWW3h7ezvjLYUHS4jwZ5526nLxy5UuvJYQ5oKIhBBCnEnfhDDCbxvP5Pca8WzZ47RQDxGmFPAeT/LMl3vYmHY7089Pwmhwyppdwgn2Hy3BuuR5LjWswIQVL8WKF1a8sOBDuT2pOuY0wwVPZ5fWhDVaEmu0RLLDetC5bWuGJEUyNTYYgyRbDd6YbrHcsGYS52evJULJY5hhHd8u+4Ldne4hIcLf1eG5BackXgsWLOD+++/nvvvuo0ePHgCsWbOGF198kVmzZmG1WpkxYwYPPfQQL7zwgjPeUngwXy8jeaEd4YQFb4p1M0p8f3y8pDtaCCHcUavIAObecRl3zw/hluynGGBIwahoPGz6lB9X7WFi2v08f20fIgLkBqs7MBlUwpR8Wqnp1X6uRTeQqkexTY9jm9aUXUozKiI6kNg8nh7xoTzaLJQQ2adJnERVFR68vBdPzb2eV4yvAfCw4QNmftOPD24dIlNJcFLi9dRTT/HKK68wYsQIx7H27dsTExPDww8/zJo1a/Dz8+Oee+6RxEsA0LVDe9Yub0V31b7Xw9e2AVzStbmLoxJCCHEmEQHevDd5OI8siGTTpjncbvwegJGGVSRl3M7UOXdzy5WjGJwU4eJIhZdRpQwvinRvKjBiwUiFbqICI2V4kaMHkEuA/aseQAaNyDZEoQXH4R0aS3xkIG0aB3JZ40Diw/wwSW+mOAutGwcS1Xssy1YtYYAhhWglh0Hpb/H5mpaM7dnU1eG5nFMSr5SUFOLi4k45HhcXR0pKCmAfjnjo0CFnvJ2oB8b1acZta6ZwR/k75BDIH9GTeK9dlKvDEkII8R+8TQaeu7Izn8U9wa0/JjDb8AaBSikt1XQ+tM7kmY83sbTHLcy4sLVMqnehMH8vUlrfzYOGe/AyqJiMKl4GFS+jirfJQKiviRA/L1r4eRHi60V0sA8hvibplRDn7M5hrRi/cTI9yu/EW7EwwbiIcf/Xk34Jk2naqGEvuKLoTtiGvnPnznTs2JG3334bLy9717PFYuHmm29m48aNbNiwgRUrVnDdddeRmpp6zkG7UkFBAUFBQeTn5xMYKJv/nYvDBWX8tOkQ/mYjozpHYzbKH2hRPZ76efTUuIU42YYDuTz98U88Vv48bdT9juN/2DrxVsg9PD52MIlR7r3qoSd+Hj0xZtGw/LnjMMs/epSHTZ8CkKaF82Djt/jgliH1bvGV6nwendJvPHfuXH766SdiYmIYOnQoQ4cOJSYmhp9++ok333wTgL1793Lbbbc54+1EPRER6M2N/eIZ0z1Wki4hhPBAnZuG8NZdV/Fq/Dzes17gOD7EkMzr+bcz5/U5vP7HLiw2zYVRCiHq2uDECEo638xqLQmAWDWb89Nf4/0Vnt0Bc66c0uMFUFhYyKeffsrOnfY5O4mJiYwdO5aAAPe+01VdcpdJCPfhqZ9HT41biH+j6zqfrD7A0v/7jGfUNwhXChznvrf14YtGU3hoTH/aRge5MMrT88TPoyfGLBqeonIrN770FfPL7sJPKQfgZtv93Hf7HbSqR/v/Vefz6LTEq6GQyk4I9+Gpn0dPjVuI/7Izq5BHPv2Dm3Nf5jzDBsfxI3ogj1knED/wWqYMSXCrUQ6e+Hn0xJhFw7Ryz1F+fP8pnja9B0C2HsTtga/ywR0j681K1nU+1PCYrVu3snDhQn744YcqDyGEEELUf60iA/jgjotZ0/sNplkmk6f7ARCmFPCa6RVa/zWF619awPJdR1wcqRCiLvRu0QjvnhP509YRgHAln6l5s3n8h00ujsw1nLKq4d69e7n00ktJSUlBURSOdaIdWxnHZrOd6elCCCGEqCe8TQZmXtiG5PYzuPWrnkzIe50Rhn8AuMCwlgHFm5jzweV81WYCD1zcgagg2fdLiPps+gVJjNt9H63z7yBKyaWvYQurNrzO9wmPM6pTE1eHV6ec0uN15513Eh8fz+HDh/H19WXLli0sW7aMbt26sWTJEme8hRBCCCE8SKfYYD688xI295vLVOtUjuj2ITh+SjkPmj5jyo4JTH/xDd79a68sviFEPeZtMvD09YOZrk/Fpts7Ze4yfsO3337J3uwiF0dXt5ySeK1cuZLHH3+csLAwVFVFVVX69evHM888w9SpU53xFkIIIYTwMGajgXtGJDFp8r1MDXuHj61D0SobXonqQT5SHyP01zu47qUFLN6WhUw7F6J+ahHuz2WXXsUc6+UAGBSd2corzPjwd4rKrS6Oru44JfGy2WyO1QvDwsLIyMgA7Bso79ixwxlvIYQQQggP1a5JEJ9MGYFh5MtcpzzNRq2549xlhuXML5pMyqczuPHtpWzJyHdhpEKI2jK6cxMOd5rCCltbACKVPKYXPMX0L9agaQ3jpotTEq927dqxceNGAHr27Mns2bNZsWIFjz/+OM2bN/+PZwshhBCivlNVhbE9m/L6fTfxefv3edByo2PxDV+lnLuM3/Jsxjg+nPsk079aT1ZBmYsjFkI426OjOvJayP1k6KEAdFN30m/XbF5bvMvFkdUNpyReDz30EJpmH5/9+OOPk5qaSv/+/fn555959dVXnfEWQgghhKgHQv28ePbKzlw26RFuC32X+dYRWHT7stKRSh6zTW8zfvN4Zj7/Cs/8vJXc4goXRyyEcBYfLwOzxw3jHnU6ZboJgLHGP8le8ga/bsl0cXS1r9b28crJySEkJMSxsmF9IXtnCOE+PPXz6KlxC+Fsmqbz/cZ0Pv/5T24qm89ww7oq51dprXlDuYYu/S9gYr94ArxNTo/BEz+PnhizECdavusI33zwIi+b3gDAohu4SX+I6bdOdMuN1s/EZft4nSg0NLTeJV1CCCGEcB5VVbi0cwwfTR/LriFvM157hBStmeN8L3UbHymP0GnpTdzy3LvMW7qHkoqGMxFfiPqqX8sw2p5/M29bLwLApNh4RXmBx+YvICOv1MXR1Z5z6vG68cYbz+q6999/v6Zv4XbkLpMQ7sNTP4+eGrcQte1wYRlzft1ByYavmKp+TXO16tCjhbbuzPe6hqEDBzO2Z1P8zOe+Haknfh49MWYhTqbrOvd9uY6Lt0xjkMG+VkSaFs69QS/yzpQLCayFHu7aUJ3P4zklXqqqEhcXR+fOnc+4BOyCBQtq+ha1olmzZgQGBqKqKiEhIfz5559n/Vyp7IRwH576efTUuIWoK6lHinn1t62YNn/FVOO3xChHqpxfaOvOh8bL6dV3KOP7NCPIt+YNNE/8PHpizEKcTpnFxqR3lnB/5t20VfcDsFFrzisxLzPvxgF4GWttcJ7T1FniNWXKFD7//HPi4uKYMGEC1113HaGhoTV9uTrTrFkzNm/ejL+/f7WfK5WdEO7DUz+Pnhq3EHVtR2Yhr/6aQuiOL7nD+B0RSl6V80ttHXhfvYzWvexzwMIDzNV+D0/8PHpizEL8m7ySCibN/ZGXi+6jiXIUgN9tnfml7Qs8P6YrqureU5fqbI7X3LlzOXToENOnT+fHH38kNjaWMWPGsGjRItkEUQghhBDnJDEqgLk39OHKyY/xYOzHPGG5liw92HF+oGETHyqPMmTlDcyc/RKzvkvhwNES1wUshKi2YF8vXpx4AdOMD1Gg+wAw1LCBXpsf47EfUupVTnHO/Xdms5lrrrmG3377ja1bt9K2bVtuu+02mjVrRlFRUbVfb9myZYwcOZLo6GgUReG777475Zq5c+fSrFkzvL296dmzJ2vWrKnWeyiKwsCBA+nevTuffvpptWMUQgghRN3pEBPMOzcN4KJbnmJWs8940HIjaVq443wPdQfvGp5lzPprefmlJ7jjkzUkp+W5LmAhRLXEhvry4ITLmKrdS0Xl9hJXGpfR7J8neX7hdhdH5zxOHTipqiqKoqDrOjabrUavUVxcTMeOHZk7d+5pz3/55ZdMmzaNWbNmsX79ejp27MiIESM4fPiw45pOnTrRrl27Ux4ZGRkALF++nHXr1vHDDz/w9NNPs2nTphrFKoQQQoi606VpCPMm9OW62x/j+cTPmGaZzC6tieN8W3U/L5veYOauq/hp3gOMe+N3ftuahabVnzvmQtRXHWKCGXfdDUyz3YFNtw8vnGBchM+KZ5n7524XR+cc57yPV3l5Od9++y3vv/8+y5cv5+KLL2bChAmcf/75qOq55XWKorBgwQJGjx7tONazZ0+6d+/O66+/DoCmacTGxnLHHXcwY8aMar/HfffdR9u2bRk/fvxpz5eXl1NeXu4o5+fn07RpU9LS0mRctRAuVlBQQGxsLHl5eQQFue++H1KPCFE7Uo8UMf+vPRSn/B/jDD/TQd1X5XyB7s0E68PMu/sqwgO8T/sanlCPSB0iGpJFmzNZ9u0bPGU6vir6S5bLiRoxjbE941wY2elVqw7Rz8HkyZP1kJAQvUOHDvqcOXP07Ozsc3m5UwD6ggULHOXy8nLdYDBUOabrun7DDTfol1xyyVm9ZlFRkV5QUKDruq4XFhbqXbp00desWfOv18+aNUsH5CEPebjxIy0trdr1S12SekQe8nD/hzvXI1KHyEMe7v84mzrknJeTb9q0KZ07dz7jZsnffvttjV7/5B6vjIwMmjRpwt9//03v3r0d102fPp2lS5eyevXq/3zNvXv3cumllwJgs9m4+eabufPOO//1+pPvMmmaRk5ODo0aNfrPDaKPZcCedkdK4q5bEnfN6bpOYWEh0dHR59zDXptqWo+4w++4Jjw1bvDc2CXumvOEekTaIhJ3bZO4a646dcg57Tx4ww03/OcH3t00b96cjRs3nvX1ZrMZs7nq8rTBwcHVes/AwECP+k98jMRdtyTumnHXoUEnOtd6xNW/45ry1LjBc2OXuGvG3esRaYtI3HVF4q6Zs61Dzinx+uCDD87l6dUWFhaGwWAgKyuryvGsrCyioqLqNBYhhBBCCCGEOFvu2af+L7y8vOjatSuLFy92HNM0jcWLF1cZeiiEEEIIIYQQ7uScerxqQ1FREbt3H18yMjU1leTkZEJDQ2natCnTpk1j3LhxdOvWjR49ejBnzhyKi4uZMGGCC6M+PbPZzKxZs04ZHuDuJO66JXGLf+Opv2NPjRs8N3aJW/wbT/0dS9x1S+KuG+e8nLyzLVmyhMGDB59yfNy4cY6hja+//jrPP/88mZmZdOrUiVdffZWePXvWcaRCCCGEEEIIcXbcLvESQgghhBBCiPrGo+Z4CSGEEEIIIYQnksRLCCGEEEIIIWqZJF5CCCGEEEIIUcsk8RJCCCGEEEKIWiaJlxBCCCGEEELUMkm8hBBCCCGEEKKWSeIlhBBCCCGEELVMEi8hhBBCCCGEqGWSeAkhhBBCCCFELZPESwghhBBCCCFqmUclXsuWLWPkyJFER0ejKArfffddlfNFRUXcfvvtxMTE4OPjQ5s2bZg3b16Va8rKypgyZQqNGjXC39+fyy+/nKysrDr8KYQQQgghhBANjUclXsXFxXTs2JG5c+ee9vy0adNYuHAhn3zyCdu2beOuu+7i9ttv54cffnBcc/fdd/Pjjz/yv//9j6VLl5KRkcFll11WVz+CEEIIIYQQogFSdF3XXR1ETSiKwoIFCxg9erTjWLt27bjqqqt4+OGHHce6du3KBRdcwJNPPkl+fj7h4eF89tlnXHHFFQBs376d1q1bs3LlSnr16lXXP4YQQgghhBCiATC6OgBn6tOnDz/88AM33ngj0dHRLFmyhJ07d/Lyyy8DsG7dOiwWC0OHDnU8JykpiaZNm/5r4lVeXk55ebmjrGkaOTk5NGrUCEVRav+HEkL8K13XKSwsJDo6GlV13w58qUeEcF+eUI9IHSKE+6pOHVKvEq/XXnuNSZMmERMTg9FoRFVV3nnnHQYMGABAZmYmXl5eBAcHV3leZGQkmZmZp33NZ555hscee6y2QxdCnIO0tDRiYmJcHca/knpECPfnzvWI1CFCuL+zqUPqXeK1atUqfvjhB+Li4li2bBlTpkwhOjq6Si9XdcycOZNp06Y5yvn5+TRt2pS0tDQCAwOdFbqoBbquM/XxZ3nNyz4nMFMPoUT3IlgpJohiDMq5jbLN131I0ZqzUWtBiiEJ3+Z9GJjUmEFJEQT5mJzxI4j/UFBQQGxsLAEBAa4O5YykHhHCfXlCPSJ1iGeb/PTrvKk8C0C2HkSB7kOQUkwQJZgU2zm9dqHuzWatGRv1FmxUkvCK78OApGiGJEUS4ufljPDFf6hOHVJvEq/S0lIeeOABFixYwEUXXQRAhw4dSE5O5oUXXmDo0KFERUVRUVFBXl5elV6vrKwsoqKiTvu6ZrMZs9l8yvHAwECp7NycpumYzWYCvezDMN6wnE92x9swqJBfXE5ZSQF6SQ5KaS5KeR7+tkJClQKilFwilRyiyCVKySFSycVfKTvl9QMpI5atXMhW4EfyMvz482AnZv0+kMiOIxjfrwWJUe77h7w+cfehNlKPCOH+3LkekTrEs3l5eRGo2v9/fWwdxI620/AxGcgrKae8OB+9JBdKc1HL8/CtbItEKrn29gg5jnZJoFJ6ymsHUk4TdjCCHcDPFGa+ydKMjjz1xwAC2p3PhH4JtGsSVMc/ccN0NnVIvUm8LBYLFovllLGVBoMBTdMA+0IbJpOJxYsXc/nllwOwY8cODhw4QO/eves8ZlH3Jg9qTkLE6ZOh0gobR4vLySooJ6ugjO35ZSwtKCMzv4y8nMPoR3YRWXGAFkoGCUo6ndQ9hCv5jucHK8VcaljBpaxgz6b5zNswEqXjNdx7fhsiAr3r6kcUQgghhBsb37cZXZqGnPZcmcVGTnEFmQVlZOWXsbugjOWV3+fmHEE/sotGZQdooWbQUkmno7qHKCXX8fwApZSLDau4mFUc2DKftzddTGHba5l+YTuaBPvU1Y8o/oVHJV5FRUXs3r3bUU5NTSU5OZnQ0FCaNm3KwIEDue+++/Dx8SEuLo6lS5fy0Ucf8dJLLwEQFBTExIkTmTZtGqGhoQQGBnLHHXfQu3dvWdGwHtIBpUr5zHcifLwMxHj5EhPi+y9XjCCnuII92UXsyCzkuQO5HNq/g/DcZIYYNjBITXbcjWqhHuJ59W22bl7Endtu46Yxl3Je60in/FxCCCGE8Ez/1RbxNhmIDvYh+l+TpGHkFlew90gRu7KKmHMgl7T9u2iUs4GBajJD1GRClCIAmqrZPKnOZ/f2Rdy/YzJXXXYFIztGO/knEtXhUYnXP//8w+DBgx3lY+Odx40bxwcffMAXX3zBzJkzufbaa8nJySEuLo6nnnqKW2+91fGcl19+GVVVufzyyykvL2fEiBG88cYbdf6zCFc5t6EkoX5ehPqF0r1ZKNf1igM6caRoNH9sP8x9mw9i3LWQa9Vf6WPYCkAbdT8faA/yxCfbST1/Kjf1b+6En0EIIYQQnupcB7WG+HnR1S+UrnGhXN2jKdCR3OJLWLLzMA9tzsC641fGKosYaNgEQIKawQf6LJ7/aiu7s+7mrmGt3HpobX3mUYnXoEGDONO2Y1FRUcyfP/+Mr+Ht7c3cuXP/dRNmUX/Y/68c//9SWxvWhfmbGdMtljHdYskq6Mwnq8Yyb/n/cT8f0Fbdj1mx8qRpPi8sLOJtfSaTBrSopUiEEEII4Xb0E9sitZPwhPh5cWnnGC7tHMORoo58seYq3l62iHu1+XRWd2NUNGaaPuetZQW8pD/CPSOSaiUOcWbuuWGFELWktm/wRAZ6c8/wRF647zbeSHibd60XOM7da/ofqYve4PetWbUbhBBCCCHcVm33NoX5m7l9SEtev28Sn7Z5i1eslzrO3WL8P8qWvcL3yem1GoM4PUm8RL118hyvuhQR4M3r1/fEMvQpnrKMdRx/3Dif9/63gCNF5Wd4thBCCCHqj9oac3NmIX5evHB1N0IvfowHLBMdx2caP+ebb78kPe/UVRJF7ZLESzQYOkqdJmKKojB5UAtCh93Le5U9XybFxkO2N3jqx011GIkQQggh3IErbgpf3yuONiPvZI71MgBURedR3uLxb9fVcSRCEi9Rb+k6KC66y3SiWwc2Z32ru9imxQLQVt2P9+bP2ZpR4OLIhBBCCFH7XN8Wua5XHBkd7mCd1hKA5momcXs+Ye2+HBdH1rBI4iUaDB3FJav4KIrCrEs78ZR6i+PYFOP3vP77ljqPRQghhBCuY2+LuOa9HxzZntmm29B0ewC3GH/ijUUbXRNMAyWJl6i3dHS36PEC+5yvHv3P5w9bJwBilCP47PiOtJwS1wYmhBBCiFrmHm2RIB8T5w8ZzI9abwAaKYU0PbCA7ZkyAqeuSOIlGgxXLrYBMKFvM+YbLnOUxxiW8L91B10XkBBCCCHqmFLHM86ruqZHUz43X+koX234ky/XHHBZPA2NJF6i3rLP8XIfAd4mmnUcwm7Nvmt8T3U7a9asQtPc406YEEIIIZxPcaM/894mA1269WGDlgBAa/UAOzcso9xqc3FkDYMkXqIBcd246mOu6tGUL2yDHeXuJUvZeDDPdQEJIYQQos7o1P6eov9lTLfYKm2RgZblrN4ri2zUBWN1LtY0jaVLl/LXX3+xf/9+SkpKCA8Pp3PnzgwdOpTY2NjailOIGnGXOV7HtGsSxFPBg6H4UwCGGtbz67YsOjcNcXFkQgghhKgdmqsDqKJZmB/pUUOxHnkPo6IxTP2H+VszGdAq3NWh1Xtn1eNVWlrKk08+SWxsLBdeeCG//PILeXl5GAwGdu/ezaxZs4iPj+fCCy9k1apVtR2zEDWiu3hc9TEd2rVjixYHQEd1Lxs2b3NxREIIIYSoC7ru+nYIQO92CfyjJwIQr2axa8t6dN29blbXR2eVeLVq1YpNmzbxzjvvUFBQwMqVK/nmm2/45JNP+Pnnnzlw4AB79uyhf//+XH311bzzzju1HbcQ/8ld9vE62fA2kfyudXWU43L+kt3jhRBCiPrK/ZoiDGsTyW+2Lo5y+5KV7MgqdGFEDcNZJV6//vorX331FRdeeCEmk+m018TFxTFz5kx27drFkCFDnBqkEM7gLvVep9gQ/jEdT7x6qdtYvfeoCyMSQgghRF3Q3WDkDUDLCH+2+fV0lO1tEZnnVdvOKvFq3br1Wb+gyWSiRYsWNQ5ICGfRT1O9uXpCK4BBVfCP70axbgagl7qVVXuOuDgqIYQQQtSGk0ffuENbRFEUohM6kq0HAdBd3cGaPYddHFX9V63FNQA2bdp02uOKouDt7U3Tpk0xm83nHJgQzuYud5kAerSI5J9diQw0bCJSyePgns1AJ1eHJYQQQoha5C6jbwB6tQhjdUprLjasIkApJT91HZrWHVV1n/ZSfVPtxKtTp04oZ0jVTSYTV111FW+99Rbe3t7nFJwQ9VXP+Eb8qLVhoMF+IyOucD3peZfTJNjHxZEJIYQQoja5w0JfAD3jQ3lLsydeAG3KN7LrcBGJUQEujqz+qvY+XgsWLKBly5a8/fbbJCcnk5yczNtvv01iYiKfffYZ7733Hn/88QcPPfRQbcT7nwoLC7nrrruIi4vDx8eHPn36sHbtWsd5Xdd55JFHaNy4MT4+PgwdOpRdu3a5JFZRu+yL8xy/t+ROPV5JUQGkmNo7yjLPSwghhKiv3LMtEhvqy16/zo5yL3Ubq6QtUquqnXg99dRTvPLKK0ycOJH27dvTvn17Jk6cyMsvv8yLL77Itddey2uvvcaCBQtqI97/dNNNN/Hbb7/x8ccfk5KSwvDhwxk6dCjp6ekAzJ49m1dffZV58+axevVq/Pz8GDFiBGVlZS6JV9QtdxhXDaCqCgHx3SipnOfVQ93O2lSp7IQQQoj6zl3aIgCNW3TkiB4IQDd1J2tTs10cUf1W7cQrJSWFuLi4U47HxcWRkpIC2IcjHjp06Nyjq6bS0lK++eYbZs+ezYABA0hISODRRx8lISGBN998E13XmTNnDg899BCjRo2iQ4cOfPTRR2RkZPDdd9/VebyidulUndDqTuOqAbo2j2C9lgBAYyWH/Xu3uzgiIYQQQjife/Z4AfRoHspazb6fV6BSQm5qsuznVYuqnXglJSXx7LPPUlFR4ThmsVh49tlnSUpKAiA9PZ3IyEjnRXmWrFYrNpvtlLllPj4+LF++nNTUVDIzMxk6dKjjXFBQED179mTlypV1Ha5wgTPNT6xrPeJDWaMdXzE0Knc9R4rKXRiREEIIIWqbGzVF6BHfiDVakqOcUJrC/qMlLoyofqv24hpz587lkksuISYmhg4dOgD2XjCbzcZPP/0EwN69e7ntttucG+lZCAgIoHfv3jzxxBO0bt2ayMhIPv/8c1auXElCQgKZmZkApySFkZGRjnMnKy8vp7z8eGO4oKCg9n4A4VS6XvW+krvdZWrTOJCXDG0cZftwwxwuaN/YhVGJ2iD1iBDiXEgd4uF09x1906yRLzt9OoDVXu6hbmNNag7NwvxcG1g9Ve0erz59+pCamsrjjz9Ohw4d6NChA48//jipqan06tULgOuvv5777rvP6cGejY8//hhd12nSpAlms5lXX32Va665BlWt9o8KwDPPPENQUJDjERsb6+SIRV1yp9TLaFBRY7tToRsAe+K1OlU2L6yPpB4RQpwLqUPqF3dZ1RDsI4FCmnWmQLevqtxD3S6LfdWiGmUjAQEB3Hrrrbz00ku89NJL3HLLLQQEuMfSky1atGDp0qUUFRWRlpbGmjVrsFgsNG/enKioKACysrKqPCcrK8tx7mQzZ84kPz/f8UhLS6v1n0E4x6lzvNynojumS4vGbNLtG443VzPZvXePiyMStUHqESHEuZA6xLMpZyi5g+7Nw1mntQIgXCngUOpmF0dUf9Uo8fr444/p168f0dHR7N+/H4CXX36Z77//3qnBnQs/Pz8aN25Mbm4uixYtYtSoUcTHxxMVFcXixYsd1xUUFLB69Wp69+592tcxm80EBgZWeQjP5U7jquHUsdVB2WspKLO4MCJRG6QeEUKcC6lD6hf3a4tUnXMeW5hMZr6s9l0bqp14vfnmm0ybNo0LLriA3NxcbDYbACEhIcyZM8fZ8VXbokWLWLhwIampqfz2228MHjyYpKQkJkyYgKIo3HXXXTz55JP88MMPpKSkcMMNNxAdHc3o0aNdHbpwMl2v2uPljjrEBLGe45Vdd2U76/bnujAiIYQQQjiLfb65e4++SYwMYLOpraPcQ93Gmn0y9aE2VDvxeu2113jnnXd48MEHMRqPr83RrVs3x3LyrpSfn8+UKVNISkrihhtuoF+/fixatAiTyQTA9OnTueOOO5g0aRLdu3enqKiIhQsXnrISoqif3GlcNYC3yYAlugeabo+rp7qdNTLPSwghhKi33KslYt9b1CeuG2W6va1sb4vIPK/aUO1VDVNTU+ncufMpx81mM8XFxU4J6lyMGTOGMWPG/Ot5RVF4/PHHefzxx+swKuESOm69quEx7VvEsjUzjnbKPhKVNDbv3gck/dfThBBCCOHmTh59467jcLo2j2TD3pb0NmwlRjnC3t07gPauDqveqXaPV3x8PMnJyaccX7hwIa1btz71CUK4EXcbVw32sdWrKsdWq4pO6KFlsp+XEEIIUU+5e1sEoHnucvYfdX2HSn1T7cRr2rRpTJkyhS+//BJd11mzZg1PPfUUM2fOZPr06bURoxA1oqOjKO56b+m4HvGhLFe7O8rD1H/4fWvWGZ4hhBBCCE9gX2HZ/XWICeYf756O8nD1H37dIm0RZ6t24nXTTTfx3HPP8dBDD1FSUsLYsWN58803eeWVV7j66qtrI0YhnEJ3uxledt4mAwGJA8jV/QEYpG5k8eYDLo5KCCGEEM5mn/bgfq0Rg6rQtE0vDuphAPRWt/JXym4XR1X/1Gg5+WuvvZZdu3ZRVFREZmYmBw8eZOLEic6OTYhz4gmrGh4ztG00i7UuAPgrZXjvXURWgSzlKoQQQngyXddx35ldVQ1v25jfbF0BMCk2mmQsZG92kYujql9qlHgd4+vrS0REhLNiEaJW6eCON5kAOK91JP+nDHSUr1IW89Va2SBTCCGEqE903HOOF0CfhEb8ZhrsKF9j+IMvpC3iVGe1qmHnzp1RzvJ/yfr1688pICGcxTPuL9n5m41EdhhG6qa3iVez6GfYwqsrV3DzgOZ4mwyuDk8IIYQQNeApc7wAzEYDbbsNZNPqeDqoqXRQU5m9ZjEFQxII9Da5Orx64ax6vEaPHs2oUaMYNWoUI0aMYM+ePZjNZgYNGsSgQYPw9vZmz549jBgxorbjFaLG7HO83Lf6G9srjk9sQx3lG8o/55NV+10YkRBCCCGcyV3nmx9zTY+mVdoik2xf8O5fqS6MqH45qx6vWbNmOb6/6aabmDp1Kk888cQp16SlSXekcB8n7xbv7jrEBDMnbgzZ6T8RruRzsWEV1y3+Py7qcBONg3xcHZ4QQgghqsmT5psDNA/3pzjxCtJ2f0esms0AQwofLfuW1M53EB/m5+rwPF6153j973//44Ybbjjl+HXXXcc333zjlKCEqA06ituOqz7mjvM78pp1tKP8mDaXmZ+vpsKquS4oIYQQQjiFvS3i3o2RO0e05WXbFY7yY+rbPPjZX5RZbC6Mqn6oduLl4+PDihUrTjm+YsUKvL29nRKUEM7gSeOqj+ncNISS9jeQrLUAoIV6iGvTH2fm1xuw2iT5EkIIITyJjmeNvgFoFRlAQPdrWWFrC0AT5ShTsh/n7s/WyI3gc1TtxOuuu+5i8uTJTJ06lU8++YRPPvmEO+64gylTpnD33XfXRoxCOIWnJGIPXdKBp81TKdDtwwuHGdYxeMsDTP7wb3KKK1wcnRBCCCFqylPaItMvaM0cvzs5qgcA0NewhSt3z2DSu8s4LNvd1Fi1E68ZM2bw4Ycfsm7dOqZOncrUqVNZv3498+fPZ8aMGbURoxA14mnjqo8J9vXigRtGc6c2DYtuX9HwYsMq7tw3hckvfcKXaw9I75cQQgjhAextEc/jZzby5PgLuZP7KdPtKxoOMSTzYMZt3PXyfD5auU96v2qgRvt4jRkzhhUrVpCTk0NOTg4rVqxgzJgxzo5NCKfyhHHVx3SKDea6seO4TbuXEt0MQDt1H59a70H7fipjn/uc1xbvIj2v1MWRCiGEEOLsuf9882MSowK47YZruEWfSYHuC0BLNZ1PtJn4/zyFG577mJd+3cH+o8UujtRznNWqhrque0yDVYhjPHFc9YnOax1JwI23MuGjRjxhe4VWajpGReMa459cU/EnK5a05ZU/+nCwUV/aJyXRJS6EjjHBRAXJXEshhBDCXXhyW6RPizD8J03kpg8a8XDFy7RX96EqOpcZlnOZZTlrlify9tK+7A3uQ9vWbekaF0KH2GCig7wldziNs0q82rZtyyOPPMJll12Gl5fXv163a9cuXnrpJeLi4mTYoXBLnlYF9IgP5bVp43jk63Yk7J7PJONPBCr2Xq6+hi30NWyBgnfYviqWDX8nMEdvwUGfRIxhrYiObER8Iz/iGvkSFeRNZKA3jfy8MBpq1NEthBBCiHPk7nuKnk6HmGDeunsss75rQ+TW+dxm/IEQpQiAHuoOeqg7oPh9dq1pwoZVCbyht+CAdyvUsFY0jggnPsyPuEZ+lW0RM2H+ZkwNtC1yVonXa6+9xv33389tt93GsGHD6NatG9HR0Xh7e5Obm8vWrVtZvnw5W7Zs4fbbb2fy5Mm1HbcQ/81Dx1WfLCLAmzfH92H57lbc9stltMv6nqsMfxKvZjmuSVLTSFLTuIY/wQpkwqFDoaRqURzQI9hGKFl6CNkEU+EdgR4QicE3BB9ff4J8vQj0NhHoYyLIx0SAtxFfLyO+Xga8TQZ8vQz4VH719jLgazJI8iaEEEKcBU+db36yED8vXr22B6v3tuCuhZeTkL6Aqw1/0lJNd1zTUk2npZrOGJaCDciCrMxg9ulR7NOi2Ekwh/UQDhNCmXck+Edg8A3F+1hbpLIdEuhjItDbWNn2MOLjpeJjsrdLfI49TAaPTN7OKvE677zz+Oeff1i+fDlffvkln376Kfv376e0tJSwsDA6d+7MDTfcwLXXXktISEhtxyxEjegenIYpikL/luH0S7iAjQf78Nbq/RzcvJzu1n8YrG6grbIPg1K1Ym+s5NDYkEMftlZ9MRuQZ3+U60YK8CNf9yMfPwp0Xwrw46juxUHMlGKmRDdTihdlju/NVKhmdIMZXTWhq15gMKEbTCgGExi8wOiFavACgxdK5fdeJhWjqmJUFVRVqfpVsX81nPxQFCb2j8fX66yqKiGEEMKteXoK1rN5I3reNpQtGd35aPUB9qasoEv5WoYYkmmv7MWoVF1wI1LJI1LJo6e6veoL2YB8+6NCN1S2QfwcXwvwJUc3k17ZFinVvexfK78vwRuLakZTvdAN9raIYrB/jxPbIseOXdcrjhC/fx/1d7aq1Zrp168f/fr1O+c3dQdz587l+eefJzMzk44dO/Laa6/Ro0cPV4clapmnDzdWFIVOscF0ig3Geml7Nh7M47cd2czZdwhr+kZaWXeQqKQRr2YSrxyikVJ4xtczK1bCySdcya9ZQDr2yvMs9lSs0A1YMWJFxYYBG+rxh35SufL8K9bLKO05QxIvIYQQ9Yant0UA2kYH8cSl7dFGtWNT+tUs2XGY11IzqUjfSEvLDlorB2hW2RYJVwrO+Fpeio1wCv7zun9Vw7aIhooVQ+XXyvJp2iPvWC8kv+NDdZ941Rdffvkl06ZNY968efTs2ZM5c+YwYsQIduzYQUREhKvDE05i3yvj+L0lXa8HNd0JjAaVrnGhdI0LBRLRtIHsPVLMrqxC1hwt5svsYg4fzoS8NIwlWYSRSyS5RCi5hCv5BCnFBFJCoFJMEMUEKLW7QqKXYsPr32rFf/mnCaQEo+p5QwmEEEIIOLbQ14nl+tUWUdXjN4ShFbren31HS9iZVcj6I8V8k11M5uEsyDuAoSSLRrq9LRJ5rC1Csb0dohQTSLFjHnttOWNbBE7bHglRijCozvl3a5CJ10svvcTNN9/MhAkTAJg3bx7/93//x/vvv++URUFW7T3KC9+uwFetqBx6ZUJRDaB6oRqNoBoxGE0YVQWjQcVU2Z1pNKiYDPbvTQbV/rXyuP2YYu8ePfZVVezfGyq/r3ye2ahiNtnHvx4bB+tjMmA22c815FVmPG1Ca3WoqkJChD8JEf4nHO0IgKbp5JRUkFVQxuGCcrKLykkrtZB/wqOwpAxbSS5aWSFYSlCtJSiWUgy2Urz1CnyVMrypwJdyfJRyTFgxYcOEFS+s9rJirVrGikmxOb73ctxb0jBiQ1V0jNgwoGFwfNUcZSsqDTXvuvvzf8jJSEU3GCvrDyOKagLVhGI0oKhGVNVorx8MSmUdYK8LjpUNlfXCsTriWNmgHi/b65wTzp9QB3mbDHgb7V99vAx4Gw14m+z1i7dJxcvQsOsTIdzZ5vR8Hvl8Gf5qub0tohpRDMaqbRGDvQ45sR3h+N5QtS1iqGx/HKszHO2VyuPH6o1jzzUb7fOEfSrri2PtEW+TocG3ReozRVGID/MjPszvlHOappNbUsHhwnKyCsrILiznYKmFgpPaItbSfPSyfLDY2yGqpQTFVoaPbm9/2NsiZfj+R1vk2HETVryUE9olJ7c7FO0/2iIGSbxqqqKignXr1jFz5kzHMVVVGTp0KCtXrjzl+vLycsrLyx3lgoL/7gYtKLVwZd57XGVc8q/XaLqCFbWyu9Pg+N6CAZuuYrE3S48fw37sWBfosfMW7F2mpahYdftrlWOiDC/HeNgyvCrLZsoUL3SDN5rRB93ki+4djME3FB8/f4J8zQT7mgj2MREV5E10sA9Ngn2IDPR22n+4unTyhFZPH1d9LlRVIczfvpJQ2+jqPVfXdcqtGqUVNkotNkoqbJRW2KiwaVhtGhabjsWmUWHTsFQ+Sqx6lbLjGquGTdfRNB2rdsJXXcdq06ueqzzmiZNnT1aTeiTnSBYfFt70n9dZTqoTrJX1hbWyLrHXL/Y/JceGeR67xv5cQ9XnoFKBkRLdQNaxugMvynWvE+oS+9cKxQubwRvNKxB8QlB8QzD7BhHs50WwrxcRAWZiQnyJCfEhJsSHIB+TNLaEqIGa1CGFZVYuzPuUm4y/nPG6E4deWTnhe93gqD9OPG/B6KhbTmyL2DBQigGrbj9WjskxH8dej1S2R3QvyhQvbKoPuskb3eiL7hOM6hOKj18AwX5eBPl4EexrIjLQTJNgX6KDvYkK9PbIhZ10HajSFmm4daCqKjTyN9PI30zrxoHVeu6xtkjZsXaI5XhbxGL997aIRTt+/sRzNg1s2glfdR2bZn+crn3i62Vwyu+gwSVeR44cwWazERkZWeV4ZGQk27dvP+X6Z555hscee6xa72HTdIyK9YzXqIqOF//S3VkXn0kNKK985NsXWcjHnzzdjzz8ydRDWaeH8b0exiHCqQhNJLxJC9rGBNO/ZRgtI/w9swHlgSG7mqIo9p4PkwFZOqdmalKPoJ25DjnG3qNowxvLqSfr6v+7FSi0P6y6Sh7+5Ot+ZOkhpOth/KaHc1APJ8unOT4x7WkXG07/VmF0iglG9cCbOkLUtRq3Rc5i0su/Dr2qq7ZIReXDsciCP3m6P3n4kaWHsl4P4ye9ERlEUBbSkkZNWtKuSTB9E8Jo3TjAI9siHhiyy53YFgn2dXU0NdfgEq/qmjlzJtOmTXOUCwoKiI2NPeNzLJpOspaAGWtl16W9YWTEirGyO9OIFWPlcCtT5fAro2KrPHf82LHnnbxinbOZFSsR5BGh5J3+gkLI3eZPypZ4vvylA1sD+tC9S3eu6x1HRIB7btirn9TH1ZDvMgnXqkk9UqoZ+cHWGwM2R11xrD4xKprj+PHzlfUIthOGTRy/xlRZ9lLOYvbxOTAqGmEUEKYU0IJDVU/aoHyfka2pzfjzz07M9u1Pzx59mNC3GcG+5z5pWYj6qmZtEY0tejN+svWq0hYxYMN0QnvjlMdpzplOqHtqk32RhTMs+FQE+dt92bw1nh8WtecJ39507NKLcX3iaBzkU6ux1dQp881dF4pwAzVKvPbs2cP8+fPZs2cPr7zyChEREfzyyy80bdqUtm3bOjtGpwoLC8NgMJCVlVXleFZWFlFRUadcbzabMZvN1XqPrnEhKGPux6rplNo0rJXdlVabhtV2wveajlWresyi6dhs9q5Rm2bv3rTYNDSbDZtmBasFTbOCrQJds4LNCprFfnfcZkWxWcBWjmItRbWV4a3bx8L6KBV4U443FnyUcnwqx8cGKcUEU0SwUkyQUkQwRfgp5af9uUKUIgYYUhhgSIGyT1m1vDUz/hpN1yFXMGlAc48YEiZ3mYQr1KQemXJRD/JK3qNc0yk+Nvyhst44NhTiWB1h0yrrjhPKJ19nH1pROXTCZqusRyzoNiu6ZkPXLCg2C7pmq6xPKlAspSjWMhRbGUabfY6ft1KBGQs+HK9bAighWCkiiGKCK+uREKXotAu2mBUrnZXddFZ3g+Vrli1rz20rrubSi0dxRdcYj7x7LURtq0kd0rZxIEVj7na0RWyajqWyHrHXCSe1RSrrD8exk9oiVk3DZq1si9gsaDZ7PYFmRT9dW0QrR7XY2yJeegU+lFe2RSoq647jc3XsbRF7/XGsXeKvlJ325wpSSuhr2EJfwxawfMG6lS15dMUltOo/hilDWuJtcs6QsNok9VzDVe3Ea+nSpVxwwQX07duXZcuW8dRTTxEREcHGjRt57733+Prrr2sjTqfx8vKia9euLF68mNGjRwOgaRqLFy/m9ttvd8p7NKmcG+Vqum4fz1pWoVFmtTnm6JRabJRZbBSX28gvtZBRUkFBqYW8Ugt5JRYKiwqx5aahFqYToR0mTsmijbKfduq+Kneheqnb6MU2vvtjKZN2Tue18f3xN7tPJ+qpc7ykohOeY2CrcFeHUIWmHR9ff6wOKbNolFqsFJRZyS+xsLekwlGP5JZUkJdzFC13P94l6TQli3ZqKh2UvbRQj/eEDTCk0FffzOvf/cPjGffyyMi20igRwgkiAr0Z2bGak3prgaMtYqmsPypsVdokJZVtkR2V7ZD8ynqksKgIS+5BDAVphGvZNFUO07qyLRKl5Dpev6u6i7fUF1m0fCkTd01n7o2D3aoHXdfr96qGonqq3UqeMWMGTz75JNOmTSMgIMBxfMiQIbz++utODa62TJs2jXHjxtGtWzd69OjBnDlzKC4udqxyWF8oin1VIbPRQBCmaj9f13WOFFWw/2gxWzIK+PlgHgd3baRDyUrGGJY4Gk+jDX/TNP1eprz/HO9NGuLWk1+luhOiZlRVsa9K5lX9uX4Wm0ZaTgkp6fl8kpbH9u1baZO3hHGGRTRVszEoOncaFzB/TQlvBDzNlMEJtfIzCCHqXpW2iE/N2iI5xRXsO1rC1kMF/J6ez/5dm2hTuJIrDUtJUtMAGGH4h9jD9zHlnWd477bz3brnS9oiDVe1E6+UlBQ+++yzU45HRERw5MgRpwRV26666iqys7N55JFHyMzMpFOnTixcuPCUBTcaOkVRCA8wEx5gpluzUAB0vSNbMkbz1oq96Bs/52HjxwQqJXRRd3NFxvO8+nsM00YkuThyu5PHVQshXMNkUGke7k/zcH9GdWoCI9uyJWM4z/8xntht73CP8SsMis4E4yLuWtySlJbTaR8T5OqwhRBuQFGOr4TXNc5+20fX27Mj6xI+/nsfhev/xyzDfBophbRR93PTked47uemzBrV3sWR20lbRJyo2l0TwcHBHDp06JTjGzZsoEmTJk4Jqi7cfvvt7N+/n/LyclavXk3Pnj1dHZJHUBSFdk2CmD2mM1dPmskkw+MU6PblZUYaVrHjr69Jz6vdze9qSkfGVQvhLtpGB/Hadd1pecUjPGg9vmz+g4aPefnn9S6MTAjh7hRFISkqkKcu68Ckyfcy2etpsnX78uSDDRvJW/MZuw8XujjK09NRZL55A1btxOvqq6/m/vvvJzMzE0VR0DSNFStWcO+993LDDTfURozCTXWNC2HmhCt52DbRcex29X+8vzzVhVEdd/K4aiGE+7m0cwyRgybxi607AOFKPk33f8v2zP/ep0gIIdo1CeLJmy7lAW2y49hUwze8s3S3C6M67uT55qJhq3bi9fTTT5OUlERsbCxFRUW0adOGAQMG0KdPHx566KHaiFG4sY6xwQR3u4oUrRkA7dV9bFv/FxXW2l1ytiZ0FEnEhHBDkwe14EPzWEd5jGEpX/9z0IURCSE8SavIABL7Xc5KWxsA4tUsslL+oLj87PZDrGvSGmm4qp14eXl58c4777B3715++uknPvnkE7Zv387HH3+MweC+ExlF7RnXN57Pbec5yv0r/mLd/twzPKNuyF0mITyDt8lAl259SNZaANBG3c/WbSkujkoI4Umu7x3H59oQR3motoIVu91g7QFdFtMQx9V4+bnY2FguvPBCLr/8coqLi8nNdX1DW7hG83B/9kcMdpT7qFv4e48bVHankHHVQrir89tFsdjW2VGOyVtLZv7p9/ERQoiTRQZ6UxA7DItu7wSwt0WOujiqU8kcr4at2onXXXfdxXvvvQeAzWZj4MCBdOnShdjYWJYsWeLs+ISHaNcygW1aLADtlVQ27d7v4ojspMdLCM/QNjqIZFNHR7mXuo3Vqe7XaBJCuK/uiTFs0O3bUTRXM9m9e4eLIwIdHUWRtoiwq3bi9fXXX9Oxo/2P448//sjevXvZvn07d999Nw8++KDTAxSeoWfzUNZq9mXkVUWHrC3ountVNPYlXeU2kxDuyKAqeDXt6rhbnaSksTPLPVclE0K4p57xoazVEh1l36NbKLfaXBjRqWS+ecNW7cTryJEjREVFAfDzzz8zZswYWrVqxY033khKiozJb6iSogLZocc6yrHW/WS4eJiQLuOqhfAoCVGhpOr2vy8tlHR2Hcp3cURCCE+SGBXADq2po5xAGqlHil0Ykcw3F1VVO/GKjIxk69at2Gw2Fi5cyLBhwwAoKSmRxTUasMZB3qQZmznKiUoaOzPd6261jKsWwr21igxgZ+UNHLNipSRzp4sjEkJ4kgBvE7n+CY5yKzWNHW7XFkHuCjdg1U68JkyYwJgxY2jXrh2KojB06FAAVq9eTVJSktMDFJ5BURSIOP7v31JNZ/fhIhdGVDmuWu4yCeExWkUGsEtr4ij7Fu7BYnO/rSmEEO7LK6qVY8hySyWdPS5viwhxnLG6T3j00Udp164daWlpXHnllZjNZgAMBgMzZsxweoDCc4SFRZJ/2JcgpYRojrAwr9TVIVUh2ykL4d5iQ304qIc7yo3JITO/jNhQXxdGJYTwJDGNgsjcF0qskk20cpT0PPdaHdU+x0vaIw1VtRMvgCuuuOKUY+PGjTvnYIRnaxzsTYbeiCClhCglh8w814+rlntNQniOIB8TRw0nJF7KUQ5J4gXAhgO5fL3uIDlF5bSJDuLaXnGE+nm5Oiwh3E50sA8ZNCKWbEKUInJcvN2RrsvoG3FcjRKv4uJili5dyoEDB6ioqKhyburUqU4JTHie6GAfMvQwWpOGl2KjNC/T1SFVoYPM8RLCjSmKgi0wGirv2UQrRzmU7149564w949dpC2exxTD90QpOazdmcjkFTfywIQr6Rgb7OrwhHArjYN9OKSHOsq2/IMujOZUMt+8Yat24rVhwwYuvPBCSkpKKC4uJjQ0lCNHjuDr60tERIQkXg1YdJAP6SdUduSnuy4Yji0fL4TwJMbgmCqJ1xo3G7Jc137YmEHB4hd51vS541gfw1ba2R7m5vc1Xr17HJGB3i6MUAj3Eh3kzVq9kaNsKEy39zq5KNuRtog4UbUX17j77rsZOXIkubm5+Pj4sGrVKvbv30/Xrl154YUXaiNG4SGODTU8xqc00632z5Bx1UK4v7DgII7ogUDlUEM3m59Rl4rLrXzy4yLuM37pOJav24ddBiqlPGWbw5M/bHJVeEK4Jfvom+NtkTDtCDnFFWd4Rt2SRKxhq3bilZyczD333IOqqhgMBsrLy4mNjWX27Nk88MADtRGj8BCNA33IPKHHK0rJ4XBBucvikXHVQniexkHeHNZDAAgnj8MFDbfH66OV+7m5/COMin1lx9etoxiqvMUmLR6ABDWDgG1fsDld9jsT4piIADNZnNAWIYcsl7ZFZB8vcVy1Ey+TyYSq2p8WERHBgQMHAAgKCiItLc250QmPEuhjJMdwvLILV/LILnJdZXcyGVcthPsLDzBzWA8GsM8VLch2bUAuous6S1av5Tx1AwAZeigb42/mt+nn85x6s+O624zf8/5fu10VphBux2hQKfeOcJTDlXw3bItIY6Shqnbi1blzZ9auXQvAwIEDeeSRR/j000+56667aNeundMDFJ5DURRsPscruwjyXNvjhdxlEsLThAd4OxIvAL0wy3XBuNCa1Bz6FC5CVex12CfWodw6tC3Bvl70HjCcJbaOAMQoR8jf8hsFZRZXhiuEW9H9T2iLKHkcLnDdkGXZU1ScqNqJ19NPP03jxo0BeOqppwgJCWHy5MlkZ2fz9ttvOz3A6li/fj3Dhg0jODiYRo0aMWnSJIqKqm6cd+DAAS666CLHYiD33XcfVqvVRRHXP0pgpOP7cCWf7EL3mZ8h46qFcH/hAWayCXKUDSWH0fWG12j5dWsW56trANB0hXUhF9ClqX0I5tU9mvKVNsRx7SiW8EvKIZfEKYQ7MlVpi+RxuNB9erxA2iINWbUTr27dujF48GDAPtRw4cKFFBQUsG7dOjp27Oj0AM9WRkYGQ4cOJSEhgdWrV7Nw4UK2bNnC+PHjHdfYbDYuuugiKioq+Pvvv/nwww/54IMPeOSRR1wWd33jGxjm2DE+Qskj280qOyGEe4sIMDvmeAGE2nIoKGt4N8e2bttMompfBjtZb0Gvju0cw5PC/M3orUaQq/sDMFhN5o8trl1FVgh3EhIY4Ph8RCBtEeE+qp14uauffvoJk8nE3LlzSUxMpHv37sybN49vvvmG3bvt499//fVXtm7dyieffEKnTp244IILeOKJJ5g7d+4p+5GJmgkL9HHcrXb1XSb7hNYTyT0mIdxdeICZbP14j5e79ZzXhbScElrk/e0o/2HrzJCkiCrXDG0XyxLNfrMzQCnFsucvyizus4qsEK4UccJc0XAlj2wXDjXkpLaILm2RBq3aiVdWVhbXX3890dHRGI1GDAZDlYerlJeX4+Xl5Vj4A8DHxweA5cuXA7By5Urat29PZOTxLugRI0ZQUFDAli1b/vV1CwoKqjzEv4s4YX5GIwo4WlDi2oBOIhNahStIPXL2vE0Gir3CHGX7/IyGdbd6dWoOvdRtjnKyuSvtmwRVuWZwUgR/aF0c5X76OlbuOVpnMYq6JXVI9ZyYeHkrFooLclwb0EmkKdJwVTvxGj9+POvXr+fhhx/m66+/5ttvv63ycJUhQ4aQmZnJ888/T0VFBbm5ucyYMQOAQ4fsY98zMzOrJF2Ao5yZmXna133mmWcICgpyPGJjY2vxp/B8EQFmsisrO1XRqShw5cT4qhNaG94sEeEupB6pHs3/eD0doeS61YpkdWHd/ly6qDsBKNbNBDTrgqpWbamF+nmRHz3QMbR7sLqBpTsb5gqQDYHUIdUTHuBNNsGOslboujmQJy/0peuSdTVk1U68li9fzqeffsrkyZMZPXo0o0aNqvJwthkzZqAoyhkf27dvp23btnz44Ye8+OKL+Pr6EhUVRXx8PJGRkVV6wapr5syZ5OfnOx6yZP6ZhZ+QeIH7rUgm1Z1wBalHqscYEOX4PlzJb3A9XgdSdxCt2O/QJ2sJdGkWftrreraJZ73eEoB4NYudOzbXWYyibkkdUj0RgeYqq6OqxYddF8xpKNIaabCM1X1CbGxsna4wdc8991RZION0mjdvDsDYsWMZO3YsWVlZ+Pn5oSgKL730kuN8VFQUa9asqfLcrKwsx7nTMZvNmM3mc/wpGo6IAG82n3CXyVR6GE3TT7lbWxdOneMlhGtIPVI9gUFBFGb4EKCUEo577QdY2/JLLYQe3QBe9vI/eiv6Nws57bX9W4bx6+/t6aluByAubzUHc0cSE+JbV+GKOiJ1SPWE+1edKxpozaG43IqfudrN3nMmGyiLE1W7K2jOnDnMmDGDffv21UI4pwoPDycpKemMDy8vryrPiYyMxN/fny+//BJvb2+GDRsGQO/evUlJSeHw4eN3Pn777TcCAwNp06ZNnfw89d2Jm58CNCKPnBL3WLhENlAWwjOcPDHelXvw1LX1B3LpWjnMEGCTkkjb6MDTXts2Oohkr86Ocn81heW7jtR6jEK4O/vom+M3LFy92NeJdJC7wg3YWaX+ISEhVRYlKC4upkWLFvj6+mIymapcm5PjugmMr7/+On369MHf35/ffvuN++67j2effZbg4GAAhg8fTps2bbj++uuZPXs2mZmZPPTQQ0yZMkXuJDlJmL9XlXHVxzZRDvOv+9+vbKAshGeKCPDmCEG04BCBSil5+fmuDqnOrN+fy/DKxEvTFayNu2I2nn7hKoOqEJLQg7ydfgQrxfRVN/Pgziyu7tG0LkMWwu34mY0UGEMd5WObKMeH+dV5LLq0Q8QJzirxmjNnTi2H4Rxr1qxh1qxZFBUVkZSUxFtvvcX111/vOG8wGPjpp5+YPHkyvXv3xs/Pj3HjxvH444+7MOr6xWhQKfcOg8pVjcMV9xkmZO/xkttMQri7k3vOtcLTL35UH6XszeBO5QAAO/UYkuLPvIhCv1aRLN/ejosNqwlSSsjbvQab1g2DC4Z3C+FONP9IqFxYOVzJd7O2iKujEK5yVonXuHHjajsOp/joo4/+85q4uDh+/vnnOoim4dL8IqFypdsIFw4Tsk9FlDtNQniaiAAz209IvJQi95oYX1ssNg0l/R+MBg2AdVorujY9/fyuY/q1DOdVrQMXG1YD0Nmyns3p+XSMDa7tcIVwa0pAlCPxiiCXnS5apEfmeIkTnfUcL03TeO655+jbty/du3dnxowZlJaW1mZswkOZgqquSOY+d5mEEJ7g5BXJfCuOUG6t/5sDb80ooL223VFeqyXSrVnoGZ4BTYJ9OBDcw1Hub0jhr12yrLwQ/oEhlOn26TDu1RaRNQ0bsrNOvJ566ikeeOAB/P39adKkCa+88gpTpkypzdiEhwoJCCBX9wfsd5lctRS0LvvDC+GRwv29qyReEUoe2W4yMb42/bM/l27qDkf5cEgnQv28zvAMu8TEtuzRGgPQRdnF2h37ay1GITxFRODxesSVG7HLfHNxorNOvD766CPeeOMNFi1axHfffcePP/7Ip59+iqZptRmf8EAn3q2OUPLIdpcVyWRQtRAeIdDHSK7BPVckq03rU7Ppou4CIFMPoWl80lk9b0CrMJZpHQAwKho+B/+mqNxaa3EK4QnCA8wcxl6PhChF5BQUuDgiOx1kvnkDdtaJ14EDB7jwwgsd5aFDh6IoChkZGbUSmPBcEQHH98/wViwUFeS6JA4ZVy2EZ1IUBZtvpKMcQf3v8dJ1nbx9yfgr9htV/2iJdItvdFbP7RnfiJV0cJR7K5tYtedorcQphKeICPCuspeXtSDLJXHouoy+EceddeJltVrx9vaucsxkMmGxWJwelPBsJ95lAtALD7kwmhNJ1SeEp1ACTki8GkCP157sIpLKNjrKa7VEuv/Lxskn8zMbqYjpjUW3LzvfX01h+W7Zz0s0bCevjqoWuSbxOpnM8WrYznoLb13XGT9+fJX9rsrKyrj11lvx8zu+L8K3337r3AiFx4kM9GbdCZWdoSgdq03DaKj2ft3nRHq8hPBcfkHhlGWb8FYsNFWy+OtIsatDqlVLdmQzUD2eeO306UzTUN+zfn73xDjWZ7Skp7Kd5momO7ZvRh/ZRoY0iQYrMtDMmhM2UfYry6S43Iqf+aybvk4hbRFxorNuCY8bN46IiAiCgoIcj+uuu47o6Ogqx4RoHubHHj3aUW6hHWDf0RIXRnSMNECE8BQto4LYqccA0EzJIjWjfi8p//e2A/RUtwFwUA+jWesu1Uqa+rcMY5nt+HDDTvl/sCEtz9lhCuExmjXyYy/H2yKJaho7swpdGNExso9XQ3bWaf/8+fNrMw5RjzTyN5Pp0xIq53a3Vg+wPbOAhAj/Oo1DVjUUwnO1bhzANi2ODmoqqqJjydyKrg+slz042YXl+O7/A7PJXmkutXVkUFLkfzyrqnbRQTwZMBjKvgLgasMfzF21jy7/sQ+YEPWVt8lAYXASVHaWt1YOsD2zkM4u+ExIj5c4pm7HfokGw6txG6y6/b9XG2Ufm9Ndv5qQpGFCeI7WjQPZpjd1lOPKd3Iwt37uHfnjxgxGqX85youVXvRLCKvWa6iqwsCePVhmaw9AnHqYvJSfyXKXVWWFcIHg6JYU6fb1Cdqo+9mcnu/iiI4tLy/tkYZKEi9RK5Jiwthe2WhKVA6yedeeOo9BxlUL4bliQ3zZZW7rKPdXU/hrV/1bMELTdBatXM+gyvldh/RQgtoMqdE8lCu7xfCZNsxRvkP5ild/3+m0WIXwNO1iQkjRmgMQoxwhddeWOo/B3hYRwk4SL1Er+iaEsVyz33lVFZ1GWStcvxy01HxCeAxVVQhv2Z2cys3Y+6hb+HNz/dsYeNGWTM7L/waTYgPga9sALusWV6PXigjwJrTLaLZo9ue3V/dRvu5T1u13zZYeQrhav4Qw/tLaOcrN81exz8UL9egyx6tBk8RL1IqucSGsUjs5yqPV5Xyz/mCdx6Eo0uMlhKcamBTJH1oXAAKUUgL2/h/7j9af1Q1LKqzM/+lPbjD8CkC5bmJF6GXVHmZ4ojuGtuJl/WpH+RHjhzz72UIOF8qQQ9HwtGkcyEZzN0d5tGEF/1uXVqcx2OebS1tE2EniJWqF2WigUdvBHNTtDYiB6iaWLPuDwjJX7vsm/92F8CQj2kbxgzrUUb7dsIBnftqErnt+I0bXdWZ+vYFppa/irdjrxY9sw7j2vO7ntIBI4yAfOg8Zw7e2fgAEKqU8W/oYU95ayMFcd1hdVoi6o6oKLTv2ZYdmXyG1m7qTTX8v4kiR60bgyHzzhk1aoqLWjO3VnA+twwH7cMN7LG/zwNcbsGl102iScdVCeDZfLyOtug1ljZYIQHM1k+67XuGFRds9Ovkqs9i476sN9N36BL0ql5A/pIfyV5ObuLhD43N+/VsGNOfbyKns0eyv1UI9xMuF93DfKx/y5doDdVYHC+EOrukZx3zb+Y7yDP09ZnyxhgqrVifvb6+q5DMn7CTxErWma1wIaQnXsV+LAKC7upMROx7m5veXk5ZT93dedcnChPA4d5zXipcME7HoBgAmGn8hevmDjH/rT9ak5nhUAmbTdH5JOcQNc75j5OY7GWNcCkCFbuA+pvHYlb2csly+0aDy0riBzPB9lHS9EWBfWOATfSbl309j7PNf8e5fezmUXz9XiRTiRIlRAVjaX8M2LRaAtup+rtn/MBPfXsKe7KI6j0cHmePVgNXt9t2iwXngks488OpU3tMfxaxYudiwitYHbubZF6/C0OZiRrSPoV/LMIJ8TE5/bx0ducskhGcL8jVx4xWjeOSznTxjeheAa42LGXZoHR+/O5SXAoaQkNSBrnEhJEUF0iLcHy+je9xT1HWdw4XlrNufy+o9R9ixeQ3Dyn7jQ8NifAwVAFh0A/fYpjDxuquID/Nz2ntHBHjz0qRLuPt9Mw8UPE4ndS8GRecG42+MLVnMsl878OLCnmSG9SY+viWdmwbTMiKA5uF+NVpRUQh3Nv3Cdty5807esz2In1LOeYYNtMqcxIuvXEVFq4sZ0aEpA1qGE+Ln5fT3ti8fL4Sdx9SuTz31FP/3f/9HcnIyXl5e5OXlnXLN2rVrmTFjBuvWrUNRFHr06MHs2bPp2LGj45pNmzYxZcoU1q5dS3h4OHfccQfTp0+vw5+kYWnayJebr72aOz7K52XDq/gp5bRQDzFXnUPWzg/4c1snHtQ7kBfcltAmrUhsHEhMiA+xob7EhPgQ5mdGVZ1VZUnVJ4QnGt42isMjp3LPj0aeNr6HWbEQoeRxj+lrKPuaA+vDSV6XwI9aU3YTTZlPYwiKwS84kmA/M0E+JgJ9jPav3ia8TQbMRhUvo3rCVwNeBvW0d6KPHbNpOuVWjQqrRrnVRrlFo9yqUVRuJa+kgpxiC7klFRwpLCP/SDpKbiqNLWl0UXYxTt1OczWzyl/dLD2Y+7UpXDd2HIOTIpz+e4sN9eWdKRfz8LdNaLL1PaYav8VHqcCoaAwxJDPEkAz5b5G2PpyN61qwWI/mbS2afN9mENQE36AwIoJ8iQgwE+LnRYC3iUBvI4E+lV+9TXh7GfA2GjAZlHq5ubWoHyIDvbn3hsuZ8l4+r6ovEqiUEKtmM0d9nSN7PmLJrk7M0jpwNKgdIU1akhgVRGyoL7GhPsSE+BLmb8bgpLaIjiL7eDVgHpN4VVRUcOWVV9K7d2/ee++9U84XFRVx/vnnc8kll/DGG29gtVqZNWsWI0aMIC0tDZPJREFBAcOHD2fo0KHMmzePlJQUbrzxRoKDg5k0aZILfqqGYWCrcHxvuo0JH0dzr2UePdQdAEQqeVxtXMLVLIFiKNjhy67tTUjTw/lbD+egHs4RgrF4N0L3DUP1i8A/MJBGfl4E+ZjsjQAfY2VjwERAZYMgoLJBIPt4CVF/XNcrjqah93L91x0YX/oBI9S1GCpXLW2qZtOUbC4xrLRfbAWOQvkRI/n4k6/7UYAv+bofhfiSo3tRjolyTFRgpLyybMHoqDFOnAB/7HsDGmYsmJUK+9fKh69SRlMK6aAUEUohjZQCfJXKyfun6cwv0018ahvKHxE38NjV/UmICKil3xoE+Zh49doe/Lm9GRMXjaRH9jeMMSwhWslxXBOrZhNL9vEnVf7+LEcMHCaYw3oIubo/hfiSpvtSgC8Flb/LEt1s/z0qXmgGb3SjN7rBG0zeYPRB8fIGgxeqwQQGI6pqQjUYMBlUDKqCUVUwGhQMqorJoGBQlSrnVMX+MKigKPbzqoLjuKqAQVWqnFMUBYOioKr26+Ia+dEpNrjWfsfCM3RrFso9t9zMxA8jmVo2j/6GzQCEKQVcYVjGFYZlUAKFO33YvcPeFlmth/O1Hk42wVjMjdB8wzD4h+EXEEwjf3NlW8RY2QY5sU1yvD2i67KqoThO0T1pgDzwwQcfcNddd53S4/XPP//QvXt3Dhw4QGysfRxvSkoKHTp0YNeuXSQkJPDmm2/y4IMPkpmZiZeXvTt5xowZfPfdd2zfvv2s3r+goICgoCDy8/MJDAx06s9W3+WXWJjz+w4OrP2JMfzGQHWjYzWvs1Wqe3GUQApP+ONfgE/lV1/H8WNfbzb8zCCDfWPSCbYHmf+E9G7WJ576efTUuN1BmcXGJ6v28/vKdXTK/50B6ia6qjsxK1ZXh3ZGFbqBDXpLfrL1Yk/EMMYO7sKF7Ro7sUf/v+m6zso9R1mwPo1DW5fT27qGbupOOip7ql0XnwtNV7BgwIYBKwbH9xYM2HQVC0ZsqNgwYENFQ0Gr/Govq+go2PSq57TKc8evUyjQ/djU+TGevbzDv8bjiZ9HT4zZXRSVW3l98S62r/qZK/RFDFGTj98oOUtluomjBDraHgW6L4WVX4+3Qfwcx682/MnFhlUA3FpxF689MQuTwT2GRItzV53Po8f0eP2XxMREGjVqxHvvvccDDzyAzWbjvffeo3Xr1jRr1gyAlStXMmDAAEfSBTBixAiee+45cnNzCQkJOeV1y8vLKS8//oHMz88H7L9kUT0KcPegpuT3uJmfNl3Ix9vTUQ+upJ2+iyQljSQ1jSjlvzb6LCeQbAKBJmfzplYoqGyPVdjK5N+tnjn27+nu94+kHnGuMR3DuLLDcLZn9mLF7qO8vf8IpZk7CSneTaySTaSSQ2Mllwgll0ClhABK8K9mw6ombLpCHv7k6X5k6GHs1yNJ0yPYZ2yGIboTHeOjuKxNJC3C7ZtCFxUV1npMJ2sX4UW781tQMTSelPSRrDiQx7wDRyjJ2oNv0T7iOUSscphwJY9wJZ9wJY8wxdlx6oAG2JM9U+XD28nvApCtB/FPafEZP2ueUI9IHeJct/VrQmG38fy8eQS3bcvAun8l7fSdJClpJKppNFGO/scrVBDAEQI4Uv22SEU5BQUFknjVI9WqQ3QPM3/+fD0oKOi051JSUvQWLVroqqrqqqrqiYmJ+r59+xznhw0bpk+aNKnKc7Zs2aID+tatW0/7mrNmzTq2QoM85CEPN32kpaU5rY6pDVKPyEMe7v9w53pE6hB5yMP9H2dTh7h0qOGMGTN47rnnznjNtm3bSEpKcpT/bahhaWkpgwYNIikpidtvvx2bzcYLL7zA9u3bWbt2LT4+PgwfPpz4+Hjeeustx/O2bt1K27Zt2bp1K61btz7l/U++y6RpGjk5OTRq1Og/JxIXFBQQGxtLWlqaRw0FkLjrlsRdc7quU1hYSHR0NKrqvncPa1qPuMPvuCY8NW7w3Ngl7przhHpE2iISd22TuGuuOnWIS4ca3nPPPYwfP/6M1zRv3vysXuuzzz5j3759rFy50vFDf/bZZ4SEhPD9999z9dVXExUVRVZWVpXnHStHRUWd9nXNZjNms7nKseDg4LOK6ZjAwECP+k98jMRdtyTumgkKCnLZe5+tc61HXP07rilPjRs8N3aJu2bcvR6RtojEXVck7po52zrEpYlXeHg44eHhTnmtkpISVFWtcufnWFnT7LuT9+7dmwcffBCLxYLJZF9q6rfffiMxMfG087uEEEIIIYQQwhncs0/9NA4cOEBycjIHDhzAZrORnJxMcnIyRUX2XceHDRtGbm4uU6ZMYdu2bWzZsoUJEyZgNBoZPHgwAGPHjsXLy4uJEyeyZcsWvvzyS1555RWmTZvmyh9NCCGEEEIIUc95zKqGjzzyCB9++KGj3LlzZwD+/PNPx9yuH3/8kccee4zevXujqiqdO3dm4cKFNG7cGLB3A/76669MmTKFrl27EhYWxiOPPFJre3iZzWZmzZp1yvAAdydx1y2JW/wbT/0de2rc4LmxS9zi33jq71jirlsSd93wuH28hBBCCCGEEMLTeMxQQyGEEEIIIYTwVJJ4CSGEEEIIIUQtk8RLCCGEEEIIIWqZJF5CCCGEEEIIUcsk8RJCCCGEEEKIWiaJlxBCCCGEEELUMkm8hBBCCCGEEKKWSeIlhBBCCCGEELVMEi8hhBBCCCGEqGWSeAkhhBBCCCFELZPESwghhBBCCCFqmSReQgghhBBCCFHLJPESQgghhBBCiFomiZcQQgghhBBC1DKjs1+wvLwcs9ns7Jd1G5qmkZGRQUBAAIqiuDocIRo0XdcpLCwkOjoaVfWc+0hSjwjhPjyxHpE6RAj3UZ065JwTr19++YUvvviCv/76i7S0NDRNw8/Pj86dOzN8+HAmTJhAdHT0ub6N28jIyCA2NtbVYQghTpCWlkZMTIyrwzhrUo8I4X48qR6ROkQI93M2dYii67pekxdfsGAB999/P4WFhVx44YX06NGD6OhofHx8yMnJYfPmzfz111+sXLmS8ePH88QTTxAeHl6jH8Sd5OfnExwcTFpaGoGBga4OR4gGraCggNjYWPLy8ggKCnJ1OGdN6hEh3Icn1iNShwjhPqpTh9S4x2v27Nm8/PLLXHDBBaftVhszZgwA6enpvPbaa3zyySfcfffdNX07t3GsSz8wMFAqOyHchKcNtZF65MyyCsp4f3kqpRYbE/vFE9fIz9UhiQbAk+oRqUOEcD9nU4fUOPFauXLlWV3XpEkTnn322Zq+jRBCiAakwqpx51s/8kjhE/hRyoz1d/HKPROJCPR2dWhCCCHEOfGMWaRCCCEahN+3ZXFh/pe0UfcTpx7mLv0jvlyb5uqwhBBCiHPmlFUNp02bdtrjiqLg7e1NQkICo0aNIjQ01BlvJ4QQop5auiObaYa1jnJPdTuvb9sD57V0YVRCCCHEuXNK4rVhwwbWr1+PzWYjMTERgJ07d2IwGEhKSuKNN97gnnvuYfny5bRp08YZbymEEKIeSk/bS6SSV+WYMWsjVttwjAYZpCGEEMJzOeWv2KhRoxg6dCgZGRmsW7eOdevWcfDgQYYNG8Y111xDeno6AwYMqBeLawghhKgdZRYbvkdSTjneSktl75FiF0QkhBBCOI9TEq/nn3+eJ554osrKOkFBQTz66KPMnj0bX19fHnnkEdatW+eMtxNCCFEPbc8spK2y95TjbdT9bM0ocEFEQgghhPM4JfHKz8/n8OHDpxzPzs6moMD+xzI4OJiKigpnvJ0QQoh6KOVgHu2U1FOOt1X2sfWQJF5CCCE8m9OGGt54440sWLCAgwcPcvDgQRYsWMDEiRMZPXo0AGvWrKFVq1bOeDshhBD1UMrBPNqr9sQrT/cjRWsGQHPlELsPZrkwMiGEEOLcOWVxjbfeeou7776bq6++GqvVan9ho5Fx48bx8ssvA5CUlMS7777rjLcTQghRD6WnpRJRubBGihZPuh5Ge3UfqqJjObQZXR/oUZvcCiGEECdySuLl7+/PO++8w8svv8zevfbx+c2bN8ff399xTadOnZzxVkIIIeqhkgorAUc3gsle3qi34LAe7DgfV7GbjPwymgT7uCZAIYQQ4hw5dW3ezMxMDh06RMuWLfH390fXdWe+vBBCiHpqc3oBHZQ9jvImvQU7leaOchtlnyywIYQQwqM5JfE6evQo5513Hq1ateLCCy/k0KFDAEycOJF77rnHGW8hhBCiHtuYlkfHExKvorBOmJu0R9PtQwvbqPvZkpHvqvCEEEKIc+aUxOvuu+/GZDJx4MABfH19HcevuuoqFi5c6Iy3EB5C13UWbDjIvf/byMer9mO1aa4OSQjhATam5dBBtQ9Vz9BDiYuLp0VMY/bpkQAkKWlsS891ZYhCCA/yS8ohpn+9kXf/2ku51ebqcIQAnJR4/frrrzz33HPExMRUOd6yZUv279/vjLcQHuKb9emkfj2LWZtH4PXTVJ5fuN3VIQkh3Jyu6+SkbiJAKQVgk9aCjjHBtI0OZKveDABvxULxwS0ujFII4SkWbs7kny+e5KGUEUT9OpnHvj91Y3YhXMEpiVdxcXGVnq5jcnJyMJvNzngL4SF+XrebO4wLCFBKucq4hG2rfqHMIneahBD/LvVIMYml6x3ltVoi3ZqF0D4miE1avON4bMlmMvJKXRGiEMKD/LD+AHcbvyZQKeViwypy1v9AfonF1WEJ4ZzEq3///nz00UeOsqIoaJrG7NmzGTx4sDPeQngATdMpT1uPSTmeaA3S17Bq71EXRiWEcHcr9hylj3q8N2ubT2dahPuTEO7PNlNbx/Fu6g7W7stxRYhCCA+Sd2AT/kqZo3yespZlu7JdGJEQdk5JvGbPns3bb7/NBRdcQEVFBdOnT6ddu3YsW7aM5557zhlvITzA3iNFtNF2VTnWUd3D5nSZEC+E+HerdmXRU90GwBE9kKiELiiKgqoq+MR1pUy3rzHfXdnBP/tknpcQ4t9l5pfRtHRblWPSFhHuwimJV7t27di5cyf9+vVj1KhRFBcXc9lll7FhwwZatGjhjLcQHmDDgTw6qburHGuj7GfLQWkoCSFOr8KqUbh7JYGV87tWam3onRDuON85PpJkPQGApmo2e/fuOu3rCCEEQHJaHp2Uqm2RFkoGuw5muSgiIY5zygbKAEFBQTz44IPOejnhgZLT8rhN3VPlmI9SQWH6NqCHa4ISQri1FXuOMNC20vHX6A9bZ2a0Op54dW8Wwt9aIr0qe8Qij6zicMEFRAR6uyJcIYSbS07LY/RJbRGDomPJSEHXB6IoiosiE+IcEq9Nmzad9bUdOnSo6dsID7J//16aKKfO5wov3EZeSQXBvl4uiEoI4c4WbjrEHYa1AFh0A0eaDCHyhKSqY2wwrxq6AN8BMNiQzJKd2YzpFuuCaIUQ7m77/nRaKQdPOR5v2cXB3FJiQ09dDE6IulLjxKtTp04oioKu61XuHui6DlDlmM0mq9rVd6UVNvyyN4J9KgZbtDjaqvatBNqp+9iSUUDfhDAXRiiEcDdlFhvpW/4iRjkCwN9aWwa0T6hyjcmgEtiyN/m7fAlSShigbuLB7Yck8RJCnMKm6ZCRjKra26JV2iLKPrZk5EviJVyqxnO8UlNT2bt3L6mpqXzzzTfEx8fzxhtvkJycTHJyMm+88QYtWrTgm2++cWa8wk1tzsing3J87sUntqGO79upqaTIpFYhxEkWbs7kYutvjvJPWi/Obxd1ynUDkhqzTLOPnAhSSijc9TcW2ZxdCHGSXYcLSbLtdJQ/tw3Bqtubuu3UfdIWES5X4x6vuLg4x/dXXnklr776KhdeeKHjWIcOHYiNjeXhhx9m9OjR5xSkcH/JB/LopBwfU/2HrTPpxkY0UY7SRtnPJ7LAhhDiJAtWbecNw0oACnQfjsZddNq70YNahfOMrTMjDasA6GdbzfLdRxicGFGn8Qoh3FvygTw6nTC/a5XWmt16E5KUNFoqB9lx3IID3wABAABJREFU8IgLoxPCSasapqSkEB8ff8rx+Ph4tm7d6oy3EG5uY1oOHdS9AGTqIWQRyhatGQABSil56TtcGJ0Qwt1szSgg9uCP+CnlAPxg68NlvVqd9tqIQG8ONx5EhW4AYKRhJT9uSKuzWIUQnmHjwTw6ViZehboPe/VotujNADApNsrTUxxTYoRwBackXq1bt+aZZ56hoqLCcayiooJnnnmG1q1bO+MthJvL3b+FgMrloJO1BK7r1ZQU7XgyHpy3lYIy2TVeCGH35p/budX4o6P8k9f5DGsT+a/Xn9c5kaVaRwCilFxytv5JSYW11uMUQniOA/v20Fixb7K+SWvO2F7NqrRFYst3kVlQ9m9PF6LWOSXxmjdvHosWLSImJoahQ4cydOhQYmJiWLRoEfPmzXPGWwg3lplfRkxxiqOcrLXgqm5N2a4cr+zaqvvYkl7givCEEG5m9+FCvLZ+41hUY4mtI337DsZsNPzrcy7u2JgftL6O8gXaXyzcnFnrsQohPENBmYXgo+sd5WS9BZd0bMJe0/EFe9opqaQclHlewnWcknj16NGDvXv38uSTT9KhQwc6dOjAU089xd69e+nRQ/Zvqu/W7c+lm3J8KOFWUxvaRAdSFt7ecayzupsNaTLPSwgBz/2YzN3Grx3l99XLuaFPszM+JyLAm9L44RTqPoB9uOE3KzbXZphCCA+SfCCPrsrxhTU2kkSHmCDUqA5oun2lbXtbJM9FEQrhxA2U/fz8mDRpkrNeTniQf/bncL1qr+zKdSNeTbtiUBXim7Vg/9EI4tTDdFL28M6eTBiU8B+vJoSoz/7cfpjEvR8SY7L3di2ztadj//MJ8jH953Mv79mSb/b1Z7zxV3yVclpn/sDGtF50jA2u5aiFEO7un/25nKceT7zKorribTLQNr4xWzPiaKfsI1FJY/PufUCSy+IUDVuNe7xWrVp11teWlJSwZcuWmr6VU+Xl5dGtWzc6depEu3bteOedd1wdksfbnZpKc9U+5GezHk/HZvZ5Gj3jG7Fas8/xMysWLPvXYJUloIVosArLLLzx7e/cZvwBAKuu8rp5IrcMbHFWzx/WJpJffEY6yjcYfuWjFXvO8AwhREOxOTWDtso+AHZqTUhqbl99+8S2iKro+B5aI/NDhcvUOPG6/vrrGTFiBP/73/8oLi4+7TVbt27lgQceoEWLFqxbt67GQTpTQEAAy5YtIzk5mdWrV/P0009z9OhRV4flsfJLLQRkrXWU/9Fa0bVZCAA94kMdlR1AR9sWth6SeV5CNFSP/5DC9LKX8a1cyfBj2zDGXDAMf/PZDb4wGlQG9OnDMpt9GHNTNRvL5u/Yf/T0f4OEEA1DmcUGaWswKvabu/9oregaZ2+LdI0LYa1+vC3STdnGhgN5rghTiJonXlu3buWiiy7ioYceIjg4mLZt2zJs2DBGjhxJv379CAsLo0uXLqSmpvLrr79yww03ODPuGjMYDPj62veJKS8vR9d1WVr0HCzfdYQBSrKjvEZpT+dYe2UXHmAmM6SL41xfw2b+2iV7aAjREH23IZ3wjW/SvXIo0AEtnCVNbuGyzk2q9TpXd4/lA473et2hfsPri2W7CiEasjWpOfTWNzjKK/V29GgWCoCf2UhxVHfHub7qFpbtyq7zGIWAc0i8TCYTU6dOZceOHaxcuZKbb76Zdu3a0aRJEwYNGsRbb71FRkYGn3/+Oe3bt//vF6y0bNkyRo4cSXR0NIqi8N13351yzdy5c2nWrBne3t707NmTNWvWVCv2vLw8OnbsSExMDPfddx9hYWHVer44bsn2LAYaNgFQppsgrh8+XsdXJkto2Ya9WhQAXZWdrNq8yyVxCiFcZ2NaHv/37Yfca/wfAJqu8KByB09d3RtVVar1Wo38zbToeTFrNfueXy3VdCybviH1iPR6CdFQLdmRzSB1IwA2XSGvcT9C/Lwc5zu2asFGrTkAbdT9bNosC/MI13DK4hrdunWjW7duzngpiouL6dixIzfeeCOXXXbZKee//PJLpk2bxrx58+jZsydz5sxhxIgR7Nixg4iICAA6deqE1Xrq+N1ff/2V6OhogoOD2bhxI1lZWVx22WVcccUVREb++/4x4vQ0TSd95z+OPTNWam3omxRT5ZqhbaP4/Z+uTFL/D6OiEXZoKYcLBxMR4O2KkIUQdWxvdhFPffAt76qvoir20QWv2i5l1KWXExPiW6PXvGVQAvetHsN8ngTgLvV//8/efUdHUbUBHP7NzG56T0hCIAm99967NAsoCgjSuzTFBhYQBPFDpYgoghQRsAJ2QYqAdCmh95ZASEhI77s78/2xYSHSEkiyu8l9ztmTndnZmTfAXOa29/K/3x/n8wHN8i1uQRDsg6ZpnDx5jIryVQAOaRVpUCXnvNEO1QLYtL0eteULAFRM+IcLMV0oV8Kt0OMVird8SSefn7p06cL06dN5+umn7/r57NmzGTZsGIMGDaJatWosXLgQFxcXli5dajkmLCyMY8eO3fEKCgrKca6AgABq167NP//8c894MjMzSUpKyvESzP69FEfjjJ2W7a1qHVpXLpHjmMZlfdmp3FpSoINygM0nrxdajIJgC4prORIRl8bERetYYHwXj+wF1v80NSSu/ss8W7/0A759b35ujlRq+ji7TdUAKCNHU/bMcv4+LcoWoWgqrmVIbpy8lky1xK2W7a2m2nc8i9Qq5ckB56aW7Q7yQTadjC6sEAXBwuYqXveTlZXFgQMH6NChg2WfLMt06NCB3bt35+oc0dHRJCcnA5CYmMj27dupXLnyPY+fOXMmnp6elldwcPCj/RJFyC9hV3lS3gWYhw6d9mlD+f+0HjnoZDwrNSdOM+9vIx/mr0MiC5lQvBTHcuR4ZCKvfPY9c7MmU0IyL1gappbju1Jv8c5TNR75/C+2rchc/WBM2evzjNH9xOc/byPTaHrkcwuCrSmOZUhu/XI4kqeUW8+A+11aUauUZ45jZFkitGojrmjmqSVN5RNsO3SqUOMUBLCzildsbCwmk+mOYYEBAQFERUXl6hyXL1+mZcuW1K5dm5YtWzJ27Nj7zkGbNGkSiYmJlldERMQj/Q5FhcGkcunoTksa+T1qVVrWq3XXYzvWLMV6k3liq6uUiW/4n0TEpRVarIJgbcWtHPn79HU+WLiURYY3CcoeinxCDWWW3/t8MrAFeuXR/+vxdNbTo2tnVpg6AuAiZTI0+XPmbjzzgG8Kgv0pbmVIbqmqxqFD+y1DCI+qZahdr+Fd5452rlmS302NAdBLJipd/5NTUaLnUChcdlXxyg+NGjUiLCyMw4cPc+TIEUaMGHHf4x0dHfHw8MjxEmDzyWg6GP62bP+sNufJWkF3PbZD1QD+0LW3bD+nbGPNwSsFHqMg2IriUo4YTSofrj/JxhUf8KU0HS/JnPDimFqGad4z+GxoezycHrxQcm49W680mwKGEKOZ/zw7KgeI2bGc/Zfi8u0agmALiksZkle7L9ygedpGy/bPpuY8VfvuzyItKvjxt3NHy3ZPZRs//CsqsELhyveKV0ZGRn6f0sLPzw9FUYiOzjkuNzo6msDAwAK7rnCnb3ec4FllOwDpmgORJTsQ4nv3ifJOeoWytdtwTjUXho3lU+zeu5sso1hMWRCKirPRyQz4fDOVd77M+/olOErmBEdbTbWZVXI2X4zojJeLwwPOkjeyLPFWj6ZMMQ217Jus+4pZ320kNVMskCoIRd3KnWd4XtkCmBdkP+bdgWol714pVWSJevWbcEitAEBVOZzTB7eKxZSFQpUvFS9VVXnvvfcoVaoUbm5uXLhg7vJ95513WLJkSX5cAgAHBwfq16/P5s2bc1x78+bNNG3a9D7fFPLT6ahkSkf8inv2ZPmfTM15pvn952z0bBjCt6a2lu3u6ev49XBkgcYpCELByzCYmLvpDB/Pn8NHMcNzzLVYZuzE2iofsXhYGzxd8q+n63bVgjyo0aEvP5paAeAhpfNa6kdM+uGAWKNREIqwiLg0nM78SgnJPFxwvdqQrs3rIUn3XqLiuQbBOZ5Feht/5nvR6yUUonypeE2fPp3ly5cza9YsHBxutWjWqFGDL7/8Mk/nSklJISwsjLCwMAAuXrxIWFgY4eHhAEyYMIHFixfz1VdfcfLkSUaNGkVqaiqDBg3Kj19FyIWFf59iiPKHZftnx8fpWrPkfb9Ts7QnZ4KeJklzBuAZ5R9+2PovqioejATBHplUjR/2R9B/1mqqbhvFQt3HlqUlkjQXXjS8jNr5f8zr0xBHnfKAsz2aEa3K83PgOMvE+YbyGeqe+phF2y8U6HUFQbCeL7ady/Es8r3clWfq3T9balk/VxLKd7cMT+4i7+OPbbswmMQIHKFw5EvFa8WKFSxatIi+ffuiKLf+g61duzanTuUta8z+/fupW7cudevWBcwVrbp16zJ58mQAevXqxUcffcTkyZOpU6cOYWFhrF+/XqzDVUjORCejHPuBsrJ5uOdOU3UaNWmdqwer/m1rsdpkzkjpKBlpF/8Dfx7LXVKUosqkasSmZGIUhb5gJzKNJr7fH8Hzs38i/aeXWZU1nk7Kfsvnf5tqM8R5LsNGjGdIi7L3bX3OL4osMaN3c16XJpCpmZenHKTbwOm/FrPtTEyBX18QhMJ1JT6NuAPrqCFfAuCIWpZy9Trg5vjg5WmHtK3GMmNnABRJ4+m0H1hzoHjPO7/5LCIqoAUvXxZQvnr1KhUqVLhjv6qqGAyGPJ2rTZs2DxweMmbMGMaMGZOn8wr5Y95fJ3hDWWvZXij3Yn7zMrn6brsq/iz26sGg5PU4SgYGKBvo98dTdKj2XIG3iNsaTdNYtTecPzZuIDTjFJccq9Ctc2d6NwqxdmiCcFfXkzL44cAVtu/awTPp6/ha2YGj7tbciBjNkxmmfgQ07cuKxyrj7FC493SIrwvDnn+OKSsu8oF+MQAzdV8ydGUAviMGU+M/6aUFQbBf8zedZqz8g2X7U+05prYpf59v3NKwjDeflHyW5BjzlImeylb6/LWJJ2v3xzUXFbeiZu3BK3z/50bKpB0jXF+e9h26MLh5mUJpNCuO8qXHq1q1anddhPjHH3+09FwJ9m/XuVgCT60gRDa3IG831aRuiy65njAvyxL9OzZhmcnc0uQoGXkhdTlf7bpUUCHbrE+3nCPut8msNr3GTP0SvlFf49LPM1i557K1QxMEiwyDifXHrjFi2U7emzWDmlsG8p1hPL10Wy3JM1I0J2YbnuXVkssZOup1Jj1erdArXTe1rexPqXYjWG1sB4CjZOBTaRbvLV1D+A2xhIUgFAWHIxLQwlZTVTbPzTqkViCoQTdKejrn6vuSJDGiUz0+Nz4JmHu9RmYuZ+G24rfG6LKdFzm9Zjqrsl7mA/2XrGYS6eunsHCbGKZdUPKlaj958mQGDBjA1atXUVWVtWvXcvr0aVasWMFvv/2WH5cQrCzLqDLvp+18qVsDmBdM/lzpy8IWZfN0nq41A+n/zwCeu74VXymZp5TdDN64hk7VRxPq61oQoducbWdiiN6ygOn6n3Lsn6j/lqG/l6F1pfEE+9w9Q6QgFLTENAN/n77OxmNXSDuznbbaXj5QduOtS8lxXJLmwkpTB7b7PMuIx5vycqUSNtFCOrptBcZHTaLUqVhaK0fwlNKYa3yPcV+68cnIJ3P9cCYIgu0xqRqz1u3mE903ln1ztef5sO2do67up2XFEqwo15+r4ZspJd2grXKYH7d/w6laE6gSWDxS9R+4HMeRPxYxR/9Njv1jdD8zdlMop6u8TuVAdytFV3TlS49Xt27d+PXXX9m0aROurq5MnjyZkydP8uuvv/LYY4/lxyUEK5u36TQDEj+3ZDL8xtSOxzt3xdM5b5nKJEni5Scb8LGxp2XfVGkR73y/t1gk2kjNNLLgx794S7fKsm+3qZrl/dvSMv73+xFrhCYUUymZRraevs7MP07ywrzfeGvGNExrRzDj3NMsU6bTX7cRb+lWpStCLcF7hr6MDVxJlRc+YvVLT9G2sr9NVLrA3LP+Ua/6LC01lcNqOQBKSnF8nPom4xb+yrXEdCtHKAjCw1r8zwWeur4QXykZgF9MTWnS/mn8PZzyfK7XnqjDLFMfy/a7ylKmfbejWMy5zjSamPXDVqbqllv23f4s8pbyNbN+OWCFyIq+fBvM2rJlSzZu3PjgAwW7s+9iHFH/LOc1/T4A4jQ3/vAfyoqHnI9UL8SbdQ0GsPvgbpoqJwiWY+h89RM+3xbC6Dy2WtmbjzecZlz6ApyVLAC+Mj7GFONAvpPeo7F8ijJyNM6n1nDuenUq+IuWJiF/JaYZOHM9maNXEjl2JYHoK2fxjD9GQ+kUT8snmCRHwF3aUjI0PX+qjfhFaod/zfY836QsdYK9Cj3+3HLUKcwf2JJhn01lZsJrlJOjCJFj+Dj1TcYv1Jg74imCvETPlyDYk2NXEzmycSWf6bYC5mHOKz2GsTKPI29uqhTgTlDzvmzctZPHlIOUkBLpEzuPjzaUZWLXqvkYue357O/zDEz8DA/FPAR7rakFEwwv8iUf0kE5RKAUT5nLPxAWUcemy3p7VPxmEQp5EpOcyUerf2eJ7ivLvndMw5jUozmK/PAt3G90rc7gk2OonfkyLlImfXR/89qmRewo/TotKvrlR+g251B4PEl7v6KF/jgAVzQ//i49isMDWjH6fxdpzNsAjFR+5YutzzOrZz1rhivYobQsI1GJGUQlZRCdlMG1xAwuxaYSFR2NduMcfhnhVJCvUlO6yNPyRXNP1j06rVM0J/5W6/CX2pCU4DZ0aVCZT2uWtJvJ5x5OeuYP68jYhQZmJr95W+XrLcZ/ZmLm0Keo4O9m7TAFQciFxDQDU1dt5AtlkWXfVGN/Xn22LQ66hx+8Nb5DJQYcH0uj5NF4Smk8oexh/87PWB/yFp1rBOZH6DbnTHQyZ7d9w8u6fwGI1Tz40W80x0Z2ZOiH0XQwHgJgqO4Ppv/djwX9m1gz3CLnof8H9fb2zvXQkri4uIe9jGBFmUYTL3+1jfcz38ddNg/P+d7Ymhod+j1yhjA3Rx0v9ezE5GUD+Uj/BQDv6ZYyeHVZSr7Yj/IlitYDUZZR5YMftvOFbqVl31R1CFOebYKns546TTuyZ+cqmsgnKS9fI/XIL0R1qkagZ96HTwhFw8o9l7melIFB1TAYVYyqRpZJxWhSMZg0UtKzyEpPQktPQMpIRM5MxM1wgwApnkApjkApnkZSHM9JUZYFRrlPHhyTJnFUK8setTr7pBroy7WkfY0Q3q3qj6+bY+H80vnM392J+SMfZ8xCmJn8FuXlawTLMXyW+QbjPk/k1YG9qB/qY+0wBUG4D6NJZcKqXUxOeQ8f2Tzs+U9TQ0q0GEyjso92/zrpFSb1asebX4xggW4OAG/pVjH0h3IE+wymelDRyoZqUjWmfr+L2cpSy773TAN4+7kWuDnqaNuyNRs31ecx5QAlpThcTq/hQkwNyhWxZzJreuiK19y5cy3vb9y4wfTp0+nUqRNNmzYFYPfu3WzYsIF33nnnkYMUCp9J1Xjj+4MMiZ5OBSUSgDNqKf4oPZ4lrcrlyzWaVfDjUIfhrNp8jr66zThJBuap7zN6sQfzRj9TpCbBf771PP0SFuClpAKwztSceu17UdbPnFBkYPMyTNrxFE04ad6Wf+er3b15o3MVq8UsWNfvu8J4J+EddBjRY0QvmdBhMr/HiBsZyNJt8yIl7lux+q9YzYOjalmOaOU4rpUnI6gxdSqG0rS8HwNDvHDSF40lHvzdnfh05OOM+UJmetJbVJKvUkJK4kt1CuO+TKRX74F0rG7fLdvnrqewYvclridmUK+MN30bh9pNz6Qg3I+maUz5+ShPh79PTeUSAOFqCVaWmMCyxyrnyzXqBHvR+PGBLPz9HCN1v6KXTHyizWL4Eg9mvdi7SCX++mrXJZ6I/pwAXQIAm0x1KdW8L9WCzAlF+jQOYdTf3XkM8/yuIcqfLN3Rj+lP17JWyEXOQ5fMAwYMsLzv0aMH06ZNy7G21rhx4/j000/ZtGkTL7/88qNFKRQqVdV4a00YbU5Opq1yGIB4zY23nN5m4QstHmmI4X+Nal2e0eGvUvl8BA3kM5SQkvgoYwrjFzkxd1jXIjEP40RkEqe3fsN43V4AbmjufO/7Il+3vDUu3c/NEf+6T3A6bBWV5Ss0lM8wd88mUttWEA9QxZSjAtXkR19eIEbz5IJWkvNqSS5oQVwiiAzfqpQsXZ6awV60KuXJiJIeRaaidTf+7k58NrIr45c6MT52Co3k07hKmSyUZ/H26lgudBzFiFblbCZBSF7sOhfLoq+WMkX6ktJSLFvP1mbQntF8OKRLkXpgFIofTdOY/tsJqh6cyhO6PYB5CPSr+jeZ2//Rhhj+V78mobx2+SW2H79EK+UonlIanxinMfYLJ2YN704ZP/u/ly7EpPDPX2tYpvsbgGTNmUXuY1jRoZLlGHcnPTUad+TA7hXUl89SRY7g2sE/iO9YBW/XPLTsCfckaQ9arTgX3NzcCAsLu2MR5XPnzlGnTh1SUlLu8U37k5SUhKenJ4mJiXh4FL2Uo0aTyuSfjlDr0BR6Z09gzdR0DNfeZOKoYVQtmf+/c0qmkRFfbGRy7KtUls2rx0eoJXjFaSr/G97d0itkj1IyjQye9xMLU8fjk50Z7iXjGIaOev2O4ZrnY1JYOHcqH+rNY9h/MzXmRpdFDGhWprDDthv2ej/mJu4Bn/7Bopj+GFAwoMOIYn6vmd8n40yS5koSLiRpLiThyg3Ng2jNhyS9Hyb3kug8gwjw8aJcCVfK+blRroQrwT4u6JX8e2CxJ6mZRl5euZunL02li/KvZf8yYycOV3uNmc/Ws9oaZA8jPjWLkR8tY4X6Fo6SwbL/mubDOOeZfDH2GXzEw9ID2WM5Yo8x54Wqarz/+wkC977HUN2fABg1mRdNrzBi2Gjqh3rn+zUzDCaGf7mNCddeoY5sXscqWvNirG4KU4c+WyDPP4Ulw2Bi4Kd/MjdhDIFSPABvGwbx5JB3aFzON8ex1xLTmT7rAxbo5wLmNVuPtF3GmHYVCztsu5GX+zFfmtJ9fX35+eefeeWVV3Ls//nnn/H19b3HtwRbk55l4uVVe3niwq3WJYOmMM70MoP79y+wQsfNUcf8Ie0Z8dk0PkqeSKh8nWA5hk8z32T8gmTG9H2W5hXsL+GGqmq8+eMhXk2ZZRmXvsHUgMBmL9x1jlz5Em4kV+hOzKVvKSEl0UXex/Pb9/BCk9B87WUU7EPNiuWZ4P0XOkVCr8jos3/qZPN7N0cdHs56PJx1BDrrqeSkx9vVgUAPJ9FLeg+ujjo+G9ict9bOIurwBwzSbQBgkG4Du05GMPizt/hwQDtKe9vHOnqf/X2W10xf4igbcuwvKcUxLW06L68owZfD2xbbirZgnzKNJt74/iBNT0ynV3YDsKpJvGp6kad7DSmQSheY53t9Oqglwz9/j6nxb1BZvkKAlMAXxncYtzCZAb1606FaQIFcuyBpmsa0X44yPO5DAhVzpWunqTpa/UF3VLoASno641jzKcJPrCJEjqGVcpTPdv3DsFblcNTZT8OUrcqXHq/ly5czdOhQunTpQuPGjQHYu3cv69evZ/HixQwcOPBRL2Ezimor05X4NF7/ehvjYqfSRDbPMzJqMhNMY3iyzxgeK4TCJioxg7GL/uC95MlUyV6RPkPTM8k4jKqdhjK0RTlkO6qA/O/Pk4TsnMTz2d36VzQ/3vD9lGUvdrrnEIk9F26wc8nrvKL/EYAlxi6U6j2HzjVKFlrc9sRe70d7jbuo0DSNz7edJ3zjQqbpluIgmQBzT/vryisM6/UM7arY9gNWepaJYe9/xsrsbKjn1CD6Zr3JNw7TKSdHAfCHqRH/NpzDlKdqWDNUm2eP96M9xpwb15MyeHXVTgZGTqOdEgaYK12TjENp9tzLdKtTqsBjuJGSyegvNzEx7m1Lz1eWpvCucSAl2oxkXPuKdtUY+tnWcyibJjNC9zsAMZoHo93ns3zcE7g43L2R7nhkIj8seJt39SsAc2I1ui+gZ4PgQovbnuTlfsyXZrCBAweyc+dOPDw8WLt2LWvXrsXDw4MdO3YUqUpXUfX36eu8/slX/C92rKXSlaY58qL6Kl17jy6UShdAoKcTC0c9zlTfD9mvmsccO0kG5ug/w/mv1xn65TauJtjH4qcLt51Hv+MjS6UrU9MxUXqJD15ofd9x6Y3L+nDQ/xkyNHOO717K36zedrRQYhaE4kKSJF5sU4GO/V5jMO9yXfMCIFiOYbn6Nlu/nsH7v5/AYMMLqf4cdpUepj8t2wtNTzJ/xONMdHyLJM3cY9dV2YfT3vn8dOiqtcIUhFzbff4GY+atZsq10ZZKV6am4yXTWBr3eKlQKl0Avm6OLBrZkY8DP+Qfk7nRwkEy8b5+CSHbJzDw801cjE0tlFge1co9l4nZONdS6TJpEq+rY5ner/09K10A1YM8uRzyDInZZUk3ZSdrtx0kH/pqir18G3/QuHFjVq1axcGDBzl48CCrVq2y9H4Jtikl08iUn46wZcUMlqlvEyzHAObkD0OlKQwdPKrQ17HwdXNk0YgOfFluHquNbS37++k2MfnKcCbOWcTynRdt9oFIVTU+Wn+KjI3TmZDdawXwumkUQ5/vRbDP/YcwSZJEz9Z1WGNqBYCblEHlyHUcCo8v0LgFoThqVyWAqWMGM8ZtNodU8xxlR8nINP1X1N0zjoGfb+RKfJqVo7y79QfP0lU2J+yJ19xIKvckjcr68PaAp3hVvZXo6lXdd/y69muORyZaK1RBuK8Mg4mZv59g3dIPWG6cSHn5GgBJmgvDtLfo0X8cz9QrXagxeTjpWTS0LWuqzuVLYxfL/h7KP3xwfQTT5i1g4bbzZBpNhRpXbmmaxsJt57n46/+Yov/asv8d42Cefa4flQLcH3iOfq2rsdrUHjCXi83i1/LP2dgCi7m4yJeKV3h4+H1fgu3ZdiaGwR9/R+cDI3hPvxxHyQjAQbUCI11mM3VU/0deH+NhuTvp+ax/U2LafMhEw1DSNfPk8DJyNF9Lk3H9cxx95/zM1tPXbar1JTnDwOiv9xC4401e0q217J9u6EuzbsNpU9k/V+fpWrMkvzh3t2wP0q1n6faz+R2uIAiY51Z+OfYpllRcwGJjV8v+Lsq/zLw+mtfnLWfD8SgrRninxHQDzhH/WMrtX01N6dHEPPG9VmkvOnTrzxxDDwAUSeMjeT5TV/xBfGqW1WIWhLvZc+EG/eespcmekczSL8JFygTghBrKEMcPeX3EEFpXKmGV2JwdFOY83wA6vc9LhtEka+Ysy6WkGyxTZlB604v0+/hHNhyPsqlnkfQsE69+dwB54zu8o19l2T/P+DTlOo/h8Vq5m7rQppI/Wz2fxqCZ53W9oGziq20nCiTm4iRfKl5lypShbNmy93wJtuPc9WSGL/mHvV+9xVeZL9FUuXUTLTN2YmnFBSwZ/zQVc9EaUpBkWWJ8h4o8MXASAxxmc0C9lU3nOd12liWP4NCKifRbsIHNJ6OtXujtPBfL4LlrGHp+LC/oNlv2TzP0I7Dzq/RqGJLrc+kVmfYtm7PRVA+AICkOp5M/cDoqOd/jFgTB3Lo9/4XGOD4+k5HGV0nQzJlUQ+QYlmtvs2/1NKb8dIQMg220bv9zNoY20iHL9napHi0r3kpA1LNBMHENxrPJVBcAbymFqWnvM2bZ36RmGgs9XkH4r8s3Uhnz9R7+WjKFJaljLUvXAHxrbMOc0AUsGt/zromoCpMkSQxtWY4Xhr3KEJd57DRVt3z2hLKHFWmjOf/Na/SZ9zt/Hr2Gqlr3WeTA5TgGzvuJZ46PY3j28EKAjw3PorWexJAWuX8ml2WJ7q0b8otqXp/XR0oh9NIPHLgsRuA8inxJrnH48OEc2waDgUOHDjF79mxmzJjBM88886iXsBn2OqH18o1Uvth6lrRDP/Cq8i2lpVvdxVc0Pyarw2nVuScDmpWxubVskjIMTPv5KG5HlvGy7kc8pVtDf5I1Z742PcZ2r+481qQez9QtVahrTVyISWHOXyfwO7GCV3Xf45rdWpep6ZlkHEqDp16kT+PcV7puSsowMHrmZ3wtTQbMf0czyn3N5wOa5Wv89s5e70d7jbs4OHY1kWkr1zMxdRb15HOW/X+barPQ+1VmvNCWCv7WbZh65buDvHHiafylBNI1B8YGr+XLoS1zHJNlVBm8cCPTro+zJNvYr1ZiQakP+XxwyyK9blte2eP9aI8xA0QmpLNo23mi/13D6/IqysrRls+iNS+mmIZSq8PzjGxV3uaSaaVlGfng9xMY9i/nVd33+Eq3GkPTNEe+MbVjo3t32jRpyLP1S+Pn5lhosUXEpTF340kcjqxmkm41HtnPSQZNYaqxP6GdxjGsVbk8nzfDYGLgB0v51vQqALGaB6+WXM6yEe1s7lnRmvJyP+ZLxetefv/9dz788EO2bt1aUJcodPZW2J28lsTiradRj61jpPKLJVsgmCdZrjB1ZGvpkUx7rrHNL7a598IN5v6ymy6xy+mjbEYn3ZrnZdIktqj1+IF2KBU68FjN0rSvEoCniz7f49A0jb0X41i1+zzKyZ8ZL/+Q4z+PK5ofr8mv8mKfZ2lZ8eGHSMzddIY624bSJrsl8F1Df54aMY16IQWTStce2dv9eJO9xl1cJGcYeHPNIaqfnMdI3W+W/dc1L95Qx9Dlqd4816C0VR48VFWj3/QvWKW+AcAmU12udF7GwOZ3tmRHJ2Uwdv63fJ71tuUhcZepGl8ETeeTga3wdM7/8tEe2eP9aG8xn7uewpfbzpF8+CdGyD9RS76Y4/NvjW34NfBF3n2umdVH3DxIWEQCH/28l9ZRyxmgbLBkRQVzBsbtai2+19pjLN+BDjVC6FAtoEDW09M0jYPhCazafYHMY78yTv7BshYqmMurV5jACz170an6w8/XX7LjIiU2jOIpZTdg7j1rOOADWllpCKgtspmK17lz56hduzapqfaR/SU37KGwyzCY+OPoNdbtPkH5yF8ZoGzIUTEA2GaqxWcOg3imcweeqx9scy1L92JSNdYcuMKPm/6he+oPPKtsy1HoASRqLmxS67FJbURqUFNqVgihUVlfapXyfOjesKQMA2HhCWw5dZ3Txw7SKPVvntdtsSxEeNNKY3v+ChrFjN7NH5hI40GSMwwM/98SvtHMD1ixmgevlPyK5SPaipambPZwP96NvcZdnGiaxrf/RrD519XMlBdQQkoCzA9Wn5me4my1sbz3TB08nAq38nIoPJ6/v3jVkrznLcNgRkyYQYjv3cubs9HJTP5iNV+Y3rW0gh9Ry/K+11RmD+5IkJdzocVuq+zxfrSHmLOMKhuOR7Fm90lKRfzGQGUDFeWcGTb3qFX5RBlAp8e62NWalZqm8cvhSFb/tYvOST/wvLIFJynnenrJmjN/q3XYqDUkIbA51cqH0risD7VKez10b1hKppEjEQn8ffo6x44dpm7S3/TRbc4xiglgjakla0qMZlrvllTwd3vo3xPMz5T9Z61mddY4dJJKkubMSJ+lrBzbxW6eHQtaoVe8kpKScmxrmsa1a9d49913OXXqFGFhYY96CZthq4Wd0aSy+8INfguLJPL4dp40buRJZTfOUs7J1AfVCizQnqNqi6cZ2aY8bna60KrBpPJLWCQ//L2Xpgm/0VPZSkkp7o7jVE3ihBbKPrUKx9QyXHcpjz6gCoG+Xvi7O+Hv4Yibow4HRcZBJ5NlVEnNMpGcYSAyIZ0rN1JJjj6Pd/xR6sjnaS0fpoIcecd1dpmqsUDpQ6dOT/JC49B8K4y+/OcCgX+N5AnFnL1svrE7FXp9QJeaYl0vsN378UHsNe7i6Gx0Mu+s3MKLCR/SSrm1tMMBtSIfuLzKW307UyfYq9Dimf3XadrteJ468nkAnnf9km9ee+6+3zl2NZGZi1fwmfa+Zah2uFqC13Wv82Lvp4t9y7U93o+2GrNJ1dh3MY7fj1zl4tFddM36i27KTtykjBzHHVPL8Knag+AmPRjTrlKBjE4pDCZV44+j11i9ZT/1Yn/led2WOypBN51Ug9mrVuW4VoZop3Io/lXx9/UhwMMRfw8n3J1uPYsYTCopmSZSMgxcS8wgIi6VpOiLeMYdobZ0npbyEareNoLppoNqBT6hD03bd2dIi7Lo8mnx9O/+Dcf08zj6ZC+Rs9zYEZdus+nZUKzrBVaoeMmyfEcLvKZpBAcH8+2339K0adNHvYTNsKXCLjHNwI5zsWw/fZ0rJ3fRMmsHTyh77nrT7zBV50vpGSo26sKwVuXx93CyQsT5T9M09lyI49u9F8g8sZ7O0i7ayYfwkO693peqSdzAg2jNm+uaF6k4kYWeTE2Ho2TElQzcSKOkFEdpKRbH/7Ri3WTSJDap9fmGLlRt9jgjW1fI9/88brY0rcwaj4NkIlPT0c9hLstfe/6+a3AUF7Z0P+aFvcZdXGUYTEz/9RiuBz7nVd336LN72ZM0F940Dqdmx/4Ma1k4C7z3nfcrX8f1Q5Y0TqrBrG38PW89Xu2B3zt2NZH3lq1hjuE9grIbqTI1Pe8aB+DVYijjO1QqtvO+7PF+tKWYkzIM7Dp3g+1nrnPxxD6aZ2zncXnPHSNtAP5VK7FIe5qg+k8yvE0FShWRHlfzsL94Vu+5SOKxv+jMLjrIB/CS7j/iK0bzIEbzJlrzIhVnMtGRpenRSyZcSceN9OxnkZg7etRuUjWJbWotVtOZ4IbdGN2uAr75PL/MaFLpM3sty1NexEXKxKRJ9FVm8cWrg+y20pyfCr3itW3bthzbsixTokQJKlSogE5XtB4OrVnYxaZkEhaewMHweI6eu4TntZ20lI7QUjli+Y80R6yaC2tMLVnv1JnGjVswqFmZQk08UdjiU7PYdDKazcciMJ7fSnMtjMbyKapI4chS/oyoNWkSB7RKbDTV56hnOx5r1oBn65Uu0ILn18ORXPnhdUbpfgXMk/z3N/+C1zpXLbBr2gtbevjIC3uNu7j78+g1Vq5ZwwfqXMu6hwCrjO3ZUuZlPujViBLuBTeh/npSBh98MIXZDgsB+Nz4JLUHzaVZeb8HfNMsIi6NV778gzdTZlBHvmDZ/5epPks8x/BGz7bFcg6pPd6P1ow5PjWLsAjzs8iRc+G4XN1JC+kIreQjOe6Lm1I1R34yteA3h07UadSawc3LFuh9Ym1JGQa2nLzOX0evkHF2G820gzSST1FduoSST88iAIfUCmw01We/exvaNW3Mc/VL53uF63ZbT19n34q3eV3/HWCuRP9abynTutcssGvai0KveG3fvp1mzZrdUckyGo3s2rWLVq1aPeolbEZhFHZGk8qlG2mcjU7mTHQKZ6KTiI84Scmko9STz1JXPkdlKfyuN7BRk9mp1uBXtSkJZbrybNPKtK8agD6fupvtRWqmkX8vxbHvYhzHzl9Gd+0Q5bRwKksRVJSv4i/F409CjgQd/5WmORKhlSBc8+eYWpbjUgUyA+vRsGp5HqsWQJVA90KZa6VpGoMXbWVG5GBLBXu08WXGjJ5A1ZL28ZBQUOzxgQnsN24BrsSnMWn1TnpGfcSTyh7L/tNqad7Rv8LY3k8+UlKd+/nu33BcfxnGE9nXHaBN5cvJ4/JUviemG5j4/b80OTubAbqNlv1JmgvvG/tCnReY0KlKkRkVkRv2eD8WRswmVSM8Lo0z0cnm55GoZOKunMYv4Qh15bPUk89SVQq/6/+jJk1ij1qN39QmXCvdlWeaVaNT9QAcdcWrVzU9y8SBy/HsvXiDI+cjkCIPUk4Np5IUQSX5CgHZzyJ66d5LVaRrDlzJfhY5roVyVKtIekBd6lWpwGPVAqlRyqPQ5n2/+NUuXj0/yJIp9XXDcHoNf5P6ocWvweZ2hV7xUhSFa9eu4e+fc4HYGzdu4O/vj8lkG2uf5Ifc/uFqmobBpGEwqRhMKlkmFYNJI9NgIjHdQEK6gcQ0AwlpWcSmZBGZkM6NuFjUhAgcU65ShkgqSVeoJF+hgnT1jvHRt8vQ9OxVq/Kn2ogrAe1oVacKXWuWpLT3oyV3KEpuVmbPRCdz7noK0UkZxCSlkZkUC1lpYMpEM2aBzgHJwRXJ0R03D2+CfV0J9nahWpAHVUu6W+0/jbPRycyfP4tPdJ8A5uEJY70+Z8XYrjjoilel+nb2+MAE9hu3YGY0qczdeIaYf77kXd1Xlrm0GZqeacb+eDQfxiudKud7g9eoFXv43/mn8ZDSSNBcebviT3z6QqM8n0fTNBb/c4GwDV8zVbfEkjgEzHNvPqIf9Vp3Y1DzMrgXcvIQa7DH+zEvzyJGNftZxKiRlf08kmkwkZRhJCEti8R0A4npBmKTM7makEFs3A3UhCs4pFyhjHaVirc9i9xvGH+mpmO/Wpn1akMu+LWnWZ1qPFGrpM1nTC5MNyuzp6OSOXc9meikTK4nppGZHIOWmQZqJhiz0BQHuPks4u5NaV8Xgr1dqFrSg+pBHlYbFhwRl8a7cz5hiTITMDfYDHGdz4qXnsbZoXhVqm9nlTle0dHRlCiRs5XvzJkzNGjQ4I7kG/YsN3+4P4dd5cvv1lJaikGPEQfJiB7zyxEDHlIaXqTgJaXgSSolpESCpNgc61Pdj0mTOK2FsFOtzh6pNlJoc5pULkXHaoH3zGwl2L///XmSertG85hyAIDfTI050+pTJjxWycqRWY89PjCB/cYt5LTrXCzzvv2Vd7M+zjHR/XdTI1b7v8oHfVs+cnbTmzKNJoZPm8dX8lQAfjE1JbPbYp5r8PCT209eS+K973fQI3YBPZQdOT7bYqrDEuU5GjTvyODmZYv0PA57vB9zE/Pmk9F8tGINoVI0DtnPIHrp9meRVLxIxUtKwYsUfKUkgqQbeEspuYpB1STOaqXYpVZnF3VQQ5rRqHIwHaoFUL7Eo2XSE2zXZ1vPEbB5vKXM2G6qyd8NFzLlqRpWjsx68lKGPNIErJsLI0uSxMCBA3F0vDW21GQyceTIEZo1K34LviqyxGDdnzyt7MyX84WrJTithXBILc8JpTJSUD2qlgmiaXlf+pXxKbYTooublx6rRP8T42iQNBpvKYUnlL2M37aCg5VfKZbzMgTB2ppV8KPyS32Y9F0FWlycR//soXuPK/uoHfsiE+e9xKDePWlfNeCRr/XvxXiaqgcguxNti6kub1X2v/+XHqBqSQ9WjOnMkh2VGbh5Ha+xguryZQDaKWG0I4zt279l3I4eVGzQkX7NyojeCzuiU2SeV7ZY/l0+qiuaH6fVYMLU8hyTK6OWrEeVMqVoUt6X3mV9i3WPR3EyvGU5+h8dS/PY4wRK8bRSjrJ+71L+qTqpwIZZFyWPVPHy9PQEzF3Z7u7uODvfyk7j4OBAkyZNGDZs2KNFaIf0ikyalvs/2ixN4ZrmS6TmRyS+XNV8uawGct25LDr/KoSWLEHlQHeeKO3FhAC3fEsPKtgXR53C273aMmXhYMuQw/eULxmysgqLxvcs0olTBMFW+bo5snBQC5buLMWLG75mprIQTymN0lIsS7UpTFx5lcgnR9GvSegjXWfLqev0lg8B5p6G2MAW+ZKgQKfIjGhdnqfrjmH2hvYYw77hZd2PlJJuANBKOUorjnLk36+Yv6cjSeW70ad5RVpVLCHW8LFxekXCkIfHPIOmEKX5ZD+H+BGp+XJZCyDKsSyKf2VCSgZQKcCdTsFejAt0L3ZzxwUznSIzrVcL3p4/gi+VDwCYrFvBwG+qUnF8PwI9i8/80IfxSBWvZcuWAVCmTBleffVVXF1FSxiAgyLzh9qYs4ZSGNBhQEcWOgya+X2q4obm6IXm7I3k4oOTqydB3i6U8nImyMuZVl7OhPi4FMhK54J9q1nak3Jt+rFu2wGeVnbiIaUzNWMWr30TyqLBLcSDkCBYgSxLDG1ZjqNlX2Loqqq8nvohDeUzOEgmZus/5+NfY5gZ9wpvdK760PfoiZNHqZS9+OwhrQINqlXMz18Bfw8nPniuLieal2fGpqdxP72GF5WfCZWvA1BLvshH8hfEXV7FdxfastClEw3qN+KZeqUoJ4aV2SQHRWaTWo8og/ddn0XSZFdURy80F29kZx8cXT0p6e1KkJcTQV7ONPdyprePy0Mv9isUXRX83WjaqTcr1h+gv24jTpKB940f8fqqMiwZ0V5Uyu8jX+Z4FSe5GceZkmkkMiEdvSKjVyQcFNn8XmfeLm5ZfYT8ZTSpDF60lcnXRlsWc/7W2IaoNh/yUofiNd/LHudmgP3GLTxYSqaRt9Ycov6J/+UY4vWNsS1htSczs0fdPFe+LsamsnTOm7ynXw7Ah4aedBr1IbVKe+Vj5Dmdjkrmsy2nUI6vYZDyJzXlS3ccc1CtwBpTKy6X7ESnBlXpUiPQLh/S7fF+zE3MaVlGrsTnfBbRZb/XKzKOujvXYBWE3FJVjVHLdzHm0ouW8uF3UyMONprLO09Wt25whaxQkmvUq1ePzZs34+3tTd26de978x48ePBhLmGT7LGAFoqe68kZjJ23mmWGibhImQBMNgygYc+JPFk7yMrRFR57vR/tNW4hdzRNY87GM6Rvm8tb+tWW/WtMLdhb8z0+eDZvla8lOy5SbsMA2iqHAeijfMTKt4YWSg/35RupfL3rEqcObOFZ0x90lffi8J/U15majs1qPdarjUgKbke72uXpVD2QADtJSW+P96M9xiwUPQlpWQyf9yNfZryCR3aCuA8NPSnd7R2ebxRi5egKT6Ek1+jWrZslmUb37t0f9jSCIDwEf3cnXnq+G28tPcsc/WcATNGtYOSPgQR5jSr2a2oIgjVJksSEjpX53vtNxv3sx8fKAvSSiR7KDqSj7/CmNJ2Zz9bJdW/DzhPh9JVPAHBN86FU5YaFNqw41NeVt5+sTlqnyvx0qBv9dx6i+o0NPKv8Q1U5HABHyUhXZR9dlX1kXvuC7Vdr8b/fGhEb1I4WNcvTtrI/FfzdRO+KIBQxXi4OvN2vK68vPM9C3UcAvKb/nnG/BBLsPZ4WFXO3uHtxIoYa5pFoZRJsyZIdF8lYP5nRul8ASNGcGKpM53+j+xSL7GP2ej/aa9xC3m06Ec2a1Qv5RJlnWSR1mbETSa2nMz4XS0GkZBqZ8N4HLNJ9CMBqY1u8e31Ol5olCzTue9E0jRPXklhz4ConD+2gQ9YWuik78ZPuXDYmS1PYq1Zlq1qHE65NKF+1Nm2rBNC0vC8uDo80xTxf2eP9aI8xC0XX9/sjuLBuOhP13wKQqekZIk3hnZEDqRzobuXoCl6hr+N1U1ZWFtevX0dVc65iHhJSdLobRWEn2BJN05jy01EaHXyVJ5S9AERp3ox1fJ95Lz5NkJfzA85g3+z1frTXuIWHs/lkND+uWsQnyhxL5es9wwtUfWYSz9Yvfd/vrj8WRey3L/KCbjMAI42v8OHbk2xiYWODSWXb6Rh+PhRO8ulttFP30Fn5F38p4a7HX1b9+Vutw06pLmpIcxpWKk2z8r5UD/JEsWJiIHu8H+0xZqFom/XnSUJ2TqS3bisAcZobo/Tv8b+RvSjjV7Qbggu94nXmzBmGDBnCrl27cuzXNA1JkjCZTPf4pv0RhZ1ga4wmlZHLdjI6/CXqyucAiFBL8IrbBywY9WS+pJy2VfZ6P9pr3MLD++t4FBtXz+ZD/ReAOSX8SNOrjBs1lhqlPO/5vYk/Hmbs0acpJd0gU9MxMuhHlo1oW1hh51qGwcS2MzFsOBpJzMl/aGPaTUd5P8FyzF2Pz9T0HFArskutzmF9LVzLNqJxhQCalfejUkDhDku0x/vRHmMWijZV1Ri/+l96n36J5spxAKI1L8Y6vs+cF5+hVBFuCC70ilfz5s3R6XRMnDiRkiVL3lFg1q5d+1EvYTNEYSfYouQMA8MXbmBq3OuWlNPn1ZK86TWLz0d0LrJLE9jr/WivcQuP5qtdl4j/Yyov6dYCkKC5MsxlLkvHP33XHixN0+g7YymrjRMA2G6qyenHVjCsVblCjTuvMo0mdp27wcYTUVw8eYjqaXtpK4fRUD51R2KOm1I1R/apVdilVueEUx28y9ajXhk/GpTxplpJjwJdv9Ie70d7jFko+jIMJoYv2sIr0a9TW74AmBfeftllJvNHPlVk1/gq9IqXq6srBw4coEqVKo96KpsnCjvBVt1IyWTUwj+YlfQGZeRoAE6rpZnsMZ1PhnexmwxjeWGv96O9xi08uqm/HKPRvy/RRfkXMKdk/6rKQub1aXjHsceuJvL7Z6/zRva8iXcN/Xlh/Ewq+NvPulmapnEmOoW/T19nz4mLOF/ZSSvpEM3lY4TcozcMIElz5pBakf1qJY4pVaFUA2qWLUn9Mj7UC/HK16GW9ng/2mPMQvGQlGFgxBd/MeXGG1SRIwC4pAbwmst7zB7+JME+LlaOMP8VSlbD21WrVo3Y2Nj8OJUgCA/J182RT4Z1YcznmXySPokgKY7K8hU+SHqDsZ9nMXv4k5T2LnoFniDYkzcfr8bA8Neofn00IXIM9eRzbDn+BRuOB9OpemCOY7ecuk5b5ZBl+7R7U8qXsK+5EpIkUTnQncqB7oxsXZ7E9NbsPBfLovOxXDh7glIJ+2kmH6e5fDzH3DAPKZ3WyhFaK0cAMEbKnLgayv7tlfleq0SCX33KlClP7WAvapf2ooK/m1XniQmCYObhpGfB0McY+bmB/yW9Tlk5mjJyNPPSJzL+8yw+GP405Yvxouv50uO1ZcsW3n77bd5//31q1qyJXp+zJaootcaIVibB1kXEpfHKwnXMzpxMacncIHJV8+Ulh6nMHPa0XbWWP4i93o/2GreQP64mpPPa3GWs0N5CJ6lkajr66T9m8St98XS+9f9n33m/syKuL4qkcV4tydcN1vDuU0VrYdKoxAx2X4hl19lYrp47TPnUgzSVT9BAPnPPJB03XdV8OayW57BanlNKRShZh8qhQdQu7UXtYE9KeTnnaq6YPd6P9hizULxEJ2UwduFvzEx5m/LyNQBiNE/G6CYzechzVA+699xWe1PoQw1l2Tz2+r8FnEiuIQjWcTUhnZcX/cbMlHduK/A8GC9NYlz/XjQp52vlCPOHvd6P9hq3kH/WHbpC1JqJjNL9CsABtSJran/J+z3qAHAtMZ2PZ03ho+xkHAuNT1Jz4FyaVyi66+JomkZEXDp7Ltxg/6UbRF46iX98GA3kM9SXz1BZvnLf76uaxDktyFwZ08pzybEyzsG1qVvWnxfbVLjn9+zxfrTHmIXiJyY5kzGLN/BuwluWdf8SNFfGaa8yqO8LtK3sb+UI80ehDzX8+++/8+M0giDkk1Jeznw66knGLnLg3cS3qCpHUEJKYok2hQlLY7jWYwhP171/GmtBEApO9zqlGH5wBBcu/0s5OYr68lnWHVzOvnqTaVTWh00nouko77ccv0PXmCFlfawYccGTJIkQXxdCfF3o2TAYqMONlGc4cDmetZfjOXUxHMdr+6nNKerLZ6kpXcBVyrR8X5Y0KklXqSRf5Tm2gwqZl/QMTF5534qXIAgFo4S7I1+M7MzoJQ68FvMmdeTzeEmpLGYGE7+O5eoTI3ihSai1wyxUYgHlPBKtTII9iU/NYsySzYyLfZfG8inA3Co8w9gXt9bjGN+hErIdz4uw1/vRXuMW8tfVhHTemv0Zy+VpgDmhxDD3z1jxUjdGLvmHzyKfw1nKIkbz5P0q65jzfH0rR2x9GQYTR68mcuByPEcjbpAYfoKSqcepLZ2nlnyBqlK4Za00MC+tsajeT7zXvcY9z2mP96M9xiwUXymZRkYv3cagyKm0UQ5b9s82PEtakwlM7Fq1QDOXFrRC7/E6cuTIXfdLkoSTkxMhISE4OhbdtYQEwVZ5uzqweORjvPKNJ5HnpvO0shNZ0nhHv5Jvtl/lxcjX+F+vRjnmlQiCUDhKeTnTquMzfL9+Gz112/CQ0umf+DlvrSuPX/jvOOuzANhoqs9jNYKsHK1tcNIrNCzjQ8MyPkB5oBExyZkcuZLApogEPomIIfNKGOWzTlNLvsANzYPawV5WjloQijc3Rx2LhrVh4g8eXD0+k77ZC8JP0P/Iz/siGX7tDT7s0xRft6JfV8i3OV73m8Cq1+vp1asXX3zxBU5O9p3SWrQyCfbIpGrM+O0Envs+Ynz2GkIAYWo5ZrhOYvqALlQOdLdihA/HXu9He41byH8mVaPf/D+ZHzccXykZgMFZrzJet9ayDs4LvM+Xb43ESa9YM1S7oWka4XFpHL6SyOGIBAY0LUOI770zutrj/WiPMQuCpmnM+es06dvn8ZZ+tWX/STWYyU4Teaf/E9Qq7WW9AB9SXu7HfOnXW7duHRUrVmTRokWEhYURFhbGokWLqFy5MqtXr2bJkiWWzIeCIBQ+RZaY/FR1vLpO4WXDi6Rr5gWV68gXWJg2gZkLvuDnsKtWjlIQih9Flnjz2ebMML5g2bfU4SNLpeukGkKFuq1FpSsPJEki1NeVp2oH8c4T1e5b6RIEofBIksSETlWo9PSbvGicQIpm7oypKkewJPM1Pv3iU77ZF05RngWVLxWvGTNmMG/ePIYMGULNmjWpWbMmQ4YMYc6cOXz88cf07duX+fPns27duvy4nCAID2lAszI8O+gVBikzCFdLAOArJbNEns7ZH6bw2vcHSck0WjlKQSheapTyxLtJP7abat7x2RytN0NalrNCVIIgCAXjuQbBDBk2lkH6/3FONQ+j9pDSWKR8SPwvbzFu9b8kphmsHGXByJeK19GjRwkNvTMrSWhoKEePHgWgTp06XLt2LT8uJwjCI2hewY+Px/Vjot98tppqA6BIGq/qf+DZY6MYPHcthyMSrBukIBQzr3aqwhf+77BPrWzZt8D4FHXb9ybYR/TYCIJQtNQP9WHB+N5MC/yUP0yNLPtf1P3CkNMjGDr3O/ZdjLNihAUjXypeVapU4YMPPiArK8uyz2Aw8MEHH1ClShUArl69SkBAQH5cThCER1TKy5mlozqyofY85hh6YNLMczQby6dYnDaexQvn8NnWc5jUotvdLwi2xNlB4Yvh7fmj/lL66WfTz2MJnk9MZ2Rr0dslCELR5O/uxJIRbdnfcC4zDH0waOYh1XXkCyzLfIXvv/wfH284hcGkWjnS/JMvyTV27drFU089hSzL1KpVCzD3gplMJn777TeaNGnC119/TVRUFK+99tojB21NYkKrUNSsO3SFtT+tYSafUFqKtez/wdiKnwLG8G7PZlQMsM3EG/Z6P9pr3IJQFNnj/WiPMQvC/aw/FsWKNeuYYZpDWTnasv83UxNW+ozl7Z4tqVHK04oR3lte7sd8W8crOTmZVatWcebMGQAqV65Mnz59cHe3zQe2hyUKO6EoCr+RxqTV//D89dk8oeyx7I/SvJlsGkrt9r0Z0aqcza2zYa/3o73GLQhFkT3ej/YYsyA8yLXEdCZ9s4cuV+bSS7fVsj9W82CaaSChLfsypn1FHHW2lWzIKhWv4kIUdkJRZTCpfLLpDNe2L2WKbgXuUrrls3Wm5nzvN5rJPVtStaTt/Lu31/vRXuMWhKLIHu9He4xZEHLDpGos2n6Bk5uWM1VZireUYvlsvakhy7zGMKlnG+rY0Pp8Vqt4nThxgvDw8BxzvQCeeuqp/LqE1YnCTijq/r0Ux4ffb+bF5Pk5VpiP0TyYbhqAf5PnGf9YZdwc82X99Udir/ejvcYtCEWRPd6P9hizIOTF0SuJzPhuKwMS5tNF+deyP0FzZYaxL84N+vFKp6p4uuitGKVZoVe8Lly4wNNPP83Ro0eRJMmSf//mosomk+lRL2EzRGEnFAfpWSbmbDxN3K6veEe3Ak8pzfLZTlN15jkNp/+THXm8Zsn7Lp5e0Oz1frTXuAWhKLLH+9EeYxaEvMoyqny65SwXt61iim4ZflKS5bP9aiU+0g3n2ce70KNeKbt5FsmXCRvjx4+nbNmyXL9+HRcXF44fP8727dtp0KABW7duzY9LCIJQiJwdFN58vBp9R0xkuPsC/jLVt3zWXDnOyqwJhH//BsO+3Mb5mJT7nEkQBEEQBCHvHHQyEzpWZuToVxjr/Tk/m5pZPmsgn2GV6TWS1r3CgM83cSoq6T5nsh350uPl5+fHli1bqFWrFp6enuzbt4/KlSuzZcsWXnnlFQ4dOpQfsdoE0cokFDeZRhMLtpzjzPbveVtZniPz4RXNj/+ZXqBEo56M61ARLxeHQo3NXu9He41bEIoie7wf7TFmQXgUBpPK4n8usH/zGt6WllJOjrJ8dl3z4gNTH1zq9ealjlXwc3Ms1NgKvcfLZDJZshf6+fkRGRkJmBdQPn36dH5cQhAEK3HUKUzoWJk3XprAu8HLmG/sTqZmnt9VWoplvm4unfcPYvSsxXz5zwWyjEVnvQ1BEARBEKxPr8i82KYC0yaM5aPyy/jQ0JN0zdzY6y8lMFv3Gc+FDWTCh5+z4O9zZBhsc5pTvlS8atSoweHD5kn4jRs3ZtasWezcuZNp06ZRrpxY/FEQioKyfq4sHtKSCr0+oJ/DXLabalo+aySfZhVv4vfXaF74+EfWH7uGSJgqCIIgCEJ+Ku3twmcDmlG/33QGOM9n421TIWrLF1ghTaHclpH0//Bbfg67iqra1rNIvgw13LBhA6mpqTzzzDOcO3eOJ554gjNnzuDr68t3331Hu3bt8iNWmyC69wUBUjONzN98lku7fuQ1eRXl5WuWzzI1PUtMXdgV+AIvdq5Pswp+BRaHvd6P9hq3IBRF9ng/2mPMgpDfMgwmFm47z5Ft63hN+pqqcoTlsyxN4WtTRzb5vcDwzo1oU7lEgSXgsIl1vOLi4vD29rZqlpGCIAo7QbglIi6ND/88hteJlbykW4PPbettJGoufGF8gpMhfRjTuQ71Q73z/fr2ej/aa9yCUBTZ4/1ojzELQkG5lpjOx+tPojuymgm6H/CXEiyfJWvOLDV1YX/JPozqXJdm5fO/MdgmKl5FlSjsBOFOB8Pjmf3LPlpGfcVAZQOOktHyWazmwefGpwgv/zzjO9WkRinPfLuuvd6P9hq3IBRF9ng/2mPMglDQjl1N5KNfD1L3ylcMV37HWbq1rnCC5soi4xOcCu3DmM61qReSf43BhVbxGjx4cK6OW7p06cNewuaIwk4Q7k7TNP44GsVXf2zj2ZTV9FC2o0i3ipdrmg+fGrsTX7kno9pVo2bpR6+A2ev9aK9xC0JRZI/3oz3GLAiFQdM0Np+8zuLfd/Bk4ip6KVvRS7cSbcRoHnxu7EZE+d6MbF+N+qE+j3zNQqt4ybJMaGgodevWve9E+nXr1j3sJQpEmTJl8PDwQJZlvL29+fvvv3P9XVHYCcL9GUwqP+y/wk+bttIvYxVPKntyfH5V82WR8Qmuln2O4e2r06jswxd69no/2mvcglAU2eP9aI8xC0JhMppUfgqL5Lu//qFX2mqelv/J0Rh8XfNisbErZ4KfY1j7WjSv4PvQ06MKreI1evRovvnmG0JDQxk0aBAvvPACPj6PXnMsaGXKlOHYsWO4ubnl+buisBOE3MkwmFi1N5y/tmxiqOEbHlMO5Pg8RvNgibErJ0s9x+AOtWlV0S/PhZ693o/2GrcgFEX2eD/aY8yCYA1ZRpXv90fwy+a/6Z/xDU/8pzE4XnNjmbEzBwKfY2D7unSo6l+gzyKPPMcrMzOTtWvXsnTpUnbt2sXjjz/OkCFD6Nixo80m1hAVL0EoPKmZRpbvusTObRsYbPqBDkrOBdUTNReWmzqxz78nfdvWpVP1QBQ5d2WHvd6P9hq3IBRF9ng/2mPMgmBNGQYTK/dcZtPfm+lv+J7O8r/It/WApWhOrDQ9xlaf5+jdtgGP1yqJXsndqluFuoCyo6Mjzz//PBs3buTEiRNUr16dF198kTJlypCSkvLgE/zH9u3befLJJwkKCkKSJH766ac7jlmwYAFlypTBycmJxo0bs2/fvjxdQ5IkWrduTcOGDVm1alWeYxQEIfdcHXWMbluBLyYO50z7L+ktf8Rvpiaomrly5SmlMV63jkU3BhL93Xien/UtX+26RFqW8QFnFgRBEARBeDAnvcLQluVYOnEQkR2/4HmHuawxtcSomatCblIGI3W/sjxxCKlrxtL3g1Us3n6B5AxDvsahy8+TybKMJElomobJ9HArRqemplK7dm0GDx7MM888c8fn3333HRMmTGDhwoU0btyYuXPn0qlTJ06fPo2/vz8AderUwWi886Htr7/+IigoiB07dlCqVCmuXbtGhw4dqFmzJrVq1XqoeAVByB13Jz0vtqnAoGZl+fbfDvTeuoNn03/gaWUHesmEq5TJIN0G+qf/xZ9/NmboX92o17Q9/ZuF4u/uZO3wBUEQBEGwcy4OOoa2LEe/pqGsOdCOPn/v5KmUH3hO2YajZMRJMtBXt5nns7awaWM9Rm/uRpVGjzGweVmCvJwf+fr5OtRwx44dPPHEEwwaNIjOnTsjy4/WoSZJEuvWraN79+6WfY0bN6Zhw4Z8+umnAKiqSnBwMGPHjmXixIl5vsZrr71G9erVGThw4F0/z8zMJDMz07KdmJhISEgIERERontfEB5BllHl18NX+XX7Ph5L+ZmnlR243Jb6FWClsT3RTd7klccq3/UcSUlJBAcHk5CQgKdn/qWpz2+iHBEE22UP5YgoQwShYBhNKn8eu8babQdolfgzzyrbcJcychzzk6kZh2tPYcqT1e96jjyVIdojGDVqlObt7a3VqlVLmzt3rhYTE/Mop7sDoK1bt86ynZmZqSmKkmOfpmla//79taeeeipX50xJSdGSkpI0TdO05ORkrV69etq+ffvuefyUKVM0QLzES7xs+BUREZHn8qUwiXJEvMTL9l+2XI6IMkS8xMv2X7kpQx45nXxISAh169a9byKNtWvXPtT5/9vjFRkZSalSpdi1axdNmza1HPf666+zbds29u7d+8BzXrhwgaeffhoAk8nEsGHDGD9+/D2P/28rk6qqxMXF4ev74LSTN2vA9tYiJeIuXCLuh6dpGsnJyQQFBT1yD3tBethyxBb+jB+GvcYN9hu7iPvh2UM5Ip5FRNwFTcT98PJShjzSHK/+/fvbbObCeylXrhyHDx/O9fGOjo44Ojrm2Ofl5ZWna3p4eNjVP+KbRNyFS8T9cGx1aNDtHrUcsfaf8cOy17jBfmMXcT8cWy9HxLOIiLuwiLgfTm7LkEeqeC1fvvxRvp5nfn5+KIpCdHR0jv3R0dEEBgYWaiyCIAiCIAiCIAi5ZZt96vfg4OBA/fr12bx5s2Wfqqps3rw5x9BDQRAEQRAEQRAEW5Kv6eTzQ0pKCufOnbNsX7x4kbCwMHx8fAgJCWHChAkMGDCABg0a0KhRI+bOnUtqaiqDBg2yYtR35+joyJQpU+4YHmDrRNyFS8Qt3Iu9/hnba9xgv7GLuIV7sdc/YxF34RJxF45HTief37Zu3Urbtm3v2D9gwADL0MZPP/2UDz/8kKioKOrUqcMnn3xC48aNCzlSQRAEQRAEQRCE3LG5ipcgCIIgCIIgCEJRY1dzvARBEARBEARBEOyRqHgJgiAIgiAIgiAUMFHxEgRBEARBEARBKGCi4iUIgiAIgiAIglDARMVLEARBEARBEAShgImKlyAIgiAIgiAIQgETFS9BEARBEARBEIQCJipegiAIgiAIgiAIBUxUvARBEARBEARBEAqYqHgJgiAIgiAIgiAUMLuqeG3fvp0nn3ySoKAgJEnip59+yvF5SkoKY8aMoXTp0jg7O1OtWjUWLlyY45iMjAxGjx6Nr68vbm5u9OjRg+jo6EL8LQRBEARBEARBKG7squKVmppK7dq1WbBgwV0/nzBhAuvXr2flypWcPHmSl156iTFjxvDLL79Yjnn55Zf59ddf+eGHH9i2bRuRkZE888wzhfUrCIIgCIIgCIJQDEmapmnWDuJhSJLEunXr6N69u2VfjRo16NWrF++8845lX/369enSpQvTp08nMTGREiVKsHr1ap599lkATp06RdWqVdm9ezdNmjQp7F9DEARBEARBEIRiQGftAPJTs2bN+OWXXxg8eDBBQUFs3bqVM2fOMGfOHAAOHDiAwWCgQ4cOlu9UqVKFkJCQe1a8MjMzyczMtGyrqkpcXBy+vr5IklTwv5QgCPekaRrJyckEBQUhy7bbgS/KEUGwXfZQjogyRBBsV17KkCJV8Zo/fz7Dhw+ndOnS6HQ6ZFlm8eLFtGrVCoCoqCgcHBzw8vLK8b2AgACioqLues6ZM2cyderUgg5dEIRHEBERQenSpa0dxj2JckQQbJ8tlyOiDBEE25ebMqTIVbz27NnDL7/8QmhoKNu3b2f06NEEBQXl6OXKi0mTJjFhwgTLdmJiIiEhIURERODh4ZFfoQu5kGEw8fb77/ORwxcAxGmu6DHhLmU83Pk0Hae0EI6pZTmqluWGRzVCylejecUSNC7ni5tjkbo9iqSkpCSCg4Nxd3e3dij3JcoR+7Vo+3ka/zOI2vIFAE6qwXhIaXiSgpuU+YBvP9h5tSRhankOa+W54tWQGlWr0b5KCWqV9hI9GYXEHsoRUYYIgu3KSxlSZJ4s09PTefPNN1m3bh2PP/44ALVq1SIsLIyPPvqIDh06EBgYSFZWFgkJCTl6vaKjowkMDLzreR0dHXF0dLxjv4eHhyjsCpk+y4SjowMeDuaHkTmGHkRUGoAxLRE1KRIlJRI/9QZB3CBQiqOkFEegFEeQdAMPKe2O83lgwp+LtOKieYcG4WdKsO1UbaZSG6lCe7o3KE+7Kv446Gxz+IlgZusPqKIcsV/Oru64O8p4yOZ/Y2+4fkrrSiVISDeQnJqGMTUO0uOR0+PRGxLxIhk/kgiQ4giU4gmU4giQ4vEnHkW6c0p1XaKoSxSwE4wrOHa4DH8ebMRnPo/zePO6PFu/NE56pZB/6+LJlssRUYYIgu3LTRlSZCpeBoMBg8Fwx9hKRVFQVRUwJ9rQ6/Vs3ryZHj16AHD69GnCw8Np2rRpoccsPLopT1Yn2McFMI+xjUvN4lpiBlGJGUQkprMv+31sbDTEnsU/K5xy0jXKS5FUkiIoK+dcSiBEjqGfvIl+bCL+4hf8fK4Z/Rw70bZVW/o1CcVV9IIJQrFWMcCdqd1q3PUzk6qRlG4gJiWTqMQMopIyOJuYwbWkDK4npJIRexGnhPOUIZLyUiRV5ctUly6jl0yWc9SQL1FDvoQhaQ1//N6YAVv60qdrW56qHWTTFQNBEAThwezqKTIlJYVz585Zti9evEhYWBg+Pj6EhITQunVrXnvtNZydnQkNDWXbtm2sWLGC2bNnA+Dp6cmQIUOYMGECPj4+eHh4MHbsWJo2bSoyGtoBDQ0px3bOhxBJkvB1c8TXzZEapTzvcoZOxKVmcSEmhfMxKXx1LZkL4RHoosOopp6jiXyCRvIpHLIfgrylFAbq/mKg6S82bGrA4G296f1kF7rXKSUegAShmDAn/jX3VKna/e97RZbwdnXA29WBSgF3G3LSBINJJTwujXPXU/g7MolPLkVhvHKIOsYjdFAOUEs298DrJRPdlF10ydzL0h+7MvrIaN5/tj5eLg75/BsKgiAIhcWuKl779++nbdu2lu2b450HDBjA8uXL+fbbb5k0aRJ9+/YlLi6O0NBQZsyYwciRIy3fmTNnDrIs06NHDzIzM+nUqROfffZZof8ugnX4uDrg4+pDgzI+2XuqYzB15HRUMjvPxbLkVDgOEf/QRdpNZ/lfnCQDAJ2U/bQ3HeTLNdsYeWgks3o3xtNFb71fRBAEq3jUJhe9IlO+hBvlS7jRqXogUAlVbcnhKwmsPxHN9MOHaZX8O72Vv/GTknCQTIzU/cqRc8cY/ulbzBn+BKW8nPPjVxEEQRAKmV1VvNq0acP9lh0LDAxk2bJl9z2Hk5MTCxYsuOcizILtMv/V3/r7/2+P18PSKzI1SnlSo5QnI1qXJzmjBX8cvcbwfacoH/kLI3S/ESjFo5NURup+5eilowz/9G3+N7grZfxc8yUGQRBsk6bdqmwV1KKXsixRN8SbuiHeqB0r88+5dkzYfJT6V79mpPILjpKRWvJFZqdO5OXPsvhkdA8CPZ0KKBpBEAShoIiMAYJdK4gRf+5Oeno1DGHF6I50Hzmdd0JX8rHhWTI1cztFTfkSn6S+yutfrOVK/J1JOwRBKLoKepSxLEu0rlSCr0a2pXLvmQxU3ueSGgBAaSmWWRlTmfDV32QYTA84kyAIgmBrRMVLsBsaIBVYm/Pd1Q72YvHgFtToM4P+uv8RrpYAIEBKYHbWu0xYsoHUTGOhxiQIQuGxRrkD5jmrnWsEMmf8AN7wnMVp1bw2TBk5msEx/2PuxjOFHpMgCILwaETFS7BbGoWX/rdT9UDmjO3La95zOaUGA+bW5wlJ/+Pdnw4XSgyCIFiXhoSUT0OccyvQ04nPR3blHdfJxGrmtOEdlENE7/qa45GJhRqLIAiC8GhExUuwG5qWX7O6Hk6QlzMLh3dkssd0IjVzco4m8kk8jyxh17lYK0YmCEJBMc/xKvwer9v5uDrw3oCuTDYNs+ybqKzik/VHrRiVIAiCkFei4iXYLa3Q257B29WBWQMf4w1esqSWHq9by7yfd2BSrftwJghCwdKQCnyO171UDnSnUpvebDTVB8zDnUue/46wiATrBCQIgiDkmah4CXbDWnMt/quMnyudunTnW5N5aQN3KZ1O8d/w1/EoK0cmCEJ+++/6gdY0vFU5luifv7Wt+43lO87d5xuCIAiCLREVL8FuWbP1+flGIfzsM5h0zbyYaU9lK6u2HbFOMIIgFArzvFLrXd/FQUeb1u3YbKoLQJAUR8qJv0hMM1gvKEEQBCHXRMVLsBu2MNfiJkWW6NOuHj+aWgHgJmVQ5drPHLsqJrsLQlFyn6UjraJng2B+0NpZtnuwhZ8PX7ViRIIgCEJuiYqXYNcKf5bXLV1rluR3526W7aeVHfwcJh6ABKGoudngY42shv/l4+qAvkpnrmteALSTD7Hl8EWrxiQIgiDkji4vB6uqyrZt2/jnn3+4fPkyaWlplChRgrp169KhQweCg4MLKk5BAA2bmWsBoFdk6jdoRNjO8tSRz1Ndvsx7YftQu1RFlm0pUkEQHpaNdXgB8GTdENafakh/3UYcJSPOEdtISGuOl4uDtUMTBEEQ7iNXPV7p6elMnz6d4OBgunbtyp9//klCQgKKonDu3DmmTJlC2bJl6dq1K3v27CnomAUBwCamvHerU4qfTc0s2/XSdnHiWpIVIxIEIb9Jt7+zfrFDi4p+bJMaWLbbywfYejrGihEJgiAIuZGrilelSpU4cuQIixcvJikpid27d7NmzRpWrlzJH3/8QXh4OOfPn6dly5b07t2bxYsXF3TcQjGk3aXt2ZoT3QEqBbhzyqO5Zbu1cphtZ8QDkCAUGbY2yQtzkg1duZakaE4AtJUP8fdJkVVVEATB1uWq4vXXX3/x/fff07VrV/R6/V2PCQ0NZdKkSZw9e5Z27drd9RhBeFSSdOshyBZ6vAAqVanJBTUQgPrSGfadvGTdgARByFe35njZjpZVS7NLrQ6Ar5RMzIUwNBusJAqCIAi35KriVbVq1VyfUK/XU758+YcOSBDu5W7PFLZQ9WpduQTb1VoA6CQVfeQ+Mo0mK0clCEJ++G+xYwtlDkDT8r7sUatZtiumH+bSjTQrRiQIgiA8SJ6SawAcOXL3tYokScLJyYmQkBAcHR0fOTBBuJvb08nbSttu/VAf1mqVGchfANTkDMcjk6gX4m3lyARByB+3shrainJ+rpx2rg3GrwFoIp9g74UblPVztXJkgiAIwr3kueJVp04dpPtMrNHr9fTq1YsvvvgCJyenRwpOEB7IBp6DPJ31xPvUgWTzdn3pDAcvx4uKlyAUQff7/68wSZKET9m6JJxxxUtKpbF8kvfOx9C7UYi1QxMEQRDuIc/reK1bt46KFSuyaNEiwsLCCAsLY9GiRVSuXJnVq1ezZMkStmzZwttvv10Q8T5QcnIyL730EqGhoTg7O9OsWTP+/fdfy+eapjF58mRKliyJs7MzHTp04OzZs1aJVcib//Zw2VLrc0iZilzTfACoI5/n0OVYK0ckCEJ+0G5bxsKWyhyAxuVLsE+tAoCPlELMeTHPSxAEwZblueI1Y8YM5s2bx5AhQ6hZsyY1a9ZkyJAhzJkzh48//pi+ffsyf/581q1bVxDxPtDQoUPZuHEjX3/9NUePHqVjx4506NCBq1fNC9vOmjWLTz75hIULF7J3715cXV3p1KkTGRkZVolXeDTWXsz0prqhPhxUKwDgJmWQeOnuQ3IFQbBvtlHimDUp58te9dYc7LLpR7gSn27FiARBEIT7yXPF6+jRo4SGht6xPzQ0lKNHjwLm4YjXrl179OjyKD09nTVr1jBr1ixatWpFhQoVePfdd6lQoQKff/45mqYxd+5c3n77bbp160atWrVYsWIFkZGR/PTTT4Uer5A3mqb9Z46X7TwC1Q324rB6K6lMQNpZbqRkWjEiQRDyg4Zmk1kNAcqXcOW0Yw3LdmP5FHsvxlkxIkEQBOF+8lzxqlKlCh988AFZWVmWfQaDgQ8++IAqVcxDHq5evUpAQED+RZlLRqMRk8l0x9wyZ2dnduzYwcWLF4mKiqJDhw6Wzzw9PWncuDG7d+8u7HCFfGAj0y0oV8KNc3JZy3Y1+TInryVbMSJBEAqCrZQ5YJ7n5VGmvmU9r4byKfZdEMOcBUEQbFWek2ssWLCAp556itKlS1OrljmF9tGjRzGZTPz2228AXLhwgRdffDF/I80Fd3d3mjZtynvvvUfVqlUJCAjgm2++Yffu3VSoUIGoKPMCk/+tFAYEBFg++6/MzEwyM2/1XCQlJRXcLyDcl0bOYT621PqsyBJG/xpww7xdTbrM0WuJtKjoZ93ABJsgyhH7ZZ7jZXtZDW9qUK4EB85WorVyhAApgasXTwJ1rB2WkM9EGSIIRUOee7yaNWvGxYsXmTZtGrVq1aJWrVpMmzaNixcv0qRJEwD69evHa6+9lu/B5sbXX3+NpmmUKlUKR0dHPvnkE55//nlkOc+/KgAzZ87E09PT8goODs7niIVHYUuPQaVKhRClmTMZVpMvceJqopUjEmyFKEeKDlsqcwAalfVhb3aCDYCgxINcTxJzlosaUYYIQtHwULURd3d3Ro4cyezZs5k9ezYjRozA3d09v2N7KOXLl2fbtm2kpKQQERHBvn37MBgMlCtXjsDAQACio6NzfCc6Otry2X9NmjSJxMREyysiIqLAfwfh7m5veQbba32uVtKdE6p5/qOnlEZs5HkrRyTYClGO2K/be9ptrcwBqFrSg2O66pbtRtIp9l0S87yKGlGGCELR8FAVr6+//poWLVoQFBTE5cuXAZgzZw4///xzvgb3KFxdXSlZsiTx8fFs2LCBbt26UbZsWQIDA9m8ebPluKSkJPbu3UvTpk3veh5HR0c8PDxyvATbYStr6gBUC/LghHYr8YxL3EkyDCYrRiTYClGOFB22VOaAeZizY2gDMjU9AI3kU/wrEmwUOaIMEYSiIc8Vr88//5wJEybQpUsX4uPjMZnMD5be3t7MnTs3v+PLsw0bNrB+/XouXrzIxo0badu2LVWqVGHQoEFIksRLL73E9OnT+eWXXzh69Cj9+/cnKCiI7t27Wzt04QFuzy5miyoHenDytopXFS5zNjrFihEJgvCocs7xsk11ygYSppmzqobK1zl3XqxNKQiCYIvyXPGaP38+ixcv5q233kKnu5Wbo0GDBpZ08taUmJjI6NGjqVKlCv3796dFixZs2LABvd7cGvj6668zduxYhg8fTsOGDUlJSWH9+vV3ZEIU7IMttT27OepI8rw116KafJkT18Q8L0EoSmypzLmpcVkfy0LKAD439pOQlnWfbwiCIAjWkOeK18WLF6lbt+4d+x0dHUlNTc2XoB5Fz549OX/+PJmZmVy7do1PP/0UT09Py+eSJDFt2jSioqLIyMhg06ZNVKpUyYoRC7mm2eZDz+08gyqRqjkCUF26xPFIkXlKEOyZlqOfyzZLoJqlPTkk3VpIuZF0in1iuKEgCILNyXPFq2zZsoSFhd2xf/369VStWvXOLwhCAdGQbGpNHYCqpbwt87yC5RguRlyxckSCIDyqHEOcbazMAXDUKZhKNSJLUwBoq4Sx5WT0A74lCIIgFLY8r+M1YcIERo8eTUZGBpqmsW/fPr755htmzpzJl19+WRAxCgJwM7uYrc6yMKtZypNjalkaymcA0EUfxmjqiE55uOUMBEGwMtsucixaVCvD7qvVaa0cobQUS/iJPZjUWiiyDdYUBUEQiqk8V7yGDh2Ks7Mzb7/9NmlpafTp04egoCDmzZtH7969CyJGQbgrTZOQbKz5uWYpT35Sy1q2q6gXOHs9haolRQYqQbB35sYf2ypzbupYPYBFGxrQWjkCQOOs3Ry4/ByNyvpYOTJBEAThpodqhu/bty9nz54lJSWFqKgorly5wpAhQ/I7NkHI4b/reNkib1cHYtxvDbmtIV/gqFhIWRDslm2XOLeE+rpy0beVZbu7vJM1+y9bMSJBEAThvx5p/JOLiwv+/v75FYsg5IkGNjnfwr10NdKyE2zUki5yTFS8BMGu3Uonb3vzSm/XvG5NtptqAua08jFHNpKUYbByVIIgCMJNuRpqWLdu3VwvGnnw4MFHCkgQ7kWzk7bn6qV9OH42lIbSGXOCjfAIoIa1wxIE4SFomn2UOwDP1S/NtE3taaWYl3bpzXpW7+3ByNblrRyZIAiCALns8erevTvdunWjW7dudOrUifPnz+Po6EibNm1o06YNTk5OnD9/nk6dOhV0vIJgYautzzcTbNykv36YLKNqxYgEQXgUOXq8rBzL/fh7OKFV7kK05gVAR+UAW7duJFn0egmCINiEXPV4TZkyxfJ+6NChjBs3jvfee++OYyIiIvI3OkG4jT3M8QKoVdqTH9UKDGIDALW1Uxy9mkD9UDHJXRDsjR11eAEwvG0VPj3dnff0ywEYZ/yKj9Y3Z2r3mtYNTBAEQcj7HK8ffviB/v3737H/hRdeYM2aNfkSlCDkhq22Pnu5OBDn18Cy3UQ+yZ4LYjFTQbBXN8sZDWyyl/12tYO9uFGpFxFqCQCaKScw/buEzWJdL0EQBKvLc8XL2dmZnTt33rF/586dODk55UtQgnA39tTwXKFCZS6qAQDUkc5x4FyklSMSBOFh2FO5c9Nrj9fmXW2oZXuybgVff/M1By7HWzEqQRAEIc/reL300kuMGjWKgwcP0qhRIwD27t3L0qVLeeedd/I9QEG43e1DDc2tz7bZ/NyknA979lWjrByNo2REDd9HprE5jjrF2qEJgpBHOed42WaZc7uyfq4079SLZesPMki3AQfJxKd8yLgvTXTr8QLd6pSydoiCIAjFUp57vCZOnMhXX33FgQMHGDduHOPGjePgwYMsW7aMiRMnFkSMggDYV3axRmV92aNVs2w30w6yVww3FAS7Y0fFTg6DmpfheI3X2WyqC4CblMEi+QMu/fg2o77azbnrKVaOUBAEofjJc48XQM+ePenZs2d+xyIID3R7W7OtzvEC8HF1IK5kK4wxn6OTVB6T97P0eBStKpWwdmiCIOSRvazjdTtJknj/2XqMTn0P46V36aTsRyepjNet5fL5HXwy7xlSKz7Fs40q0LKSn+iNFwRBKAS56vGyp54Goeiyt3+GTapX4F+1CgBl5WjOnjgo7iVBsDP2sn7g3TjoZD4b2Jy/a33EHEMPjJr5v/xQ+Tof6xcy42IvLq8ez4hpcxn11R6W77zIofB4MgwmK0cuCIJQNOWqx6t69epMnjyZZ555BgcHh3sed/bsWWbPnk1oaKgYdigUiJzp5G276bljtQC+2VSfpsoJAOqm7eLo1W7UKu1l3cAEQcgTe8pq+F96ReaD5+ryW+Xp9FvXiBeNX9NSOQaAr5TMEN2fDOFPki44c/BcJbZr5fmM8mT4VMHNL5Qy/u6U9XOltJcz/h5OBHg44uaos9n5tYIgCLYsVxWv+fPn88Ybb/Diiy/y2GOP0aBBA4KCgnByciI+Pp4TJ06wY8cOjh8/zpgxYxg1alRBxy0IgG0/BFXwd+OER3NI/xqAp5Rd/BgWKSpegmBHikon9RO1gmhZcQiLt7dl8c7f6aFtpLO8D0fJCICHlE4b5TBtOGz+QhJkJOq5eC6QS1ogJzU/tmpeRGveJOl8MbkFIrn6oXPxwsPFCQ8nHZ7Oejyc9Xg663F11OHsoOCiV8w/HRSc9AouDjqc9QpOellU3gRBKHZyVfFq3749+/fvZ8eOHXz33XesWrWKy5cvk56ejp+fH3Xr1qV///707dsXb2/vgo5ZKMb+m9XQlkmSRIM69Ti4swL15HNUlcM5cWgXxi5V0Cl5zmsjCIKVSNLN0sa+Kwqeznpe7VSZlDbl+SXsWYb+ewKfa9tpKx+ihXwMPykpx/FOkoGqUgRVibjzZGnZLyBJcyYJVxI1V5I0VxJxJRUn4jQH0nAiHQcyNEfScCQdB9I1R9JxRFUc0RQHVNkBZD3o9KDoQXYAxQFJZ/4p68zvdTodekVGJ0sosowic+undJd9sowiSVQP8qBDtYCC/wMWBEF4gDwl12jRogUtWrQoqFgK1YIFC/jwww+Jioqidu3azJ8/35IeX7Aftptew+zpeqVYtr0l9eRzALTN3MI/556ibWV/K0cmCMLDse0yJzfcHHX0aRxCn8YhxKa0ZfuZGGaejSU64gxe8UepIV2gvHSNstI1QqRoHKT7z/nykNLxIJ3SUuzDBaRmv4z3P8ykSRjQYUTBhJz9Mr83oqBqkvnnzW1kbmju/Fb7c1HxEgTBJjxUVkN799133zFhwgQWLlxI48aNmTt3Lp06deL06dP4+4sHYlulaXdmNbR15Uu4cSmwE1mxK3CQTHRTdjLjQLioeAmCHbKHMiev/NwceaZeaZ6pVxqoQ3LG0xyPTOJibCr7Y1O5FJNEWswl5JQo3LJiCZDi8ZfiCZDi8SYFTykVD1LxkFLxJPWBlbRHoUgaCgbAcPcD7vLXc13z4g+56P29CYJgn4plxWv27NkMGzaMQYMGAbBw4UJ+//13li5dmi9JQfZcuMGHa3fiImchKQ6g6JBkByRFh6RzQFF06BQFnSJZhk3oFBm9IqGTs3/e9l6vyDk+N3/vtmOz9zkoMo56Occ4eme9gpODjINSNMfT28Ov9Fj9qvz9Z106KfsJkBJIPbmJ5IzauDvprR2aYMNe/mY/NyIvIMk6UHSgOICsQ1Z0SIoDkqxDpzOXDTo5uxyQJUu5otx8L5vf6xXzUCxzmSGhKDL67LLn5vdunkevk3HSyTg7ZJch2a+b20oxfpC1hzLnUbg76WlSzpcm5Xxv29sYgAyDietJmUQnZxCdlEFEmoHj6QYS0w0kphlITMsiIy0FNSMeKSsVLSsd2ZiGZEzHQc3AiUxcpEycycKJTJylLPSY0GO0vBwkY85tjOgwoc/e75jd36Wg3npJ/9nO/lyHShY6dMX03+uRKwlM/W4HblImyDLICsg6JEkBWUGSdUiKApKCJJtf5mGcErIsoUggy9KtfVLOnzrLcbd+6pSbn3PP4/SKhJNOMT+v6BQc9QqOOvOzi+WnXsbNQYdcTP/uhKKr2FW8srKyOHDgAJMmTbLsk2WZDh06sHv37juOz8zMJDMz07KdlJR0xzH/lZRuoGfCEnrptt7zGIOmYETBgPmnEZ35vaZguPn+P/vNx+tyfC8T3W3n0pGJnrTs8fMZlrH0DmRKjqiKE6reGfQuoHNGcvZG7+qJh4szXi56vJz1eLno8XVzpJSXM0Fezvi5OdhMhU1D+88cL9uI60GerB3EW7+3opOyH4Cu/MP6Y315rkGwlSMTCsvDlCNxsdGsSB5232OMmpxdTtzldVsZY/rPZwbNvO9mOXLzffptZUkGDpa5ORnoSceRdM2BDBwxyo6YdE5oijOak3t2WeKDh4sTni56vJwd8HHVE+TlTClvZ0p5OdttQ8PtS0DY+rzSguakVwjxdSHE1yXP3zWaVDKMKmlZRjKyVNIMRjIMKkaTSpZJxWDSMBhVDNnbqSYNgyl726hiVM2fZ5nM71VVw6hqmG6+NA2TKfunmvPVOsijAP40CtfDlCEpmUYej/+awbr1ub6OQTMP0bw5lFPl1vBNyz5Nvm2o583jcg79vHWclHMfMgZ0pKEnXtOTgQOZ6MnQsn/etp0suWB08EZz9kZz8UHv6kOQtxuhvi6U9XOlakkPgrycH+rPUxCspdhVvGJjYzGZTAQE5BzvHRAQwKlTp+44fubMmUydOjVP1zCqGjrp/oPV9ZIJPSbuKDIKui5hyn4BpIB6XSIJFxI0NxJwJVFzIxZPtmp+RGp+RMslMHqE4BVUkWqlvKhVyosGZbxx0ovFNnPLx9UBrWIHEi8uxFNKo7P8L2MPnhcVr2LkYcoRTPcYTnUbnWRu1b/r0KvCaJcwAanZr1hI0lxI0FxJwI04zYNIzZcDmh9XNT/iHEujK1mdiqUDqBfiRfMKfnZTGbu9wcc+mntsj06RcVNk3ByL3WNHvniYMkRVQUbN03f00u0PCfdQmDeBhiWRixojce2SD5fUQC5rAWzWynLJvS5lKtWmZ8MQapf2LPCG4oS0LE5eSyYyIZ3ryZnEJGeSkmkgw6CSnmXEYMhERkOTZCRJRpIUJFnGQSfh4qDDzVGHi4OCa/ZPH1cHSrg54ufuiJ+bI17OetHLV8SJEvABJk2axIQJEyzbSUlJBAff/4HZYFI5rJbHEQN6TOZhEtnDJXTZFS7dzeETNz+7y37z8IqCXchSljS8SMVLSr33QamQfMaZk6dDOKyWZ6lUF335lvRoWJb2VQMKbdiRueHZPlufn6hXlt/PNaGPbguuUibul/8iOqkJAR5O1g5NKAQPU46kqwq/mJqi3FYeWF7S7duqpdy4vYxRLGWP+X1Bzr25yUNKw0NKI4SYOz/UwHRV4sKVIP7dVYlXqYepTBueb16ZtpX9bfZhQ8vx3jZjFIq+hylDTJrGca0Mv5maWIZfyrcNyZRR0Ul37lPusS1LKrrsfqtbn2s5h3dKeavo5YUsaZTiBqWUGzTnuHlnBlwMC+CbA+2YF9KLt55uQAV/93y7pqZp7Dx3gz+PRnLp9CH8k09SRQ4nWIqhoZRACRJwl9JwwoAjWSjS3Z9MMjUdaTiRihOpmpP5veZIHB6c0jyJ0byIxYMbkjcGjzK4+JclpIQn5Uu4UTvYi0oB7sV6iHdR8lAVr/Pnz7Ns2TLOnz/PvHnz8Pf3588//yQkJITq1avnd4z5ys/PD0VRiI6OzrE/OjqawMDAO453dHTE0dExT9doUMYHpddEjCaNNJOK0aRhVM1DKYzZwyQM2fsNavbnJhVD9lCK2z83mkyoJiOaKQvVZARTFqhGNJPB/N5kAJMRSTWAakQyZiCb0nHUsnDOHkPvTGaO905k4Spl4EEaXlIKXqTgJaXgQRryPQoNdymdRtJpGsmnGcYfJF104ftzrenj0YPR3dvQqlKJPP0Z5RcbGQX5QB2qBjBCaUUftgDQXd7BL2GRDGtVzsqRCYXhYcqRMU80IT6tHhkmjVTVXFaYVC1HOXKzbDG/N5cxJjV7n0mzlCWm7LLFpBqRVCOq8WY5Yi5TNNUEqsFcnqgmMGVZyhK9KQMnDDhZ5uZk4SyZyxFnMnGT0rMbb8xliaeUihcpd30AUySNitJVKspX6cPfJEZ8zjer2rGkRF/efLY5NUp55tcfeb7K0eNlJ2WOULQ8TBlSNdCdxJ4vY1BVMlRQ1VtDMVXNXH6o2q2hmqqqYVLBpKrZx3Hr85vfUW8ed9t3tOzvZH9fU03mMkUzoRqNaJoJSTWhaaZbzy/GDCRDBpgykI2ZSKYM9GoWjhhwzH5W8cxuFPaSkvEmBV8piRDpOt5SSo7fs6wczZvyN1yL3MCkT0fT7/l+tK/66Fksd52P5ZOfd9I8bi0j5J2EyDHg8HDncpSMOJKCNykP7jFMA8NFhfAL/pzXgvhFrcgJXRX0wQ1oWzOEjtUCKeGet38Lgu3Ic8Vr27ZtdOnShebNm7N9+3ZmzJiBv78/hw8fZsmSJfz4448FEWe+cXBwoH79+mzevJnu3bsDoKoqmzdvZsyYMflyjVJe5jkN1qJpGlkmlYwslXSDyfzKMv/MyH6fmmUkIt3A0TQDCekGEtIMJKWlk5WagJp0DYeUSEoSQykplvJSJNXkyzlSBXtIaQzV/Umf1C18/NVz7Gw2mje6VC3QVmsN+8tqeJOTXsG/ehuuHPejtBRLS/koiw4cFxUv4Z6s1ZjxXyZVI+Nm2ZH9M8NgLlvSskykZBiJSs/iVJo5yUJCWhbxqVlkJsVAQjgu6dcoJcVQUbpKDfkSlaQIS++bp5TGSN1vPBu3nbc+H8YTzw3lydpBVv6Nc7p9AWV7KnMEwd/Diads7H66H6NJJdNofqUbTCSmZZcnaQaupWdxNDmL8Lg0YmOiUGNOU91wnNbyEZoqJwAoKcWxmPcZvToTn2EvUjfk4daV1TSNeZvOcH3rFyzRrcRVl3nPYxM08/p1GThYXqomI2f3JMpoyGg4YsCFDFyljOyf9z4nmId8lpeuUZ5rdFQOAJAW4cjWy7WZ9ktj1MpPMKRNZeo95O8oWE+eK14TJ05k+vTpTJgwAXf3W9257dq149NPP83X4ArKhAkTGDBgAA0aNKBRo0bMnTuX1NRUS5ZDeydJEo46BUedgicPN4fCpGpcT87ganw6Z6+nsDAykUvh4XhH76adfJAu8j6cJAMuUibv6Ffy7e4rTDW8x9TutfL5t7k/W1/H63bd6wXzy5FmvKj7BZ2kUi52C+E32j/URHVBKCyKLOHqqMP1IefmZBhMRCakcyY6mQ2RScy+FIUu/B86SXt4Qt6No2TET0riM2U2r3+fipN+Ao/Z2JpLUo739lPmCII90WVncHbN7sy5dwN2bVS1I6ejk9l0Ipq5e3YxJmMRLZVj6CUTH8vzGbKqLCte7fVQ89EXbDmL07ZpvK//zbLPoCnsVauwU63JaaksRu/yKJ6B+Hh44OWix8mSoVFGliQ0zTzUU9U0NA0yDSZSs0ykZRlJzTSRnpmFIT0ZU2osSup1HDNvUEJKJFCKo4wURTkpirLSNZylLEsMLlImXZV9dFX2EXV+JYtOP8Gyav1558la+ItpC3Yjz/+THj16lNWrV9+x39/fn9jYh1w8sZD16tWLmJgYJk+eTFRUFHXq1GH9+vV3JNwozhRZoqSnMyU9nWlQxid7b03iUh9j08loBv5zmC43ljNAtxGA3rqtzPl3LutCp/F03dIFEpOm5cxqaG+alPPlM4fmvKj+AkA7+RCbT0UzqHlZK0cmCAXHSa9QroQb5Uq40blGSaAyKZnN+ePoNfr9vY8hSZ/RSdmPImnM1C1m4Pch1Hx5CIGetvEgodlxmSMIRZUsS1Qt6UHVkh4MbVmOt9bWIOn4RB5X9uEupTMwbRkrdjdmeKvyeTrv/ktxXNyyhI9vq3StNrblJ89+tGtUh6cqlaCivxs6Rc7X3yfLqHIjNZPIhAwuxabyR2wqF2OSSLhykqDkozSUTtNeOYivlAxAoBTPZP3XnDq9lXHnx/NK/x40tDyrCbYszxUvLy8vrl27RtmyOR8WDx06RKlSpfItsII2ZsyYfBtaWJz4uDrQs0Ewz9Yrzaq9NRj3+xLmKPNRJI1xurX0/bUp7asOxKMQspVpSHY130KRJQIrNyHqhDeBUjzN5WOsPhEuKl5CsePmqKNng2C61Qlixq8ViT4wjf66jThIJt5RP2f2hmbM6lnX2mFa3Gzw0RBzvATB1jg7KMx6ri6DbrxOo+tDKCEl0ln+l+d37WNoi3K5ngKhaRpzft3H57qvLPveMgymTKexfNuibIFOpXDQyZbG7vqhtw8fbMj15Az2Xojj3WNXST29lV7aeh6TDyBLGlXkCJaY3mLCl1Gk9htBm8r+BRajkD/yXGXv3bs3b7zxBlFRUUiShKqq7Ny5k1dffZX+/fsXRIyCDZJliX5Ny9Cux0jmGXsA5onzQwzf8N2+iAK5pnmOl323PneoFsAWk/mB0kkyoLu8neSMB6cNF4SiyFGnMLV7TfZXfZ0jqrkBoqocQdrhdUQnZVg5OjPNvoscQSgWdIrM+Mfrs8zYGTBnQGyWsoED4fG5Pseu8zeoH/UDHlI6AGtNLQjqMJphrXJfeSsI/u5OPFk7iPl9GzLvzZcIf2wxfaWZHFdDAXCVMvlEmcvylSs4HplotTiF3Mlzxev999+nSpUqBAcHk5KSQrVq1WjVqhXNmjXj7bffLogYBRvWvW4pzlQcTKRm7uJuLx9k8+59ORYdLSj/TbZhD1pU9GMb9SzbLTnE7vM3rBiRIFiXJEm881Rt5mnPW/b1lLew9uBVK0aV060eLzHDSxBsVf1Qbw76dLFst5EPs/3MXZa2uIc1+8Ppq9sEmBen/9b1BUbYWAIsdyc9w1qV49NXBzM3dAG/mZoA5qyJs+U5TFu9hQxDwS8dIjy8PFe8HBwcWLx4MRcuXOC3335j5cqVnDp1iq+//hpFEYvqFkdD2lRltbE9YG5lqp60nbPXUx7wrbzTNPuraP2Xu5OezNItMWjme6WJfJK9F+OsHJUgWFcJd0d8a3YkQjVncmwhH2PX0TNWjspMdHgJgn2QJIna1apwIrsnqKZ0kUOnz+fqu5lGEzdObidASgDgb7UuXVo2yfe5XPnF182RhYNasLnadP421QbAR0phaOInfL41d7+zYB0P/S8qODiYrl270qNHD1JTU4mPz313rlC0NAj15oBLS8t2e/kQm09eL4QrSwW+Sn1BqFuhFEc187CqCnIkp8+fs3JEgmB9T9YtzXq1IWBuwHGP2kOSjQzDvVnKaHZa5ghCcdGyQgl2qdUAcznicP0IRtODF3U+ciWRRqYDlu0/1UZ0rVmywOLMD4os8b/n6rPI93ViNPMaiI8pBzn8z6/Eptw/Xb1gPXmueL300kssWbIEAJPJROvWralXrx7BwcFs3bo1v+MT7IAkSZSvVpfw7Nbq+vIZwi4VRMWraLQ9Ny7nw57s/xgAvK7/S2K6bTxgCoK1NAj1YR81LdtNpOPsv2T93mAxx0sQ7Ef1IA9LjxdAefUyl26kPvB7By7HU18+a9mO8m1MgB2kaHfQyUzp3YoPjLeGao/QfmTF7stWjEq4nzxXvH788Udq1zZ3a/76669cuHCBU6dO8fLLL/PWW2/le4CCfWhQxpdDWkXAPNY47crxArnO7ck17HGOF0CdYC8OSLcqXo2kkzbxgCkI1uTsoJAZ1NCyXV2+xMlryVaM6BZ7T+ojCMWFt6sD110qWLaryOG5KkcOX4qhtmQeondF86NcuQoP+IbtqBLoAbV6cl4199A1VU6wb+8ODLno6RMKX54rXrGxsQQGBgLwxx9/0LNnTypVqsTgwYM5evRovgco2IcapTw5qt5Kix6YfpqY5Pzt6i4qLc9OegVjUCOMmvn2aySfYp+Y5yUIhAaV5IrmB0BF6QqnryVZOSIoKj3tglBcOJasavn/tbJ0hbPRD654pVy/YFms+LBajjrB3g/4hm0Z0rIiX5ses2x3zNjAttO5TywiFJ48V7wCAgI4ceIEJpOJ9evX89hj5r/otLQ0kVyjGCvr58p55Vb2n2rSZU5H5X9r9e09XPa2jtftapcvzQnNPByiknSFkxcLJgW/INiTSoHunFaDAfCQ0omLumjliG7Kzmqo2W+ZIwjFRXAJb65pvgCUkmK5mnD/pSk0TUNJumLZjtACCPV1KdAY81u1IA/OlXyCTM28PG8XZR/rj0ZaOSrhbvJc8Ro0aBA9e/akRo0aSJJEhw4dANi7dy9VqlTJ9wAF+6DIEibvWxWv0lIMEfFp+XqNotTuXC/UmwNqJcA8AVgfdYBMo0gBKxRvlQPcOauVtmw7xJ1FVa175xeVnnZBKC6CvJyIxFzx8pZSuBF3/xEl8WkG/NRYy3ak5kOQl3OBxlgQ2tWpxA7VPE+2pBRH9Ekx3NAW5bni9e677/Lll18yfPhwdu7ciaOjIwCKojBx4sR8D1CwHy6+pSytLcFSDBFx+Vvxgv/O8bLfDGN1Q7w5oFWybNfWTnHsqi0MqxIE6wn1dSFc87ds+2sxxKbaTnYu87xS+yxzBKG4CPJyJjK7xwtATbz/iJLIhHSCuLWe5jX8CHB3LLD4CkrnGoGWzLAAjY3/EhaRYL2AhLvSPcyXnn322Tv2DRgw4JGDEexbaR93rp7zo5wURbB0Pd8rXkWp5dnTWU+CT13IHo1ZXzrLwcvx1A+1r3HlgpCf/NwciebWA1OQFEtkQgb+7tbLLlYU1g8UhOIkyMuZ3bdVvJTkSFRVQ5bvfidHJqQTJN3q8cp0CbLZ9bvuJ8jLmat+LSBxEQDN5OP8c+4GDcv4WDky4XYPVfFKTU1l27ZthIeHk5WVleOzcePG5Utggv0J9nHhilaCckThKmWSeCOqAK6i3eWdfQouW4mrh30pJd2gjnyOlZdioFW5B39REIooRZbIcguC7E6uICmOawnp1An2smpcN9nzvFJBKC5KeTkTmZ2kByAgu+f8Xg04kQnplJNu9XhJXsEFHmNBqVqxImf3laKifJVa0gXmnb0MHSpaOyzhNnmueB06dIiuXbuSlpZGamoqPj4+xMbG4uLigr+/v6h4FWPBPs5c0Urc2pF45d4HPwTN7qtaOTUI9ebgoYqUUm7gKmWSdPkwmtbIbodPCkJ+kD2DIXsZwJLc4FTi/SfGFzQNTaSTFwQ78t+e8wDiuZ50n4pXYgYts3u8UjVHPLxL3PU4e9C8gi8791SnonwVnaTieHUPaVltcHF4qH4WoQDkuS/15Zdf5sknnyQ+Ph5nZ2f27NnD5cuXqV+/Ph999FFBxCjYiRJuTsTgadnWpcfm+8T4HFUSO6+g1L8twQZA+YxjRMSlWzEiQbA+Lx9fkjXzxPaS0g0iE2znntDEDC9BsHmKLGFwvlV5CpDiuZ587wacq/FplJTMCTiuab4EedtfYo2bGpX1Za9W49Y2xzh4OcF6AQl3yHPFKywsjFdeeQVZllEUhczMTIKDg5k1axZvvvlmQcQo2Ak/dwditVsVLx8SSEw35Nv5i9IcLzAnEjjrWN2y3UA+w/7LYj0voXgL9HQiSjPPSQiU4olOtG7FyzzHq4gVPoJQ1HmUtLwtISUQnXTvJD3J8ddxkcyfR2q+lLLDjIY3uTnqSAlsZNluKJ9m38Ub9/mGUNjyXPHS6/XIsvlr/v7+hIeHA+Dp6UlEhFiLqDjzdXXMUfHyI5HYlPzNSJbzAci+254lScIjtA5pmjl7Uj35LAcux1s5KkGwLn93J65rXgC4SJkkJyVYNZ7badh9R7sgFAsOHv6YNPPN6i8lcP0+FS8Sbk2LuKr5UdLTfiteANUqlOFU9nqINaSLHD6fv9M+hEeT54pX3bp1+ffffwFo3bo1kydPZtWqVbz00kvUqFHjAd8WijIHnUy6w63sOX5SIjH5WPEqaj1eAHXKlOCwWh6A0lIsly+etXJEgmBdJdwdcwxZ1pILIklP7plTyAuCYE/8PFy5kV2O3G+oocGk4pR+a6HhSM2XIC/rZVHND43L+rBPNa+rq0gaush9ZBjEOqG2Is8Vr/fff5+SJc1duDNmzMDb25tRo0YRExPDokWL8j3AvDh48CCPPfYYXl5e+Pr6Mnz4cFJSUnIcEx4ezuOPP25JBvLaa69hNBqtFHHRo7reGlftJyUSm5J1n6PzTipCWQ0BGpTxZv9t63l53ThEUkb+Dc8UBHvj7+7Ide3WsgpK6nUrRpOTPa8dKAjFSYCHI9HZPed+JHI98e7L20QlZlAyxxpe9j3UEKB+qA/7tKqW7XraCY5cSbRiRMLt8lzxatCgAW3btgXMQw3Xr19PUlISBw4coHbt2vkeYG5FRkbSoUMHKlSowN69e1m/fj3Hjx9n4MCBlmNMJhOPP/44WVlZ7Nq1i6+++orly5czefJkq8Vd1EhuAZb3fiQSm1xwi58WhWnu1YM8OUJly3Y96Qxh4QnWC0gQrMzf3ZGY24YsuxpukJ5l3dZaMcdLEOyLeciyuQFHJ6lkJt29AedaYkaONbxuKP54OusLJcaC4umsJ96vgWW7kXxKzPOyIfa3Qtw9/Pbbb+j1ehYsWEDlypVp2LAhCxcuZM2aNZw7dw6Av/76ixMnTrBy5Urq1KlDly5deO+991iwYMEd65EJD8fN3ZtMzVxo+UlJ+TrHy5zW+fZt+694OekVMgLrWbbry2fYL+Z5CcVYCXdHyxwvMM/PiCnABpwHuX2Ic1EocwShOPD/Tzki3WPIsnnx5FuVEqN7qSLRq12pQgUuqIEA1JbOc/D8tTuOSc4w8NfxKP48eo3UTDHyq7DkueIVHR1Nv379CAoKQqfToShKjpe1ZGZm4uDgYEn8AeDsbO4u3rFjBwC7d++mZs2aBATc6pXp1KkTSUlJHD9+/J7nTUpKyvES7s3vtvkZ5qGG1ntgshdVy4VyRi0FQHXpEscvRj7gG4K9EeVI7rk56khUbq3BU0JKuG8q6MJwe4+X/T+SCfZIlCF54+/hyHVuDVnWpV2/6/I2V/9T8dJ7ly6U+Ara7fO8HCQTpoh/MZhUy+fHriYy9KOVJHwznJTvhjNo1gqOXRXDEQtDniteAwcO5ODBg7zzzjv8+OOPrF27NsfLWtq1a0dUVBQffvghWVlZxMfHM3HiRACuXTPX9KOionJUugDLdlTU3VtDZs6ciaenp+UVHGy/K5oXBj+3W5kNfUgmLjn/UkEX1bTO9UJureelk1SMVw5iyuf1zwTrEuVI7kmShOrmb9n2lxK4bs0eryJY5gj2R5QheRPg4ZSjx8uXeOLS7hzZdHuPV4zmib+35x3H2KOGZXzYq96a51XHdJzjkebKemK6gWnL1rHI8CY9ddt4TredL41vMfWrX0nLEj1fBS3PFa8dO3awatUqRo0aRffu3enWrVuOV36bOHEikiTd93Xq1CmqV6/OV199xccff4yLiwuBgYGULVuWgICAHL1geTVp0iQSExMtL5Ey//5K3DY/Q5Y0DMkFODG+iDQ91w/15sBtCTaqm05yKkq0ZhYlohzJm9vnipbAukMN4b8Lt1srCqE4E2VI3vi6OhCDl2U7gPi7ppSPjk8mAPPwfnNGQ/tOrHGTr5sj133qW7YbyafYe8Fcwfxsy1lezvwCT+lWwhEPKY2h6ctYvutSYYda7Ojy+oXg4GC0Qszr/corr+RIkHE35cqVA6BPnz706dOH6OhoXF1d/8/efYdHUXUBHP7NbEvvnTQIJPTQe68BBUSlSkeUJoIoYANEAcECIigKAvqJWOkiRaRLh9A7ARISkpCQXnd3vj8WFiI9bEi77/MsYWennE2ykzlz7z0XSZL4/PPPza97eXmxb9++PNvGxsaaX7sXnU6HTqd7wndRerjZ6YhT7iwFbdnES5JK3t1nd3sdMQ7V4WbjYD35DHsvJlLFp2TceRPEeeRx2Tq6kh2nQSfl4i4lsbcwuxqWvFOOUAyJc8jjUatksqw84WZdHg8pidjULCrjkGe93BtXkW9eV5SkxAsgMKgiUeFu+ErXqS2f5dsTEXSs5s2l3ctpqD4JwHXFAStysJOyaK86QM9du3i1WRAqWdxhKiiP3RQ0e/ZsJkyYwKVLlwognLu5u7tTsWLFBz60Wm2ebTw9PbGzs+OXX37BysqKtm3bAtCwYUOOHTtGXNztZGDTpk04ODhQuXLlp/J+Sjp3ex3X75iDR50ZV4CJeompDYN32apcu1mBqaF8ku3HLhRyRIJQeNztrcxjRQu7uIaJcvNfqURUUxWEUsH+dsu5h5RE/L0mUU69av5vtOKGj2PxnsPrTk0ruLPZUBMAKykXj6sbGLV0H2/JS83rvJc7iNn6F8zPm2duYtf563ftS7CcR7pydXZ2xsXFBRcXF3r27MnWrVsJCgrC3t7evPzWozDNnTuXQ4cOcfbsWebNm8fIkSOZPn06Tk5OALRr147KlSvTt29fjhw5woYNG3jvvfcYMWKEuJNkIaauhk7m587GZJIzLTMvlWmMV8nUrooX6w11AdBJuThFbS70ggKCUFg87hif4SqlkpCSXmixiAYvQSietA6eGBXTVcO9JlFOycrFOSfW/DxacSlRLV4tK7qzUd3S/LyvahO1rv1KedlUwOuAMRifBt056d6BXMVUHO951Q6WH7xcKPGWFo/U1XD27NkFHIZl7Nu3j0mTJpGWlkbFihX55ptv6Nu3r/l1lUrF2rVrGTZsGA0bNsTW1pb+/fszZcqUQoy6ZHG105qLa4CpIll8ajZONtoHbJU/SgnKwpoFu/OqqhED2AjAM/Je1h6JYVCTsoUcmSA8ff+9gZOTHHv/lZ+CW6ca0wTKhRqKIAiPyNXBjgTscScFdymJ2P+0eF1JyMBfut0DKgoPvJ1KTouXTq2ifI1mnDgUQBX5MtXlCKrLEebXP5cHMq91BVa62rB1fQ3aqg7iKSWRdGobWbk1sdIUXqXykuyREq/+/fsXdBwW8cMPPzx0nYCAANatW/cUoimddGoVmdo7S0EnE5+WTQVP+yfet6m6WMm8/2ylUeFcsSnXTjvjJd2gpXyYl3YfYECjQGTR11ooZdztdUTdkXjJaYWXeD3NMc2CIFiOp4OOeMUZdynFVKQnJSPP61cSM/CXbydeGTZ+6NQlK9kY2rI8bx/sxxI+zLN8sb49HZ7piLOtlmeqezN1XUPaqg4C0Ma4iy2ne9OhmndhhFziPfIgGaPRyIwZM2jcuDF169ZlwoQJZGZarlS4UHIotrdLQbtJyQU4PqNkJSTd6wbys8HULUAtGWmavIYdoq+1UAp5/KfFS5MVVySmWFAoaWcdQSi53B2siL15HtFKBjJT8v49vZyQgb90+6aO7Fryeph4O1rTuWtPxuYOJUWxQa/ILNW35njVcfRpEACAh70VKf5tyFRMPZM6qPbx55ErhRl2ifbIidfUqVN55513sLOzo0yZMnzxxReMGDGiIGMTiinJ/nbiZclS0CX9xnPDIFf2OHUy97Xuq9rEtxsOijvuQqnjbq8j7o5S0O4kkZBeOAU2TMmW+AwKQnHjYa8jTrk9ibKSkne+1iuJ6QTcTLziFUe83NyeanxPy/O1fOnx8njeqbCaYQFrkDvPZmb32kh39JtuVzOIzcZagGlcbeaZLaRlizm9CsIjJ14//PADX331FRs2bGDlypWsWbOGpUuXYjQaH76xUKrYOziToZiKlXhKN4hNsVyRiLwXQCXr3rMkSXRpVocVhiYAOEoZNIv9gZXhVx+ypSCULK62Oq7nSbyS7zkHz9MmxngJQvHh8Z8bOJqMa+gNt69Zr8Zcw1NKAuCS4om/q81TjvDpqVfWhbl96rJgUBN61fO/q1x8WBUv/jQ2uv1c2cXfJwt3bG1J9ciJ15UrV+jYsaP5eZs2bZAkiejo6AIJTCi+yjjbEKWY7hz5SteJTEizyH5Lwz3nF2v78odDX7IVDQCDVX/x66pVXEsWFQ6F0kMlS+RauZufe0g3iE8rpBYvRbR4CUJxFOBqyyXj7TlayytXuHjdVCHVYFSQrx01v3bSGEBlb4e79lFaONtq0Qe1JlUxVXVsrzrA+iOXCjeoEuqREy+9Xo+VVd5qLxqNhtxcy5QKF0qOAFcbLium+TN0Ui5p1yMttm/pAc9KAo1KZmDHpnyl7wyYxnpNNc5h7PdbyMgRzf5C6aE8yhw8T5mYx0sQig8XWy2xtiHm51XkSxy/mgzAxfg0go3nza8dV8pStYzjXfsoTcJCA9lorA2Ag5SBcn4zyRniGt/SHqmqIZgqOw0YMCDPfFdZWVkMHToUW1tb87Lly5dbNkKh2AlwseGUcvuiSZ10CUVR8vQnzo/SMtYprKoX66sMI/z0YWrIFyknX+P165MYtsiKOf2b4GitKewQBaHA6Ry9MNyQUEkKPlICx29kPHyjAiAKaghC8WXrW4WsixqspFyqSRdZEpnE87V82XMxgTryWfN6MTYVcbcv3fO5tq3iydiVjXmBnQB0lHaz4cQ1utf1K+TISpZHbvHq378/Hh4eODo6mh99+vTBx8cnzzJB8He14bJyu8CGpyGG62k5Ftl3ni4/JXiwxQfP1WCq1VvEK6auD/XkM4yNHsOQeWs5EZ1cyNEJQsELcHfkkmLqJlRBusrZmKTCDYibSVjJPe0IQolTzc+Fo0o5AMrKsZw9FY6iKGw7GUUT+TgA8YoD7uVqFGKURYODlQZNhZYkKabGlLbyQdYfvlDIUZU8j9zitXjx4oKMQyhBvB2tiZTLmJ9XlK5wMiaF5vbuD9jq4UpHe5eJo42G6S93ZtTXyXxjnIKDlEF1OYL5qSOZNG8w3o16MqxFeZxtLT8xtSAUBRW9HTil+BNEDDopl/SYM0D9px6HoihijJcgFFMtK3qwanMt6slnAKiZuo3fDzbH/uKf2GhM3Zf/MdSiVWUxZxVAxxoB/HW2Hr3UW7CRsvG9vJxjUTWp5isaVizlkVu8BOFRqWQJg2eo+XmofIGjkUkW2XdpugAq72HHuy/35mX1VK4qpkmpXaQ0vtR8Qeu9A3lj5lwmrTzG6WsphRypIFheJW97Thv9zc+dU04XgfEGYoSXIBQnlb0dOGLfFKNi+uQOUq/nqz82MEr1h3mdP+XmtAh5shvDJUXrip6s1oSZnw9Xr2bWX0dKzVCPp0EkXkKBKOfvR4TRNM6rinSZo1fin3ifpfFzX7WMI5+N6MlYpzmsM9QzL68vn2ax9AG9DvXi5y/fpddny5m5/jQHLiWSrTcUYsSCYBnlPew4IZc3P28gn2T3xac/obiYx0sQii9Jkghr2oi1xgYAuEkpbNGNpaxsKpW+3xhMSL322FuJsdMA1loVzZq3ZYOhDgBe0g1qX14gprWxIJF4CQWipr8Th5RgwFTZ0HBxB5k5T54Q5LnbLJWOX18/Fxt+eK0jJxt/yfDcMVww3u4SUVGOZLLmB5alDqTDvz3YvXAMIybPoN+8DUz98yR/HIziWFSyqIgoFDs6tQrZv6F5aoVmqmNsORVXqDEpSGKQlyAUM73q+bPU8RViFJc8y5MUWz7RjWJkq+BCiqxo6tcwgO90fchRVAC8qlrLqhW/iPHlFvLIY7wE4XE0q+DO+8ZavKDaAUArZS+bT8fybHWfJ9hr6b3rrFXLvBlWkct132LOpmcxHvuDl1R/56nKVE2+RDX5kulJPJyP9eG04sffRl/mU4Zk23LIzgG4ubrg62SNj5M13k7WuNlpcbXV4WKrRasuHcmsUDw0CPFj75WKNFMdw1e6TszxLaR3roKt7in+6VJEVUNBKM6sNCo+GRjG6O8MdEv9gSpSBOcUX3607ssHAzvhaCNau+5kq1Mz6LkOfLlsJ2M1v6OWjMzmM4Z9a8OY/j2oV9bl4TsR7kskXkKBcLbVkubXnMyYr7GWcnhOtYtXth2jY1VvZDn/lzF5u/yUvsuhAFdbPutZh8h2lfll/wA+3b+LBlk7aCMfouqtpOum8nI05YkG1V7TghwgFpKu2XJVceOq4sYlxYWDiiOJ2HNdcSRL64TR2hXJ1g21jTM21lbY6dTYW6mx0918WKmx16mx0amxUstYaVQ3HzI6temrlUaFTi0/8RQCQunWsbo3n25oSjPVMQD6GVeyYEdHRrcpnDvUorS8IBRP/q42fD/mef463pB18en4udiwpLo3NlpxGXwvYVW9+Cd0OFuOnaOl6ghOUjoLlUm8v/Aymxv1Y3jLCiJhzSfxGycUmK4NKvLH703po96MnZRFu9iF/G9PCP0bBeZrf6VxjNf9+LnY8Gb7EAxtgwmPfI61J+OYfuoUzgkHqSWdo5Z8lsrSZbTS3d07naR0nKR0qnD53jvPvPkAMhQdaViTqliThjVpN7+mYkOcoiMLLdloyFK0ZN/6P9qbzzUYVDqMKisUlRWSSoOiUoOsBlkDKg2otEgqNchaJLUalUqLSq1CrZLRyBJqlcz7z1YWc5eVUmWcrMks/yzXLi3DS7pBG9Vhlm/9mf3lR1I38OncdVVQKM2t7YJQUlhpVHSt6VvYYRQbHz5fg1cSJmET8w715dPYStl8rvmanXu289q+7pSr1YYuNctQw89J3GR9DCLxEgpMx2re9F7fgxcyd2At5TBAvZEx6yqgVQ+lZ12/fH1Q79xCEZ9zVLJE7QAXage4QIeKJGc+S3hkEv9cvsFXV2+QEXsOu5RzVCCKQPkaZaQEynAdLykRzT2Ssv+ykbKxIRsPKenJAjXcfDzKqoqEHjW5qHg/dyD6Dh882bGFYu31sGrMnNuTzzVfAzBTNY/Xv9Ny+ple9K7nj+oJWtAfl4IkhngJglAq6NQqvh7cnNE/fk5UxHTz0JEmqhM0YRJnDi5g1b5GfGpVF5dytahbzo1K3g4Ee9qLm6UPIBIvocBoVDIjn2/FJ9/3YKLmfwDMUs9j/urL9D84mP4tqtAixOORL5zEPeeHc7TW0DzYnebBt0rj1icr18D5uDQuJ2RwLCmT9UmZRN9IIzMxGjktGk1WIs6k4EoKLlIqrlIyrqRiJ2ViRyZ2Uib2ZGBHJiqp4H8KKklBRS46cpFRUKvEuLPSrJK3A26N+rF2z2GeVe3BTspigfwxP6/dS6+t3WlStw6tKnpQ2dvhibox34+iiKqGgiCUTjZaNV/1b8j8bbMY/s8yJsg/4C+bqlSHyFGMk38Fw68knbXl0OkKHFD8+NHoR6JNELKLP04u7pRxsqaMszVudqax5M42WlxstThaa57qjbOiotgkXlOnTuXPP/8kPDwcrVZLUlLSXevs37+fCRMmcPDgQSRJol69esycOZPQ0NtzSh09epQRI0awf/9+3N3dee211xg3btxTfCelS/Ngd3Y3HsGP/0bTR70ZgKHqtXS/tpXfljZnkbYp7hXqUa+cO1XLmO6UWGlUD9hj6R7jlR9WGhVVyzhStcy9J0A0GhWSMnNJTM8mIS2HhPQcItNzSMvSk5adS1qWntRsPamZueRmpqNkJ6NkpyJlp4EhG9mQhcqQjRU56MjFSsox/193x/81GFBjQC3p0WBAgx41hpvL9Wikm6/fXKZBTxK2aFTi51zajQuryIjY99FGTKSd6iCypNBb/Q89M7ewf1sI6/4JZaamCpJnVYJ8vSnrZoOvsw1+LqYiMpYax2Ea4yV+HwVBKD3UKpmRrSpwvuobfLopDM3JP+ip+oe6dxT3cpLSaaUKpxXhpgV6IA5SYq2JVtyIVly5rjgSgT2Jij03MD1ytc4oWjvQ2oHODpWVHVZWNtho1djpVNjq1OYx41q1aRy56at8z2VWGhmNSkYlS6hlGbVKQi1LqGTpjuVSoXaNLDaJV05ODt26daNhw4Z89913d72elpZGWFgYnTt35quvvkKv1zNp0iTat29PZGQkGo2GlJQU2rVrR5s2bZg/fz7Hjh1j0KBBODk58corrxTCuyodxoVV5GPlI6b968Gb6l/RSgZcpDReVf/Jq8Y/SThtz6GTwWw2BjKXsmQ6lkPjGoifmyP+rrb4OVvj5WhFQlpOYb+VEkmWJVxsTXegynvkbx9Go0KOwUhWroFsvelrVq6RbP3tr3qDQq7BiN5o+ppuUNAbjOQaTV/1BoVco+nrreXlDEa0osWr1FOrZOb2b8TkVZ+z7+C3jFb/gZ2UhSwp1JdOU18+DYDxmsTlGA8uK15EKu7sUdyJVlxJUzujt3ZFsnNHZ+eGk501zrZaHKzU2FtpcLBW42ClMf/f3kqDg5UaW636ZouXIAhC6Vbew445L9Xj0vUqrDg8mM8OHSIk5V8ayCepL5/CRUq7axsHKRMHKZKKRN57pwqQffORalqUo6hIx5p0rEhXrMxjyXMUtXkseQ5qbihaclCTjebm66avuajRo8KATC5qDMjolbzPjZIKo6wGSQ2yCkVWo0gaJFmFolKjyKb/I6lAVpGpsmdWv6aUcbJ+4u9jsUm8PvjANM5jyZIl93z99OnTJCYmMmXKFPz8/ACYNGkS1atX5/Lly5QvX56lS5eSk5PDokWL0Gq1VKlShfDwcD7//HOReBUgWZZ455nKrA+YTJ/VTemVuZSO8l50kmluKVcplbaqg7RVHTRtkAGGdImYK65cNnoSpbhzChdiFBdayTF37FlcDhUVsixhJase0lopCPmnUclMfb4G/1afwit/diQ0bg0vqrYRdMc5QZYUykqxlCX27h3c/ONuvC6RhC03FHtSsSFFsSEFG2IVG85jQ4piSwo2pCo2pEo2pBht0OhMAxTFGC9BEEq7QDdbxrQNRmlTgcsJndhzMYEPLyYQH30R68RTlFciKSdF4yMlUEa6jreUYL7eexRayYCWNJxJe3qXecabXw1A7t0vf5jbB72hsUUOVWwSr4cJCQnB1dWV7777jnfeeQeDwcB3331HpUqVCAwMBGD37t00a9YMrVZr3q59+/bMmDGDGzdu4OzsfNd+s7Ozyc7ONj9PTjZNIJeSklKwb6gEauRvQ61XO7LmSA367j+JX/xWGsknqC+fwkHK/M/aCg7EU414qt252AgpNz+/OWSJn0Mpd+vnrxTxkpfiPGI5VT20fD2gKeGR1Vh4fBDnzxzDP/UwlaRLVJIjKS9dxVq6x19OMwU1qbiTivsD1rq5KgApN390aUYDWelp4udWwhSH84g4hwhFkYsWOlZ0omNFJyCIHH1rLiWYxpQfTs7iz6QsYpPSSU+Og4wEVFlJWOUm4yyl4kwqjlIGNmRiJ2VjTRa2UjY2ZOVZZkXuUxlf/iAZuXrSU1NJ0dy7SthjnUOUYmbx4sWKo6PjPV87duyYEhQUpMiyrMiyrISEhCiXLl0yv962bVvllVdeybPNiRMnFEA5efLkPfc5adKkW7WExUM8xKOIPiIjIy12jikI4jwiHuJR9B9F+TwiziHiIR5F//Eo5xBJUQrvFs+ECROYMWPGA9c5deoUFStWND9fsmQJo0ePvqu4RmZmJi1atKBixYqMHDkSg8HAp59+yunTp9m/fz/W1ta0a9eOsmXL8s0335i3O3nyJFWqVOHkyZNUqlTpruP/9y6T0WgkMTERV1fXhw7OS0lJwc/Pj8jISBwcHB64blEi4n66RNz5pygKqamp+Pj4IMtFdyxYfs8jReF7nB/FNW4ovrGLuPOvOJxHxLWIiLugibjz73HOIYXa1XDs2LEMGDDggeuUK1fukfb1008/cenSJXbv3m1+0z/99BPOzs6sWrWKnj174uXlRWxs3r7/t557eXndc786nQ6dTpdnmZOT0yPFdIuDg0Ox+iW+RcT9dIm488fR0bHQjv2onvQ8Utjf4/wqrnFD8Y1dxJ0/Rf08Iq5FRNxPi4g7fx71HFKoiZe7uzvu7g/tZf9IMjIykGU5z52fW8+NRtOouYYNG/Luu++Sm5uLRmOa3G3Tpk2EhITcc3yXIAiCIAiCIAiCJRTNNvV7uHLlCuHh4Vy5cgWDwUB4eDjh4eGkpZnKV7Zt25YbN24wYsQITp06xYkTJxg4cCBqtZqWLVsC0Lt3b7RaLYMHD+bEiRP88ssvfPHFF7zxxhuF+dYEQRAEQRAEQSjhik1Vw4kTJ/L999+bn9esWROALVu2mMd2rVmzhg8++ICGDRsiyzI1a9Zk/fr1eHt7A6ZmwI0bNzJixAhq166Nm5sbEydOLLBS8jqdjkmTJt3VPaCoE3E/XSJu4X6K6/e4uMYNxTd2EbdwP8X1eyzifrpE3E9HoRbXEARBEARBEARBKA2KTVdDQRAEQRAEQRCE4kokXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMJF6CIAiCIAiCIAgFTCRegiAIgiAIgiAIBUwkXoIgCIIgCIIgCAVMbekdZmdno9PpLL3bIsNoNBIdHY29vT2SJBV2OIJQqimKQmpqKj4+Pshy8bmPJM4jglB0FMfziDiHCELR8TjnkCdOvP766y9+/vlnduzYQWRkJEajEVtbW2rWrEm7du0YOHAgPj4+T3qYIiM6Oho/P7/CDkMQhDtERkbi6+tb2GE8MnEeEYSipzidR8Q5RBCKnkc5h0iKoij52fmKFSsYP348qampdOzYkXr16uHj44O1tTWJiYkcP36cHTt2sHv3bgYMGMCHH36Iu7t7vt5IUZKcnIyTkxORkZE4ODgUdjiCUKqlpKTg5+dHUlISjo6OhR3OIxPnEUEoOorjeUScQwSh6Hicc0i+W7xmzpzJrFmz6NChwz2b1bp37w7A1atX+fLLL/nxxx8ZM2ZMfg9XZNxq0ndwcBAnO0EoIopbVxtxHhGEoqc4nUfEOUQQip5HOYfkO/HavXv3I61XpkwZPv744/weRhAEQRAEQRAEodgrHqNIBUEQBEEQBEEQijGLVDV844037rlckiSsrKwoX748Xbp0wcXFxRKHEwRBEARBEARBKFYskngdPnyYQ4cOYTAYCAkJAeDs2bOoVCoqVqzIV199xdixY9m5cyeVK1e2xCEFQRAEQRCE+zAajeTk5BR2GIJQImi1WotMN2GRxOtWa9bixYvNgzyTk5N5+eWXadKkCUOGDKF3796MGTOGDRs2WOKQgiAIgiAIwj3k5OQQERGB0Wgs7FAEoUSQZZmyZcui1WqfaD8WSbw++eQTNm3alKeyjqOjI5MnT6Zdu3a8/vrrTJw4kXbt2lnicIIgCIIgCMI9KIpCTEwMKpUKPz+/YjMptCAUVbcmLI+JicHf3/+JKqBaJPFKTk4mLi7urm6E8fHxpKSkAODk5CSavAVBEARBEAqQXq8nIyMDHx8fbGxsCjscQSgR3N3diY6ORq/Xo9Fo8r0fi9wG6dKlC4MGDWLFihVERUURFRXFihUrGDx4MM899xwA+/btIzg42BKHEwRBEARBEO7BYDAAPHGXKEEQbrv1ebr1+covi7R4ffPNN4wZM4aePXui1+tNO1ar6d+/P7NmzQKgYsWKLFy40BKHEwRBEARBEB6gOE0ILQhFnaU+TxZJvOzs7FiwYAGzZs3i4sWLAJQrVw47OzvzOjVq1LDEoQRBEARBEARBEIodi464vHbtGjExMVSoUAE7OzsURbHk7gVBEARBEARByAdJkli5cuVTP25gYCCzZ882P7927Rpt27bF1tYWJyen+y4riSySeCUkJNC6dWuCg4Pp2LEjMTExAAwePJixY8da4hCCIAiCIAhCCTVgwAAkSbrrcf78+cIOjRYtWpjj0el0lClThk6dOrF8+fLCDu2xxMTE0KFDh3xvv23bNlq1aoWLiws2NjZUqFCB/v37m4vnLVmy5J5J0/79+3nllVfMz2fNmkVMTAzh4eGcPXv2vstKIoskXmPGjEGj0XDlypU8FXR69OjB+vXrLXEIQRAEoZSIS8li+rpTTPjjKOdiUws7HEEQnpKwsDBiYmLyPMqWLXvXeoVRJXvIkCHExMRw4cIF/vjjDypXrkzPnj3zJBRFnZeXFzqdLl/bnjx5krCwMOrUqcP27ds5duwYX375JVqt9qEFJ9zd3fPkBxcuXKB27dpUqFABDw+P+y4riSySeG3cuJEZM2bg6+ubZ3mFChW4fPmyJQ4hCIIglAIGo8Kob/+ky54ejD7amSnzFhKdlFnYYQlCsWQ0KiSkZRfqw2h89GEnOp0OLy+vPA+VSkWLFi0YOXIko0ePxs3Njfbt2wOmFph69eqh0+nw9vZmwoQJ5iJvYGqpeu211xg9ejTOzs54enqyYMEC0tPTGThwIPb29pQvX56//vrrobHZ2Njg5eWFr68vDRo0YMaMGXzzzTcsWLCAv//+G4BWrVoxcuTIPNvFx8ej1WrZvHkzYOp2N23aNAYNGoS9vT3+/v58++23ebYZP348wcHB2NjYUK5cOd5//31yc3PNr0+ePJkaNWqwaNEi/P39sbOzY/jw4RgMBmbOnImXlxceHh5MnTo1z37/29UwKiqKXr164eLigq2tLXXq1GHv3r33fP8bN27Ey8uLmTNnUrVqVYKCgggLC2PBggVYW1uzdetWBg4cSHJysrl1cPLkyeb3fKurYWBgIH/88Qc//PADkiQxYMCAey67Fe/ChQvp2rWruYVt9erVeeI6fvw4HTp0wM7ODk9PT/r27cv169fNr//+++9Uq1YNa2trXF1dadOmDenp6QBs3bqVevXqmbs3Nm7cuMDzFosU10hPT7/nXBGJiYn5zqwFQRCE0uf41WTq3VhHZY3pj99byvcs29eRse1CCjkyQSh+bmTkUPujvws1hoPvtcHV7smvBb///nuGDRvGrl27ALh69SodO3ZkwIAB/PDDD5w+fZohQ4ZgZWVlvuC/td24cePYt28fv/zyC8OGDWPFihV07dqVd955h1mzZtG3b9+7em09iv79+zN27FiWL19OmzZtePnllxk5ciSfffaZ+fr3xx9/pEyZMrRq1cq83WeffcaHH37IO++8w++//86wYcNo3rw5ISGm85y9vT1LlizBx8eHY8eOMWTIEOzt7Rk3bpx5HxcuXOCvv/5i/fr1XLhwgRdffJGLFy8SHBzMtm3b+Pfffxk0aBBt2rShfv36d8WelpZG8+bNKVOmDKtXr8bLy4tDhw5hNBrv+V69vLyIiYlh+/btNGvW7K7XGzVqxOzZs5k4cSJnzpwByFNk75b9+/fTr18/HBwc+OKLL7C2tiYnJ+euZbd88MEHzJw5k08++YQvv/ySl156icuXL+Pi4kJSUhKtWrXi5ZdfZtasWWRmZjJ+/Hi6d+/OP//8Q0xMDL169WLmzJl07dqV1NRUduzYgaIo6PV6nnvuOYYMGcKyZcvIyclh3759BV4N1CKJV9OmTfnhhx/48MMPAVOGajQamTlzJi1btrTEIQRBEIRS4EhUEjXlc+bn1eUIZpw4DSLxEoQSb+3atXku1jt06MBvv/0GmHpRzZw50/zau+++i5+fH3PnzkWSJCpWrEh0dDTjx49n4sSJyLKpU1doaCjvvfceAG+//TYff/wxbm5uDBkyBICJEyfy9ddfc/ToURo0aPBY8cqyTHBwMJcuXQLg+eefZ+TIkaxatYru3bsDpnFPt8av3dKxY0eGDx8OmFq3Zs2axZYtW8yJ1614wdRC9Oabb/Lzzz/nSbyMRiOLFi3C3t6eypUr07JlS86cOcO6deuQZZmQkBBmzJjBli1b7pl4/fTTT8THx7N//35cXFwAKF++/H3fa7du3diwYQPNmzfHy8uLBg0a0Lp1a3PCpNVqcXR0RJIkvLy87rsfd3d3dDod1tbWeda71zIwjf3r1asXANOmTWPOnDns27ePsLAw5s6dS82aNZk2bZp5/UWLFuHn58fZs2dJS0tDr9fz/PPPExAQAEC1atUAU+NQcnIyzz77LEFBQQBUqlTpvnFbikUSr5kzZ9K6dWsOHDhATk4O48aN48SJEyQmJprvTAiCIAjCw4RfvsGz8oU8y2yvHyUr91msNKpCikoQhKehZcuWfP311+bntra25v/Xrl07z7qnTp2iYcOGeRKaxo0bk5aWRlRUFP7+/gBUr17d/LpKpcLV1dV88Q3g6ekJQFxcXL5iVhTFHIOVlRV9+/Zl0aJFdO/enUOHDnH8+PG7usfdGdOtROXO4//yyy/MmTOHCxcumJMHBweHPPsIDAzE3t4+z/tQqVTmhPPWsvu9r/DwcGrWrGlOuh5GpVKxePFiPvroI/755x/27t3LtGnTmDFjBvv27cPb2/uR9vO47vxe2dra4uDgYH5PR44cYcuWLfdsWbtw4QLt2rWjdevWVKtWjfbt29OuXTtefPFFnJ2dcXFxYcCAAbRv3562bdvSpk0bunfvXmDv4xaLjPGqWrUqZ8+epUmTJnTp0oX09HSef/55Dh8+bM4iBUEQBOFh4q6cxkVKy7OsEhGcuSaKbAhCSWdra0v58uXNjzsvgu9Mwh6HRqPJ81ySpDzLbiVN9+ti9yAGg4Fz587lKQDy8ssvs2nTJqKioli8eDGtWrUyt7Y8KKZbx9+9ezcvvfQSHTt2ZO3atRw+fJh33333roIiD3tf/93vf93Zne9xlClThr59+zJ37lxOnDhBVlYW8+fPz9e+HsWD3lNaWhqdOnUiPDw8z+PcuXM0a9YMlUrFpk2b+Ouvv6hcuTJffvklISEhREREALB48WJ2795No0aN+OWXXwgODmbPnj0F9l7AQi1eAI6Ojrz77ruW2p0gCIJQyiRn5OKSdAy0eZdXlS9xPDqZUD+nQolLEIorZxstB99rU+gxFIRKlSrxxx9/5Glx2rVrF/b29ncVeyso33//PTdu3OCFF14wL6tWrRp16tRhwYIF/PTTT8ydO/ex9vnvv/8SEBCQ55q6IAo+VK9enYULF5KYmPjIrV7/5ezsjLe3t7lYxaNUOLSkWrVq8ccffxAYGIhafe+URpIkGjduTOPGjZk4cSIBAQGsWLGCN954A4CaNWtSs2ZN3n77bRo2bMhPP/302F1OH0e+E6+jR48+8rp3NhMKgiAIwr2YxnfdPWdPVTmCOVeTCyEiQSjeZFmySGGLomj48OHMnj2b1157jZEjR3LmzBkmTZrEG2+8kae7naVkZGRw7do19Ho9UVFRrFixglmzZjFs2LC76hncKrJha2tL165dH+s4FSpU4MqVK/z888/UrVuXP//8kxUrVljyrQDQq1cvpk2bxnPPPcf06dPx9vbm8OHD+Pj40LBhw7vW/+abbwgPD6dr164EBQWRlZXFDz/8wIkTJ/jyyy8BU/fHtLQ0Nm/eTGhoKDY2No9dsORxjBgxggULFtCrVy/GjRuHi4sL58+f5+eff2bhwoUcOHCAzZs3065dOzw8PNi7dy/x8fFUqlSJiIgIvv32Wzp37oyPjw9nzpzh3Llz9OvXr8DihSdIvGrUqIEkSXnuNICpryuQZ9nTzH4FQRCE4ik8Mommd4zvOmkMoLJ8GW8pkaioK4C4iScIgkmZMmVYt24db731FqGhobi4uDB48OA8hSksacGCBSxYsACtVourqyu1a9fml19+uWdi1atXL0aPHk2vXr2wsrJ6rON07tyZMWPGMHLkSLKzs3nmmWd4//3381RqtAStVsvGjRsZO3YsHTt2RK/XU7lyZebNm3fP9evVq8fOnTsZOnQo0dHR2NnZUaVKFVauXEnz5s0BU2XDoUOH0qNHDxISEpg0aZLF476Tj48Pu3btYvz48bRr147s7GwCAgIICwtDlmUcHBzYvn07s2fPJiUlhYCAAD777DM6dOhAbGwsp0+f5vvvvychIQFvb29GjBjBq6++WmDxAkjKrUzpMd3Z7Hn48GHefPNN3nrrLXOWvHv3bj777DNmzpzJc889Z5Fgi4KUlBQcHR1JTk6+a6CjIAhPV3H9PBbXuAvaK4t38eWlzugkPReNXvxtrM0r6j8BGKyfwNeTx6NVW/5OtlC6FcfP44NizsrKIiIigrJlyz72Rb9gGZcuXSIoKIj9+/dTq1atwg5HsIAHfa4e5xyS7xavOwcKduvWjTlz5tCxY0fzsurVq+Pn58f7779fohIvQRAEwfIURSEj8ig6yTT5abhSnuPGQPPrIcpFzsWlUsXHsZAiFARBeLDc3FwSEhJ47733aNCggUi6hLtY5NbhsWPH8lR0uaVs2bKcPHnSEocQBEEQSrCoG5mUyz5lfh5uDOKEEmh+Xk2O4MTVlEKITBAE4dHs2rULb29v9u/fX6CV/h7GqChk6w0YjPnq1CYUIIskXpUqVWL69Ol5Sl3m5OQwffr0pzIZmSAIglC8HY5MosYd47sidBUJqVyTNMXUpaOqdIljosCGIAiPwZi/0TT51qJFCxRF4cyZM3nmCnuakjNziYi5TmJsFJExscSnZhVKHMK9WaSc/Pz58+nUqRO+vr7mCoZHjx5FkiTWrFljiUMIxciKw1FsPhlDzQA3XqrvX+omPc3KNXA+Lg0vRyvcSmg1KUGwtIOXEhkknQUgW9Fg7RdKFV9nTp4LoJ50Bj85nojIKKBq4QYqCEKRl6M3cjUxBTk3A7T2lHGxRa0q+eND07L0JCfGUY44btW4u5aSxQ3ZB2fbginrLzweiyRe9erV4+LFiyxdupTTp08D0KNHD3r37p3vCe+E4um3A5FErpjMJ+rVbDpdmxmJnzCpc+Hc9SkMKw9f5fdVy6mrP8R5pQzOdbrzbqcq6NSlK/kUhMd1IeIiAXIcAEeUcoQGelKtjCPHjWWpJ58BwCruEFm5bUvdzRxBEB7P1cQ0yuRGopX0ZObcICY5ED+Xkn09qigKsUmpBBLPHYXF8eQGF5NtcbB2QyVL99+B8FRYbAJlW1tbXnnlFUvtTiimVm7by//UfyBLCp1Vu1mzfw1p7Sthp7PYr1qRtev8dbb//iU/ar42f7I2H9rJ6JRpfNmnQam42yYI+ZGWrcc+/iBoTM8PGoOpHeBMJW8HflGCGcR6AGoppwiPTKJBOddCjFYQhKIs12BEnZuM9mahHmspB31mKopik2eqo5ImMT0HZ0MCKilv90pJAlflBjcyHEQvnCIg31eCe/bseeR1MzIyOHHiRH4PZVFJSUnUqVOHGjVqULVqVRYsWFDYIZUYadl6HBOOIN/xoW+p7GfX+euFGNXTYTAqzFmxjWnqhXmWt1YdpuP5D5i86hj5nLlBEEq88CtJ1LrZzRDgsBJCqK8TjtYaktzrmpfXl0+x92JiYYQoCEIxkZatx46845qsySIrt+TOKas3GklKScGZVAAMiswpox+5iql3gCPpJKeli+uQIiDfiVffvn1p3749v/32G+np6fdc5+TJk7zzzjsEBQVx8ODBfAdpSfb29mzfvp3w8HD27t3LtGnTSEhIKOywSoST0SlUkyPyLAuVL3AsquQPiP/zWAxhyb9gJeUCEG4sR6Zi6k/dSbUHv0Mz+HrbhQftQhBKrd0Xr1NPPm1+nuFVB2ut6YIhJKg8540+AFSXLnLk4tVCiVEQhOIhPUuPLZl5llmTTWYJTrziU7LxVK6buxjG4YSLgx2J2AOmVi8bQ+p9vweKopCSmUtMcibX07LRG4xPK/RSJ9+J18mTJ3nmmWd47733cHJyokqVKrRt25ZOnTrRpEkT3NzcqFWrFhEREWzcuJF+/fpZMu58U6lU2NjYAJCdnY2iKOIOgIUcv5pMFelSnmXBUhSnouILJ6Cn6I9/T9JTtQWADEXHoJxxvG54HYNiOgu+qv6ThE2fM2/LeXFCE4T/CD99jlD5IgAnjAFUKR9ofq1+ORf2GSsCoJEMyJG7ycjRF0aYgiAUAznZmWilvAmGjlyy9SXzb2+23kBO+g3sJFMrX7aiJkfnioe9jjT59mS+TqSRlJF71/ZGReFKQjpZiZE4p11Am3yJy7EJpGeL82xByHfipdFoGDVqFGfOnGH37t0MGTKEqlWrUqZMGVq0aME333xDdHQ0y5Yte6ySmtu3b6dTp074+PggSRIrV668a5158+YRGBiIlZUV9evXZ9++fY8Ve1JSEqGhofj6+vLWW2/h5ub2WNsL93Y8Komq/2nx0kgGsqOOlOjk9nJCOl5R67GWTNMp/G5oxoe9W9Cl+2De1w8yr/e+Zime/4yh3/TFDP1hH9P/OsXvB6NIvseJUBBKi7jULDxid5mfbzOG0iLYw/y8QVlXdiih5ufNlQPsOFfyuy8LgvD4cvQGdMaMu5Zr0ZNTxFu8lixZgpOT02NtExgYyNSPP8WL2z23ruGKl6M1kiRha2NLumIa12Ut5ZCVkXbX9di15Cycs6/iISVjJeXiIGUQqFwl9npCie6eWVgsMtq/Tp06jB49mlmzZjF//nw++ugjXnjhBVxcXB57X+np6YSGhjJv3rx7vv7LL7/wxhtvMGnSJA4dOkRoaCjt27cnLi7OvM6t8Vv/fURHRwPg5OTEkSNHiIiI4KeffiI2NjZ/b1zII+7qeVyktLuWB+acI+pG5j22KBn+OHSVF1XbzM83aFrTtrInz1T3plzYSGbrnze/9qJqOz/p3+CLCx15YfcL6FYOZugnC9l+tuS3CgrCvWw7E09LVbj5+V5VLeoEOpufO9poyPRrTrZiqrzRRnWIv09ce9phCoJQwAYMGIAkSXc9zp8//8j7SMvO280wRzFVupIlBYM+536bPVSLFi2QJImPP/74rteeeeYZJEli8uTJ+d5/fikKqAzp6G4WEklTrNDYOqO7Wfl17qfTsPOtglSmFmr/urSoX5ORo0aTlma6VsvRG8hNT8RBypusqiQFP2K5mpCCsYhNwhwYGMjs2bMLO4x8K3Jl1jp06MBHH31E165d7/n6559/zpAhQxg4cCCVK1dm/vz52NjYsGjRIvM64eHhHD9+/K6Hj49Pnn15enoSGhrKjh077htPdnY2KSkpeR7C3TJy9Ngl3i6gstNQxfz/6tLFEjvxqdGosHf/PurKpsIAZ4y+VKjRFK3a9NF6uWk5rNu+x1u5r5CqWJu300m5BMtX6aTaw/fGd/n+f99xvIR+jwRxHnmQjUcu0VIOByBFscYmqCGa/1QAbVo1kF1G0znFW0ok+vQeDEXsYkAQClJpOYeEhYURExOT51G2bNm71svJuXcSlZalx+5m4qVXZJK4XUJeNmQ/Ue8bPz8/lixZkmfZ1atX2bx5M97e3vneb34ZFQWD0Yj9zferKBAnueLhcLtyoVolUyGkItGHNnJpz1qmvzOKxYsWMnbsWADiU7PxIMm8/hWjh3nSeo1kwMMQy7WU24VKFEVBry8ZXRDv9ztU0Ipc4vUgOTk5HDx4kDZt2piXybJMmzZt2L179yPtIzY2ltRUU9WX5ORktm/fTkhIyH3Xnz59Oo6OjuaHn5/fk72JEupUTCqVpdvdDH8zNEevmH69qskXOVpCC2zsiUigScYm8/PfDc14sU7e35FXmwfxwuAJDHVdzIe5fVhvqMtZYxlztSGtZOAT+Uum/b6jyN1ZEixDnEfuLSEtG83Fv7GXTBcOGwx1aV/t7u9N20qe/G2sbX7eOGcX/14Q3Q2F0qO0nEN0Oh1eXl55HiqVihYtWjBy5EhGjx6Nm5sb7du3B2Dbtm3Uq1cPnU6Ht7c30ya9DQbTBXU6VnR58SVee28Goyd+Qo0qFfHy8mLBggWkp6czcOBA7O3tKV++PH/99ddDY3v22We5fv06u3bd7hr9/fff065dOzw8PPKse+PGDfr164ezszM2NjZ06NCBc+fO5VlnyZIl+Pv7Y2NjQ9euXe8q9HbhwgW6dOmCp6cndnZ21K1bl7///tv8+vW0bGSMyJiuGxKxx9HBEbWc99Jeq9Vi6+GPr48nL3VpS5fnurJ69Wpy9UZ+/WEhTTt2wz64CZ412jJq9BhO3lCbr08O7t5BGWcblq9aS+3atdHpdOzcufOhsYGpZeqjjz6iX79+2NnZERAQwOrVq4mPj6dLly7Y2dlRvXp1Dhw4kGe7nTt30rRpU6ytrfHz82PUqFHmIn4tWrTg8uXLjBkzxtwi+ijb3Yrnww8/pF+/fjg4OPDKK6+Qk5PDyJEj8fb2xsrKioCAAKZPn/6A34InV6wSr+vXr2MwGPD09Myz3NPTk2vXHq3ryeXLl2natCmhoaE0bdqU11577YFj0N5++22Sk5PNj8jIyCd6DyXViehkqt2ReB0whnBWMf1hCJaiOBNZMrsGrTgYSVfVTgAMisRxl/ZU8XG4a70G5Vz58bUwer3+MUmdFvFHwz/o5vo7mwy1AHCR0mge/xP/nI67a1uh+BPnkXtbd/waneTbFzHrpca0rex513r+rjZEuLU0Xww8r9rB7/svP7U4BaGwiXOIKcnRarXs2rWL+fPnc/XqVTp27EjdunU5cuQIs+fM47dlS/noC9O0LmlYY0Ti+9/W4ubizIY1y3ll2HCGDRtGt27daNSoEYcOHaJdu3b07duXjIy7x4bdSavV8tJLL7F48WLzsiVLljBo0KC71h0wYAAHDhxg9erV7N69G0VR6NixI7m5pjHde/fuZfDgwYwcOZLw8HBatmzJRx99lGcfaWlpdOzYkc2bN3P48GHCwsLo1KkTV65cITvXQEZKEjKmgiF6RSZZ7Y6LrfauWFSyxA3szM8drFRk5+QQn5aNlT6FD98azpFNPzN/4WJir0Yx8a1RXMU9zz7efWcCU6dN59SpU1SvXv2Bsd1p1qxZNG7cmMOHD/PMM8/Qt29f+vXrR58+fTh06BBBQUH069fP3BJ54cIFwsLCeOGFFzh69Ci//PILO3fuZOTIkQAsX74cX19fpkyZYm4RfZTtbvn0008JDQ3l8OHDvP/++8yZM4fVq1fz66+/cubMGZYuXUpgYOD9fwksoOTPavsf9erVIzw8/JHX1+l06HRiwrmHORqZRIebhTVuKHYkaT05YixHZfkyKkkhN/ooitK8RE1emJGj5/rxzfjKpjvv243VaVW3+n3foyRJlPewp7yHqbxrTtsQ+sy6QbO0V9FJufRSbWHU9uO0uceFp1C8ifPI3RRFYf2+Eyy62c0wXnHAtmJrbO8z2XrbulXZuqEGbVUH8ZJukHFqI8mZoThaa55i1IJQOJ74HPJNc0grhBt7dh7w6raHr3fT2rVrsbO7nSR06NCB3377DYAKFSowc+ZM82vvvvsufn5+zJ07F0mScPYJ5L2xw5k47XMmjnmFbJUtkiQTWrkC741+mVTFmsDqbzHr009wc3NjyJAhAEycOJGvv/6ao0eP0qBBgwfGN2jQIJo2bcoXX3zBwYMHSU5O5tlnn80zvuvcuXOsXr2aXbt20ahRIwCWLl2Kn58fK1eupFu3bnzxxReEhYUxbtw4AIKDg/n3339Zv369eT+hoaGEht4uLPThhx+yYsUKVq1axTPd++HL7Z/nNVzwcra95/WHBKBzRJ99nSPHjrN85WrqNWpOTnoSw3o9A0CmokVXsSxf+nhTt25dFFlLgtHevI9JY4dSpW5j/FxMXTddXFzuGdvq1avzJDsdO3bk1VdfzfN9rlu3Lt26dQNg/PjxNGzYkNjYWLy8vJg+fTovvfQSo0ePNv/M58yZQ/Pmzfn6669xcXFBpVJhb2+Pl5eX+TgP287KytR9slWrVuZulgBXrlyhQoUKNGnSBEmSCAgIuNeP3aIsnnhlZWWZ36Clubm5oVKp7iqGcesHJhSea5HncZdMfc6PGcvSp2Egx3aVoxemEuvlc89xOSGDQDfbB+2mWFl//BrPKLf/oCw3NuP9Gj4P2CIvrVqme6v6rFjRmJ7qrThIGXhG/kVkYkP8XGwKImRBKDLCI5OoGrsancZ0B3i1oTGdavrfd/3napbhnfUtaItpTsgX2czvB3sxuMnd4z8EQfiPtDhIjS7sKB6qZcuWfP311+bntra3rxlq166dZ91Tp07RsGFDJElCURTSMjJoVbcqb6RncD76BnbBlZFkmarBwYCppHyKIuHq6pqnp9OtXlR3Fmm7n9DQUCpUqMDvv//Oli1b6Nu3L2p13kvpU6dOoVarqV+/vnmZq6srISEhnDp1yrzOf2sZNGzYME/ilZaWxuTJk/nzzz+JiYlBr9eTmZnJ6fMR9NXHmkvmZ6NBsnHFRnvvS/pjx45RJdALg15PTm4uz7RuwrQP38GTRA4ePcnkz77h0MkLpKSkYDSaWtAyEq+B++0JmJuGVkCVGU9ShgYnG+19Y/tvi1f16tXv+j7f73vv5eXFkSNHOHr0KEuXLjWvoygKRqORiIgIKlWqdM/3+Kjb1alTJ892AwYMoG3btoSEhBAWFsazzz5Lu3bt7nkMS7FI4mU0Gpk6dSrz588nNjaWs2fPUq5cOd5//30CAwMZPHiwJQ6DVquldu3abN68meeee8587M2bN9/VnCg8PRk5euwTj8HNG89HlCDaVfFk1sEQuFmJNFS+wJGopBKVeP154Dxz5L0ApCg2ZJVrj4fD49106BTqzaC1YfRUtgLQWf6X1UeiGdGyvKXDFYQi5YddFxirvj0+cqPNsywNcb/v+i62WlQh7Yk7/x0eUhJt5YP02r6Lfg0D7irGIQjCf9h5PHydInBcW1tbype/99+/O5Ow/8rKNWBtSOVWXeUUrCljrUGSQFabut9pJT25uXokSUKjud1SfquV6FbS8TCDBg1i3rx5nDx58rGnM3ocb775Jps2beLTTz+lfPnyWFtb8/zzL5CdnoyTdHvsUqpkj5ej9X33ExISwspVq4i5kU599xysdKb3np6RTPveI2nTvDFzFiyhSjk/rly5Qvv27cnNzcXHxZbdOAFga2ONIzeIuGGNtdbtnrG9+OKLdxWsuNf3+UHf+7S0NF599VVGjRp11/vw97//jblH3e6/v0O35hv+66+/+Pvvv+nevTtt2rTh999/v++xnpRFEq+PPvqI77//npkzZ5qbbgGqVq3K7NmzHyvxSktLy1M6NCIigvDwcFxcXPD39+eNN96gf//+1KlTh3r16jF79mzzIEmhcJyITqGadNH8/LgSxMvejtj6VSMzQou1lEN9+RRfXrhOlxplCjFSy4m6kYHT5b+w1WQDsNZQn051gh57Pzq1ioBqjYkI96SsHEtD+STzDh8XiZdQol1OSEd/YhW+alM33S2GUFo0aoj6IQnUgKYVWHw6jPGan5ElhS4Zf7DmSAuer+X7FKIWhGLsMbr7FReVKlXijz/+QFEUEtJzcCOVlfvDsbezxalMMDZaFbIkYUBl3saoz37i4/bu3Zs333yT0NBQKleufM+49Ho9e/fuNXc1TEhI4MyZM+b1K1WqxN69e/Nst2fPnjzPd+3axYABA8wtY9dvJHPp8iVa1rtdNdqACgcbHSr5/sM4tFotwRUq4JaeQ1pSJFaYeiedPh9Bwo0kXnvnA+rVqolGJecpdKFTq3Cwv93dUJLAV4kjKkF3V2xpaWlcunTpod+7h6lVqxYnT568b/J96/0YDHnnF3uU7e7HwcGBHj160KNHD1588UXCwsJITEzM15RYj8Iitwl/+OEHvv32W1566SVUqtu/4KGhoZw+ffqx9nXgwAFq1qxJzZo1AXjjjTeoWbMmEydOBKBHjx58+umnTJw4kRo1ahAeHs769evvKrghPD1HIpOoLl0wP09zrYa1VkXdIC/2G00VI32kRC6fP15YIVrcj3uu8JLqdgWfdapWtMvn2KzONcqwxtgQMM01Ui5hK5eupz9kK0Eovr78+wyj5D/Mz//HM/Ss+/AqbXUDXTju84J5aoYXVDv4efNecg2Pdqe6pErNyiUlS0zELpQuw4cPJzIykhEjR3Lm6AE2bNzEpM/mM3JIf+zs7JEkCVkCwx2Xuirjkydezs7OxMTEsHnz5nu+XqFCBbp06cKQIUPYuXMnR44coU+fPpQpU4YuXboAMGrUKNavX8+nn37KuXPnmDt3bp5uhrf2s3z5csLDwzlw8DA9evQAo4FbKVa84oAky2jVKh6Fs42GdJ0nyYppKIOvjzdarZZffvqJyMuXWL16NR9++GGebeysTK1TaTcnYdZKejz0MZQJKGuO7ciRI/Tu3fuRWwwfZPz48fz777/moiPnzp1j1apVeXq1BQYGsn37dq5evcr169cfebt7+fzzz1m2bBmnT5/m7Nmz/Pbbb3h5eT32RNaPwyKJ19WrV++ZZRqNRnMFl0fVokULFEW563Hn3AkjR47k8uXLZGdns3fv3jz9aIWn7/Cl64TKphavWMWJMv7lAGgY5Mpu4+07MwEpB4m68eCqQcVBVq6BY/u2UEs2tcyeNAZQvlYrrDSPdvL7r3qBLuzRNTE/byMf4u9TYlJvoWQ6H5eG4ehvVJCvArDfGEz5Bp1wvkc1rnvp1zKUpYbWgGk+vG7JS/h535WHbFUyXUvOYtjiXUyYMoX3pkzi5QVbiU4quZPVC8KdypQpw7p169i9Zy8d27Vh6IRpDO71HINfH4+rnel8IklSnsRLRy6WmLTFycnpgV0fFy9eTO3atXn22Wdp2LAhiqKwbt06cze7Bg0asGDBAr744gtCQ0PZuHEj7733Xp59fP755zg7O9OoUSM6d+5E5+a1qVWtIgCpijVZ1l7Ij1GwTJIk/FxtybIPIEIVSKZ3Hb7+diFrVi6ncuXKfPzxx3z66af33DZe5WEe72UnZTH9vTewtXekUaNGdOrUifbt21OrVq1HjuV+qlevzrZt2zh79ixNmzY1N7zcOQ/vlClTuHTpEkFBQbi7uz/ydvdib2/PzJkzqVOnDnXr1uXSpUusW7cOWS647uuS8iSzyd1Uu3ZtxowZQ58+fbC3t+fIkSOUK1eOKVOmsGnTpgdOUFzcpKSk4OjoSHJyMg4Od5cNL22MRoV+H83nR+MEANYYGpDVZSHd6viZXvvwK35U3gFM3fHSOy+kR93799MtDn47EIm0ajgvqrYDMD53CK+MnkyQu91Dtry/8b8dYdTxrpSREshW1Az1/o3FQ1tZKuQSq7h+Hotr3E9KURReXridD6IG4SuZ7lQOML7PZ+New9Xu0Sq2GY0Kfef+xVcJg3GUMjAqEr1VM/nmzYE42pSeCofxqdkMn7eCGRmTKCebpuuINLrzhs1Uvn2t6yMnskLx/Dw+KOasrCwiIiIoW7ZsgRU7Kypy9AaiY2MJlEw3K7MVDXHWZc3V9wAuRsdTjigAEhU7VC4BOFoXj89HrsHIlfgUyhiisJJMDRlZioZotR+B7g7ID+hiaElZuQai4xMIVKKRJVPaEKs4ITl442Ffsn/HbnnQ5+pxziEWSekmTpzIyJEjmTFjBkajkeXLlzNkyBCmTp1q7iIolExn41Kpkn3E/HyPsTINyrkCIMsSTkF1SVJMJ8Dm8lH+OV685x4xGhX+2H6ITrJpwu5kxYbrgZ2eKOkCaF3Zk8035/TSSXqsIreTnCG6Dgkly/rj16hxeZE56dpuqEaVxs8+ctIFpvPKa8/W50u9aWyBLCmM03/D1LXHCiTmokhRFN5a+i8fZ0w2J10AfnI872dMZ9qfJadbtyDcj6IoXE1Mx5vbEw/H4nxXIiCpb59fdOSSrS8eXZOz9QauxCXhe0fSla2ouaoqg7+b/VNLugCsNCrcXZyJumN+L08pCUNKLNfTnrz7ZmlikcSrS5curFmzhr///htbW1smTpzIqVOnWLNmDW3btrXEIYQias+FBBrIJ83PL9rVylMKvU2VMmw2mhIKeykT44VtpGXrn3qclrLxZCwtE39Bd/Mk+LOhJT0bV3zi/Tap4MZ26Xap3NbyQbaeFZMpCyXH9bRslqxcxyuqtQDkKCrmWr3C8BaPPxi6QTlXooP7cMHoDUAt+TwORxay5Uzp+Mz8sj+S5lFfEySbJg+9YnTnqmK64VVdjkB35AeORCYVYoSCUPDiU7NxyI1DJ5muKdIUK9S2Luj+0+1fo1GTc7ObnI5csnOLfuKVkaMnKu4GfsYo8/VGjqIiSvbB383xoYWICoK9lQY7Jzeib55rALylRHKTrxGbkoUFOtCVChb7yTVt2pRNmzYRFxdHRkYGO3fuLPBa+ELh23U6inqyqYBKnOKEb1C1PK+3rOjBJmM98/M27GVzMR2/pCgKSzYdoO/NohrZioZ/nLrRuuKTl+m10aqRyzUjTTHdqWspH+afkzFPvF9BKAoUReG93w4yOXeW+SJpgeEZ+ndqe98Jkx/mvS41mSwNw6iY7vq+qf6V+b+tJS4ly2JxF0VxKVmsWbeK/qqNgGni0365E3g9Z4R5nRHqVXy75fEKWwlCcZKckUNuajyuUioABkUiVvbA0+Hu1nOdWkXOzflu1JIRvb5o9ya5kZ5DXHw8gcpV81xdWYqGSNkXP3cnNOrCmz7DxVaH1sGTa4qzeZm3lIicGk1MUqZIvh6BmPxEyLe0bD1SxDZsJVMz81ZDKE1D8iYhjtYacsu2NCcUHVV7Wb3v3FOP1RI2nLhG84Sfsbn5fn8ytOKltvUt1tzfvLIvO4ymxNVVSiXx7L+lvlqbUDJ8tzOCuhfmUEk2dTU+ZfTjaLmhdKyW/4nvfZys6fhMV5YY2gNgJeUyLWcmb/5vR4n+3ExZFc5E43zzOIvP9S8yeUBnWrd/jk0GU6u5t5SIzZk/OB+X9qBdCUKxlJyRQ9KNeHy4bl4WjSuezg6o7lEUQaeWyeaO8Z/6otk6Y1QUopMyyUqKIYBrqKSbkxkrWq6qffH3cHrkCoYFyc1eh2zvlSf5cpeSsc64yuWENPQWqG5YkuU78XJ2dsbFxeWRHkLJtO1MPC25PefDZurQ4h4ToHaqXY41BlO5dHspE5fLf3I5oXiVS8/WG1i0dhsDVaZyr9mKmr8cu/NMNW+LHaN1RU9zt0yARvp97L+UaLH9C0Jh2HY2ntPrv2Gw+i/A9NmZrBnN1O51zJNn5lfPun7sKTuCk8YAAILkGF669jGTVh4tkhdWT2r98WuUO72AENlUKOCosSyJ1QbTIsSDPg38+V7V1bzuq6q1LNx2/n67EoRiR1EU4lKzSL0Riz9x3Dp9xCuOWDm4m0uf/5eVRiaL28U0NEoOemPROj9k5xq4FJeCbXok3lKi+b0lKzbEavwIcHcoUhPFezhYoXb0Jkpx49ap1llKwyM7kstxSWTlGh68g1Is3z/F2bNnM2vWLGbNmmUugdm+fXsmT57M5MmTad/edBfy/ffft0ykQpGz6cRV2qgOApCh6NAHtsThHie+sKperFXfHuvXW/UPPxWz8s+Ldl6iX/oi8wDX7w3t6dOu0QMnLXxcXo5WxHg0N3edaiMfYtPJ4tktUxAAjl9NZsFPy5iqXmheNkXfj1e7dcLtMQpq3I8kSczo2YBJVhPMc9O0Vx0g+PBUPttw5on3X5QkZ+ayeOVfjFSvAECvyExTD+fdTtUB0/iL0IZt2Ws0jTktL0eTeGQt8ali4HtpVZJuPuToDVy+noYqJQpf6bo5MUlU7Mix8Xrg+USjksm5I/GyIqfIJAaKopCYnkN0XDy++ss4SrdvSl9TnEmz8SPA3R51AZY3zy83Ox12zp5cwRPDzesWGykbf0MUMXHxJKRnl6jfQUu9l/x1rgf69+9v/v8LL7zAlClT8kxUNmrUKObOncvff//NmDFjnixKochJz9aTfGor7rJpBvQdxmq0qHrvMvFWGhXBtZpz8kAAleXL1JTPM2vPBpKbly8W5Z8jEzPY+88KhqlMs8xfVxzY6T2Q76tbrrXrlnpVgjmwPZh60hkqyFc5fewgyrOVn7hlQBCetrOxqUz97mfmK9PN47r+p2+DZ6vhtKpouQnvnW21TOr/DG/Mv8w38kzUkpEB6o3M3mnHHM07vNaqfIn4/Hy4+hjjcuahlU0XjN8anqVX12dwuaNsfP9Ggby/oxP1b467HSD9yf/2dOeNtsGFErNQODQaDZIkER8fj7u7e7H+/TcYFZIycshMT8GDG2glPbdGcd5Q7MixcsPDSiI7+8E3GPSKiiyD6cJZUrJITc9EQ+EmXzl6A/GpWVjlJOJFKkYJsjDdVInFBVt7J1ytZXIe8t4Kk5UMBnsHLiSBl3IdraQH9HgTTeL1VC5qXfB0sC6UYiCWpCgK8fHxSJJknostv/KdeN1pw4YNzJgx467lYWFhTJgwwRKHEIqYdcdi6KRsMT//U2nEe5XvfzHVt2Eg8/Z24DN5PgAvG39n8b8dGd2maF8QGI0K7/+6lynMNy/7VN+Dt7rUK5A/Zu2revLblrrUk01362um7+T41S5U83W0+LEEoaCcjE7hg0W/85XhQxwl06TpOw1V2FdxPHNaPX4Vw4epWsaRbj0GMH5ZEp9pTJ/V0erlzN+Sy9SMt3m3mN+8+ONgFL7HvqS22jQ+9qLRi/CyrzAsNO/koB72VjhWf4YLx38kSI6hkeokX+7eQlaLoHxP8C4UPyqVCl9fX6Kiorh06VJhh5MvRqNCeo6erKws7MjAmmyu3nxNQSIJO9RWauytrvEoPfJvZOSQnZOACiMGZFI0epILaa47RVFIy9aTnZWBE2nkYCDl5mvZaEmRHHC0TSUnI50bhRLh4zMYFY6k6bEzJmNFzs2l8eRyhUPYY2tjjY3WIilHoZEkCV9fX1SqJzuXWuS74OrqyqpVqxg7dmye5atWrcLV1fU+WwnF2dr9Z5gv7wNMfZCzyoXh4XD/SfTKuduhr/Iil08vJ0COo5nqGN9tX0tsveF4PmC7wrbk30u0ipqHvzoegH3GEORafQosEQrxtOeEQ1PI/BGA9qr9rD8RIxIvodjYdf468/+3lG+YgdPNbjMHjMEsLDOV+T1qF1gCFFbVmxtdRjBlVToTNf8DYKh6Dcv2pjEqeQIfd6uT7wqKhen0tRTWrvqZ71S3uxhOkobz8Qv3HiM3uFl5Fh3pwFR5EQDdc1fxx6EwXqof8FTjFgqXnZ0dFSpUIDe3aFfwu5PeYORoVDKbTkSTfm4HHfiXhqqTedY5ZgzkO01vXgqrSq2yj15D4ODBKAJ3fkUdlenmxXjHT5jRt6VF438UJ64m8d3Gg3RI+YW2qkPm5bmKikX6MDKq9GRoiwpYF8MkJTvXwFdbzqE78Ssvq9fdbP0yvbc/DM045t2N4e2qUsbZ5iF7Kpo0Gs0TJ11gocTrgw8+4OWXX2br1q3Ur18fgL1797J+/XoWLFhgiUMIRciZa6n4Ra3FWmO6q7HK0Jiudcs9dLvhrSoy98RzfCJ/C8AEFvHxn02Z1atugcabXwcuJXLwryXM02wCTOPYPrV6ne+eqVJgx5QkidDqoRzfHUhV+RKh8kU+PnoM2j/5XGGCUJAURWHp3ivsXruYBaq55vGQR4zl+MJzGvMHNi3wVpde9fz5wfAm767V8KF6MbKk0Eu9haCz0Qyc+w4f92tDuSec7PxpiknOZOJ3K/lGmnVHFcNudOvejTJO1vfcJsTLntiyXUmM/BUXKY1n5T28tG0/ver6P9UJV4XCp1KpLHKhWJBSsnLZezGRf07FcuXEbhrn7GSQvBs/OT7PejGKC1/on0dVux8zOlTG0frxunsFejhxJlVHE7Wpsmpy8gm02rCn9plIyshh5l+nUA59z3T1MnNPADDdmPpE8yoDuz5LWNX8V3otbFZW8HbnGvxd3pshv5flA/0XVJQjsQIGcInIC3/x4VcDCGr0AsNalr9nTYDSwCKJ14ABA6hUqRJz5sxh+fLlAFSqVImdO3eaEzGh5Phu+3mGqv4yP1+vbcviSg+fyyrEyx65Ri+OHttEdTmCSnIkLseXsOVMAC1DnnwuLEuKTcli5o+rWaS+3cVwmr43o/q0x76ATxZhVbxYv7MuVeVLAFRM2sb5uDDKe9gX6HEFIb/Ss/W8v/wwFU58wTz1GvPy7YZqfOc9mXmDmj+11qZ+DQNZrhvLmOW2zFR9jU7SU08+wxcpY3h7zjBaduhO3wYBRT4JiUvJ4vUF6/k0ewrOsqks/FZDKEm1RtD5P10M/6t/80r87/u2vK5egUYy0CplJVvONKV1JcuNrROE/IhLyeJIVDJHIpM4ci4Cu5g9NJSOM1Q+QoAcd9dVaZTixiJ9B6KCevFau6r57v0R7GXHasXX/DzQeJmoG5n4uxZs64uiKKwMv8rPa/5inP4bamtuT6eTpNgyQ98LVe1+fBtWuViMeX8UbSp7EjqmHxOXV6HC2e8YoV6JTtLjJ8fzLZ+w49+/eGV/fzq0aUfv+v5Fqlrj02Cxv4T169dn6dKlltqdUETFpWSRcnQN5dTXAPjXUJk6jVuie8S5Jd7qUIXRJwbzI6ZKmOPUv9Dv15pUHd0Xd/snr3JmCckZuYxauJGPc6ZjJ5uG8a4wNMahyas0qeBW4McP9XVihk0TyP0NgDDVftYfv8bIViLxEoqefRGJfPbbZt5I+5T66tuT9i43NGFLyPt806PuUx9f9HwtX1ztXmfgT958pnyKt5SIt5TIEtVUvl+3j0FHR/L2c7UJ8Sqan6krCRmMXbiWmenv4X/zzv8poz/feb3Pgs5VH7p9k/JufOXSlaHJa9BJenqrNjNq23GReAlPTUaOnovx6VyIT+NCXBpnY26QFnmcMpmnqS5dJEy+wBvSZWTN3ZXiDIrEDmN1lintsKkSRr/GQdT0d77HUR6du52OGG1ZuHm4YCmSM7GpBZp4XYhP46PlB6gfuYClqnWo5dvzW/1haMJvLkMZ90ITaj3heyuK3O11fN2/ERtOlKPvyuaMzvqaRje7jTZVHaexYRzL/1rDSzv60r1NI56r4VPsC3A8KoskXleuPLg0uL//vavdCcXPN9suMERebX6+mE5Mb/joYwfc7HSEhXVm8dodDFRvQCfl8mHu54z9XwDfDmlV6APA07L1jFy8lfeT3qecbEouTxn9Wes/nm+fUnc/WZYIqVKbC4e8CZJjqCudYXb4SUa2qvBUji8IjyIlK5fP1p8id/8SFqp/wl7OBEz9+T/W90LVaASzO1Sy6JQLj6N5sDuBrw1g9BJfXkuaQRPVCQD6qzfRNuYgn8zthX2dXrzeJhhXC5S2t5TtZ+P5+qff+UKZiY9sqhoQpbgxyX4y3w5s/kjnSEmSeL5ZLVaubEIP9VYcpAzKRq7gWFQdMV5UeGJGo0JyZi5xqdlEJ2cSk5RFdFIm0cmZJCbewJBwEYf0SwRJ0QTJ0bSTohkuXTV1P75Po06uomKPsRLrjPW57NGKZjUqMbW2r0WmnQDTZ0L2qAg3Z2gJkaPYE5tK2wcUBcuvrFwDX2+9wOltv/KhajG+6tsTPV8wevMhL9O47fP82DiwxCcb7at40bh8bz7fUJuf9/3Mm/Iy/OV4ZEnhRdV2Omfs4rcVLei9qTsvtG7E87V8S3wLmEUSr8DAwAcOmDYYisZ8CcKTibqRwdW9y82Vtc4YfXGv0fGxT4wv1fdn6MnXaXDpFJXkKwTLVxkcM4Vxvzoyq1fdQrtQS0zPYeR3m3nz+nvmbn7RigsTbd/nm94Nn2pcYdV8WHegPq/JK5ElhUoJmzh9rSUVvRyeWgyCcC96g5Fl+yNZu3EjY/Tf0UBzyvxajOLCW4yhT69uhFW1/HQLjyvA1ZbFrz3DjHVl2bh/IW+rl2Et5eAjJTJLPY9Dhzbw5sGeBNXryJDmQYVa6CcjR88n60+TvXcRS9Q/oLs5Ru6i0YsJdh8xe0hHnGwevQpb5xo+9FvfhR76rQC8rF7Hh3/3Zv6ARgURvlAEZeYYuJaShcGoYDAq6I3Gm18VjDe/GowKOQYjGdkGMnL0ZOQYSM/Rk5ljID3bQHq2nhvp2WSn38CYnoCcmYgmOxEnUvEgCW8pAR8pgZpSAt5SgrmgDg/5VTUqEmcUP3Yaq7KPauT6NqR+RX+GVvMiwNW2QL4fAT6eRF1zw1e6TrAUxZKYlIdv9Jj2XEzg8z+2Mijla8ao95uXZysa5uq7cD54MFO71LzvGM2SyE6nZmLnKlxoOJ6P13XA++yPvKZeiZOUjlYy8JJ6M90zt/LHqqa8tKk77Zs1onsd3wIf1lFYLJJ4HT58OM/z3NxcDh8+zOeff87UqVMtcQihCJi18RRj5J/Nz2cbu/NOy8cvBy9JEtO612HoF+NYkDMeJymd5qqj3Dg9iTd/mcLM7rWf+h2P83GpvLNkI1PSJlFRNg2+TVTsGKOdxOdDns0zV87TUK+sC19Yt+K13JUAdFXtYMXhq7zdQSReQuEwGBX+PBbD0k176Zq0hGWqbcjy7W5CP+tbsMZrOFN7NiHQrWAunPLDRqvmg+eqs7PKRPr/WpdXs76jtcr0N6uWfJ7FfMTh/T/zwd4uaCt3oEf9cjQo5/LUys/rDUaWH77Kbxu3MSrza5pqjptfO2AM5hPH95j7Sns87B8vKdSpVTRr3IzN/9SkteowZaQEvM4t49CVSiWya5Nwtz0RCSz8fhHVpAhkjKgxopIMqDGgQkGF6f8a9FhL2diTjSdZ2EjZ2JKFNdnYSpk4k4Zaut1N7n6tVvdjUCQuK54cV8py1FiOc+oK4B1KpQAfGpd3pU+AC9bagu/tEuxpzxmjH76q69hLmdyIuQjUssi+kzNy+XjdcdSHl/Cd+hfsVZnm17YbqvGl9VCGdG/D2CrFt3jGkwpyt+Or/o3YF1GREWs70yB2GQNUG7CXMtFIBnqqt9ItexubNtRhxKZnqVCnLQMal8XPpXhWQbwfiyReoaGhdy2rU6cOPj4+fPLJJzz//POWOIxQiPZFJGJ99H+EaKIAOGQsj2fdF/L9gXC10/HBwC6M+iaBBcqH6CQ9z6n+xfrkBF5dNInPejfA+SklO6uPRPP7Hz8zT5qNu5wMQLziyGuq95gyOP/v8UmoZInQWvU4urss1eUIqsmXmHpoL4b2FQutRVAonbJyDawKv8ryLXvpkPIr36u2YKW+XaL6stGDD5SXaRzWnR8aBRbZ388mFdyo9VYv5m+tx7Idyxkv/Y8KsmlmoJryeb6SPyPmzGJ+PdmC2fbtqVm9BmFVvQj1dSyQJCw5I5ffDkay8d8DdEn9mZ9UW9GobvcO+UHflr3Bb/JdjzrY5bMwSf9GgQzZ0YvWRlOiOVK9kjfXPc/iV1sU63nNhEejliXaygcZoN5Y4MfKUVRcU1yIwZUoxY0LRh8u4kO6fTl0HkEEeLhQxceBnr5OlHOzLZTiNiFe9uxX/GiN6fNglXiaHL0RrfrJbvRuOR3H17+tZULuPGppzpuXxysOTDX0w6PhSyxpE1wsp7MoCPXKuvDjyPZsPVOLVzf1ol7sLwxSrcdBykAlKYSp9hPGfo7uX8LnezqQHdKZ7vWDaFrBvcj+fXkcBfpbEBISwv79+x++olCk5eiNfPbHNhaob7d2zaIPs1o/2Zijyj4ODOnzEqO+T2GO6gt0kp72qgP4Ro7k1S/GM653B+oEPvo8HY8rLjWLKSvDKXd6AYvUy8139K4Y3XnT6gOmD+lCUCGWnn6+pi8/72xCdTkCgCaZ/7Dn4rM0Ll/wBT4E4UJ8Gj/tucyZg1t5zrCeH+VdaNS3E4MUxYY5+q5cDe7DpGdDC6x7kCXZaNW80S6EqLqvM2tjO/THVjBMXmlu5faWEnldvZzXM5dzbHcg63fWY6Z1fdzL1aRuOTfqBDoT5G6XrxZ5o1EhIiGdPRcT2Hr8CnLENl6U/mGZfAiV+nbL4VXFlcnGl6nXridzm5Z9ogTJTqemdcu2rNnYgE6qPbhJKVSNXMqOc1VpFuye7/0KxYNKljCSv6QiS9GQgY4MrLih2HFDsScRe9NXxZ4b2JOgOJBh7YXi4IuNsxdeTrb4OFlRxsmGTu62lHWzLfRx23cK9rTnR+PtyoblieRSQjrBnvkrspOZY2D6nydQHfiW/6l/RiffviH1k74laz2H8t4LjajsI3qq/JckSbSs6EGLkPZsP1eb4Rt7EhrzO/3UG/GUkgCoLkcwS/6K+As/8sfZ5nxtE0ajevXpXtcXb8fi21XTIolXSkrefrKKohATE8PkyZOpUEEUBCju5m45T9/k+TjcbDr/Td+MFmHPWWTQa9MK7hj7DWPo/6yYK3+KrZRNFfkyC7PHMmPBAZbX6s/odhUfu5vNg6Rm5bJwRwThO//kbeU7Kmoiza9tN1Tja9cJfDGwdaF/sEO87Dnt1g590lLUkpHnVLuYfTBSJF5CgYlNyWLdsRj+CT+HX/R6XlL9TRX5Mtxx7ZSu6FhqaMM/rr0Z1bkBjYKK3++jr7MNn/WozZU2lfhmWzfiDv9Jd/6mlXwI1c35sqrJl6gmXwL9rySdsWX/qYqsMAZzQfIj2yUEB8+yeDla4+mgw9VWh04jo1HJqCSJ9JtjZRLSsom6kcm1+ESM145QIfcMdeWzzJaPYqvOzhNTqmLNQn1Hjvr14f0X6llsvrE+DQLot70PHXL2oZaMvKpey8urn6HhmC4lfhB7aaeSJH41tGCvsRJ6ZBRJjSKrQFJhlNVIkgpFVoOsRtHaIt18qHS2WFnpsNWqsNGqcbLR4GKrxdlGS/DNry62WpxtNY9c0bgocLTWkGgbBDfzo2A5ijPXUvOVeF2MT+Pt7zcxIvkzmmmOmZdfMHrzAa/QqkNX/tew6PYAKCokSaJ5sDvNKrRj/6W6fLBjELozqxmkWmc6/wLuUgpD1WsYmrOG3dsqM2NLS9KDOtK5djnaVPJ8Kt1ULUlSFOXuWp6PSZblu+7KKYqCn58fP//8Mw0bNnzSQxQZKSkpODo6kpycjINDyb+LsS8ikZ8XzuRzzdeAadzTSJdv+GFkR4tW4zl4OZGZP6xkWu5MguQY8/JwYxBzlW741e1En4aBT9QCFXE9naW7L3H64FaGGH6mueqo+TW9IjPX8BzXQkcx+bnqReYu3YLtFym/aQAtVUcAGGR8j9nvjC61Ew/+V3H9PBaVuBVF4VRMKtvPxbP7ZAQuUZt5Rt5NM/koWilvUaRkxYYlhvbsce9G/9a1aFfZq8jPhfWobqTnsPzwVTbuPkjtpA2EqfabW5rvJ1PREqs4E4sziYo92WjIUTQYkLGVsrAlCxcpFV8pDnfp/oP4YxQXlulbccCjK6+E1aN5sLvFuwH+sv8K+lWjeUm9GTBNj3G97VyGNHv4xPelQVH5PD6OR4n5VkENtSwjS4jupcDL3+1k/pVOqCUjJ4wBrG/yG2PbhTzWPraeiWPBsl/4XPnU3DoDsFDfgb3lRvLB87XxKUXFMyztSkIGS3ZFcP7gJrob19FOPnDX36MkxZZ1hnpskJvhVrkFnWv50TjItdCqRD7OOcQiide2bdvyPJdlGXd3d8qXL49aXbL6tBbHE3R+3UjP4dXZv7Ao503sJNN8VqP0oxgy9M0CKUkcl5LFmz/uoFP0HLqpt+d57ZgxkF8MLYnwCqNepSCaVHCjsrfDA+90pGTlcjwqmb0Riew/fprA61vppdpsvoty576nqkfQq/MzdKlRxuLv60nEpWQxZcY05mq+AGCtoT43On5L34aBhRtYEVFcP4+FFbfeYORMbCqHryRx6FICsecOUC3rIM3ko9SWz6KT9HdtE24MYqmhNYmBz9K3WaUCSQyKCkVROBqVzIYT1zh89CghyTtoKJ+knnwaZynNYsdJUOzZbKjFZuqiqxTGSw3LUa9swRX0MBgV+ny5jq8SXzG/j37GSXzy5vBCreRYVBTH80hxjLkomLbuFN33PE95OZpsRcOocn/yTf/6j7z9T3uvcHD1PKapF5rPl9cUZ942Dqf1Mz14qb5/iT0/Pm2pWbmsDI/mrz1HqRK/jp6qLXluzN8Srbiw2tCIbboWhFRvSIdq3tQJdHmqrY1PPfHavn07jRo1uivJ0uv1/PvvvzRr1uxJD1FklJaTXY7eyJAFW5gQM5pKsmmetl/0Lbje+jNGtCxfYMfVG4ws+fcSOzctZwJLzGMvzK8rMuFKeXYZq3BG8SfVPgiVvRdqG0dklRq9Ppfc9BtIyVE4Zl6munSROvJZQqULyFLeX/UoxY3Z+heQqvdgwjNVi9RcPnca8cMeJl/ojruUTK6iop/jYn4a01mc3Cm+n8enEXdyRi5n41I5G5vKudg0Ll6NRY45RGXDGWrK56kln8NVSr3ntjGKC+sM9dmsbUm1us3oVde/SFUqfBoURSHiejp7IxLZdyGe+IhjuKWdIUSOJESKxE+Kx1O6gYOU8cD9XFOciVTcuWD04YgSxBXrSriWq0mbKj60CHF/aq3Xh67c4PdvPmSa5jvANB3IF0ELmNevYak/lxTH80hxjLko+P1gFNYrB/KMah8Afazn8eP4Po+07Xc7I7j612dM1PzPvGyPsRKznN5lap+WlPcovDHhJd2xqGSW7bvM1SOb6Wz8mzB5P7ZS9l3rnTOW4S9jXfZoGxFQpQHtqnrTKMi1wLvEPvXES6VSERMTg4eHR57lCQkJeHh4lKh5vErDyU5RFCb8fpg2R8fSVnUIMPVb/qjMfBYOaf5U7iJEJ2Xy5d9nSD28gldVK+9qpfovoyKhgHl8xoMcNZblR0Mb0oKfZ3RYtXwPrH1adpyL58j3YxmpXgXAJ7ndafXKDGoHFFzhkeKiuH4eHzVug1Eh12Akx2AkV28k12B6np6jJykjl6SMXJIzc7iRkcu15Cyib6SRmRiNnBKFS/ZVguUoKkhRBEtR+ErX77r5cKcrRnf+MdbkH1UT3Cs15ZkaZWhS3v2JK36VJLeS2dPXUom6kUFcSjZJyUkY0xNRDDlgyAGjAbQ2SDp7VNYOeDo74udiTTk3W0L9nAp17Oj43w7R+9hgQuWLAMzVd6HMC9PoWtP3IVuWbMXxPFIcYy4KjkUl8/fXYxij+QOAYbmj+WzS+9hoH9w764fdl7i69mPe1iwzL/tR35q9FScwo3uth24vWEZ6tp51x2JYf+gCdpc30VneRTP5KBrp7jwj0ujOBmMddqga4FyxCW2rlKFpsFuB3Ox6nM+jRX5TFEW55x2zhIQEbG1L113S4k5RFD7dcJqQIzNoqzYlXcmKDW/rJvBlrwZPrenWx8ma6S/WILJVMD/s7sG0Q7tonb2ZFvIRysvRd63/oAtKMN3d3WSszS5NI6rWb8aw+gGULSZ38BsHuTHXviPDM1YjSwq91P/w+e4IkXiVcJ0/34jX9X/R3JxnRyPp0aJHgx5bsnCS0nAiDRcpnXJSKl7cwEtKvP0H6CGzMSQptuw3hrDDWI3z9vUpF1KN5iGefFvBrciMcSxqHG001A10oW4BVlstSOM6VGH4qeH8zzAerWRgmGo1fVfVp2G5wXg5ii6HQslX3sOOr/AzPw8mkvNxaVT3dbrvNn+fjOXo2q/59I6ka7b+eQxNxzOnXUipbzF+mmx1arrV8aNbHT/iUhqw5mgMiw6dpGzsJjqr/qWOdNZ8Pegnx/Oy/Bcv8xfxpx34+0Rtxis1yfBtTINKZWkR4k5FL/un/vN7osTr1vxckiQxYMAAdLrbXbUMBgNHjx6lUaNGTxah8FTN+fscNjunMUi9HjB17RttfIOJA7oWylgAPxcb3n2mMrlhFdl1vgs/nb3OmbMncUg8RgWiKCvH4EwaTlIaoJCLmgzFimjFlauKG6elsmR7hBJSrhxtKnsyNMC50AZf5pcsS7RqUJetf4fSShWOr3SdzOPriEupgocYn1FiOUlpfKudZZF9pSlWnFfKcMIYyCFjBWIdq+HmX5naZV0ZVN6t1HUjLK1c7XQM6Posc37ey5ua31BJClOMc3n316osGNysxBRLEYT7sdaqSHWoADfnNw6WIzlzLfW+ideF+DR+WPYjC9ULzMtm5nbHvu0EhrUIegoRC/fj4WDF4CZlGdykLBfim7PmSDSzj56kbMI22sv7aSifNN+IdJdS6KXeQi+2kHvtCw5Eh7BiUyjHrevhX7E2LSp60Li8G/ZPoev3EyVejo6mAguKomBvb4+19e0uFFqtlgYNGjBkyJAni1B4KhRFYdbGM8g7ZjBCvdq8/B39y/To1YeqZSxfTONxaFQyLUI8aBHiAVQmM6crF+LTiLieTmRmLsczclAU0KhlbLQqvB2tqe5kxWsedsWq3O39vFjblwl/t6cV4QAMlNew5N9ujAurWLiBCQVH/XgTiN9Q7IhWXIlW3Lh688ZDBGXIdgnGyascFbwcqeTtwNv+ThaZCkIonjpU82bj8Vc5cuogofJFysvRtLv8OfO2+PDaE87NKAjFgZ13BbIvaNBJuYRIUfwSe++xrjl6I+//tJVZ8hxzVb3v9W3RNH9TJF1FTJC7HaPbBEObYC5db8uGE9dYcOwcbtFbaa86QHP5CNZSDgAayUBD1Ukaqk6CfhlXj7myLTyU8Up1Ur0bElqhLI3Ku1I7wLlArh+fKPFavHgxAIGBgbz55puiW2ExlWsw8t7ycCodmZ5nhvv3cgdS+7nXCKvqXYjR3Zu1VkXVMo6FnhA+La52OtxrdOTskR8Jlq9STz7DnD2bSG9ZHlud6FteEhnUtnyc25Nc1OSgJhc1uYrpa5akIUfjjNHaGcnaCZWtC072dvg6WePjZE05J2uaOFkT4Goj5moS7jKpSyivXHidJbnjsJGy6aHeyrh/vmVXwHgxT6BQ4gV7OXHufBmqSpcIlK5xISbhnuvN+fss/a7PxlOVBMAOQ1WOVZ3AJ22Dn2K0wuMKdLPl1eZBvNo8iNiUFmw8GcvIY5eQL++iMYdpKYcTIMeZ1y8jJdBb/Q+9+Qdj/Bccjwvk3x1V+U6qCv4NqVPBl8blXani42iR4TYWuWKbNGmSJXYjFILE9Bze+mk3L175kA7q/eblH+T2pWKnMfSo61+I0Ql3GtS0PN8ceoZP5G8B6GVYza8HnmVg47KFHJlQEKZ3r0+OoS5alYxGLaFRmSbo1apktGpZTMwp5JuTjZY3enfi3e/OMEvzFQBT1IsZsCyYsq/1EXMQCSVasJc9ZxRfqnIJtWQk59ppoGmedS4npHNu5++8efO6KFGx40vHN/nhhZpiTFcx4ulgRd8GAfRtEEBGTmN2X0hgwelYzp86QuX0vbSQw6kvn0YnmWbVliWF6lIE1eUIhrKG7Cg1h64Es2hjC0LavWyRls58J161atVi8+bNODs7U7Pmg38RDx06lN/DCAXo8JUbTP/fWj7InkEllalse66i4m39EGp2Hs5L9QMKOULhTuU97Eit0JW4iF/xkJIIk/fTa/u/9G0QUOzGrQkP5+9qU9ghCCVYg3KuHGo7mKV/n+El9WaspFw+yZ3BmEWefDeiI3aiJV0ooUI87fnd6Ac3e5G5ZVwkKSMHJ5vb3btn/nmEt+UfzM8nGwYxqXcrUXioGLPRqmldyZPWlTxRnqvGhfjn2HomniWnrqC68i/1OEYT+bh5CiUAnaSnoeoke4yVaFDOMkWV8n1m7dKli7mYxnPPPWeRYISnQ28wsmBHBMf+/pGFqq9xkE2jTNMVHa8bx9C990DaVfEq5CiFexnUvCJLzrVnnOYXVJLCc+m/szK8OS/WLt3loAVBeHxDmwUx7OJYql26SHU5Aj85nvFJH/DGj858NaCxuKEjlEiBbrZckG5XNgyRIzkbm0a9sqYL61MxKTif+ZVATSxgmqvLo35PqviUjqENpYEkSZT3sKe8hz0vNy1HZk5T9l9KZOWF60w9ewGnuD00ko7TWD5BgBzHIU0or1loaEu+E687uxeKrobFx/m4NCb9spPn4+bylXrH7eVGH97VjmNc3+eoHeBciBEKD1I30Jk53s+TGr8aeymTbqptvLRpJ11qdBdjeQRBeCyyLDGzZz2GzHufL9LexFtKpJZ8nk6XPmLK6k/44LlqoluVUOJoVDLZziFws6ZGBSmKs7Gp5sRrwbZzvK7607z+HFV/vm4jxnWVZNZaFc2C3WkW7A4dKpGU0Y49FxNYeD6B8+dO4OhhuZ5FFu1LkJOTQ1xcHEajMc9yf38xTqiwpWXrmbv5HFf+/Y3PVIvwUt0wv7bG0IDffcbxZZ/GeNiL8uRFmSRJDG5bi0X/C+N19Qo0koHn05ax/FAjMR5PEITH5mij4ZNBYYyZe53vjO9jK2XTSbWHuIPT+NxmMmPbi8qpQsnj6FWWtBQr7KQsgqUodtysbBiTnEnOsVUEaEzFF7YbqlG/eRscrQu+zLhQdDjZaAmr6n2zuFxVsvV3T9CcXxZJ386ePUvTpk2xtrYmICCAsmXLUrZsWQIDAylbVgz8L0x6g5FfD0Qy+JP/0Wj3EL5Sf4aXZEq6UhRr3sp9hRMNZ/PdKy1F0lVMtAh2Z79XT1IU0xigF1Xb+WPTDnL0xodsKQiCcLcAV1vG9u/GG4ZRGBRTC9dg9V+od3zM11svFHJ0gmB5wV4OnFfKAOAvx3M55joAyw9dpaf8t3m97+lMv4ZivHtpZ8my8hZp8Ro4cCBqtZq1a9fi7e0tuiYUAXqDkVXh0fz69y66pP7MUtVW1KrbF+bbDNWZZz+K8T1aUzvAMgMGhadDkiRebV+bhUs68obmd9SSkW4ZP/PrgSb0aSD+QAiC8PjqBrpwrdsg3v41mZkaU+XU19UrmLbJiiWa8QwQ1VOFEiTY046zRl9qyKYbC4b4UyhKc3YcOMww+SQAEUZPPGqE4Wz7eHMqCsKDWCTxCg8P5+DBg1SsKLokFLbkzFx+OxDJpl376Jz2K/9TbUWrvt1EGqW4MV3fhzKNevB92xCstaJCT3HUpLwbC3x6khT3F05SOs+rdtB702aeq9lPVCMTBCFfOoX6kJ79GpNXZTFZY6ro9o5mGdPWwXz9WwxtLiaNFUqGCp72LFNuF6XyyLzEtrPx1Ez6G1mjALDc0JQX64jCVYJlWaSrYeXKlbl+/boldiXkg6IoHIlM4t3lRxk9bTb+G19mWeZQXlJvNs+2nqpY83nui3zgv4Q3Rr/FO89UFklXMSZJEkPDajJf3wkAlaQwLGcJ32wT3YIEQci/nvX88evwBp/kdjcve0ezjOxNH/HZhtMoilKI0QmCZQS42BBxR2XDCnIUX225QHvV7flM9zm0pZa/KDYmWJZFEq8ZM2Ywbtw4tm7dSkJCAikpKXkeQsGIuJ7OnM3n6PXJ76yfP54B4T1ZrPqIdqqDyJLpj2OaYsWX+ucY6rqYWv2ms2BwU4Lc7Qo5csESGgW5cTGoH1GKGwAtVEc4tWM50UmZhRyZIAjF2eAmZbFpPS5P8vW6ejkOOz5gypoTGI0i+RKKN7VKJtvldqXCECmSi5cizF0PTxoDqFEtVAydESzOIn2S2rRpA0Dr1q3zLFcUBUmSMBgsVw2kNMvWGzh0OYl/Tsdy/MQxKiTvoqNqH6PkU/Cfgjsxigv/07fhiGdXBrerzcgQD3ECKYHGPVudT+b04gv1lwC8Jf3IZ+vb8lnPOoUcmSAIxdmIVhVYqHmbD9brmKT5HwBD1Ov4bV8ao5Lf5dOedcVkskKx5uJVlpRkaxykTCrIV2mpOmx+bbOxJq0qehRidEJJZZHEa8uWLZbYjfAfiek5HL+azP5LiRy9EIU6ej91lOO8IIfzrhx5V7IFsNdYkR+N7aBSJ/o1Ls9bAc4i4SrBynvY41inB4cO/UUt+TwhchRWx5ZyqFGQ6CIhCMITeblpOZbpxjF+lRXT1QuRJYVu6u34nE1g8PxJfDGwBW52usIOUxDyJdjLnvOny1BLOo+vdJ1O8m7za3vVdRgm5jQVCoBFEq/mzZtbYjelUq7BSExSFpE3MohMzCAiIZ3z0Qlkx5zEK/M8laXLtJXPMFq6hEp17+4dF4zerDA0Yb99a+rXrs079fzwdrR+yu9EKCyvtwnm9fAB/Mh7ALyl/oWhf7Tmx1EdLTbhnyAIpVOvev7YaMcw6jdbPlXPw0rKpbHqBB7xYxg2dyLTBj5DBU/7wg5TEB5bBU97zhp9qSWfB6CZ6hgACYo9LiENxd9PoUBYJPE6evToPZdLkoSVlRX+/v7odKXnrtipmBT+On6NXIORHL3R/DXHYCQ9K5estGSMGdeRMxLQZifgSxx+Ujx+Ujw1pVjKSTFoJFyi8rEAABcWSURBVMM9W7QAjIpEuBLEZkMtDlvXp1yVenSt5cdYfyfRulUKudrpaN7qGVZs+ouuql04Sel0S5zP4l2VGdKsXGGHJwhCMdelRhnc7F7j5f+5MVuZiZuUQgX5Kl9nvsXYeVfp/mJvnqnuXdhhCsJjCfa050fl7qqFW42htKzkVQgRCaWBRRKvGjVqPPCCX6PR0KNHD7755husrEr+JL1nY1Ox2vYhz8oH0aJHI+nRkosWPTZkm5KqWx5xMvRTRj/2GyuyT6nIDY/61K4cQvtKHoz1cUSWRbJV2g1oHEjfA6/SKvkwjlIGL6h20H/TcjpWH0EZJ9H6KQjCk2lc3g334QMY9p0rH2dNIUiOwU1K4Ts+YvovFzgSOZJxYRVFK4FQbPi72BAh+921fKuxJlOCxfguoWBYJPFasWIF48eP56233qJevXoA7Nu3j88++4xJkyah1+uZMGEC7733Hp9++qklDlmkaVUyntINguWrj71tjqLiguLDKSWAU0Z/zsuBGDxDqRQUQP2yLnTxd8HR5hGzNaHU0Khk3nqhKTO+7cU0zXcATJQW8uHKRswf0KiQoxMEoSQI9rRn3mvPM2axM6/GT6WZ6hhqycj7mqWs2XOBwZHj+eSlRnjYl/wbrELxp5Il9J6h6ONl1JIRgFxFRbJPUzFpslBgLJJ4TZ06lS+++IL27dubl1WrVg1fX1/ef/999u3bh62tLWPHji0diZda5rqiI02xIgc1OWjIVdTkoCYTHYmKPTewN31V7LmquJGg8cLgFICNSxmCPB2p5O1AD28HAl1txB1E4ZHUDnDhj1r9OXhkO7XlcwTJMVQ6v4A1RwLpFOpT2OEJglACeNhb8d2wdkxc4cnRo7MZqV4FQCfVHipGj2TU7DG82q0LLUVFOKEYaFI9mO2bqtNKFQ7AGmNDwupUKtyghBLNIonXsWPHCAgIuGt5QEAAx46ZBivWqFGDmJgYSxyuyPN0sOKPSu9wUCWjUclo1aavOrWMlUaFi60WZ1stFWy0ONtqKONkjaO1RozPEp7Y+A6VeeXEUJYa3kItGRmuWkW/lfWoX3YgHg7iLrQgCE/OSqNiRrea/BTwIUPXlGem6itzSe7v9W8z/X9H2VbvVSZ0rCRKzgtFWs96/rz871Ck9K/JRMfvrkNZXLtMYYcllGCSYoFp6GvWrEloaCjffvstWq2peTY3N5chQ4Zw5MgRDh8+zK5du+jTpw8RERFPHHRhSklJwdHRkeTkZBwcHAo7HEG4y+oj0UT89i6vq5cDcNZYhs8CFzB/YKMSl9wX189jcY1bEP7r8JUbTPvfWj7I/oTK8mXz8n8MNfjGeSxTerckxKtoVz0sjp/H4hhzUZWYnsOaI9FoVDLP1fTBRmuRNgmhFHmcz6NFfrvmzZtH586d8fX1pXr16oCpFcxgMLB27VoALl68yPDhwy1xOEEQHqBTdW9ePzaU42cPUVW+RLB8lVoX5/HbAX+61717ILEgCEJ+1fR35pvRPXjn1yDqXpjDYPVfALRShVMteSQT5w6hSquevNo8CI3oNi8UQS62Wvo3CizsMIRSwiItXgCpqaksXbqUs2fPAhASEkLv3r2xty/ad7oel7jLJBQHiek5DPv8R37Qv4VO0mNUJAYwmamvv4Kfi01hh2cxxfXzWFzjFoT7URSFH/deYdufPzFd/gp3KcX82ipDI352HcF73ZtSxcexEKO8t+L4eSyOMQtCSfU4n0eLJV6lhTjZCcXF3ydj2b90Em9rlgEQpbgxzm0eS4a1Q6suGXeei+vnsbjGLQgPczY2lYlL/2HIjVm0Vh02L7+uOPCBfiBlm7/EiFbl0amLztiv4vh5LI4xC0JJ9TifR4tefZ08eZL169ezevXqPA9BEJ6+NpU9uRH6CvuMIQD4StfpH/cJn6w/VciRCYJQUgV72rPktWfZ1/Ar3sgdRpJiC4CblMKXmi+otGMEfT9fwc5z1ws5UkEQhKfPImO8Ll68SNeuXTl27BiSJHGrEe3WQH6DwfCgzQVBKCATu1RnYMSbfJsxBmcpjfaqA/y7+xs2B71L60qehR2eIAglkJVGxdsdKxNebQJDf63PwKS5tFcdAKCDaj/N0o8ye8kL/Fp5IO88Wx0vR1FxVRCE0sEiLV6vv/46ZcuWJS4uDhsbG06cOMH27dupU6cOW7dutcQhBEHIBzudmkl92jPeMMy87B31Uhb+upzopMxCjEwQhJKuhp8T37/emeNN5jFKP4rriqkLjq2UzbuanxhxZiDjPvuKhTsukmswFnK0giAIBc8iidfu3buZMmUKbm5uyLKMLMs0adKE6dOnM2rUKEscQhCEfKpaxpGmz/ThW/0zAOgkPR8bPuetH7aSlStaowVBKDg6tYqx7SvyyrA3GeW2gP/p22BUTL1hQuQofpA/wGXja/T5fAWbT8Uihp0LglCSWSTxMhgM5uqFbm5uREdHA6YJlM+cOWOJQwiC8AT6NAjgWMVRhBuDAAiQ43g1firvLw8XFzqCIBS4qmUc+XFEe1SdZtFHmsYRYznza8+rdrI4bRjHlk5g0LfbOBGdXIiRCoIgFByLJF5Vq1blyJEjANSvX5+ZM2eya9cupkyZQrly5R6ytSAIBU2SJKa+WJupthPM3X2aqY5R/thnLN51qXCDEwShVJBlid71/Zn71sssq7aId3MHmYtv2EjZjFYv5+Po/nw/7yPG/XqI2JSsQo5YEATBsiySeL333nsYjab+2VOmTCEiIoKmTZuybt065syZY4lDCILwhBysNEwd0IE3lDHkKqZSzq+q/+TY+gXsOi8qjAmC8HS42Gr5uFtNnn9lIsNdFrJY3958TvKUkpip+ZYBxwfw9idfMH3dSW6k5xRyxIIgCJZRYPN4JSYm4uzsbK5sWFKIuTOE4m7DiWvs+OljPtIsBiBL0TBI/pCPhvelnLtdIUf3eIrr57G4xi0IlmY0Kqw6cpVl67bwctZi2qkO5nl9j7ESX0m9qNW0A4OblMXeSmPxGIrj57E4xiwIJVWhzeN1JxcXlxKXdAlCSdC+iheuzYexTN8SACspl9nGj5nw3Vqup2UXcnSCIJQmsizRtaYvP4zrzblW3zLAOJFjxkDz6w3kU/wgTaTGtpd5dcZC5m+7QEaOvvACFgRBeAJP1OI1aNCgR1pv0aJF+T1EkSPuMgklgdGoMPJ/uxlwYTT1ZFMBnHPGMkxy/5zvXm2LtVZVyBE+muL6eSyucQtCQYtLzWL2xjNkHP6VUfLvlJOv5Xl9vaEui7W9aNO8Jb3r+2Ore/LpSIvj57E4xiwIJdXjfB6fKPGSZZmAgABq1qz5wMpoK1asyO8hCkRgYCAODg7IsoyzszNbtmx55G3FyU4oKdKz9bw8fwMfJYwlSI4BTN16vg/6nLl9G6KSi36LdXH9PBbXuAXhaYm4ns6cTSfRHP+VUerl+Ep5x6GuN9Tle/ULNGjchgGNAnG0yX8XxOL4eSyOMQtCSfXUEq8RI0awbNkyAgICGDhwIH369MHFxSW/u3tqAgMDOX78OHZ2jz+eRZzshJIkLjWLEV8u5+vs8bhJKQCsMjTiYK0ZfPBctSLfXbi4fh6La9yC8LSduZbKnI3HcDnzC6+pV+IhJeV5fZuhOovk56nUwDQGzN1e99jHKI6fx+IYsyCUVE9tjNe8efOIiYlh3LhxrFmzBj8/P7p3786GDRvE3ECCUAx42Fsx/eVOjJImkKloAeii+peyBz/i0w2nCzk6QRBKuxAve+b1a0S3YR/wrt//+DD3JWIVJ/PrzVVH+V6aTKvd/Xh75udMWnmMKwkZhRewIAjCAzxxcQ2dTkevXr3YtGkTJ0+epEqVKgwfPpzAwEDS0tIee3/bt2+nU6dO+Pj4IEkSK1euvGudefPmERgYiJWVFfXr12ffvn2PdQxJkmjevDl169Zl6dKljx2jIJQk5T3seb1fT8YYRmFQTC1cA9Ub0O38mK+2ni/k6ARBEKC6rxMLXm7GM69OZVLgT7ybO4hIo7v59XryGRaqPqb7oZeY9fmHvPbjPsIjkwovYEEQhHuwaFXD/7d3/zFRnHkYwB9Y2AWjZRXOXaGsYtMcrfgDQSjapGkk0dbWH216bYJ1pTmNFhUk0XI2ltw1CImXnLXamjYRk6uW1gSstZc03FrJkVIQDCi1Ih705KoLNQYW1KLd+d4fl05dAcsPZ2eGPJ/kTeCdl+XZzeYJLzs7GxoaipCQEIgI/H7/qG7jxo0bmDt3Lvbv3z/o8U8++QT5+fkoLCzEmTNnMHfuXCxZsgRdXV3qmnnz5iEpKWnAuHLlCgCguroaDQ0NOH78OHbt2oWzZ8+OKivReJE+MxrP/+GPKPh5vTq3JewYeir/ir/XfK9fMCKiu8x3TcaB7EVYvenP2P37I8i/sxGtSpx6fFbof/C38Pfwp9aXceLADrjf+ycqz3dCUXgWDhHpb8yf49Xf34/y8nIcPHgQ1dXVeO6555CdnY2lS5ciNHRs+7qQkBBUVFRg5cqV6lx6ejoWLFiAffv2AQAURUF8fDw2b96MgoKCEf+Obdu2YdasWVi7du2gx/v7+9Hf/+sltnt6euByudDR0cHzqmncKW/4Ly58sRc7wo+oc3+5sxpzn9+MFclx9/lJffh8PsTHx6O7uxtRUVF6xxkSe4RIG+3X+lD6r3/jxrkv4Lb8A3NCvw847pMIZP+8Ewe2vozfTYoY9DbM0CPsECLjGlGHyBhs3LhRJk+eLHPmzJE9e/bIjz/+OJabGwCAVFRUqN/39/eLxWIJmBMRWbNmjSxfvnxYt9nX1yc+n09ERHp7e2X+/PlSV1c35PrCwkIBwMHBYeDR0dEx4n4JJvYIB4fxh5F7hB3CwWH8MZwOGfPl5F0uF5KTk+979bPy8vJR3f69r3hduXIFcXFx+Prrr5GRkaGu2759O6qqqlBbW/ubt9nW1oZVq1YBAPx+P9atW4fc3Nwh19/7XyZFUXD9+nVER0f/5hXfftkBm+0/UswdXMw9eiKC3t5exMbGjvkVdi2NtkeM8BiPhllzA+bNztyjZ4Ye4d8izK015h69kXTImD55cM2aNYa/3PS9Zs6ciaampmGvt9lssNkCL09rt9tH9DsfeughUz2Jf8HcwcXco2PUU4PuNtYe0fsxHi2z5gbMm525R8foPcK/RZg7WJh7dIbbIWPaeB06dGgsPz5iMTExsFgs6OzsDJjv7OyE0+kMahYiIiIiIqLhMuZr6kOwWq1ISUmBx+NR5xRFgcfjCTj1kIiIiIiIyEjG9IqXFvr6+nDp0q+fHdTe3o7GxkZMmTIFLpcL+fn5cLvdSE1NRVpaGvbs2YMbN24gOztbx9SDs9lsKCwsHHB6gNExd3AxNw3FrI+xWXMD5s3O3DQUsz7GzB1czB0cY76c/IN26tQpPP300wPm3W63emrjvn37sHv3bni9XsybNw979+5Fenp6kJMSERERERENj+E2XkREREREROONqd7jRUREREREZEbceBEREREREWmMGy8iIiIiIiKNceOlkf3792PGjBmIiIhAeno66urq9I4UoLi4GAsWLMCkSZMwdepUrFy5Ei0tLQFrfvrpJ+Tk5CA6OhoTJ07Eiy++OOAz1PRWUlKCkJAQ5OXlqXNGzf3DDz9g9erViI6ORmRkJGbPno36+nr1uIjgrbfewrRp0xAZGYnMzEy0trbqmBjw+/3YuXMnEhISEBkZiUceeQRvv/027n5rqBFzjxfsEe2ZqUMA9giNDDskOMzUI+wQnQk9cGVlZWK1WuXgwYPy7bffyrp168Rut0tnZ6fe0VRLliyR0tJSaW5ulsbGRnn22WfF5XJJX1+fumbDhg0SHx8vHo9H6uvr5YknnpCFCxfqmDpQXV2dzJgxQ+bMmSO5ubnqvBFzX79+XaZPny5r166V2tpaaWtrky+//FIuXbqkrikpKZGoqCg5duyYNDU1yfLlyyUhIUFu3bqlW+6ioiKJjo6WEydOSHt7uxw9elQmTpwo77zzjqFzjwfsEe2ZqUNE2CM0MuyQ4DBTj7BD9MeNlwbS0tIkJydH/d7v90tsbKwUFxfrmOr+urq6BIBUVVWJiEh3d7eEh4fL0aNH1TXfffedAJCamhq9Yqp6e3vl0UcflcrKSnnqqafUsjNq7jfeeEOefPLJIY8riiJOp1N2796tznV3d4vNZpOPP/44GBEHtWzZMnnttdcC5l544QXJysoSEePmHg/YI9oyW4eIsEdoZNgh2jNbj7BD9MdTDR+w27dvo6GhAZmZmepcaGgoMjMzUVNTo2Oy++vp6QEATJkyBQDQ0NCAO3fuBNyPxMREuFwuQ9yPnJwcLFu2LCAfYNzcx48fR2pqKl566SVMnToVycnJ+PDDD9Xj7e3t8Hq9AbmjoqKQnp6ua+6FCxfC4/Hg4sWLAICmpiZUV1fjmWeeAWDc3GbHHtGe2ToEYI/Q8LFDgsNsPcIO0V+Y3gHGm2vXrsHv98PhcATMOxwOXLhwQadU96coCvLy8rBo0SIkJSUBALxeL6xWK+x2e8Bah8MBr9erQ8pflZWV4cyZMzh9+vSAY0bN3dbWhvfffx/5+fnYsWMHTp8+jS1btsBqtcLtdqvZBnve6Jm7oKAAPp8PiYmJsFgs8Pv9KCoqQlZWFgAYNrfZsUe0ZcYOAdgjNHzsEO2ZsUfYIfrjxouQk5OD5uZmVFdX6x3lN3V0dCA3NxeVlZWIiIjQO86wKYqC1NRU7Nq1CwCQnJyM5uZmHDhwAG63W+d0Q/v0009x+PBhHDlyBLNmzUJjYyPy8vIQGxtr6NwUfGbpEbN2CMAeofHNLB0CmLdH2CH646mGD1hMTAwsFsuAK9d0dnbC6XTqlGpomzZtwokTJ/DVV1/h4YcfVuedTidu376N7u7ugPV634+GhgZ0dXVh/vz5CAsLQ1hYGKqqqrB3716EhYXB4XAYMve0adPw+OOPB8w99thjuHz5MgCo2Yz2vNm2bRsKCgrwyiuvYPbs2Xj11VexdetWFBcXAzBubrNjj2jHrB0CsEdo+Ngh2jJrj7BD9MeN1wNmtVqRkpICj8ejzimKAo/Hg4yMDB2TBRIRbNq0CRUVFTh58iQSEhICjqekpCA8PDzgfrS0tODy5cu63o/Fixfj3LlzaGxsVEdqaiqysrLUr42Ye9GiRQMukXvx4kVMnz4dAJCQkACn0xmQ2+fzoba2VtfcN2/eRGhoYE1YLBYoigLAuLnNjj2iHbN2CMAeoeFjh2jLrD3CDjEAnS/uMS6VlZWJzWaTQ4cOyfnz52X9+vVit9vF6/XqHU21ceNGiYqKklOnTsnVq1fVcfPmTXXNhg0bxOVyycmTJ6W+vl4yMjIkIyNDx9SDu/tKQiLGzF1XVydhYWFSVFQkra2tcvjwYZkwYYJ89NFH6pqSkhKx2+3y2WefydmzZ2XFihW6XwrV7XZLXFycegnX8vJyiYmJke3btxs693jAHgkeM3SICHuERoYdElxm6BF2iP648dLIu+++Ky6XS6xWq6Slpck333yjd6QAAAYdpaWl6ppbt27J66+/LpMnT5YJEybIqlWr5OrVq/qFHsK9ZWfU3J9//rkkJSWJzWaTxMRE+eCDDwKOK4oiO3fuFIfDITabTRYvXiwtLS06pf0/n88nubm54nK5JCIiQmbOnClvvvmm9Pf3q2uMmHu8YI8Eh1k6RIQ9QiPDDgkes/QIO0RfISJ3fewzERERERERPXB8jxcREREREZHGuPEiIiIiIiLSGDdeREREREREGuPGi4iIiIiISGPceBEREREREWmMGy8iIiIiIiKNceNFRERERESkMW68iIiIiIiINMaNFxERERERkca48SIiIiIiItIYN15EREREREQa48aLiIiIiIhIY/8DHpEipM1ba+8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2nd Order Case\n",
+    "# First we need to find the modal damping ratios from the damping matrix\n",
+    "zeta_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,C_proportional,E_2ndOrder)/(2*mm_2ndOrder*omega_2ndOrder)\n",
+    "\n",
+    "# Now Construct the FRF matrix from modal parameters\n",
+    "num_modes = omega_2ndOrder.size\n",
+    "H_2ndOrder_fromModes = np.sum([\n",
+    "    phi[:,r,np.newaxis]@phi[:,r,np.newaxis].T/ # The newaxis turns the array from a 1D array back into a 2D array that can be transposed\n",
+    "    (-mm_2ndOrder[r]*omegas_bc**2 + 2j*zeta_2ndOrder[r]*omega_2ndOrder[r]*mm_2ndOrder[r]*omegas_bc + omega_2ndOrder[r]**2*mm_2ndOrder)\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_proportional.reshape(omegas.size,-1).T,\n",
+    "                      H_2ndOrder_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "031edfd9-54c1-44e0-9ddb-851744aca873",
+   "metadata": {},
+   "source": [
+    "We see we get identical FRFs.\n",
+    "\n",
+    "For the complex mode cases:\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "Note that for a frequency response function, which assumes a single frequency line excitation, the Laplace variable $s = j\\omega$.  The pole $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "Here is the proportional damped case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "43e01c80-adc6-41e4-b882-09c24e203d50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_proportional.size\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "# Compute FRFs\n",
+    "H_proportional_fromModes = np.sum([\n",
+    "    psi_proportional[:,r,np.newaxis]@psi_proportional[:,r,np.newaxis].T/\n",
+    "    (ma_proportional[r]*(s-\n",
+    "        (-zeta_proportional[r]*omega_proportional[r] + 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n",
+    "    + psi_proportional[:,r,np.newaxis].conj()@psi_proportional[:,r,np.newaxis].T.conj()/\n",
+    "    (ma_proportional[r]*(s-\n",
+    "        (-zeta_proportional[r]*omega_proportional[r] - 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_proportional.reshape(omegas.size,-1).T,\n",
+    "                      H_proportional_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "811671ce-53fa-48a5-8851-bfad6a333687",
+   "metadata": {},
+   "source": [
+    "And here is the general damped case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4c6bbc32-5c2a-4f85-af36-18c8856b976a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general.size\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "H_general_fromModes = np.sum([\n",
+    "    psi_general[:,r,np.newaxis]@psi_general[:,r,np.newaxis].T/\n",
+    "    (ma_general[r]*(s-\n",
+    "        (-zeta_general[r]*omega_general[r] + 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n",
+    "    + psi_general[:,r,np.newaxis].conj()@psi_general[:,r,np.newaxis].T.conj()/\n",
+    "    (ma_general[r]*(s-\n",
+    "        (-zeta_general[r]*omega_general[r] - 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_general.shape[1]*2,H_general.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general.reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "335edf60-cc3b-4bcf-8671-5f36dec4965b",
+   "metadata": {},
+   "source": [
+    "We can see that we understand the relationships between the modal parameters and the frequency response functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0912a4-8590-4b7e-83b4-860e436cd0ac",
+   "metadata": {},
+   "source": [
+    "## Solving for Modal Parameters from FRFs\n",
+    "The process of Experimental Modal Analysis involves measuring FRFs and then fitting modal parameters to those FRFs.  Many mode fitting software packages utilize a two-step process.  First, the poles (frequency and damping) are identified, then the shape information is found.  Some curve fitters identify modal participation factors in the first step, which are essentially the mode shapes at the reference degrees of freedom.\n",
+    "\n",
+    "This section of the document will follow the implementation of the paper [Peeters, Bart et al. ‘The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation?’ 1 Jan. 2004 : 395 – 409](https://content.iospress.com/articles/shock-and-vibration/sav00272), which implements the popular PolyMax modal parameter estimator.  This is the algorithm implemented in SDynPy's `PolyPy` fitter.  Other modal parameter estimators may give similar results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99330d1c-fe11-4cb4-91c8-339a638b8c7e",
+   "metadata": {},
+   "source": [
+    "### Setting up the System of Equations\n",
+    "\n",
+    "Let us consider the frequency response function that we may measure during a modal test.  We understand that we will only measure a finite bandwidth during the test, therefore there may be contributions to the measured FRFs from modes outside the bandwidth.  We will turn our typical FRF matrix equation\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "into a \"measured\" version.  We will replace the value $s$ with $j\\omega$.  We will also introduce lower and upper \"residuals\" ($\\mathbf{R}_l$ and $\\mathbf{R}_u$, respectively), to try to fit portions of the FRFs from out-of-band modes.  We will explicitly add the notation to show that the frequency response function matrix is a function of frequency $\\omega$.  Additionally, we will call the mode shapes of the references the *modal participation factor* $\\mathbf{l}_r$ and the mode shapes corresponding to the responses $\\mathbf{v}$.  Finally, We will remove the modal mass term, which will make the assumption that the modes we fit from the algorithm will be mass normalized.  Note that the format of the residual terms implies that the frequency response functions we are investigating here are *admittance* frequency response functions, which relate the force at a location to the displacement at a location.\n",
+    "\n",
+    "$$\\mathbf{H}(\\omega) = \\sum_{r=1}^N\\left( \\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}\\right) + \\frac{\\mathbf{R}_l}{(j\\omega)^2} + \\mathbf{R}_u$$\n",
+    "\n",
+    "Recall the poles $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$ and $\\lambda_r^* = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "The PolyMax approach assumes that the frequency response function can be represented by a right matrix-fraction model.\n",
+    "\n",
+    "$$ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$$\n",
+    "\n",
+    "Note that these are not the same $\\mathbf{A}$ and $\\mathbf{B}$ matrices from the previous sections, which represent the state space formulation.  However, this document attempts to match the notation from the referenced paper, so we will override those previous definitions and apologize for any confusion it may cause.\n",
+    "\n",
+    "We can see similarities between the two formulations.  The FRF equation has a numerator and a denominator, as well as a summation over a number of modes.  Similarly, the right matrix-fraction model has a matrix that is not inverted (representing something like a numerator), a matrix that is inverted (representing something like a denominator), and a summation operation inherent in the matrix multiplication that is performed.  Terms in $\\mathbf{B}$ will relate to the shape information in the numerator of the FRF equation, and terms in $\\mathbf{A}$ will relate to the pole information in the denominator of the FRF equation.\n",
+    "\n",
+    "The numerator matrix $\\mathbf{B}$ will have a number of rows equal to the number of responses or outputs $N_o$ and number of columns equal to the number of references or inputs $N_i$.  The denominator matrix $\\mathbf{A}$ will have a number of rows and columns equal to the number of inputs $N_i$, and is therefore square.\n",
+    "\n",
+    "We can split up the above equation and look at an individual row of the FRF matrix, which we will denote $\\mathbf{H}_o$ to represent a single output.  This will involve selecting a single row from $\\mathbf{B}_o$.\n",
+    "\n",
+    "$$ \\mathbf{H}_o(\\omega) = \\mathbf{B}_o(\\omega)\\mathbf{A}(\\omega)^{-1}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23ba4d5b-112a-42e4-8034-3e2616448aad",
+   "metadata": {},
+   "source": [
+    "Each entry in the $\\mathbf{B}_o$ and $\\mathbf{A}$ matrices is represented as a set of coefficients multiplied by a set of basis functions, similar to how we represent displacements in modal analysis by a set of modal coefficients multiplied by a set of mode shapes.  The basis functions $\\Omega_r(\\omega)$ are a set of functions that depend on the frequency $\\omega$.  The coefficients are constant with respect to $\\mathbf{\\omega}$.  We will call the coefficient of the $\\mathbf{B}$ matrix corresponding to the $o$th row and the $r$th basis function $\\beta_{or}$.  Similarly, we will call the coefficient of the $\\mathbf{A}$ matrix for the $r$th basis function $\\alpha_r$.  The number of coefficients and basis functions will be equal to the polynomial order $p+1$.\n",
+    "\n",
+    "$$\\mathbf{B}_o = \\sum_{r=0}^p \\Omega_r(\\omega)\\beta_{or}$$\n",
+    "$$\\mathbf{A} = \\sum_{r=0}^p \\Omega_r(\\omega)\\alpha_{r}$$\n",
+    "\n",
+    "The secret of PolyMax is that instead of using a polynomial basis function for $\\Omega_r(\\omega)$, it instead transforms to the $z$-domain.  For larger model orders, polynomials will have large exponents, which when applied to large frequency ranges will result in huge differences between the largest and smallest values, which is a sure-fire way to run into numerical issues.  Instead, by using the $z$-domain, the PolyMax approach avoids these numerical issues and generally achieves better results.  For a test where the minimum measured frequency is $\\omega_0$ and the maximum measured frequency is $\\omega_{end}$, this frequency range is mapped onto a half-unit circle.\n",
+    "\n",
+    "$$\\Omega_r(\\omega) = e^{-j\\omega_z \\Delta t_z r}$$\n",
+    "where $\\omega_z = \\omega - \\omega_0$ and $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.  If instead of angular frequencies, the frequency range is represented in Hz with minimum measured frequency $f_0$ and maximum measured frequency $f_{end}$, then the values are instead $\\omega_z = 2\\pi(f - f_0)$ and $\\Delta t_z = \\frac{1}{2(f_{end}-f_0)}$ where $f=\\frac{\\omega}{2\\pi}$.\n",
+    "\n",
+    "The polynomnial coefficients are stacked into matrices.\n",
+    "\n",
+    "$$\\beta_o = \\begin{bmatrix}\\beta_{o0}\\\\\\beta_{o1}\\\\\\beta_{o2}\\\\\\vdots\\\\\\beta_{op}\\end{bmatrix}$$\n",
+    "\n",
+    "$$\\alpha = \\begin{bmatrix}\\alpha_{0}\\\\\\alpha_{1}\\\\\\alpha_{2}\\\\\\vdots\\\\\\alpha_{p}\\end{bmatrix}$$\n",
+    "\n",
+    "Here $\\beta_o$ has number of rows equal to the polynomial order $p+1$ and number of columns equal to the number of references or inputs $N_i$.  Each row of $\\beta_o$ corresponds to the coefficients of one polynomial basis function for all of the references.\n",
+    "\n",
+    "$\\alpha$ also has a number of columns equal to the number of references or inputs $N_i$.  However, each row partition $\\alpha_r$ of $\\alpha$ has a number of rows equal to the number of inputs $N_i$ as well.  This means after stacking $p+1$ of these matrices on top of one another, the final number of rows of $\\alpha$ is equal to $N_i(p+1)$.\n",
+    "\n",
+    "We finally stack all of these coefficients into a single coefficient matrix $\\theta$, recalling that there is one matrix $\\beta_o$ for each of the $N_o$ outputs of the system.\n",
+    "\n",
+    "$$\\theta = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\beta_{3}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "Each row partition in $\\theta$ has columns equal to the number of inputs $N_i$.  Each of the $\\beta_o$ row partions has number of rows equal to the polynomial order $p+1$, and there are $N_o$ of those partitions, resulting in the $\\beta$ portion of the $\\theta$ matrix having $N_o(p+1)$ rows.  Added to the $N_i(p+1)$ rows of the $\\alpha$ partition, the final $\\theta$ matrix has $(N_i+N_o)(p+1)$ rows.\n",
+    "\n",
+    "At this point, we can represent the FRF matrix as a function of frequency $\\omega$ as well as a function of the coefficients $\\theta$, $\\mathbf{H}(\\omega,\\theta)$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81e2b187-5adc-488c-a62d-9ce63f2ddc6f",
+   "metadata": {},
+   "source": [
+    "### Selecting Coefficients that Minimize Errors\n",
+    "It may be obvious, but the goal is to eventually find the parameters $\\theta$ that best fit some measured frequency response functions.  In a real test, we will have measured frequency response functions $\\hat{\\mathbf{H}}$ at $N_f$ discrete frequency values $\\omega_k$.  We would like to find the coefficients $\\theta$ that minimize the error between the fit frequency response functions and the measured frequency response functions.  These coefficients can be found by minimizing the weighted non-linear least-squares error for the $o$th output $\\varepsilon_o^{NLS}$\n",
+    "\n",
+    "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{H}_o(\\omega_k,\\theta) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n",
+    "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o)\\mathbf{A}^{-1}(\\omega_k,\\alpha) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n",
+    "\n",
+    "where $w_o(\\omega_k)$ is a weighting function as a function of the output degree of freedom and the frequency line that allows taking into account data quality in the least-squares solution.  The previous equation is a matrix equation, and we would ideally like to transform it into a scalar equation that can be minimized which is only a function of the parameters $\\theta$.\n",
+    "\n",
+    "$$l^{NLS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "We multiply the value $\\varepsilon_o^{NLS}(\\omega_k,\\theta)$ complex-conjugate-transpose ($\\bullet^H$) by itself, and compute the trace ($\\mathrm{tr}(\\bullet)$).  We then sum over all frequency lines and all outputs.\n",
+    "\n",
+    "This equation is solved by setting the derivatives of the equation with respect to the unknown modal coefficients to zero.  It is reasonably clear that this leads to nonlinear equations due to the matrix inverse in the error equation.  We can construct a linear set of equations that approximates the original equations by simply post-multiplying the equation by the matrix $\\mathbf{A}$.\n",
+    "\n",
+    "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o) - \\hat{\\mathbf{H}}_o(\\omega_k)\\mathbf{A}(\\omega_k,\\alpha)\\right)$$\n",
+    "Substituting for the basis functions and coefficients:\n",
+    "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\sum_{r=0}^p\\left(\n",
+    "\\Omega_r(\\omega_k)\\beta_{or}\n",
+    "- \\Omega_r(\\omega_k)\\hat{\\mathbf{H}}_o(\\omega_k)\\alpha_r\\right)$$\n",
+    "\n",
+    "We can stack this equation for all frequency lines ($\\omega_1$, $\\omega_2$, ... $\\omega_{N_f}$)\n",
+    "\n",
+    "$$E_o^{LS}(\\theta) = \\begin{bmatrix}\n",
+    "\\varepsilon_o^{LS}(\\omega_1,\\theta) \\\\\n",
+    "\\varepsilon_o^{LS}(\\omega_2,\\theta) \\\\\n",
+    "\\vdots \\\\\n",
+    "\\varepsilon_o^{LS}(\\omega_{N_f},\\theta) \\\\\n",
+    "\\end{bmatrix} = \\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o \\end{bmatrix}\\begin{bmatrix}\\beta_o\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "This will result in $E_o^{LS}(\\theta)$ having a number of rows equal to the number of frequency lines in the test, and the number of columns equal to the number of references or inputs $N_i$.\n",
+    "\n",
+    "The $\\mathbf{X}_o$ matrix can be constructed from pulling off the coefficients of the $\\beta$ terms, which are generally the basis functions $\\Omega_r$ evaluated at each frequency line $\\omega_k$.  Each row will correspond to a separate frequency line, and each column will correspond to a basis function order.\n",
+    "\n",
+    "$$\\mathbf{X}_o = \\begin{bmatrix}\n",
+    "w_o(\\omega_1)\\Omega_0(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1) & \\cdots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\\\\n",
+    "w_o(\\omega_2)\\Omega_0(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2) & \\cdots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\\\\n",
+    "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f}) & \\cdots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\\\\n",
+    "\\end{bmatrix} $$\n",
+    "\n",
+    "The $\\mathbf{Y}_o$ matrix can be constructed by pulling off the coefficients of the $\\alpha$ terms, which are generally the basis functions somehow multiplied by the FRFs.  We will explore this expression a bit more here.  The relevant portion of $\\varepsilon_o^{LS}(\\omega_k,\\theta)$ containing the $\\alpha$ terms is \n",
+    "\n",
+    "$$\\mathbf{Y}_o \\alpha = -\\begin{bmatrix}\n",
+    "    \\sum_{r=0}^pw_o(\\omega_1)\\Omega_r(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_r \\\\\n",
+    "    \\sum_{r=0}^pw_o(\\omega_2)\\Omega_r(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_r \\\\\n",
+    "    \\vdots \\\\\n",
+    "    \\sum_{r=0}^pw_o(\\omega_{N_f})\\Omega_r(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_r\\end{bmatrix}$$\n",
+    "\n",
+    "Expanding the equation out in terms of the summation can help visualize which terms go where in the $\\mathbf{Y}_o$ matrix.\n",
+    "\n",
+    "$$\\mathbf{Y}_o \\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix} = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1) \\left( \\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_0 + \\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_1 + \\dots + \\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_p \\right) \\\\\n",
+    "    w_o(\\omega_2) \\left( \\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_0 + \\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_1 + \\dots + \\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_p \\right) \\\\\n",
+    "    \\vdots \\\\\n",
+    "    w_o(\\omega_{N_f}) \\left( \\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_0 + \\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_1 + \\dots + \\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_p \\right) \\\\\n",
+    "    \\end{bmatrix}$$\n",
+    "or in matrix form\n",
+    "$$\\mathbf{Y}_o \\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix} = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1)\\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & \\dots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) \\\\\n",
+    "    w_o(\\omega_2)\\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & \\dots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) \\\\\n",
+    "    \\vdots & \\vdots & \\ddots & \\vdots \\\\ \n",
+    "    w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & \\dots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) \\\\\n",
+    "    \\end{bmatrix}\\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix}$$\n",
+    "\n",
+    "We can recognize the pattern $\\begin{bmatrix}\\Omega_0\\hat{\\mathbf{H}}_o & \\Omega_1\\hat{\\mathbf{H}}_o & \\dots & \\Omega_p\\hat{\\mathbf{H}}_o\\end{bmatrix}$ as the [Kronecker Product](https://en.wikipedia.org/wiki/Kronecker_product) $\\mathbf{\\Omega} \\otimes \\hat{\\mathbf{H}}_o$ where the matrix $\\mathbf{\\Omega}$ has only one row.  Therefore, \n",
+    "\n",
+    "$$\\mathbf{X}_o = \\begin{bmatrix}\n",
+    "w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\\\\n",
+    "w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\\\\n",
+    "\\vdots \\\\\n",
+    "w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\\\\n",
+    "\\end{bmatrix} $$\n",
+    "$$\\mathbf{Y}_o = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\otimes\\hat{\\mathbf{H}}_o(\\omega_1)\\\\\n",
+    "    w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\otimes\\hat{\\mathbf{H}}_o(\\omega_2)\\\\\n",
+    "    \\vdots\\\\ \n",
+    "    w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\otimes\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\\\\n",
+    "    \\end{bmatrix}$$\n",
+    "\n",
+    "where $\\mathbf{\\Omega}(\\omega) = \\begin{bmatrix}\\Omega_0(\\omega)&\\Omega_1(\\omega)&\\dots&\\Omega_p(\\omega)\\end{bmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "873cdaf8-293e-46d6-837b-29eedf756c3b",
+   "metadata": {},
+   "source": [
+    "Similarly to the nonlinear least squares solution, we can construct an error cost function to minimize based on the linearized equation.\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "or \n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\\left(E_o^{LS}(\\omega_k,\\theta)\\right)^HE_o^{LS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "If we substitute the matrix equations, we get\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\n",
+    "\\begin{bmatrix}\\beta_o^T & \\alpha^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o^H \\\\ \\mathbf{Y}_o^H\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o\\end{bmatrix}\\begin{bmatrix}\\beta_o \\\\ \\alpha\\end{bmatrix}\\right)$$\n",
+    "\n",
+    "Ideally, we would like to remove the summation in the previous equation and end up with an equation of the form $l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$.  We recognize that we can stack the equations for $E_o^{LS}$ for all outputs and arrive at an equation\n",
+    "\n",
+    "$$\\begin{bmatrix}E_1^{LS} \\\\ E_2^{LS} \\\\ \\vdots \\\\ E_{N_o}^{LS} \\end{bmatrix} = \n",
+    "\\begin{bmatrix}\n",
+    "    \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{Y}_1\\end{bmatrix}\\begin{bmatrix}\\beta_1 \\\\ \\alpha\\end{bmatrix} \\\\ \n",
+    "    \\begin{bmatrix}\\mathbf{X}_2 & \\mathbf{Y}_2\\end{bmatrix}\\begin{bmatrix}\\beta_2 \\\\ \\alpha\\end{bmatrix} \\\\\n",
+    "    \\vdots \\\\\n",
+    "    \\begin{bmatrix}\\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o}\\end{bmatrix}\\begin{bmatrix}\\beta_{N_o} \\\\ \\alpha\\end{bmatrix}\\end{bmatrix} = \n",
+    "\\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n",
+    "               \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n",
+    "               \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "               \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$\\mathbf{J} = \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n",
+    "               \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n",
+    "               \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "               \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}$$\n",
+    "and as we've seen previously,\n",
+    "$$\\mathbf{\\theta} = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "Therefore our cost function can be simplified to\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347b489b-ba1c-4564-a8ef-ad96fa26c986",
+   "metadata": {},
+   "source": [
+    "In general, the quantity $\\mathbf{J}^H\\mathbf{J}$ will be conjugate-symmetric or [Hermitian](https://en.wikipedia.org/wiki/Hermitian_matrix).  This means that its upper triangle will be equal to the complex conjugate of the lower triangle transposed, and the diagonal will be real valued.  While the matrix $\\mathbf{J}^H\\mathbf{J}$ is therefore complex, in the previous equation, because it multiplies by real valued coefficients $\\mathbf{\\theta}$ and we take the trace, which should only select the real-valued diagonal, we can replace the quantity $\\mathbf{J}^H\\mathbf{J}$ with its real part $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$\n",
+    "\n",
+    "Computing $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$ gives\n",
+    "\n",
+    "$$\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right) = \\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n",
+    "                                                          \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n",
+    "                                                          \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n",
+    "                                                          \\end{bmatrix} $$\n",
+    "where\n",
+    "$$\\mathbf{R}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{X}_o\\right)$$\n",
+    "$$\\mathbf{S}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{Y}_o\\right)$$\n",
+    "$$\\mathbf{T}_o = \\Re\\left(\\mathbf{Y}_o^H\\mathbf{Y}_o\\right)$$\n",
+    "\n",
+    "We minimize the cost function by setting the derivatives of the cost function with respect to the coefficients $\\mathbf{\\theta}$ to zero.\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n",
+    "                                                          \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n",
+    "                                                          \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n",
+    "                                                          \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}\\right)$$\n",
+    "Expanding the multiplication gives\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\n",
+    "    \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{R}_o\\beta_o\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{S}_o^T\\beta_o\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{S}_o\\alpha\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{T}_o\\alpha\\right)\\right)$$\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\sum_{o=1}^{N_o}\\left(\n",
+    "    \\beta_o^T\\mathbf{R}_o\\beta_o + \n",
+    "    \\alpha^T\\mathbf{S}_o^T\\beta_o + \n",
+    "    \\beta_o^T\\mathbf{S}_o\\alpha + \n",
+    "    \\alpha^T\\mathbf{T}_o\\alpha\\right)$$\n",
+    "\n",
+    "We can therefore compute the partial derivatives with respect to the unknown coefficients and set them to zero.\n",
+    "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\beta_o} = \n",
+    "    2\\mathbf{R}_o\\beta_o + \n",
+    "    2\\mathbf{S}_o\\alpha = \\mathbf{0}$$\n",
+    "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\alpha} = \n",
+    "    \\sum_{o=1}^{N_o}\\left( \n",
+    "    2\\mathbf{S}_o^T\\beta_o + \n",
+    "    2\\mathbf{T}_o\\alpha\\right) =\\mathbf{0}$$\n",
+    "\n",
+    "As a first pass, we only care about the poles, which come from the denominator terms $\\alpha$.  We can eliminate the $\\beta_o$ terms using the first of these constraints\n",
+    "$$\\beta_o = -\\mathbf{R}_o^{-1}\\mathbf{S}_o\\alpha$$\n",
+    "\n",
+    "We can then plug this into the second constraint to get an equation for $\\mathbf{\\alpha}$.\n",
+    "\n",
+    "$$\\left[2\\sum_{o=1}^{N_o}\\left( \n",
+    "    \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)\\right]\\alpha= \\mathbf{M}\\alpha= \\mathbf{0}$$\n",
+    "\n",
+    "This matrix $\\mathbf{M}$ is a square matrix with number of rows and columns equal to the number of inputs times the polynomial order $N_i(p+1) \\times N_i(p+1)$, and depends only on the measured FRFs from the test.  Note, however, that this equation is not unique.  For example, the trival solution $\\alpha = \\mathbf{0}$ satisfies this equation.  Additionally, one may see that in the right matrix-fraction formulation $ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$, one could multiply both $\\mathbf{A}$ and $\\mathbf{B}$ by the same matrix, and the equation would still be satisfied.  Therefore, there is not a unique value for $\\alpha$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc9b2e09-e498-4663-9717-d1ffa3085ee7",
+   "metadata": {},
+   "source": [
+    "### Solving the Constrained Problem\n",
+    "To solve a problem of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{0}$ for the non-trivial $\\mathbf{x}$, we must apply some constraint to the problem.\n",
+    "\n",
+    "To solve the problem, we will partition the matrix $\\mathbf{M}$ as follows\n",
+    "\n",
+    "$$\\mathbf{M} = \\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n",
+    "                              \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}$$\n",
+    "\n",
+    "where the $a$ indices are the first $N_ip$ rows or columns and the $b$ indices are the last $p$ columns, recalling the size of $\\mathbf{M}$ is $N_i(p+1)$.  Therefore the matrix $\\mathbf{M}_{aa}$ matrix will have size $N_ip\\times N_ip$, the $\\mathbf{M}_{ab}$ matrix will have size $N_ip\\times p$, the $\\mathbf{M}_{ba}$ matrix will have size $p\\times N_ip$, and the $\\mathbf{M}_{bb}$ matrix will have size $p\\times p$.\n",
+    "\n",
+    "Because we must apply constraints to our problem, we will make the assumption that our solution matrix $\\mathbf{\\alpha}$ will have the form\n",
+    "\n",
+    "$$\\mathbf{\\alpha} = \\begin{bmatrix}\\hat{\\mathbf{\\alpha}}  \\\\ \n",
+    "                                   \\mathbf{I}\\end{bmatrix}$$\n",
+    "\n",
+    "We can see that if we plug this version of $\\mathbf{\\alpha}$ into the equation, we get an expression for the portion of $\\mathbf{\\alpha}$ that was not constrained.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n",
+    "                 \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}\n",
+    "  \\begin{bmatrix}\\hat{\\mathbf{\\alpha}}  \\\\ \n",
+    "                 \\mathbf{I}\\end{bmatrix} =\n",
+    "  \\begin{bmatrix}\\mathbf{M}_{aa}\\hat{\\alpha} + \\mathbf{M}_{ab}\\\\\n",
+    "                 \\mathbf{M}_{ba}\\hat{\\alpha} + \\mathbf{M}_{bb}\\end{bmatrix} = \n",
+    "   \\begin{bmatrix}\\mathbf{0} \\\\\n",
+    "                  \\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can then simply take the top partition to solve for $\\hat{\\alpha}$.\n",
+    "\n",
+    "$$\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38d7b7fb-bb03-4710-b443-d5f1989876a8",
+   "metadata": {},
+   "source": [
+    "### Extracting Poles and Participation Factors\n",
+    "Now that we have the polynomial coefficients $\\mathbf{\\alpha}$ for the right matrix-fraction model, we would like to extract the roots from it.  The roots of this polynomial are the poles of the frequency response function.  We will do this by setting up a [Companion Matrix](https://en.wikipedia.org/wiki/Companion_matrix).  A Companion matrix of a polynomial is a matrix whose characteristic equation is equal to that polynomial.  Then the eigenvalues of the matrix (which are the roots of the characteristic equation) are also the roots of the polynomial.  In our case, the eigenvectors are also useful, as they are related to the participation factors.\n",
+    "\n",
+    "For a polynomial $p(x) = \\alpha_p x^p + \\alpha_{p-1} x^{p-1} + \\dots + \\alpha_2 x^2 + \\alpha_1 x + \\alpha_0$, the companion matrix will be \n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} 0 & 1 & 0 & \\dots & 0\\\\\n",
+    "                                0 & 0 & 1 & \\dots & 0\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                0 & 0 & 0 & \\dots & 1\\\\\n",
+    "                                -\\frac{\\alpha_0}{\\alpha_p} & -\\frac{\\alpha_1}{\\alpha_p} & -\\frac{\\alpha_2}{\\alpha_p} & \\dots & -\\frac{\\alpha_{p-1}}{\\alpha_p} \n",
+    "                                \\end{bmatrix}$$\n",
+    "\n",
+    "Readers can verify that $\\mathrm{det}(\\mathbf{I}x - \\mathbf{C}) = p(x)$.\n",
+    "\n",
+    "Our present case is slightly different, because our coefficients are matrices rather than scalars.\n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\alpha_0^T{\\alpha_p^T}^{-1} & -\\alpha_1^T{\\alpha_p^T}^{-1} & -\\alpha_2^T{\\alpha_p^T}^{-1} & \\dots & -\\alpha_{p-1}^T{\\alpha_p^T}^{-1}\n",
+    "                                \\end{bmatrix}$$\n",
+    "However, because we applied the constraint such that $\\alpha_p = \\mathbf{I}$ when solving for $\\mathbf{\\alpha}$, the quantity $\\alpha_p^{-1} = \\mathbf{I}$, and the remaining terms are those in $\\hat{\\mathbf{\\alpha}}$.\n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n",
+    "                                \\end{bmatrix}$$\n",
+    "\n",
+    "The $\\mathbf{C}$ matrix will be a square matrix with size $N_ip \\times N_ip$ where $N_i$ is the number of inputs or references, and $p$ is the order of the polynomial.\n",
+    "\n",
+    "We can therefore notate the companion matrix compactly as\n",
+    "$$\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{I}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b34449b0-790f-426e-9f61-7590aa486efc",
+   "metadata": {},
+   "source": [
+    "We can then solve for the eigenvalues and eigenvectors of this matrix.  The companion matrix is set up such that its characteristic polynomial is identical to the polynomial it represents.  Therefore, its eigenvalues, which are roots of the characteristic polynomial, will be roots of the polynomial it represents.\n",
+    "\n",
+    "$$\\mathbf{C}\\mathbf{V} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n",
+    "                                \\end{bmatrix}\\mathbf{V}= \\mathbf{\\Lambda}\\mathbf{V}$$\n",
+    "The diagonals of the eigenvector matrix $\\bar{\\lambda}_r = \\mathbf{\\Lambda}_{rr}$ are the eigenvalues of the companion matrix.  These are the poles of the system; however, we must remember they are represented in the $z$-domain, so we must return to the $s$-domain $\\lambda_r$ in order to solve for the natural frequency and damping ratio.\n",
+    "\n",
+    "$$\\bar{\\lambda}_r = e^{-\\lambda_r \\Delta t_z}$$\n",
+    "$$\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$$\n",
+    "\n",
+    "where $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.\n",
+    "\n",
+    "We can then solve for the natural frequency and damping ratio from the poles.  Recall that the frequency was shifted by $\\omega_0$ during the mapping, so we will need to take that into account.\n",
+    "\n",
+    "$${\\omega_z}_r = \\left|\\lambda_r\\right|$$\n",
+    "$${\\omega_r} = {\\omega_z}_r + \\omega_0$$\n",
+    "$$\\zeta_r = -\\frac{\\Re\\left({\\lambda_r}\\right)}{\\omega_r}$$\n",
+    "\n",
+    "Note that there will be in general $N_ip$ eigenvalues.  Half of them will be complex conjugates of the other half, so we can ignore those.  Similarly, we can ignore non-physical eigenvalues.  A criteria that is successfully used is to only keep poles where the real part is less than zero and the imaginary part is greater than zero.\n",
+    "\n",
+    "The modal participation factors $\\mathbf{l}_r$ can be determined from the eigenvectors of the companion matrix.  They will be the last $N_i$ rows of the eigenvector matrix.  One must also be sure that for each eigenvalue discarded for being non-physical or complex conjugate, the corresponding eigenvector should also be discarded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724700d5-1b64-4f54-8c17-505f115c560f",
+   "metadata": {},
+   "source": [
+    "### Stabilization Diagrams\n",
+    "Solving for a polynomial of a particular order will in general result in a large number of poles.  For a typical problem, only a subset of these poles will correspond to true modes of the structure.  For a typical modal test, it may not be obvious how many modes are in a given bandwidth anyways.  Therefore, we need some kind of metric to determine if a pole is valid or not, and for this we utilize the stabilization diagram.\n",
+    "\n",
+    "A stabilization diagram is constructed by computing the poles over a large number of polynomial orders.  As we increase the polynomial orders, more poles may be found in the system.  We declare a *stable pole* to be one which does not change significantly as the polynomial order changes.  Computational poles may bounce around the measurement bandwidth.  Real poles of the system, on the other hand, will generally result in a similar frequency, damping ratio, and set of participation factors regardless of the polynomial order.  We then construct a final set of poles by selecting from the stable poles in the stabilization diagram."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e84ee11-8387-4196-8c61-dc3e1c944e31",
+   "metadata": {},
+   "source": [
+    "### Back to the Example Problem\n",
+    "To demonstrate the equations shown here, we will implement the previous equations in code.  We will demonstrate solution of one polynomial order to demonstrate the process, and then rely on the implementation in SDynPy to perform the rest of the analysis.  We will initially focus only on the general damping case rather than repeating the analysis for all the damping matrices.\n",
+    "\n",
+    "To start, we will pull in the measured frequency response functions and corresponding frequency values.  We will shape the frequency response function such that it is a 3D array with shape $N_o \\times N_i \\times N_f$.  We will also assume that we only peformed measurements with inputs at the first and second degrees of freedom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0e5df1a5-cdc8-4e07-a7f9-e041e22c4a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "H = H_general.transpose(1,2,0)[:,:2,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c834190f-3f0d-41fe-866f-e616e36e9cfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 2, 1000)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20e3730a-73f0-4316-9c23-6545c19572aa",
+   "metadata": {},
+   "source": [
+    "We will also assume that we only performed measurements over a limited bandwidth from 10 rad/s to 80 rad/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a274ff4c-0012-48b5-9e62-48351fac2513",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frequency_lines_to_keep = (omegas > 10) & (omegas < 80)\n",
+    "H = H[:,:,frequency_lines_to_keep]\n",
+    "omegas = omegas[frequency_lines_to_keep]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b873134-0289-4846-8bcc-824d57174fc3",
+   "metadata": {},
+   "source": [
+    "Now that we have truncated the frequency domain, we will define some helper variables to make the code a bit more intuitive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f7dcec4f-fbf1-4afd-bccb-e9f1b84364b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Outputs: 3\n",
+      "Num Inputs: 2\n",
+      "Num Frequencies: 778\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_output, num_input, num_freq = H.shape\n",
+    "print('Num Outputs: {:}\\nNum Inputs: {:}\\nNum Frequencies: {:}'.format(num_output, num_input, num_freq))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f5f1720-01be-4a77-bfd2-074f1d23d420",
+   "metadata": {},
+   "source": [
+    "We will also set our polynomial order.  In this case, we know that there are 3 modes of the structure in the bandwidth.  However, let's select 5 as the polynomial order, as we generally would like to overfit the model.  Keep in mind that this will result in 6 polynomial coefficients: 0, 1, 2, 3, 4, and 5.  Therefore, you shouldn't be confused when dimensions of arrays associated with the polynomial order have length 6 instead of length 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3c9da069-ffcd-4640-a8c7-fa281bd33fa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae930b28-a34b-4a65-a03a-05977c5809d7",
+   "metadata": {},
+   "source": [
+    "We will assume no weighting, or rather the weighting is identically 1 across all outputs and across all frequencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4df3d9a0-b12a-4ba5-b2e3-11d818d4cca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weighting = np.ones((num_freq,num_output))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e27b2ab-82df-4618-bae9-7dfb65ec472a",
+   "metadata": {},
+   "source": [
+    "The first thing we will do is to set up our $\\mathbf{\\Omega}_r(\\omega) = e^{-j\\omega_z\\Delta t_z r}$ vectors.  We will set this up as an array with number of rows equal to number of frequency lines and number of columns equal to the number of polynomial coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4942b941-1e13-4e88-97b1-f03a17335542",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(778, 6)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "omega_z = omegas - omegas[0] # Perform the shift\n",
+    "deltat = np.pi/(omegas[-1] - omegas[0]) # Compute the scale factor\n",
+    "Omega = np.exp(-1j * omega_z[:,np.newaxis] * deltat * np.arange(order+1)) # Compute Omega\n",
+    "Omega.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ea75991-908b-41c4-b425-29b5e46c286a",
+   "metadata": {},
+   "source": [
+    "In the previous code, we use broadcasting to set up our $\\mathbf{\\Omega}_r$ array.  We add a `newaxis` to our shifted frequency vector $\\omega_z$ to make it a `num_freq` $\\times$ 1 array.  This is then multiplied by an array `[0,1,2,3,4,5]` constructed from the `arange` function.  Per [Numpy's Broadcasting Rules](https://numpy.org/doc/stable/user/basics.broadcasting.html), a `num_freq` $\\times$ 1 array and a `order+1` array will be broadcast to a `num_freq` $\\times$ `order+1` array.  Multiplying by scalars `deltat` and `-1j` get performed elementwise, as does the exponential function `exp`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b46fbf38-0e64-49fa-b8c2-4fcb128ef1a3",
+   "metadata": {},
+   "source": [
+    "The next step will be to assemble the $\\mathbf{R}_o$, $\\mathbf{S}_o$, and $\\mathbf{T}_o$ matrices.  These are built from the $\\mathbf{X}_o$ and $\\mathbf{Y}_o$ matrices.\n",
+    "\n",
+    "Before we do this let's think about the dimensions of the arrays.  The matrix $\\mathbf{R}_o$ for each output will have dimension `order+1` $\\times$ `order+1`.  The matrix $\\mathbf{S}_o$ for each output will have dimension `order + 1` $\\times$ `num_input*(order+1)`.  The $\\mathbf{T}_o$ matrix for each output will have dimension `num_input*(order+1)` $\\times$ `num_input*(order+1)`.  Because we will have `num_output` of each of these matrices, we will consider assembling them into a 3D array with dimension `num_output` $\\times$ `...` $\\times$ `...` ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "97c9d6a9-be8a-4e44-a440-7ff0ac71a911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  R: (3, 6, 6)\n",
+      "  S: (3, 6, 12)\n",
+      "  T: (3, 12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "R = np.zeros([num_output, order + 1, order + 1])\n",
+    "S = np.zeros([num_output,order + 1, num_input * (order + 1)])\n",
+    "T = np.zeros([num_output, num_input * (order + 1), num_input * (order + 1)])\n",
+    "print('Shapes:\\n  R: {:}\\n  S: {:}\\n  T: {:}'.format(R.shape,S.shape,T.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f86ddef-0627-4f7d-8554-66900ad82bc1",
+   "metadata": {},
+   "source": [
+    "We will then loop through each of the outputs and assemble the corresponding portions of the matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c050e1b7-3872-4bfe-9344-9758c8403463",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accumulating Data: Progress = 0.00%\n",
+      "Accumulating Data: Progress = 33.33%\n",
+      "Accumulating Data: Progress = 66.67%\n",
+      "Accumulating Data: Progress = 100.00%\n",
+      "Shapes:\n",
+      "  Xo: (778, 6)\n",
+      "  Yo: (778, 12)\n",
+      "  Ro: (6, 6)\n",
+      "  So: (6, 12)\n",
+      "  To: (12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for o in range(num_output):\n",
+    "    print('Accumulating Data: Progress = {:0.2f}%'.format(o / num_output * 100.0))\n",
+    "    # The Xo matrix depends only on the weighting and Omega.  We use\n",
+    "    # broadcasting to extend the weighting across all polynomial coefficients\n",
+    "    Xo = weighting[:, o][..., np.newaxis] * Omega\n",
+    "    # The Yo matrix will need the measured frequency response functions\n",
+    "    # at the output o.\n",
+    "    Ho = H[o, ...]\n",
+    "    # We note that rather than re-multiplying the weighting by Omega in the\n",
+    "    # Yo matrix, we can simply use Xo.  Here we must loop over all frequency\n",
+    "    # lines to perform the kronecker product.  The frequency lines are the\n",
+    "    # rows of X, and the columns of Ho, so we transpose Ho.\n",
+    "    # Xk is then the polynomial coefficients at the k frequency line and\n",
+    "    # Hk is the FRF for each input at that frequency line.\n",
+    "    Yo = np.array([-np.kron(Xk, Hk) for Xk, Hk in zip(Xo, Ho.transpose())])\n",
+    "    # We then construct the R, S, and T matrices for that output from their\n",
+    "    # definitions\n",
+    "    Ro = np.real(Xo.conjugate().transpose() @ Xo)\n",
+    "    So = np.real(Xo.conjugate().transpose() @ Yo)\n",
+    "    To = np.real(Yo.conjugate().transpose() @ Yo)\n",
+    "    # We stick the matrices into the 3D arrays for future usage.\n",
+    "    R[o, :, :] = Ro\n",
+    "    S[o, :, :] = So\n",
+    "    T[o, :, :] = To\n",
+    "print('Accumulating Data: Progress = {:0.2f}%'.format((o+1) / num_output * 100.0))\n",
+    "print('Shapes:\\n  Xo: {:}\\n  Yo: {:}\\n  Ro: {:}\\n  So: {:}\\n  To: {:}'.format(\n",
+    "    Xo.shape,Yo.shape,Ro.shape,So.shape,To.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7004a057-06f0-42bc-882c-39cdb34aa075",
+   "metadata": {},
+   "source": [
+    "We will now compute the $\\mathbf{M}$ matrix, which is equal to $\\sum_{o=1}^{N_o}\\left(\\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)$.  Note that we dropped the factor of 2 in front of the expression because the final answer $\\alpha$ is only defined up to a matrix product.  Multiplying the answer by 2 will not result in any meaningful change.\n",
+    "\n",
+    "We will compute the contribution $\\mathbf{M}_o = \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o$ for each output and then sum them together to construct $\\mathbf{M}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "cbee950b-972c-45aa-8ead-6d65fdd1afc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  M: (12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Because S is a 3D array, to transpose it, we will only want to\n",
+    "# swap the last two dimensions and leave the first output dimension\n",
+    "# out front.\n",
+    "# We compute R^-1 using np.linalg.solve, which solves a problem.\n",
+    "# np.linalg.solve(R,S) is equivalent to R^-1@S, but is faster and\n",
+    "# better conditioned.\n",
+    "M = np.sum(T - S.transpose(0,2,1) @ np.linalg.solve(R, S),axis=0)\n",
+    "print('Shapes:\\n  M: {:}'.format(M.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26545d69-262d-459a-919c-72a6fcfc2dc6",
+   "metadata": {},
+   "source": [
+    "Now we will solve for the parameters $\\mathbf{\\alpha}$ and construct the companion matrix.  Recall that in our solution we will assume $\\mathbf{\\alpha}_p$ is equal to the identity matrix and drops out of the companion matrix computation, so we really only need to compute $\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "40d56445-b74b-4f9b-af2e-1e98251a22ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  Maa: (10, 10)\n",
+      "  Mbb: (10, 2)\n",
+      "  alpha: (10, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Maa = M[:order * num_input, :order * num_input]\n",
+    "Mbb = -M[:order * num_input, order * num_input:]\n",
+    "alpha = np.linalg.solve(Maa, Mbb)\n",
+    "print('Shapes:\\n  Maa: {:}\\n  Mbb: {:}\\n  alpha: {:}'.format(\n",
+    "    Maa.shape,Mbb.shape,alpha.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f24809a1-e67f-4446-b842-19f139beadd7",
+   "metadata": {},
+   "source": [
+    "We will then construct the companion matrix $\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{1}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$.  The partition $\\mathbf{0}$ will have shape `(order-1)*num_input` $\\times$ `num_input`, and the partition $\\mathbf{I}$ will have shape `(order-1)*num_input` $\\times$ `(order-1)*num_input`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "788bc574-8350-4be2-b8db-acfab146d5b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  C: (10, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "C_top_left = np.zeros([(order - 1) * num_input, num_input])\n",
+    "C_top_right = np.eye((order - 1) * num_input)\n",
+    "C_bottom = -alpha.transpose()\n",
+    "C = np.concatenate(\n",
+    "    (np.concatenate((C_top_left, C_top_right), axis=1),\n",
+    "     C_bottom), axis=0)\n",
+    "print('Shapes:\\n  C: {:}'.format(\n",
+    "    C.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab985b8c-3070-4041-a128-e24fa02ba8ec",
+   "metadata": {},
+   "source": [
+    "Finally, we can compute the eigenvalues and eigenvectors from this matrix, which should be the solutions to the polynomial.  Remember that the eigenvalues will be in the $z$-domain, and must be transformed back to the $s$ domain for us to extract natural frequency and damping ratio: $\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5f2a5fec-6a50-4f5e-9286-ab7ab4b95ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zpoles, V = np.linalg.eig(C)\n",
+    "spoles = -np.log(zpoles)/deltat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be16c045-b456-42dd-acf1-c4704cc460fb",
+   "metadata": {},
+   "source": [
+    "We will immediately discard any poles where the imaginary part is less than zero or the real part is greater than zero, ensuring that we also discard the corresponding eigenvectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9070751d-05f8-4510-8fbf-41d66fb9942f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keep_poles = (np.imag(spoles) > 0) & (np.real(spoles) < 0)\n",
+    "spoles = spoles[keep_poles]\n",
+    "V = V[:,keep_poles]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbc83e04-6f70-44ce-8720-e4cd0bf0a96b",
+   "metadata": {},
+   "source": [
+    "We can then extract the (shifted) natural frequency and damping ratios from the poles.  We unshift the natural frequency from the pole to recover the natural frequency of the structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b06e5f64-ca4f-4ffe-8e8e-56304150103d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omega_general_fit = abs(spoles) + omegas[0]\n",
+    "zeta_general_fit = -np.real(spoles)/omega_general_fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f11f8a6a-7f07-4e72-bd4f-bbf181f354cf",
+   "metadata": {},
+   "source": [
+    "The modal participation factors $\\mathbf{l}_r$ are then the last `num_input` rows of the eigenvectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "cd6279de-eb9f-401c-9baf-12ba4ebdc193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = V[-num_input:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681c6a98-1297-4e84-b1a2-c787d2853b53",
+   "metadata": {},
+   "source": [
+    "Let's compare the fit natural frequencies to those from the test problem.  We will sort them ascending so they match those computed directly from the eigenvalues of the state space formulation.  Make sure that we also sort the damping and participation factors identically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "74d9e3ed-63cc-4595-860f-2d2cad63f99c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sort_indices = np.argsort(omega_general_fit)\n",
+    "omega_general_fit = omega_general_fit[sort_indices]\n",
+    "zeta_general_fit = zeta_general_fit[sort_indices]\n",
+    "lr = lr[:,sort_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5091ad-1e98-4063-a1c7-7cd06a1053b5",
+   "metadata": {},
+   "source": [
+    "Let's now set up a table to show the differences.  We will compare the participation factors against the same degrees of freedom from the corresponding mode shapes.  Note that the participation factors may be scaled differently and rotated differently than the mode shapes, so we must rescale and rotate them to judge how well they fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "71fa8ee2-5eab-48e8-a0cb-6933f8b9d67e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frequency From System Matrices</th>\n",
+       "      <th>Frequency From FRF Fits</th>\n",
+       "      <th>Frequency Error</th>\n",
+       "      <th>Damping From System Matrices</th>\n",
+       "      <th>Damping From FRF Fits</th>\n",
+       "      <th>Damping Error</th>\n",
+       "      <th>Participation From System Matrices</th>\n",
+       "      <th>Participation From FRF Fits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.25</td>\n",
+       "      <td>27.30</td>\n",
+       "      <td>0.19%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>2.60%</td>\n",
+       "      <td>-3.90%</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "      <td>[-0.04318693+0.04408044j -0.09857685+0.09838141j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.78</td>\n",
+       "      <td>54.86</td>\n",
+       "      <td>0.15%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>2.76%</td>\n",
+       "      <td>0.67%</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "      <td>[0.06534612-0.06998435j 0.00118367+0.00286075j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>59.91</td>\n",
+       "      <td>59.88</td>\n",
+       "      <td>-0.06%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>2.69%</td>\n",
+       "      <td>1.02%</td>\n",
+       "      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>\n",
+       "      <td>[-0.06070207+0.05458388j  0.03320861-0.0344524j ]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n",
+    "                             'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n",
+    "                             'Participation From System Matrices','Participation From FRF Fits'],\n",
+    "                  dtype=object)\n",
+    "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n",
+    "    omega_general,omega_general_fit,\n",
+    "    zeta_general, zeta_general_fit,\n",
+    "    psi_general[:2,:].T, lr.T)):\n",
+    "    df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n",
+    "    df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n",
+    "    df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n",
+    "    df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n",
+    "    df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n",
+    "    df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n",
+    "    # Find the maximum participation degree of freedom from the\n",
+    "    # system matrices\n",
+    "    check_index = np.argmax(np.abs(lm))\n",
+    "    # Compute the angle of that index\n",
+    "    check_angle = np.angle(lm[check_index])\n",
+    "    # Compute the same angle from the fit\n",
+    "    this_angle = np.angle(lf[check_index])\n",
+    "    rotation = check_angle - this_angle\n",
+    "    # Compute the magnitudes of the vectors\n",
+    "    check_magnitude = np.linalg.norm(np.abs(lm))\n",
+    "    this_magnitude = np.linalg.norm(np.abs(lf))\n",
+    "    # Correct rotation and scaling\n",
+    "    lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n",
+    "    df.at[i,'Participation From System Matrices'] = str(lm)\n",
+    "    df.at[i,'Participation From FRF Fits'] = str(lf)\n",
+    "    \n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4b3d62-f721-4cd0-93ad-60ca93746ee3",
+   "metadata": {},
+   "source": [
+    "We can verify that we get very good agreement between the quantities derived from the system matrices and those fit by the FRFs.  However, we may have simply been lucky: our system of equations actually solved for `order*num_inputs` or 10 poles, and it just so happened that 3 of them ended up being physically realizable with positive damping and frequency.  It is possible that more of them would have been physically realizable, in which case we would have had to go through and try to figure out which poles are \"real\" and which are \"computational\", and without *a priori* knowledge of the true pole information, it could be very difficult to do this.\n",
+    "\n",
+    "Instead, we use a stabilization diagram.  We solve for a range of different polynomial orders and we plot the resulting frequencies on a figure.  We then identify poles that appear in many different model orders to be \"stable\" and we accept these as real poles.\n",
+    "\n",
+    "Rather than code all this from scratch, we will simply rely on SDynPy's `PolyPy` implementation to help us out.  `PolyPy` relies on the FRF being structured as a `TransferFunctionArray`, so we must first create one from our data.  Note that `PolyPy` works with frequencies in Hz rather than in radians per second, so we must convert our `omegas` variable by dividing by $2\\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "547b6ff7-d71a-4f9c-9950-26f4d3cf9066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sdynpy as sdpy\n",
+    "tfarray = sdpy.data_array(sdpy.data.FunctionTypes.FREQUENCY_RESPONSE_FUNCTION,\n",
+    "                          abscissa = omegas/(2*np.pi), ordinate = H, \n",
+    "                          coordinate = sdpy.coordinate.outer_product(\n",
+    "                              sdpy.coordinate_array([1,2,3],'X+'),\n",
+    "                              sdpy.coordinate_array([1,2],'X+')))\n",
+    "pp = sdpy.PolyPy(tfarray, displacement_derivative = 0) # We tell PolyPy these are displacement FRFs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c771ea7-f4a6-46ab-a8e9-1e71f32fd5e1",
+   "metadata": {},
+   "source": [
+    "We can then tell `PolyPy` which polynomial orders to solve for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "eefd5ef6-b2a6-4ba8-912b-d8428a3f7925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accumulating Data: Progress = 0.00%\n",
+      "Accumulating Data: Progress = 33.33%\n",
+      "Accumulating Data: Progress = 66.67%\n",
+      "Solving for 29 roots (1 of 13)\n",
+      "Solving for 27 roots (2 of 13)\n",
+      "Solving for 25 roots (3 of 13)\n",
+      "Solving for 23 roots (4 of 13)\n",
+      "Solving for 21 roots (5 of 13)\n",
+      "Solving for 19 roots (6 of 13)\n",
+      "Solving for 17 roots (7 of 13)\n",
+      "Solving for 15 roots (8 of 13)\n",
+      "Solving for 13 roots (9 of 13)\n",
+      "Solving for 11 roots (10 of 13)\n",
+      "Solving for 9 roots (11 of 13)\n",
+      "Solving for 7 roots (12 of 13)\n",
+      "Solving for 5 roots (13 of 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pp.compute_poles(np.arange(5,30,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d394d513-7cd6-4323-8ae9-fa540a477736",
+   "metadata": {},
+   "source": [
+    "We can then plot the stabilization diagram.  The stabilization diagram will have different markers to denote the stability of a given pole.  We generally overlay these poles with a mode indicator function to help us figure out which poles are \"real\".  In this case, a red X denotes an unstable pole, a blue triangle denotes that the frequency has stablized, a blue square means that the frequency and damping have stabilized, and a green circle means that the frequency, damping, and participation factor have stabilized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a5ff5306-cdbf-46db-95cc-b539e9adc423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Polynomial Order')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmif_axis, order_axis = pp.plot_stability()\n",
+    "cmif_axis.set_ylabel('CMIF')\n",
+    "cmif_axis.set_xlabel('Frequency (Hz)')\n",
+    "order_axis.set_ylabel('Polynomial Order')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e544c5-0ad6-4d0d-853e-5f91a09066ae",
+   "metadata": {},
+   "source": [
+    "We can see that as we move up to a higher model order, we start to see more computational poles appear, as shown by the red Xs throughout the frequency band.  However we see the three main columns of green circles denoting three stable poles in our fits.  We would then select these three stable poles.\n",
+    "\n",
+    "In `PolyPy` we can select them by index, and we can determine the indices by plotting the stabilization diagram with labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "56fd5c29-a2d3-4dd3-84f4-5d21ad3e38ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Polynomial Order')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmif_axis, order_axis = pp.plot_stability(label_poles=True)\n",
+    "cmif_axis.set_ylabel('CMIF')\n",
+    "cmif_axis.set_xlabel('Frequency (Hz)')\n",
+    "order_axis.set_ylabel('Polynomial Order')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96f1fb64-126c-4b63-9cd3-c2ea7bb2bf84",
+   "metadata": {},
+   "source": [
+    "We can then extract a final pole list from those indices.  For example we may wish to select poles `(4,0)`, `(4,1)`, and `(4,2)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "550a58ed-8f09-4a2c-a4d9-bb7d532ffe4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pole_indices = [(4,0), (4,1), (4,2)]\n",
+    "pole_list = pp.pole_list_from_indices(pole_indices)\n",
+    "omega_general_stab = pole_list['omega']\n",
+    "zeta_general_stab = pole_list['zeta']\n",
+    "lr_stab = pole_list['Lr_complex']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e01bd62-660d-4575-9f35-3d5295933814",
+   "metadata": {},
+   "source": [
+    "The stabilization diagram actually reveals that for our test case of `order=5`, our poles may not have yet stabilized, and therefore our results may be inaccurate!  Let's compare the same quantities extracted from the stabilization diagram with those computed from the system matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "43de5b7e-a171-48b8-a89a-94fcddfe309f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frequency From System Matrices</th>\n",
+       "      <th>Frequency From FRF Fits</th>\n",
+       "      <th>Frequency Error</th>\n",
+       "      <th>Damping From System Matrices</th>\n",
+       "      <th>Damping From FRF Fits</th>\n",
+       "      <th>Damping Error</th>\n",
+       "      <th>Participation From System Matrices</th>\n",
+       "      <th>Participation From FRF Fits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.25</td>\n",
+       "      <td>27.26</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>-0.02%</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.78</td>\n",
+       "      <td>54.79</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>59.91</td>\n",
+       "      <td>59.92</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>\n",
+       "      <td>[-0.06059006+0.05448316j  0.03373322-0.03429902j]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n",
+    "                             'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n",
+    "                             'Participation From System Matrices','Participation From FRF Fits'],\n",
+    "                  dtype=object)\n",
+    "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n",
+    "    omega_general,omega_general_stab,\n",
+    "    zeta_general, zeta_general_stab,\n",
+    "    psi_general[:2,:].T, lr_stab)):\n",
+    "    df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n",
+    "    df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n",
+    "    df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n",
+    "    df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n",
+    "    df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n",
+    "    df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n",
+    "    # Find the maximum participation degree of freedom from the\n",
+    "    # system matrices\n",
+    "    check_index = np.argmax(np.abs(lm))\n",
+    "    # Compute the angle of that index\n",
+    "    check_angle = np.angle(lm[check_index])\n",
+    "    # Compute the same angle from the fit\n",
+    "    this_angle = np.angle(lf[check_index])\n",
+    "    rotation = check_angle - this_angle\n",
+    "    # Compute the magnitudes of the vectors\n",
+    "    check_magnitude = np.linalg.norm(np.abs(lm))\n",
+    "    this_magnitude = np.linalg.norm(np.abs(lf))\n",
+    "    # Correct rotation and scaling\n",
+    "    lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n",
+    "    df.at[i,'Participation From System Matrices'] = str(lm)\n",
+    "    df.at[i,'Participation From FRF Fits'] = str(lf)\n",
+    "    \n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef16ad2f-08bb-458e-bf22-5143e52820b4",
+   "metadata": {},
+   "source": [
+    "We now see that we have extracted nearly identical modes to those that were computed directly from the system matrices, meaning the fit has significantly improved by using a higher-order polynomial.\n",
+    "\n",
+    "Note that this section has used the code-based `PolyPy` implementation.  However, in SDynPy there is also a `PolyPy_GUI` which can be used to interactively select poles."
+   ]
+  },
+  {
+   "attachments": {
+    "188f4ca3-1e6a-465f-8085-438e9f6aac2b.png": {
+     "image/png": 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"
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "31e870ab-8990-4de7-8137-d3c2324bb9ed",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "ppgui = sdpy.PolyPy_GUI(tfarray)\n",
+    "```\n",
+    "![image.png](attachment:188f4ca3-1e6a-465f-8085-438e9f6aac2b.png)\n",
+    "![image.png](attachment:628e4999-feea-4f9c-888c-9e86b6bd0f32.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "278d5451-ab7a-4f7e-8a54-c4f12ace5031",
+   "metadata": {},
+   "source": [
+    "## Fitting Mode Shapes to FRF Data\n",
+    "Once the poles and participation factors are known, we can go ahead and fit the mode shapes of the system.  Looking again at the frequency response function equation\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n",
+    "containing the frequency response function $\\mathbf{H}$, the participation factors $\\mathbf{l}_r$, the frequency lines $\\omega$, and the poles $\\lambda_r$.  We wish to solve for the mode shapes $\\mathbf{v}_r$ and the residuals $\\mathbf{R}_u$ and $\\mathbf{R}_l$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "913fad3e-aa05-4d5e-a13c-8c08d3f5cc48",
+   "metadata": {},
+   "source": [
+    "We want to pose this problem in an entirely real set of equations $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$.\n",
+    "\n",
+    "The mode shapes will generally be complex $\\mathbf{v}_r = \\mathbf{v}_r^R + j \\mathbf{v}_r^I$, so we will want to solve simultaneously for the real and imaginary parts.  Similarly, the frequency response function is complex, so we will need to split that up into its real and imaginary parts.  Ignoring the residuals for now:\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{H}^R\\\\\\mathbf{H}^I\\end{bmatrix} = \\mathbf{P}\\begin{bmatrix}\\mathbf{v}^R\\\\\\mathbf{v}^I\\end{bmatrix}$$\n",
+    "\n",
+    "The goal is to then find the coefficient matrix $\\mathbf{P}$ which allows us to solve for the mode shape coefficients.\n",
+    "\n",
+    "$$\\mathbf{P} = \\begin{bmatrix}\\mathbf{P}_{RR} & \\mathbf{P}_{RI}\\\\\\mathbf{P}_{IR} & \\mathbf{P}_{II}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25037b11-5fbc-4520-83d5-b2ae1d418a15",
+   "metadata": {},
+   "source": [
+    "We will start with the term\n",
+    "\n",
+    "$$\\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}$$\n",
+    "\n",
+    "We will substitute each term with its real and imaginary parts to ensure that all of the variables in the expression are real.\n",
+    "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right)}{j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right)}{- j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega}$$\n",
+    "\n",
+    "As we wish to collect real and imaginary parts, we will want to multiply each term's numerator and denominator by the denominator's complex conjugate.\n",
+    "\n",
+    "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(- j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}$$\n",
+    "\n",
+    "We would then like to collect real and imaginary terms, as well as collecting terms that have the real and imaginary part of the mode shapes.  $\\mathbf{P}_{RR}$ will contain real coefficients of the real part of the mode shapes.  $\\mathbf{P}_{RI}$ will contain real coefficients of the imaginary part of the mode shapes.  $\\mathbf{P}_{IR}$ will contain imaginary coefficients of the real part of the mode shapes.  Finally, $\\mathbf{P}_{II}$ will contain imaginary coefficients of the imaginary part of the mode shapes.\n",
+    "\n",
+    "$$j v^{I}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + v^{I}_{ir} \\left(\\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n",
+    "$$+ v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + j v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n",
+    "\n",
+    "We can substitute this into a matrix form where the real equations are on the top row and the imaginary equations are on the bottom row.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\j H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\j \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) & j \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n",
+    "\n",
+    "Cancelling out the $j$ terms in the bottom row reveals the final matrix equation for the contribution to the frequency response function from one mode shape $H_r$.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n",
+    "\n",
+    "We can then solve this equation in a least squares sense by using the pseudoinverse.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right]^+ \\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "600deb13-308c-4883-b3d3-66147f5f863e",
+   "metadata": {},
+   "source": [
+    "Note that this equation must be generalized to solve for all modal coefficients simultaneously in terms of all frequency response functions for all degrees of freedom at all frequency lines.  For this we will give some thought as to how to structure the matrices.\n",
+    "\n",
+    "At the end of the computation, the matrix $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ should have real and imaginary parts for each degree of freedom for each mode being solved for.  We will solve for these terms over all frequency lines simultaneously.  However, because there is no frequency line dimension in the mode shape matrix, this reveals that the frequency line must be on the inner dimension of the matrix product.  The frequency lines must correspond to the rows of $\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$ and $\\mathbf{P}$ (which will be the columns of $\\mathbf{P}^+$).  We also see that the columns of the FRF matrix corresponding to the references at each frequency line do not appear in the final mode shape matrix (they are related to the participation factors, which are known).  Therefore this dimension must also be encoded in the inner dimensions of the matrix product.\n",
+    "\n",
+    "We can therefore start to envision what the structure of each term in $\\mathbf{P}$ should look like.  The columns of $\\mathbf{P}$ (the rows of $\\mathbf{P}^+$) must correspond to the mode shape matrix, and they should correspond to the mode index rather than the response degree of freedom index because we need matrix multiplication of $\\mathbf{P}\\mathbf{v}$ to sum over modes, and summation occurs on the inner dimensions of the matrices in matrix multiplication.  The rows of $\\mathbf{P}$ (the columns of $\\mathbf{P}^+$) must correspond to both the mode participation factor (reference degrees of freedom) and frequency lines, separated into real and imaginary parts.  If we compute each partition of $\\mathbf{P}$ as a `num_input` $\\times$ `num_freq` $\\times$ `num_modes` array, and then reshape it into a `num_input*num_freq` $\\times$ `num_modes` array, this should produce the correct size matrix.\n",
+    "\n",
+    "Each partition of the mode shape matrix $v^{R}_{ir}$ and $v^{I}_{ir}$ will then be revealed as a matrix with number of rows equal to the number of modes and columns equal to the number of response degrees of freedom.\n",
+    "\n",
+    "We can similarly construct the dimensions of the partitions $H_r^{R}$ and $H_r^{I}$, as they must now be consistent with the rest of the terms in the equation.  The number of rows of these partitions must match the number of rows of partitions in $\\mathbf{P}$, meaning that the FRF must be similarly structured with mode participation factor (reference degrees of freedom) and frequency lines along its rows and response degrees of freedom along its columns.\n",
+    "\n",
+    "This generalization into matrix form can be somewhat abstract to visualize, but should become more obvious when applied to real data arrays."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c129812-1273-49d1-b18f-5d2851a401f0",
+   "metadata": {},
+   "source": [
+    "### Fitting Residuals for Out-of-Band Modes\n",
+    "We note that the terms $v_{ir}$ are not the only unknowns in the frequency response function formulation.  In general, we can never fit all of the modes of a structure, so there will always be some contributions from out-of-band modes in our frequency response functions.  As we try to solve for the mode shapes of the structure, these out-of-band modes can contaminate the response.  Therefore we also include terms to try to match the contributions from these out-of-band modes, and we call these terms residuals.\n",
+    "\n",
+    "We will in general have a lower residual $R_{Lij}$ and an upper residual $R_{Uij}$, and as the expression suggests, there can be one term in the residual for each pair of input and output degrees of freedom.  We must therefore solve for these terms simultaneously with the mode shapes.  We can think of these terms as \"extra modes\" of the structure, which therefore should imply that the $\\mathbf{P}$ matrix should have columns added corresponding to the coefficients of the residual variables, and the $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ matrix should have rows appended corresponding to the real and imaginary parts of these terms.\n",
+    "\n",
+    "Indeed, the coefficients are easily picked directly from the frequency response function.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]\n",
+    "= \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & -\\frac{1}{\\omega^2} & 0 & 1 & 0 \\\\\n",
+    "\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & 0 & -\\frac{1}{\\omega^2} & 0  & 1\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\\\R^R_{Lij}\\\\R^I_{Lij}\\\\R^R_{Uij}\\\\R^I_{Uij}\\end{matrix}\\right]$$\n",
+    "\n",
+    "When generalizing to multiple references, responses, and frequency lines, each residual term $R^R_{Lij}$, $R^I_{Lij}$, $R^R_{Uij}$ and $R^I_{Uij}$ will have a number of rows equal to the number of references and a number of columns equal to the number of responses.  Similarly the coefficient terms ($0$, $1$, $-\\frac{1}{\\omega}$) will have a number of rows equal to the number of `num_freq*num_input` and number of columns equal to `num_input`.  Because of the structure of the matrices, they are easily constructed using Kronecker products of identity matrices multiplied by either $1$ or $-\\frac{1}{\\omega^2}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533195b0-3bd2-4e9a-8314-dcf73de1d52d",
+   "metadata": {},
+   "source": [
+    "### A Note on Types of FRFs\n",
+    "Note that the mode shape derivations described in the previous sections are valid for *receptance* FRFs, meaning FRFs between displacement and force.  When transforming to from *receptance* to *mobility* (velocity over force) to *accelerance* (acceleration over force) FRFs, we multiply the FRF expression by $j\\omega$ at each step.  This will clearly change which terms are real and which terms are imaginary, as well as the coefficients on each term.  As this document is already quite long, these will not be derived, but the same approach as described above can be used to find the coefficient matrices for those cases; simply substitute for real and imaginary parts of each variable, collect imaginary and real coefficients for imaginary and real parts of each term of interest, and place them into a matrix form."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22c07fae-f815-4be9-8a10-04fec78dd6c2",
+   "metadata": {},
+   "source": [
+    "### Normalizing Mode Shapes\n",
+    "Once we solve for the mode shapes, our job is not quite done.  Due to how the problem is formulated, the particpation factors have some arbitrary scaling and rotation applied to them.  This means when the shapes are found, they will also have an arbitrary scaling and rotation applied to them.  There is no constraint that the participation factors are identical to the mode shapes at the input locations.\n",
+    "\n",
+    "To perform the normalization, we compute the residue matrices for each mode\n",
+    "\n",
+    "$$R_{rij} = \\psi_{ir}l_{jr}$$\n",
+    "\n",
+    "We recognize the at the drive point, the residue is equal to the mode shape squared or the participation factor squared if the mode shapes are scaled such that the participation factors are the mode shapes.\n",
+    "$$R_{rii} = \\psi_{ir}l_{ir} = \\psi_{ir}^2 = l_{ir}^2$$\n",
+    "\n",
+    "However, we currently do not have such a scenario.  In our case, the fit shapes $\\bar{\\psi}_{ir}$ are scaled by some complex coefficient $c$, and the participation factors $\\bar{l}_{jr}$ are scaled by some factor ${\\frac{1}{c}}$.\n",
+    "\n",
+    "$$\\psi_{ir} = c\\bar{\\psi}_{ir}$$\n",
+    "$$l_{jr} = \\frac{1}{c}\\bar{l}_{jr}$$\n",
+    "\n",
+    "We note that due to the reciprocal scale factors, we can still compute the residuals.  We can therefore set up a set of equations to solve for the scale factor that gets applied to the mode shapes.\n",
+    "\n",
+    "$$R_{rii} = \\psi_{ir}l_{ir} = c\\bar{\\psi}_{ir}\\frac{1}{c}\\bar{l}_{ir} = \\bar{\\psi}_{ir}\\bar{l}_{ir}$$\n",
+    "$$R_{rii} = \\psi_{ir}^2 = c^2\\bar{\\psi}_{ir}^2$$\n",
+    "$$\\sqrt{R_{rii}} = c \\bar{\\psi}_{ir}$$\n",
+    "\n",
+    "Since we have participation factors and shapes at multiple degrees of freedom for a multiple-input solution, we can solve for the scale factor $c$ in a least-squares sense for each mode.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = \\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}c$$\n",
+    "\n",
+    "$$\\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}^+\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = c$$\n",
+    "\n",
+    "**BE AWARE:** when taking the square root of the residue matrix terms, there are two values that can be obtained (e.g. $\\sqrt{4} = \\pm2$).  In the case of real numbers, most square root algorithms will only return the positive root.  However for complex numbers, it is not so obvious which sign the root will have (positive real part?  positive imaginary part?).  Flipping a sign on one term in the matrix $\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix}$ will make the least squares solution incorrect, therefore we must make sure the signs are consistent between the mode shapes $\\bar{\\psi}_{ir}$ and the residue terms $\\sqrt{R_{rii}}$.\n",
+    "\n",
+    "We can do this by dividing the termes $\\sqrt{R_{rii}}$ by $\\bar{\\psi}_{ir}$ and looking at the direction of the complex vectors.  If the vectors are all pointing nominally the same direction, then the least squares problem will solve for that direction successfully.  If some are pointing opposite due to the sign of the output of the square root, then the scale factor found by the least squares solution will be incorrect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13f2483d-e733-4d8d-94e5-b4cfbfcfde11",
+   "metadata": {},
+   "source": [
+    "### Back to the Example Problem\n",
+    "While the symbolic mathematics in the previous sections was straightforward (though perhaps a bit tedious), the discussion of the construction of a system of equations to solve for each coefficient of the mode shape matrix was perhaps a bit abstract.  We will therefore make it more concrete by applying it to our specific example problem.  Note that we will take advantage heavily of broadcasting and dimensional manipulation to help us put these equations together.  Readers are encouraged to run these problems themselves to understand exactly what operations are being performed at each step, and the resulting shapes of all of the arrays.\n",
+    "\n",
+    "We will first generate the $\\mathbf{P}$ matrix described above.  We will start with only the mode shape coefficients, then show how it can be expanded to include residuals.  This will involve translating our symbolic code into numeric Numpy code.  To help us repeat this process for different sets of modes and frequency ranges when we introduce the residuals later, we will define the process to generate the $\\mathbf{P}$ matrix as a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9cc21035-44bd-4128-ac92-0709ab740be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def P_modal(omegas, poles, participation_factors):\n",
+    "    '''Construct the mode shape coefficient matrix\n",
+    "\n",
+    "    Constructs the coefficients from the system poles,\n",
+    "    participation factors, and frequency lines.\n",
+    "\n",
+    "    Arguments should be passed as arrays and will be broadcast\n",
+    "    together.\n",
+    "    '''\n",
+    "    # We want the output array to be n_input x n_freq*2 x n_modes*2\n",
+    "    # So let's adjust the terms so they have the right shapes\n",
+    "    # We want frequency lines to be the middle dimension\n",
+    "    omegas = omegas[np.newaxis,:,np.newaxis]\n",
+    "    # We want inputs to be the first dimension and modes the\n",
+    "    # last dimension\n",
+    "    participation_factors = participation_factors.T[:,np.newaxis,:]\n",
+    "    # Split up terms into real and imaginary parts\n",
+    "    pr = poles.real\n",
+    "    pi = poles.imag\n",
+    "    lr = participation_factors.real\n",
+    "    li = participation_factors.imag\n",
+    "    P_blocks = np.array([\n",
+    "        [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n",
+    "         (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n",
+    "        [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n",
+    "         (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n",
+    "    return np.block([[P_blocks[0,0],P_blocks[0,1]],\n",
+    "                     [P_blocks[1,0],P_blocks[1,1]]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cef05146-3d81-4003-86fe-1500592485de",
+   "metadata": {},
+   "source": [
+    "We can now create our pole vector from the extracted frequencies and damping ratios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "0693d32a-2136-4c72-8f6e-fb67515ce6f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  omegas: (778,)\n",
+      "  poles: (3,)\n",
+      "  participation_factors: (3, 2)\n",
+      "  P: (2, 1556, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute poles from the extracted data\n",
+    "poles = -omega_general_stab*zeta_general_stab + 1j*omega_general_stab*np.sqrt(1-zeta_general_stab**2)\n",
+    "# Compute P with our function\n",
+    "P = P_modal(omegas,poles, lr_stab)\n",
+    "\n",
+    "# Check shapes\n",
+    "print('Shapes:\\n  omegas: {:}\\n  poles: {:}\\n  participation_factors: {:}\\n  P: {:}'.format(\n",
+    "    omegas.shape, poles.shape, lr_stab.shape, P.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68336074-9c72-4186-9c32-76c4597712f8",
+   "metadata": {},
+   "source": [
+    "We must now set up the frequency response functions to perform the least squares computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "b9a0bf64-820f-4786-b4e6-f78f7f209dad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  H_LS: (2, 1556, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "H_LS = H.transpose(1,2,0)\n",
+    "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n",
+    "\n",
+    "print('Shapes:\\n  H_LS: {:}'.format(\n",
+    "    H_LS.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0af6fc0-5e43-4fe6-838d-d30266bfb2f5",
+   "metadata": {},
+   "source": [
+    "To construct the least-squares set of equations, we simply squash the first two dimensions into a single dimension, in which case the least-squares solution will solve over all frequency lines and all inputs simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0ef24c44-cb79-4317-9aae-5a3b99ec51ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n",
+    "                            H_LS.reshape(-1,H_LS.shape[-1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818bdad7-6a69-422e-9d1e-f5d2480acd83",
+   "metadata": {},
+   "source": [
+    "We can reconstruct the FRF by muliplying the output shapes by the coefficient matrix and extracting real and imaginary parts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "570bd80c-83d0-4f5e-a6ca-289637763d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reconstructed_frfs = P.reshape(-1,P.shape[-1])@shapes_LS\n",
+    "H_recon = reconstructed_frfs.reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_recon = H_recon[:H_recon.shape[0]//2] + 1j*H_recon[H_recon.shape[0]//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a6e4c568-2976-4d5b-a4cb-76547020ee30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general_stab.size\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(num_output*2,num_input, sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n",
+    "                      H_recon.reshape(omegas.size,-1).T,\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['Truth','Reconstructed from\\nLeast-Squares Solution'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "669682c7-558a-4ab6-995c-c24c8dccdea1",
+   "metadata": {},
+   "source": [
+    "We have now solved in a least-squares sense for our modal coefficients.  We should extract the shape arrays from the matrix.  The top partition will be the real part of the shape, and the bottom part will be the imaginary part of the shape.  Remember also that the terms are transposed from the typical mode shape matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "aa810667-ba40-42ca-8b2b-fab6ecfcbf3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_stab = (shapes_LS[:shapes_LS.shape[0]//2]+1j*shapes_LS[shapes_LS.shape[0]//2:]).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39a5a890-5f7e-478a-b0b2-985454864c4a",
+   "metadata": {},
+   "source": [
+    "Note that because the participation factors were arbitrarily rotated, these shapes will also be arbitrarily rotated.  Therefore we need to normalize the mode shapes.  We will do this by computing the residue matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "09077e1b-1ba5-4527-8898-cf6539d8c7b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residues = np.einsum('ik,jk->kij',psi_stab,lr_stab.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a8143f5-66d3-41b2-a024-931218b1a1ba",
+   "metadata": {},
+   "source": [
+    "We can get the drive point response indices corresponding to the participation factors and pull out the drive point residues.  In general, the drive point residues should have a negative imaginary part, so we can use this to discard 'bad' drive point data where the drive point does not excite the mode well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d6a3f830-7704-46ef-a7a0-a6388757dfd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drive_indices_response = np.array([0,1])\n",
+    "drive_indices_input = np.array([0,1])\n",
+    "drive_residues = residues[:,drive_indices_response,drive_indices_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56cd126e-1a06-4234-99ae-8929d52116a7",
+   "metadata": {},
+   "source": [
+    "We will now go through each mode and compute the correct shape scaling using only the good drive points where the imaginary part is negative and not close to zero.  First, let's go through and make sure that our participation factors and mode shapes that we solved for are actually scaled versions of the true mode shapes.  I'm going to mass normalize my mode shapes for better comparisions to the fit data without having to carry around the modal mass term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "883ccaa0-1f86-41ae-947e-2d0796702b0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mass normalize the mode shapes\n",
+    "psi_general /= np.sqrt(ma_general)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aed6d5e9-2bb7-43a1-957b-b76d1bda87d6",
+   "metadata": {},
+   "source": [
+    "Let's find the scale factor between the participation factors and the mode shapes at the participation factors.  We will average over all of the degrees of freedom for each mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "bcf8a804-ecc4-45d6-8f55-e1e0874bfde2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_scaling = np.mean(psi_general[:2,:]/lr_stab.T,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a6b122a4-87ab-476a-8d7a-510fb951cd0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.18452662-0.18416078j,  0.08928666-0.0956242j ,\n",
+       "       -0.093857  +0.08439714j])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83c5bce4-a5cb-4e7d-9214-83b2800f6bf2",
+   "metadata": {},
+   "source": [
+    "We can then check to see if we reconstruct the mode shape by applying the scaling to the participation factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "aa40a7de-d7e4-47e3-985a-f4f24fe5e3ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267784-0.03499731j,\n",
+       "        -0.03029502+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.01686661-0.01714951j]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_stab.T*lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e887ee89-7ad0-4fae-bee5-be261d25caa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,\n",
+       "        -0.03029503+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.0168666 -0.01714951j]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general[:2,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2245282f-b4cc-4448-8e05-72d35a076f38",
+   "metadata": {},
+   "source": [
+    "Now we will do the same thing for the mode shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5458c528-b3dd-449f-87b7-01181b91c076",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_scaling = np.mean(psi_general/psi_stab,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "349fc0a1-850a-41f4-aab0-8c1198a09e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,\n",
+       "       -5.88158134-5.30751768j])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "805218db-fbff-419b-a61f-248fad8c7c63",
+   "metadata": {},
+   "source": [
+    "Let's reconstruct the mode shapes by scaling the fit mode shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "ec397820-33e5-4bd5-b968-8d6ea7d63cc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02159971+0.02202864j,  0.03316166-0.03473379j,\n",
+       "        -0.03033178+0.02722321j],\n",
+       "       [-0.04928856+0.04919178j,  0.00094139+0.00090236j,\n",
+       "         0.0168663 -0.01716533j],\n",
+       "       [-0.02186329+0.02176496j, -0.03539605+0.03236945j,\n",
+       "        -0.02735936+0.03018341j]])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_stab*psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3e49f237-ee6c-48e2-8854-e52abd728337",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,\n",
+       "        -0.03029503+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.0168666 -0.01714951j],\n",
+       "       [-0.02186239+0.02176446j, -0.03496025+0.03263839j,\n",
+       "        -0.02739619+0.03019566j]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0854c79-1cc4-492b-bf1c-c43526067a1a",
+   "metadata": {},
+   "source": [
+    "Since the particpation factors times the mode shapes should be equal to the scaled mode shapes squared, we should be able to see that the scale factors are reciprocals of one another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7b80d0c2-c92a-479c-97a9-823c368e0cad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,\n",
+       "       -5.88158134-5.30751768j])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "87376ad2-6f5c-4c78-8b9b-f92a5191f2a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.71501354+2.70963081j,  5.2165319 +5.58679996j,\n",
+       "       -5.89109483-5.29733029j])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050511f9-7ba5-4c09-a5e9-1f92d713f291",
+   "metadata": {},
+   "source": [
+    "This was a sanity check to make sure the mode shapes and participation factors were identified consistently.  Unfortunately in a real situation we don't have the true mode shapes, so we need to compute them from the residue matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "5676966d-8021-4f4b-8da1-9fdff6a48330",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scale for mode 1: (-2.709418738512416-2.7149722671668157j)\n",
+      "Scale for mode 2: (5.211812790651124+5.590252990269536j)\n",
+      "Scale for mode 3: (5.886705065831037+5.299694143958719j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "psi_stab_normalized = []\n",
+    "for k,drive_residue in enumerate(drive_residues):\n",
+    "    # Throw away non-negative imaginary parts\n",
+    "    bad_indices_positive = drive_residue.imag > 0\n",
+    "    # Throw away small values compared to the average value (only considering negative imaginary parts)\n",
+    "    bad_indices_small = np.abs(drive_residue) < 0.01*np.mean(np.abs(drive_residue[~bad_indices_positive]))\n",
+    "    # Combine into a single criteria\n",
+    "    bad_indices = bad_indices_positive | bad_indices_small\n",
+    "    # Get the good indices that are remaining\n",
+    "    remaining_indices = np.where(~bad_indices)[0]\n",
+    "    # We will then construct the least squares solution\n",
+    "    shape_coefficients = psi_stab[drive_indices_response[remaining_indices],k][:,np.newaxis]\n",
+    "    residue_coefficients = np.sqrt(drive_residue[remaining_indices])[:,np.newaxis]\n",
+    "    # Before we compute the scale, we need to make sure that we have all of the signs the same way.\n",
+    "    # This is because the square root can give you +/- root where root**2 = complex number\n",
+    "    # This will mess up the least squares at it will try to find something between the\n",
+    "    # two vectors.\n",
+    "    scale_vector = (residue_coefficients/shape_coefficients).flatten()\n",
+    "    sign_vector = np.array((scale_vector.real,scale_vector.imag))\n",
+    "    # Get the signs\n",
+    "    signs = np.sign(np.dot(sign_vector[:,0],sign_vector))\n",
+    "    residue_coefficients = residue_coefficients*signs[:,np.newaxis]\n",
+    "    # Now compute the least-squares solution\n",
+    "    scale = np.linalg.lstsq(shape_coefficients,residue_coefficients)[0].squeeze()\n",
+    "    print('Scale for mode {:}: {:}'.format(k+1,scale))\n",
+    "    psi_stab_normalized.append(psi_stab[:,k]*scale)\n",
+    "psi_stab_normalized = np.array(psi_stab_normalized).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab437cbd-420d-4421-8cff-040a1f1bf4cb",
+   "metadata": {},
+   "source": [
+    "Now that we have our mode shapes, we should resynthesize frequency response functions from them to make sure they fit our original data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "576a0ff3-1f51-4d50-85a6-a9b9985a4a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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gtQFY0NqCPlreaH10jBVTW44ONkwcoBwowxcMXwzDHyx+WCx+KKs/Fqs/dpsPjqMXCR1WAx+bhQCH9WgZZCHQp7QsKi2PSqcH+lipE2DHz37Ond4IIWqBc+7IlJmZidfrJTIy8rjpkZGRbN++/YT5n376aR577LEK2bZpavwOmkz80vznedXRwTh+7DVOPr3sM+MvljUUpgKPAR4reCzgtvy/8dHpHovCfWy6tXQZlxVcdsizanJtoG0KbTEwrQbasGLVNpTXFzz+eLxBlHhC8aq6WH0aEVanKQnhAcTX8ad5VCCtooPxtVsqZJ/WFKapySx0cijPSW6xm7xiN3klbvKKPeSXuHF6TJweE5fXxOUpHZwuD6Z2Q2kKzrHu8o71mnes/zylFIZhw2a1YDWMsqvKVouB7ejYevTqs81i4Guz4Gu3HD+2WfD502s/u4VAHxs+NkPudgpRC1Vk2eSjMum0ysWwVbqsLDlWnnj/VLb8ebrnJNNKX6vjyqOTLeOxlJYpbiu4LeromOPHVvBawGVonBbIt5Quq20G2mpgWi1YtA3DtKG8djAdaNMHj+lLiScUjw7HsEdj9Y0jODCCOv526of6EhfmR2yYH3FhfkQEODDkzpsQogqdc4lXeU2aNIl77rmn7H1eXh6xsbGntS6vqUmOhGeuMDA0pYP5/8YaLObJpxvHTdcnrOO45f7fui1/msfqBR8X2Dwaqxds3tJpx17bPBqrCVYP2L1/9W00UJpAepWLYgfk+eaS5w+5/opcP8j1h8wgRU6+JjHTYKvDwXxnBPklzTD8e9O+cQsGtoykc3woVkvtuDtmmprE9DwS0/JITM8j+cA2SgoTMd2pWK2ZWKy5aIsTr+HCxI3F7cVwm+DVKA14QZmAV6GO/r/8lT+fDngUuI9daDZ02QXn0gvN6uh0hVZH31stoAwMbBimFWVaQdvRpg1t2jFNHzymA68OQlnCULZwrPYoHL71CPLzJ8jXSpi/g7qBDiKDfKgb6KBukIPwAAe2WvJ/JcTZrCLLJq09LGthsD9cYzH/KEtOfK3LypG/nuf4aTbPsbLn6Lq9YD06/Vh5c+y1/ei8f897dHBTbC+h2E7ZUOJQFDogzx/yfEvLojw/KPaFdB/Nfj8ba/BHuUNwuSIo9DbEv875tKhfj9b1g+neqA71Q3xPax8KIcSpOK3Ea8+ePXz44Yfs2bOHV199lbp16/LTTz8RFxdHq1atKjrGChUeHo7FYuHgwYPHTT948CBRUVEnzO9wOHA4HBWy7S4NwrDt/xQzWOPxekG7MU0XHtMLphvT9KC1G609aNMFpgeNB9PrBjxo01s61l609sCxQXmPzmsCXtClBZM+OlZ4UcpE4cUwvCjlwVAelHKDKn2PKl2PVl60MsvGJl7QJoZporwmhluDR2O4QXkUfs7SJM7PCYHFmuBCCC6ExjmakEIIKdBHW3Dx4rQWsS8ymd31UthV/2e2oti5I5YjrqFc2GUQ15wXR52AitnXFcnjNfltx2HmrNtBxoFZ2O0bcVuzsOa5Cc1SNMrRhOVDnXwIy9f4l5TuD4fn79b6j2cX5XCydXkwFZTYinHZoMQGzrJB4bRBiR0KfaDABwp9FAW+pe9z7JpsOyQ5DEy7DavpB+5AvN5gSjx1wFIf38D21K8bQ3wdfxLC/WkUEUCjCP9ak0ALUdtVZNkUWm8EXncfcoNdpWWQ6UZr1x9j7T5axrhBl5ZTaG/pmNJySOMF7cE4WqYoZaKUF0XpmKPlEMo8vswxPH+UN6q03DK8GqWPXrAyNcqrSy9YucHwKOwu8D02OMHXpcte+5dAo3RNUCEEF2lsx108dJHj5+JQSDaHQvZxoM4akku+ZGexYs++IL6a35Ri2yDOa9meER1jaBYVWCH7VwghjlH6WH2mU7Ro0SIGDx7M+eefz+LFi9m2bRsNGzbkmWeeYc2aNXz99deVFWuF6datG127duX1118HwDRN4uLiuP322/+xcY28vDyCg4PJzc0lKCioKsI9Y6ap8WqN19R4TI3LY+I+VuXtT1XfTpjm/WNaiduk0OWhyOmlwOmh0OmhqMSJ03kEtzMLjysTjzsD7T2MzZKDxZqHthbioRBLsQtbviIyGxqma5qkaaJySqtBbo+BX9sqsuMd5BVczvDe1zCue3yNOIF3e02mr07l+8XvY/eZj3GkgNZ7NW32aeIOl16ZNYEjgaVDVpDiSAAU+CqKHKVJTKEPaJsuvTNl4Y87U8cGxUmq+umTvlXm0ZMQk9J6iebRu2dm6Tzq2F00L9jd4OMGR9mg//T66GcuCCgpTRT9S8DPdeI+MBXk+EN2AGQHKLID4UiAIiMUCoM1ngArFhWAcoVR4GqCI7APTWIb0zE+hPMbhVM3yKei/1vEOa42HoOrQk3YL1prTA0e08Rr6rLB86ex52i5UuIuLWOcZWPv37/3mBS7vBQUuyh25uJ2HsbtzsZ0Z+P15KLNPLQuwGoUYrHko61FeC1F4HVjcXpRxZqAfEXdXIjM0dTNKT2OB5SUxp4VCJsaKDYlQF59K7qkNd6Aa7i1f2f6NouQKtlCiL9UnuNvuROv7t27c+WVV3LPPfcQGBjIxo0badiwIatWreLyyy9n//79ZxR8VZgxYwbjxo3j3XffpWvXrrzyyit8+eWXbN++/YRnv/6/mlC41WSmqckpdpNZ4CQz38n+7GL2ZRWSnHmEnMy1eEtWYfdJxm0eoW6q5rztmvZJmhx/+LyPIicuFNN3Mq+N6VOtd7+2puXyxPSPsVg/JHaHiyGrTSLy/iicd8QossI1ZqANiw4ETxBudxjFngiUJQLDVgebIxK7bzhBvn44bKUPh/+5RUm7xYLV8kdhfqxcV0crGR57b2qN16txHz1p8Zgat7f0xMbtPX6ay2PichfjceZienLwenIxPflgFqB1IegiLKoEi+HEMIoxLMVoSzGmxYWpXRgeD4bLRLk0FqciuBBC8yG0QBNWUDoOz4Wg4j/2VY4/pIXB3ijFnnqK/HDAxx9vUVPc9kF0btGZYW2jaRUdJCcv4ozJMfjkZL+UJn75Tg9HClxkFTrJKnCRVegiq8DJwTwnGZlJFOdtwePai82ajmk/hHYX4pcJTQ9o2iZp4g+X1hBY2UyxuCNY7A3xBtzJ/644nwbh/tX9FYUQNVClJl4BAQFs3ryZhISE4xKvffv20bx5c0pKSs4o+KryxhtvlHWg3L59e1577TW6dev2j8tJ4XZmCp0ethzI5fc9WazeugSrZyZunUT/5dAzUbOpgWLmYIMS931MvWU04dWQfM3dks4nP9yHrWg9N/5kEpoPi1srlrUFgoJwFzWnyNKXmHqtaREdTKOIAOqF+FA/xJfwAEeNbiZZa43TY1Lk8pJf4ia32H20MRBP2etjQ2Z+AYV5e3EXJ6E9aditWVgtOZi2fLy6AGuBB/9cRb0jEJupScgovZMJkOtXmqBubKg4Ul9h8TSiwBjB8PN6cWXnWPwd8nipOD1yDD452S+nzjQ1h/KdbEvPY2taLompyWRl/IjDvgGXeZCmO6HfRpOonNIE7Mc+ipKi4dw2/A4uan3iIwlCiHNbpSZeMTExfPnll/To0eO4xGvWrFn85z//Yc+ePWcUfE0nhVvF2pqWy5Rft3Ik/Wks+Xu49TtNgQ+8O9LAtDzOJ7dcWqXNAs/dksG0ubdSb99Oxv1ssqWBYuYAMLxtcfnfwPBOTenbrC6xYX5VFlNNYJqa7CIXGXkl7M8uJjmrkKTMItIPp1CUuw6L3obVJxWvzsGeZdJ0v6ZdkqZhRmlLZhsbKpa0gsJoP/IKhzGo+xjG90zAx3Z2tW4pKp8cg09O9suZ8ZqaLQdy+WlzGuu3TMfm8xMhyYVctUjj44a3LzbID+zM5f2fYFSXuOoOVwhRg1Rq4vWf//yHlStX8tVXX9G0aVPWrVvHwYMHGTt2LGPHjmXy5MlnFHxNJ4Vb5ViVdIRnvnwfm+Uj7pyuyfWHaSMcJMR8yJOXd6iSGJIyC5n0/r3UT1vCjfNNZndTbO0YTD4P88DF59G9UZ0qiaM2OlLoYsuBXLak5bJ+XzrZ6XPw81mLS2VQf6+m11aTZgfgcBB8380grYmDwpLx/PuSK+nXvG51hy9qETkGn5zsl4rj8ZrM3pDG5wu/wOb4hIsWeumyS/PuYEVGRA9uu/x/9G0mxy0hRKlKTbxcLhcTJkzgo48+wuv1YrVa8Xq9XH311Xz00UdYLGf3FWwp3CpPRm4Jt777Pg7zLSZ+pvm1rWJN+1bcM2oKneJDK3XbHq/J2Dc+RRc8y4PTNPM7KjZ2qE+9uOd54rK20hl0OXm8Jhv357Ig8SCrNv9EgO1HXM4D9FsN5ydqDoXApwMM3EFNadz0cR4c2kr2sTglcgw+OdkvFa/Q6eHRWWs5nHEXnVfkcsFGzYtXGGT5juK92+8nUhoPEkJQyYnXMampqWzevJmCggI6dOhAkyZNTivY2kYKt8q153ABD7z3MA3TFjDuZ5MnxymMoGf47LZhldoww4zVKXy/bATjviygxAbTL/ejbt33eGlUB2kQ4gxprVmx9wgfLt5E4eE38epELvsV2u7T/NhZsb6LD1a/Z3lzbD959kv8IzkGn5zsl8qhtebpHxLZtucGLpqfT5MDmhfHGYSGvcU74/pUd3hCiBqgPMff077EHBsby5AhQxgxYgSFhYVkZ2ef7qqEKNMoIoCLet1JehM/0sPg4qVgFL/LupTK+325vSZf/foZEUlFRGfB54MUue6JPHV5W0m6KoBSiu6N6jDl+n5MGjMVS+Dz/DCoDh8MNBi4XnPZnGJKiu7hhinfkV/iru5whRCijFKKiUNaEhT2It8PNDAN6L9EU3DoFdbsO1Ld4QkhaplyJ1533XUXU6dOBcDr9dKnTx86duxIbGwsv/32W0XHJ85BY7s3IL9kHN92V3Tco3GbSXywZHulbW/e1gx8HbO5ZIXJ8hYKi7UZD1zSt0ob9ThXtK4fzGe3DuHC7p+xL7ovz41WxB2GsTM9KPNR7vjkF1wes7rDFEKIMoaheGpEV5wlI5ne26BXosbr3cEbC9ZWd2hCiFqm3InX119/Tbt27QD4/vvv2bt3L9u3b+fuu+/moYceqvAAxbnHbjUY2n0I2fE+5PhB581wKHVapd0N+WrZKuyHC4jKgV87gdvvJno1iaiUbYnSK8jXnBfPI9c+Rb71al4crQjPhSvneHEXT+Kp7zdVd4hCCHGcED87l/S6lswEXw6GQJ814DwylaTMwuoOTQhRi5Q78crMzCQqqrQfix9//JGRI0fStGlTbrjhBjZv3lzhAYpz08jOsbgKu7K8heK87Rq7/2oWbjtU4ds5lFeCK3cG3bdqkiPA4h/OmJ7tK3w74kRtY0J4cfxdFDOW10YoGqdBlxUl7Nn9KPO3ZlR3eEIIcZyrusZRVDSE+R0MemzTaMcWpq9Kqe6whBC1SLkTr8jISBITE/F6vcydO5eBAwcCUFRUdNa3aCiqTrCfjeCo0axvBnXyQeUW8NOmvRW+nZ+2ZGD4bqPjHs3yFga5xX25sGVkhW9HnFzjugE8PuYWiq0D+HiAwaD1Gr8je3jr+w/JLZLnvYQQNYePzUKntqPZ20yhNNTfq1m9ZT6n2UaZEOIcVO7E6/rrr2fkyJG0bt0apRQDBgwAYOXKlTRv3rzCAxTnrkFtGmGGBpDjBy33Qlba93i8Ffv8z29bd+A45MbXBTsbaxIShkunvlWsXWwIIy74D5nxkaxqqrh+vomP7+c8P3dLdYcmhBDHGd6hPjZPAlviFd12aPyN+Ww5kFfdYQkhaolyJ17//e9/ef/997n55pv5/fffcTgcAFgsFiZOnFjhAYpzV5+mEXgLm7ClgaJNksbPsZ5NB3IrbP0lbi/5mXNotU9zKBis9lAGta5XYesXp+6qrrH4hD3Kj30VDject9LLnl3PsT1DTmiEEDVHy3pBFOihrGyuaJWi8VhT+HnbweoOS9RQ2YUuZm84wCs/7+Tpn7YxZfEeVu87UuEXkUXtcVrNtl1xxRUnTBs3btwZByPEn9UN8sHtGMimhA30SNR4jAx+35VJx7iK6Ux5XXI2fr5baJ2s2RKvcBY3o0ej8ApZtygfpRSPXtqVO9++iK96zmXMrybLO27klXnrpa8cIUSNoZSiY4vebDNex9AQkGGy1rEdBjat7tBEDZJ6pIgX527kYPJUbP6bsDgL0SZ4HRZWrKnLEfdQLu5xKWO7N8BuPe2enUQtdFqJV2FhIYsWLSIlJQWXy3XcZ3feeWeFBCYEQNvGXdjhLb0165NpsiF5P1AxnXWv2JuFWx8m7jB8ex4YARcR7GerkHWL8osJ9aN313+xOuAXclaX0H8ZLO7+KhtT29EuNqS6wxNCCADOb1yHXbvrkBZ6mFbJmt+Df6DYNRRfu1RTFzB7wwE++fFFfOxz6bYZzk/UBJSUfmZisi/qAD+3f4/F675gwfr/8MzVQ0kI96/eoEWVKXfitX79eoYMGUJRURGFhYWEhYWRmZmJn58fdevWlcRLVKiOcaHsSgokxy+Xhmma9QEL0bpvhXRsvCnlAH6ZpQ9F50Yp2jZqc8brFGdmfM8Efl09gm/O/4Ib55r80nU7r89fw/vjB1R3aEIIAUCXBmG8XdSCLQ0yaZ2sWddtO2uTs+nZRGpMnOumLN7D0pX3EH9oF9cu1JTYYUEHxY76Co8dQnOh607NjXM1yZH5fHLxf7n7vd08NW4CraKDqzt8UQXKfX/z7rvvZtiwYWRnZ+Pr68uKFStITk6mU6dOvPDCC5URoziHtY8NQZfEsjta0SQNfC2bSc4qOuP1mqYm9/AvNMjQ5PiBYQukg9xVqXaBPjaG9LiKwwm+ZAZD/1VQnPUuuw7mV3doQggBlB6njMAL2R6jqH8E3OoIG/fnVHdYopp9s3Y/y1beTeMdu7hprmZJK8VL19lY1XQAJLxJnSafkR57P3P7NOLJaxU2D9w7TRPk+YwHP3pd+oQ7R5Q78dqwYQP33nsvhmFgsVhwOp3Exsby3HPP8eCDD1ZGjOIcFhPqS7HZjl3RisbpGtPnABtSc854vUlZhfhbE2mcBnvqKXRJjFRnqyHGdm9AftFwfuhi0H2bxmNJ5P1Fu6o7LCGEKNM8rhl5EaWv7Vkmm1LSqzcgUa12ZOQzfd4zBKft5sqlms/7GKzsGkPDyI/48M4X+Hh8X167qgPf3DWOe674mGLfB3h/pJWUCLjjG42f9XPu+/hr8kukG5WzXbkTL5vNhmGULla3bl1SUko7DwwODiY1NbVioxPnPKUUdaL6sa8e+DlBFRVVSOK1ISUH7dhPo3TN7miFU7elXrDPmQcszpiv3ULvTleyv4mVYgd02qjZu2cKh/Od1R2aEEIApd1g4OtPgQ80yICsjEXVHZKoJi6PyYPTf8KmfuHGeZoFHRRJzWLo1eltnh3ZkVB/+3Hzd24QxrQJY7A5nufLYVaOBMLNMzU260s89f3mavoWoqqUO/Hq0KEDq1evBqBPnz48+uijTJs2jbvuuovWrVtXeIBCtIurizvMggmEHVLsSNl0xuvcuD8H7SwksASSo6BOVL8KeW5MVIxru8fjLuzJz+0V/TdqbH7L+HRFcnWHJYQQALSNCUGVRLO7nqJxmsahNpKRW1LdYYlq8NmKZPzUS1w1X5MWBst6+NAw4Xlu6dPoL5fxd1iZcsMA3N5JTL1UEVII5y9zk570BEt3ZVZh9KKqlTvx+t///ke9eqV9HT311FOEhoZy6623cvjwYaZMmVLhAZbHunXrGDhwICEhIdSpU4ebb76ZgoKC4+ZJSUlh6NChZY2B3HfffXg8nmqKWJyKdrEh2HQd0upAozSNJ28pLs+Z9YGxPXkXgUePbSV1DNrGSf9dNUndQB8SGt/AhrYKuxvidnpZsvYLil3ecq/L4zX5YVM6E7/ZxL+nr+f9JXvJLnT984JCCPEXGob7U+huzZ560Dhdoxyp8pzXOSi32M3sxZ8TsTePhunw2SCF2/NvHry41T8u62e38srY4XjcQ/ikv0H/jRqzeAcvfveD9PN1Fit34tW5c2f69esHlFY1nDt3Lnl5eaxdu5Z27dpVeICnKi0tjQEDBtC4cWNWrlzJ3Llz2bp1K9ddd13ZPF6vl6FDh+JyuVi2bBkff/wxH330EY8++mi1xS3+Wdv6IZQUN2BPPUWjDI2vzy52nkFjCyVuL+78RTRO0xwMAasKkYY1aqAb+zTF4mnF7y0VQ1ab+Pt+z9dry1edOS2nmDFvzmb6wtGk7hrNkZ1j+HX9CK577Vl+2S6dngohTo9hKALC+5BUr/RuhenJZ2MFVIMXtcvUpUn4B3zN5cs0v7dUeH2bMumyYfjYTq1rgbg6fgzvezuHEkLYVQ+uXqgJtbzN12v3V3LkorqcNb22zZkzB5vNxptvvkmzZs3o0qUL77zzDt988w27d+8GYP78+SQmJvLZZ5/Rvn17Bg8ezBNPPMGbb755Qn9kouYI9rPhtXVhTz1F/CHw2A6f0ZXFrWl5BDh20Thds6eewlMcR5sYaca1pmkaGYhP2L/4rTNE5EFwahEzl8w55SuBh/JK+NeUz9Cehxn+4yEe/kjz4OeaOz4qISJ/Ou9+O5GfEyX5EkKcnlaxsZTUKa2i7pcJ21KlOvS5xOnxsmzN19Tf5SK0AH49D0Ii7qFzg7ByreeabvG4uIPPBygaHILAlDxm/PrVadXwEDVfuROvgwcPcu211xIdHY3VasVisRw3VBen04ndbi9r+APA19cXgKVLlwKwfPly2rRpQ2RkZNk8gwYNIi8vj61bt/7levPy8o4bRNWLrteNjEiNzQuWPCcbk7NPe10bUnPw2tNJyIBd0Qrl6Eagj3ScXBPd1K89NmsCaxspLlmhCXB8zpxN/9x6mMdrcvsnC/C1v8KEGSZ1c+GFEQaPjVPsrK+4e5ZJcM4q3pr9EikV0D2BEFVNyqbq16Z+MIY1iCMBkHBQk5+1GK11dYclqsiPm9MJ8fmOi1eZLG2p0KoFdw5qX+71GIbiziH9MXwSWNVUMXyZxtd3OtNWSiJ/Nip34nXdddexbt06HnnkEb7++mtmzpx53FBdLrjgAjIyMnj++edxuVxkZ2czceJEANLTS0/UMjIyjku6gLL3GRkZJ13v008/TXBwcNkQGxtbid9C/JV2caF4g+14DIg4pEhOW3va61qffARVUILDA2mRmpj651dgpKIindcwDLfjJuaeB3GZYD+Uy2e//vKPJzdv/Lobh+cpRv1gYvXCG6MUWSET0CGv83P/YJY3V9zyo4nV9j33f/EjXlNOlkTtImVT9WsbE4xZUp+kKEXDdPCxbGN/dnF1hyWqyNdLV2I7nEfdXFjYSaGCxtM8Kui01tWrSQSOoDuZfx5E5UBYciE/LJt9xs+zi5qn3InX0qVLmTZtGrfeeivDhw/n0ksvPW6oaBMnTkQp9bfD9u3badWqFR9//DEvvvgifn5+REVFkZCQQGRk5HF3wcpr0qRJ5Obmlg3SZH71aBcbgtUTQWoENErXmMUrTvs2fPKB9dQ9pPAq8ITYaB8fUcHRioqilOKGC87DCIhiewwMW2Hip6by05aTXygBWJ+Sze+rXyB+ez7NU+GdSxRu8xbev3U8X/zrAkLrvsov/axkB8C1czQ2z4tMX51Shd9KiDMnZVP1iwn1pdDTir1R0DBDg+MAWw7kVndYogqsT8nGr/hD+q6DndGgAsIZd36HM1rn7Rd2wbA3ZFVTxeXLNCGO6czecKCCIhY1RbkzktjY2Cq9lX7vvfeybdu2vx0aNmwIwNVXX01GRgYHDhwgKyuL//73vxw+fLjs86ioKA4ePP6ZjmPvo6KiTrp9h8NBUFDQcYOoeq2igygqbljawEa6xtdnL1vTyl/AZRU4cXhW0DhNk1IXrN4I2kvDGjXagBaRFOjr+O48g2YHwJ55mCk/fUOJ+8TEu8jlYfKX32NhKaMWm3x3nsLraM8jo64lPMCBYSj+N6Ijxa67+XCwonEGhCXn8fUvH5BTJM95itpDyqbqp5QiuG5vkiMVQcWg3QVsksTrnPDJ0l14zT203af5qbNBQcnlDGgR+c8L/o1O8WF4/W7hx+5QLxt80/L5ZvE8TKmRcVYpd+L1yiuvMHHiRPbt21cJ4ZwoIiKC5s2b/+1gtx/fOV1kZCQBAQHMmDEDHx8fBg4cCED37t3ZvHkzhw4dKpt3wYIFBAUF0bJlyyr5PuL0+NmtGH492FNPEZsJHiOT9Sk55V7PhtQc/Hz30jhds7uewlnShKaRARUfsKgwhqEYP2Ag3rBINjZQXP2Lib/PB7w0f/sJ8z41ZyuB9pe54QdNagRs6ehLlw6P0iEutGwef4eV+4YPQ/k0YVFrxdW/mTj8ZvLyvMSq/FpCiLNAy5josgY2fDNhuzSwcdY7lF/CoaSPaL1ZkeMPB+PtDO06FKvlzNurG9+vC4ZPDBsSFJcs1wTyMQu3H/rnBUWtcUq/ktDQUMLCwggLC2P06NH89ttvNGrUiMDAwLLpx4bq9MYbb7Bu3Tp27tzJm2++ye23387TTz9NSEgIABdeeCEtW7bk2muvZePGjcybN4+HH36YCRMm4HA4qjV28c8axXXgcKTG0GDP8rBqT/mrh63Ym4VbHSbmMOyOVviE9KmQg6WoXEPaROH1uZdv+0FELrRdW8LWrf89rlXCz1emkLxnMu1XO4nKhk8GK0xjInf0b3rC+no0Dicw6n5+PV9h80KnNV527HyZ3YcKTphXCCH+Sts/NbDRMEOTn7VIGtg4y32xMhW7/zJ6b9H82lZhFnRjdJe4Cll3v2Z1KeR6vu+uiD8MRlYWny2U39TZxHoqM73yyiuVHEbFWLVqFZMnT6agoIDmzZvz7rvvcu2115Z9brFYmDNnDrfeeivdu3fH39+fcePG8fjjj1dj1OJUndewDqn7A8nxz6Npqmat7094vL3LlTit3rWH8EMmBnA4Gro27lh5AYsKo5TioeG9mPxxL6b1W8q4hSZ7ohN5d/ZktqXfS5Hby/oN/yPy4E4uXq35qL+BV/fl2VGD//L38eDFHbjr7QF8e97PXLnUZE3b1Tz3wyqmXH9BFX87IURt1br+sQY2cmiYDoktE0k5UkR8Hf/qDk1UApfH5OfVC2h42IWvE1a3VjSJGU+Yv/2fFz4FhqG4rk8vZiypw/aYwwxZrvlxyBRWJvXnvIZ1KmQbonqdUuI1bty4yo6jQnzyySf/OE98fDw//vhjFUQjKlr3hnX4Ym5jtsatp1WyZnub9Ww6kEvHP1Uj+ztHCl2owp9omXy042RbKOc3Dq/coEWFaRkdxOBeE/lt9ViWpWVxx2yTjwcsYVniSkDTMNnN1b9p5ndQpDeK4speE2kY8dfVSGNC/eja8VY2+S8iZ72Ti5bCL71eZNmetvRoJL8LIcQ/iwn1pcjbjp3RiVy6wkQ79rN6X7YkXmepuVszCHd8Q//1mo0NFT4qnut6nlir4kwMaxfNlJ/HMKv7a0z6yuT7/AN8uHAl5zUcUqHbEdXjlG8VmKbJs88+y/nnn0+XLl2YOHEixcXSbKqoOnWDfHDb+7MpQdE4DVxGGgvK0QHuz4kHCQjYQpt9mi3xCndxC2lYo5a5/vwGhEe9wG99/VnYXjF+vsl975Vw/xQnY341+bGLYm23YFq3eJ2RXf65ee1b+zYiv/hqZvRRnLdDo4uTeGXOAnmYWQhxSpRSREYPIClG4+cCCopYsftwdYclKoHWmg+XbMVVkk6TdFjYXmHxGUvr+sEVuh2bxeCqnoMoqhvEvrowYKVCZ7/F5v3ScMvZ4JQTr6eeeooHH3yQgIAA6tevz6uvvsqECRMqMzYhTtC1ZQ8OxypQEJmsWbnl1Os+/7ApFU9JJjFZsL6JIjxqBHarPN9VmyileH5UVxo3mMqaDs157DrF1+cbfNXL4PEbFBtat6Fzq4+476IWp7S+QB8bV/YdRW5sCHuiYOQvGod+g5nrpQlfIcSp6dooEjPEgcsCMQcUSSmLqzskUQlWJR3BWvAx3TZDViAUR/kx6vzelbKtUV1iySu8klk9DNrt05jOPUxZuL5StiWq1imfdX7yySe89dZbzJs3j2+//Zbvv/+eadOmYZrSuZuoOhe1isJqxpEYq+ixTePPd6xNzv7H5TJyS8hK/5YWu6DIAcV17VzYoW0VRCwqmsVQTL60LXePnEp0gw9JavIvkpv8iwYNPuWRa9/jzoHNUEqd8vpGd4ml0HMbX/ZTNEmHwLRsPlvwObnF7kr8FkKIs0W3hmEYrvrsrK9os0/jyyL2HpaGes427y7ajfbdQO/Nmp/bG1icgxjc+uRdEZ0pH5uF4eddQmaMH2mh0HO1ouDgq2xMzamU7Ymqc8qJV0pKCkOG/FG/dMCAASilSEtLq5TAhDiZtjHBFJiXsKgNtN2ncekkPlq66x+X+2xFMiFBv9Jvk2ZFM4UqacMFzetWQcSisnSKD+Wl0d344p47mXb3nTw7shMt6pW/LyOrxeCOiy+EgHhWNVWM+UXj4/spT3wnVxeFEP+sRVQQeZ5erG+saJOsMR17WbhNmgA/m+zIyCcz7TPabjLRCna3VFzQ/YZKbRX5mvPiKcy/hNndDbrt1JToHTzzwwpp4bCWO+VfjMfjwcfH57hpNpsNt1uuCouqo5RicLeBZMfbyfOF89ZD5v7X/7Yz5cP5TuatmkvAgULC82BpR0hoeCsBjlNqW0acA/o2jcAW8h9+6KtwuKHvEi8HUx9j/taM6g5NCFHDGYaieaNBJDXQ2D3ge9DNL5s3V3dYogI981MivoG/cdFazS/tFIb7fEZWUBPyfyXIx8aI868ms4EPh4Jh6GJwFL7IPCmXarVTTry01lx33XVcfvnlZUNJSQm33HLLcdOEqGwjO8dSUtCf77sZDNigwbKOyV/9itPjPWFerTUPz9xEZPDHjFys2ZCgsNljGNerdTVELmoqpRRPjOiBu+QyPumv6LNF45e5j3e+e4VdB/OrOzwhRA03sFU9bPZwUiKge6JGFU5jj1Q3PCv8vjuT3PT3abvBi90DazoqOrS7E/8quHg7tnsDSkqu5dMLDDrt0dizDvDmnC/JKXJV+rZF5TjlxGvcuHHUrVuX4ODgsuGaa64hOjr6uGlCVLYQPzt9u91CUksbWUFw1U8ah3qc2z5dQV7JH3dgXR6TR2ZvofDQU7ReW0JELszuC/XqTaJJZGC1xS9qpugQX6668F/kxESyrIXilh9M7Oo77vtkGhm5JdUdnhCiBuvdNIL84gv5ta1B150a05HItOX7qjsscYaKXB4mz1yGxWcZQ1dpfuiqsHn7cHPfZlWyfR+bhXEXXklR3QjWNVSMWajx85vK5FlSFb62Uloqi5ZLXl4ewcHB5ObmEhRU/udJRMUodnm55vWp+BS/wsTPNWuaKBb0t5OZdx2tm/bFZhis3r2XGPvr1NuTwXULTT7ub7C/QU9e+ter1A3y+eeNiHOO1pq7vljJ4cx/MeYbF4HF8MYohdP8N2+Mv5bYML/qDvGcJ8fgk5P9Uv2enJPIlt0jmfSel2/ON9jS4CKm3Pk/wgMc1R2aOE0PzdpM8r47GPzzIRoc1Lx1rY3BXWdzTfeEKotBa81NH/xMYfa/ue8TzdrGimU96tOn+xSuO7/q4hB/rTzHX2lLW9RKvnYLT149GrdlAK8ON+i4R3PDdCfNsqdwMPUaUvePIa7oIS78JZ3rFprM7qZIaxTF+EuekKRL/CWlFM9e2QUvj/LxpQZeA+6YofFRr3L7uy+zZJf0zyOEOLmx3RuAsxO/tFNcvMrE7j+f/32/qbrDEqfp85UpbEl8hzoph+i4R/PRIAM/681c1a1BlcahlOKpK3vhdF/KlIsUPRM1oalp/LD4KXkOuRaSxEvUWi3qBXHfVf8lN2gwT1+jOBiquO0HzcPveXnkHQ8PfGkSnaV5ZbhBYttGXNHvY/o1k5YMxd/zsVl4/bqhuPV/eHekgdMK936mCS36nLdn3cZD36wnt0gaFRJCHC+ujh/14+9gZWeFxYSey7wc2v8Uc7ekV3doopxmbzjA+3M/xZ9fGT9f83N7hSesIZNGXofFOPXuSipKZJAP44feSX5kAnM7Km6YbxKWt5x3v32S2Ruk38naRKoalpNU56h59mcX8fTsReRlvY6XFHyOaJSG4iCNxTcYm+Vq7r18LK2i5RlEceoO5zu55b2vsVmeZtg8k/Z7NfM6KVZ1tVJSeCm9Ol/DdT0aUEeqEVUpOQafnOyXmuFgXgm3vflf4g5+z81zTV4cYZDpN4RJV02kW8M61R2e+AemqXnj1918u+QLwn2nc+90TXoYzLzUl8t7fsvwjpXbkuE/eXn+NtZtu44+v+XTdafm9UsMioLb06bdI9x7YVNsldi8vfhr5Tn+SuJVTlK41Vx7DxewZFcmO9MzAU1MnTB6NQmnVXRQuTrUFeKY3GI3981YRknOg0TvymHUYpMSO8w+zyClmQ1XYXciosdyWecEejYJx88uXRRUNjkGn5zsl5pjxuoUflg6hgt+zaL9Xs0LoxRFxiAuu+DfjOkWj1ENd0zEP0vKLOShWZtxZb6Lj2sVd3xrkhUEn15uoWPDd7hvWM/qDhGtNfd/uYq0gxPo/0sR3bdrpvc22NsqiAL9ABMv6SUJfjWQxKsSSeEmxLlFa83UpUnMXfI6Fsc8+qyE3ps1hT6wuLVibSuw+URQWNSOoLrDOa9JPB1iQ2gTEyyJWCWQY/DJyX6pOf44Of4Xl81x0iQN3rtIkRcdRbHjfm4f2IneTcLlgmANsS+zkHcW7eG3jUtpGP4BDROLGb3IZGu8YvZgg5axr/HQZf1qzP+XaWoe/249O5LvpMXmI1y5VLM9BmZcqLDqthB6K+P7tKB3k4hqqRZ5LpLEqxJJ4SbEuWl/dhHP/LCe7AOv4bFspstG6LVFE1QM++vA+kaKrQkKVx0rVm9dCouboHx7EBXRnCZRQTSuG0CjiADqh/oS5GOr7q9Ta8kx+ORkv9Qsbq/JHZ/+Qn7effRa7KTPFs26Rorv+oJNNSaHq7mgbQf6t4ikdf1gOUGuYvuzi1i08zCzN6SRnraEuLDZOA7nMHKRSWwmzD5PsaN9ID3bvsuNfdvVmKTrGK01HyxN4pflD0HJJm780aRuLixqo/itK/jqRqS7L6dX2270b1GXTvGhOKyW6g77rCWJVyWSwk2Ic9uug/m8/Usiqfs+xuG/Ep+MEjrs1rTfqwkrAK+C5EjYVU+RFKXICtN4gixYVSC4wnC6I3Dp+lh9GxMY3JR6IYFEBNgJ87cTFuCgjn/p6zr+dkL97VJn//+RY/DJyX6peTxek//9sJndux7AkZXK2AUmYfmwtoliSWtFUT07lDTkiKsbEfV606p+CM2jAmkWFUhsqF+VdNB7Lihyedh5sIAtB3LZmpbLir2ZULCM6ICVeGz7qL/XZOB6kwaHYFsMfN1f4efoynUXP0fPpjW7Qa4tB3J5/MsZ2CzvUH+nk8uWmwQVwqYExaK2UFjPCu4EDhd3JbxeX9rF1aFVdDCtooOIDfWTaq8VRBKvSiSFmxACSp//mrMpjbnrNuLKmY3dbyu6KI+6GYrGaZomaZrorD+ajs3xh7QwyAxSZAaXjo8EgtdPY/pYMG1WrNqB8vqiPf54vUGUeELQKghlCcGwhmG1R2DzCSfAx48AHyuBPlYCHVZ87BZ8bRZ8bH+MfWzGH9PsFnysFnzspdMcVgs2i6pxV3FPhRyDT072S821ZNdhXp89DZv9E8KTnFy0tvQkv8gO2+IUW+IVB6I0niAbNh2MxxlNgTsKl0rAJ7AlkaGh1PnTRZnSCzMOQvxsBDis+Dus+DtK//Zr49/06XJ5TAqcHo4UOskscJFV4CKzwElmgZMDOcWkZBWxPysLm3sbEY49+Pvsw207hFHgJD5V0X6vpkWqxjBhQ0PFgq5gBNejadxE7hzar9Ykvh6vybSVKcxe+j5+jjmEprgZsN6kaRq4LbA1TrE1XnGgnsYVasfiDcdVHEOupwnavz0x4XWJC/MnOsSHiEAHdQOPjoMcBDqs59Rv6nSdlYnXU089xQ8//MCGDRuw2+3k5OScMM/q1auZOHEia9euRSlF165dee6552jXrl3ZPJs2bWLChAmsXr2aiIgI7rjjDu6///5TjkMKNyHE/5dX4ub3XZks3nmIffvXYhb9jsN3Dx5LDpYCF345ivqZmqhsqJOvCc+DOnlg+dPR1wQKfCHPD/J9Ic9PkecHRQ4ociiKHaWvix3gsYG2abRNoa0GpqV0MJQFw7RiaAuYVtA2MG1o046p7ZimHa/pg1fb8WorKHvpgANlOFDKByw+KMMHw/DFsPhisfqgLH7YrA5sFgO7VWGzGEcHhcVQWA3j6FhhtSgshoHVUGXTLEbpMn9+HxHooO9pdO8gx+CTk/1Sszk9XmatO8C3S77Gbvket+sgTXcrWiVrmh3Q2D2l82WEQGqE4lAwHA5WHA4Gd4BGOwy8diuGsmPx+KK9/phefzxeHzymPy7TD7f2ByMYZQlB2cMwrCHYbA7sFgO71cBhLR0fe1/62oLdWvr3ahgKi1JYjNK+oyxH35dOB8NQGP9vutYaDaBBo9EaNBwd//Geo/NpXVpNzmNq3F6N22vi9pq4vCZuz/9779W4PSaFLg9FLi+FTg9FJYWY7izw5GAlDx8jHx9rDj7WHKzWPJSlAK+1BFMXYyn04MhTROZAvWxNQoYm7hDYveCyliYkGxoq9jdS+NCYJgl3Mr5/D+oG1s6+PkvcXr5df4DZy+ZgN2dSotOJT4IOe0p/Y74u8BiwPxz2h6ujQ+nvS/laMK0OlDcQrzsIjycEpycMp1kHbY1E2+vh4xdKkI+VIF8bQT42gnyt+Nms+NqNoxf7SpN/32MX+45eAPSxWbD/qcywWQ1sxh/lx9mQ2J2VidfkyZMJCQlh//79TJ069YTEq6CggPj4eC655BImTpyIx+Nh8uTJLF26lNTUVGw2G3l5eTRt2pQBAwYwadIkNm/ezA033MArr7zCzTfffEpxSOEmhPgnRS4PW9Py2JGRz+5DBaRkJFOQsxrlTcJuy0TZcnEbhVhcLowiE2uxIrAYgoogqEgfHUNgscbPCb5O8HOWnjD8HbcFnLbSkwqX9Y/XTpvCZftjmttaWgB7LH8Mbos67r3H8qd5rOA1AAO0RZeODUCBNlTpWAGGKhuDQhsKrUoHpRQKA6VLx6aazBcThpR738ox+ORkv9QOWms27s9l9roUdu75HsNcgde+H6PASUiWIu6wJiYTInI1Ebng8By/fLG99OJMni8U+CpK7KXTiu2UvnaostdOG5gGaAuYR/9usZT+zWrL0b9P44+/09K/WwOFQgHoo6+O/s3++T0olP5zNWhdOpn/f0p59L3Sx73XgFZetDLRSqPxYpgabWqUaaLM0m5hlKnRXo3ygPIo7C7wcYFv2VjjXwLBhRBcpEvHheDn+iOCEhtkhEJyXcXeKMXBuhpviAO7J4GIsKH07TSEC1rUPauegdp1MJ/ZGw6wacc8tOtXXD6pqPxiYtIVcYc0MZma2EwIKPljGacVsoJKa2McuwCY76vIP/p7K/EpveBnWBXYFFgMvBYrKCvKtJZe5NN2lFl6wQ9twzSPXvDTNrzajgcbHtOOR9txawce7CgcYNiPXvxzYDEcWCwOMHywWGygLFgsRtkFAMvRCwDHLgL8/+mW4y4iHJv2/z4vG4NxtHw69vr8xuF0b1S+liHPysTrmI8++oi77rrrhMRrzZo1dOnShZSUFGJjYwHYvHkzbdu2ZdeuXTRu3Ji3336bhx56iIyMDOx2OwATJ07k22+/Zfv27ae0fSnchBCny+nxkpFbwoGcYtJySkjPKSYtt4Ts/AKKC/bjKknB68lAmVlYLblYLAVgcaINF17DjTbdWLxelMcEt8Zwg3IrHG5wuMHuAfvRscOtj5vmKPtMY/OA1aR07C19bfX+v8Gs+O/vMUoTuLeHGhQ1eJZPb7243OuQY/DJyX6pnQ7llbAy6QiJ6Xkk7U8kL3sFSu/FYjuCx5oHHifWIi9GicL//12c8S85loCU3s0oS0acx99NLy+vKk3YzD+PTzbtT3mX+lN+9ef8S53sM8AwS48xlqPHm/LG67b8kWwW+kCunyLXn6ODIscPCoM03gADbfPF6g7GYTQmNLQHjeJ70K1R+DnTqElOkYsVe7NYn5xNyv7V5OatAiMJZctCeYugyIujUBGe+0eNjMBiCCzSZRcE/648KLGV/j8cG7uspRf33BZVepHPUjrt2Lj0M3BbFW5LaZngPTY++ts6Ns1Uf77IV5rc66MX/speK1X2XilK/ym9NlD6z7H/YlV64aD0esHRi4RKoQGlSi84WF0hdG3/DhP6NS7XPi7P8bd2VGA9Bc2aNaNOnTpMnTqVBx98EK/Xy9SpU2nRogUNGjQAYPny5fTu3bss6QIYNGgQzz77LNnZ2YSGhp6wXqfTidPpLHufm5sLlO5kIYQor1AbhEbYaR1hB/58gG5V9srjNcktdpNT5CLf6aHA6aWwxEOB002B00NBiZcCl4eCEjeFJU6KXfkUuPLxePLR3nxMbyHaLEabRWhdDJRgMZwYyo0ynCjlhf83aGUC5tEr0CYm3qNXnf+4Ao0GTI3ylr5WJkenHT25MlXpyZUuPbGyHB2MP4817A42iXC5T+s4emyZWnbNsMJJ2XR28AH6JATQJyEAiAYGUOzykpZbTFpOMWm5xRzKdZJVUEJBQQYHilLZ58rA6zmMNvMxjGIwSlCGC224QLnxKg9oD8prYpgavLr0b9XU4C39u1WmgqMXXOye0r9N4+jftHH05tixaYb+02sTlNZl74/R6o97Xfroia6G0hPmY2e+6k/zqj/dVT96kv3nu/DmsTvshkapY7WmDbRVoQ0LKBuGLr3LYjF9QAdhM+pgs0Xg41uf4KAEWkTEEBvmT2yoLzGhfvjaj7+jVViQXzn/qTWMAfSI86dHnD8Qg9bDycx3kpJdVPocXHYxqZkZpOfvItWZgvakY5KLthWijWK8yoXFdGNxesGj0R7Aq8oSfYf7jwsAPu7SZNp+9KKe3VU69vfqsmk2D9i8pYPV80c5Yfl/v6mq5lXw0vVFFBfml/s4Wq5ySdcyH374oQ4ODj7pZ5s3b9aNGjXShmFowzB0s2bN9L59+8o+HzhwoL755puPW2br1q0a0ImJiSdd5+TJkzVH74rLIIMMMshQM4bU1NQKK1dqIymbZJBBBhlq1nAq5VK13vGaOHEizz777N/Os23bNpo3b/6P6youLmb8+PGcf/75fPHFF3i9Xl544QWGDh3K6tWr8fX1Pa0YJ02axD333FP23jRNjhw5Qp06dU7rgcC8vDxiY2NJTU2tVdVBJO6qUxtjBom7qp2rcWutyc/PJzo6uhKiqz2kbCpVG+OujTGDxF3VJO6qU5XlUrUmXvfeey/XXXfd387TsGHDU1rX559/zr59+1i+fDmGYZRNCw0NZfbs2YwePZqoqCgOHjx43HLH3kdFRZ10vQ6HA4fDcdy0kJCQU4rp7wQFBdWaH+SfSdxVpzbGDBJ3VTsX4w4ODq7gaGofKZuOVxvjro0xg8Rd1STuqlMV5VK1Jl4RERFERERUyLqKioowDOO4K33H3ptm6VOB3bt356GHHsLtdmOz2QBYsGABzZo1O+nzXUIIIYQQQghREYx/nqVmSElJYcOGDaSkpOD1etmwYQMbNmygoKAAgIEDB5Kdnc2ECRPYtm0bW7du5frrr8dqtdKvXz8Arr76aux2O+PHj2fr1q3MmDGDV1999bjqGkIIIYQQQghR0WpNq4aPPvooH3/8cdn7Dh06APDrr7/St29fmjdvzvfff89jjz1G9+7dMQyDDh06MHfuXOrVqweU3gacP38+EyZMoFOnToSHh/Poo4+ech9eFcHhcDB58uQTqojUdBJ31amNMYPEXdUkblGRauv/S22MuzbGDBJ3VZO4q05Vxlzr+vESQgghhBBCiNqm1lQ1FEIIIYQQQojaShIvIYQQQgghhKhkkngJIYQQQgghRCWTxEsIIYQQQgghKpkkXkIIIYQQQghRySTxEkIIIYQQQohKJomXEEIIIYQQQlQySbyEEEIIIYQQopJJ4iWEEEIIIYQQlUwSLyGEEEIIIYSoZJJ4CSGEEEIIIUQlk8RLCCGEEEIIISqZJF5CCCGEEEIIUckk8RJCCCGEEEKISmat6BU6nU4cDkdFr7bGME2TtLQ0AgMDUUpVdzhCCHFO0VqTn59PdHQ0hiHXDo+RskkIIapHecqlM068fvrpJ6ZPn86SJUtITU3FNE38/f3p0KEDF154Iddffz3R0dFnupkaIy0tjdjY2OoOQwghzmmpqanExMRUdxg1hpRNQghRvU6lXFJaa306K581axYPPPAA+fn5DBkyhK5duxIdHY2vry9Hjhxhy5YtLFmyhOXLl3PdddfxxBNPEBERcVpfpCbJzc0lJCSE1NRUgoKCqjscIYQ4p+Tl5REbG0tOTg7BwcHVHU6NIWWTEEJUj/KUS6edeHXv3p2HH36YwYMH/+1ttQMHDvD6668TGRnJ3XfffTqbqlHy8vIIDg4mNzdXCjchhKhicgw+OdkvQghRPcpz/D3tqobLly8/pfnq16/PM888c7qbEUIIIYQQQohaT55MFkIIIYQ4TUcKXXjN06o8JIQ4x1RIq4b33HPPSacrpfDx8aFx48ZceumlhIWFVcTmhBBCCCGq1faMPB798kcsxd/hMmPo1/VGJlzQRFqVFEL8pQpJvNavX8+6devwer00a9YMgJ07d2KxWGjevDlvvfUW9957L0uXLqVly5YVsUkhhBBCiGpxKL+E+z6cir96jwuXQ1LkRlb6LsfH/jE39mpY3eEJIWqoCqlqeOmllzJgwADS0tJYu3Yta9euZf/+/QwcOJCrrrqKAwcO0Lt377OicQ0hhBBCnNv+++1a/H2ncsfXmsbpmlGLTeK3H2He0ldIyymu7vCEEDVUhSRezz//PE888cRxLXkEBwfz3//+l+eeew4/Pz8effRR1q5dWxGbE0IIIYSoFrsPFXDkwDv0WGli9cKD4yz80FUx4ncTw7GQt37ZXt0hCiFqqApJvHJzczl06NAJ0w8fPkxeXh4AISEhuFyuiticEEIIIUS1mPLbbnCsp88mzQ9dDYKzLmJdJwteAzps0uzc8T45RXK+I4Q4UYVVNbzhhhuYNWsW+/fvZ//+/cyaNYvx48czfPhwAFatWkXTpk0rYnNCCCGEEFXuYF4Je3ZPpc1mE68FdrY0aNL0aryF57OwvaL/Bo3Vbymfr0qp7lCFEDVQhSRe7777Lv3792f06NHEx8cTHx/P6NGj6d+/P++88w4AzZs35/3336+IzQkhhBBCVLkPfk/C4b+YC9dpfm6v0MXdmTS0JdENxrOuLfg6IXq3h4WrvsbjNas7XCFEDVMhiVdAQADvvfceWVlZrF+/nvXr15OVlcWUKVPw9/cHoH379rRv374iNieEEEIIUaXyStysWD+duJ0efFywpr2iYeNbqBvow/jeTbCazVnVTHHRWhNfx2wWJB6s7pCFEDVMhXagnJGRQXp6Ok2aNCEgIACtpUNBIYQQQtR+01akEBgwh6GrTJa2Uljdbbi5b2kXOm1jQnD7Xs8vnaD+EbBl5vHF0uXVHLEQoqapkMQrKyuL/v3707RpU4YMGUJ6ejoA48eP5957762ITQghhBBCVItil5cfl88mdF8x4fmwqDOERN1GQrh/2TxjenbECAgnKRIGrNPowg/YnpFXjVELIWqaCkm87r77bmw2GykpKfj5+ZVNHzVqFHPnzq2ITQghhBBCVIvpq1MI8ZvB8BWa1U0UNksC/7qg7XHzDG5dj6ySK5jbSdFxj8at9/Lx0p3VFLEQoiaqkMRr/vz5PPvss8TExBw3vUmTJiQnJ1fEJoQQQgghqpzT42Xmkh8JOJBP9BGYex7YQ+6gXWzIcfPZrQb9O19EVgMHeb7QeSMk7Z4iTcsLIcpUSOJVWFh43J2uY44cOYLD4aiITQghhBBCVLnPVqQQ4vMpw5dp1jdUWH3iuGVA15POO6ZbHMX5Pfm5g6LfJo3yW8FHy/ZVbcBCiBqrQhKvXr168cknn5S9V0phmibPPfcc/fr1q4hNCCGEEEJUqZwiF7MXTyM0JY+4TPihB+B/G10ahJ10/sggH2IajGVTa4XdAwnbvSxe/TH5Je6qDVwIUSNVSOL13HPPMWXKFAYPHozL5eL++++ndevWLF68mGeffbYiNiGEEEIIUaVenr+DAP+vGLVY83sLhfJpzL8H9/rbZW7r3wzD3ZZFbRSXrDDx8Z/DJ8vlsQshRAUlXq1bt2bnzp307NmTSy+9lMLCQi6//HLWr19Po0aNKmITQgghhBBVZtP+HLZte4WWG90EFMOCnhAWeR+d4kP/drnmUUEERU1gSVfwL4EG2zz8smKqPOslhMBaUSsKDg7moYceqqjVCSGEEEJUC5fH5L9fzcOulnPpCs233RW4enLf0E6ntPwdA9rw1Gft+bXtBi5dYfJyizm89vMYHr2kdSVHLoSoyU478dq0adMpz9u2bdt/nkkIIYQQogZ4deFO/CwvMGquJj0MdrTzoUeLe4kNO7EhsZNpXT+YsOg7+d02nl5bNR3XeFlf/DxJPd46ru8vIcS55bQTr/bt26OUQmuNUqpsutYa4LhpXq/3DEIUQgghhKgav+04xLoNj9NiZyEND8L/xihc5j3cdkGTcq3ngSFtufvdC5jV4xeuXGKyts1q/jtzCR/dNOi4cyQhxLnjtJ/xSkpKYu/evSQlJfHNN9+QkJDAW2+9xYYNG9iwYQNvvfUWjRo14ptvvqnIeIUQQgghKkVyViEvfjMVR+FGRvyu+bKXgdfahclXDsNmKd8pU1wdPzq1v409rexkBcGIn8Eseoqv1uyvpOiFEDXdad/xio+PL3t95ZVX8tprrzFkyJCyaW3btiU2NpZHHnmE4cOHn1GQQgghhBCV6Uihizs//Bo/+6fc+pVmU4Jib6tgBnScTOv6wae1zjsuaMw1m2/gowvf4YEvTVYkHeHLktfp0+xxIoN8KvgbCCFqugpp1XDz5s0kJCScMD0hIYHExMSK2IQQQgghRKUodHq47aOf8bE9z83faAp8YPYgA7/gpxnf88Tzm1MV6GPjrktH4Qlsws/tFdf8amKxzeG+L37Da+oK/AZCiNqgQhKvFi1a8PTTT+Ny/dFUqsvl4umnn6ZFixYVsQkhxDlkz+ECnvlpO2OnzGfsOz/xxJxEdh/Kr+6whBBnoQKnh5um/ozyTOKq77z4O2HKCIVHT+SF0T3O+Hmsfs3rEhHzMMu6WynwgZFzNabzEV77eUcFfQMhRG1RIc3Jv/POOwwbNoyYmJiyFgw3bdqEUorvv/++IjYhhDgHFLu8PPfTNhITX0T5riYwQ2MxYWsRPLylI107PMRdA5thGPJguhDizGUXurj1o/ngfZBR37uJPQwvjlK4vON5+6YrCPSxVch2Hr2kLeNev4kpw95m4uea1msLWVvyEIsbvEPvphEVsg0hRM2n9LFmCM9QYWEh06ZNY/v27UDpXbCrr74af/+zq9nUvLw8goODyc3NJSgoqLrDEeKskVng5LYPfsSuH6PVehdDV2tsRxtE9Sr4vptiW/tgGsS/zWPD20mrYOcoOQafnOyX8kvKLOTuj77Ex/oiV33nJSYLXrpSUWy5gv9d/x+aR1Xsflyz7wjPfXE/DfcvZ9xCk5cvM8gKGsbTNzxE08jACt2WEKLqlOf4W2GJ17lCCreq5/GazNt6kE0HcmgRFcSQNvWwWyuklqyoIZKzCrnzg+k4bC8x9luT2MMwu7sisaVCWxTNt2quXGKytJViWc9QurT+gDsGNKvusEU1kGPwycl+KZ8luw7z/FdT8LVN418zNf4l8OoVimLLaJ6+7q4KT7qOeWfRHpatG0/fRYfosEfz/GhFie1G3rn1NuoGSmMbQtRG5Tn+nvbZ64oVK0553qKiIrZu3Xq6m6pQOTk5dO7cmfbt29O6dWvee++96g5J/A2nx8vNH/7KjIWj2LrlCr5ZPIIbpsym2CV9w50tNu3P4Y4pb+JQL3LrdJPQAnjmWsXmRr1p1fQr2jb+kh2NOvPWMEWvLZpmm7JZsXYyK/ZmVXfoQohaxu01efbHRN6a+W/CnJ/zn2karwEvjlEUW6/j2evvrrSkC+DmXg0JCH+Khf3spETAnd9obMb73Pb+DI4Uuv55BUKIWu2073g1adKEhg0bcuONNzJkyJCTVilMTEzks88+48MPP+TZZ59l7NixZxzwmfJ6vTidTvz8/CgsLKR169asWbOGOnXqnNLyclWxaj3w5ToOZNzE0J+KaZ+k2RMFn19mp03jL5g0tGV1hyfO0G87DvHqNy/iq7/n319r8vzgvcsVLs9tvHD9OBrXLa1+88OmdD798T6aJK1n9CKT50ca5PqP59O77iTAcfqPqq5LyWbq4h1kHFgIZjYWnxZ0anE+47o3ICpYrj7XRHIMPjnZL/9s58F8Hv7yN+z6CZpvKOCyZZrNDRTfDrZgMonXrru8Sv7uS9xern3nRwxzEjfP8KI0vDPKQPMw7998BcF+FfNcmRCialTJHa/ExESGDh3Kww8/TEhICK1atWLgwIEMGzaMnj17Eh4eTseOHUlKSmL+/Pk1IukCsFgs+Pn5AeB0OtFaI7Uta6ZluzPZv+9/dF5RTKsUzYcDDGIyoeVGN+s3vcGhvJLqDlGcga/X7uftmQ8SWjSH+6Zr0sLg/ZEWtOVhptx6Y1nSBTC0bT16dfsv+1oGs7mBYvxPJj4+H/HC3NO7k+41NU/N2coLX9xKVtoNtN7zOd0TfyQ6+SU27LiSO9/6Ny/O247TI3dWhajtnB4vLy3YyUMf3IcqeYAR3+UzfLlmRh+DHy8KJjj4NT68ZWSVXWzxsVl4+/oLKXLdx5TLFQ433PSVidJP8q/3vySrwFklcQghqt5pJ142m40777yTHTt2sHz5cm666SZat25N/fr16du3L++++y5paWl88cUXtGnT5pTXu3jxYoYNG0Z0dDRKKb799tsT5nnzzTdp0KABPj4+dOvWjVWrVpUr9pycHNq1a0dMTAz33Xcf4eHh5VpeVD6Xx+S5b+eCK5GB6zSf9TNIbxrM3M6Koas1Fr8lfL4qpbrDFKdBa81rC3fx7cI7iTy0jHu+MdmUoPjqUgc+vi/wwS1XUvckHYve2qcRyue/zBwIASXQZZWHndufZkNqTrm3/9A369m++0baJW7ikQ80F68yab9Hc+M8k/vf99B69xLW77iaMa9/Js3YC1FLaa35cXM6I1/+iHWJV9Ni3xIe/EgTUgBPX63YmdCV3p2m8c64vvifwZ3z0xEe4ODtG6/E6b2N10YrfF3wr69MTO//uPGdT0k9UlSl8QghqkaNa1zjp59+4vfff6dTp05cfvnlzJo1i+HDh5d9PmPGDMaOHcs777xDt27deOWVV/jqq6/YsWMHdevWBaB9+/Z4PJ4T1j1//nyio6PL3h88eJDLL7+cmTNnEhkZeUrxSXWOqjFl8R6WrB/D6Fm5oGH65UEsSX+YbpETeWSKyQcXGiTH3MhX994lTYvXIi6PycSv15KZ/h8abz3MlUs18zso1pwXSFjEKzw/stvfNpyyL7OQe6Y8RNMDC7h2ocn/rlG4Ah7iizuuwmo5tetIr/68i7Wbb+C85Vn03qz5vK/BvpZ2rN4QnBym3UaTYSs1GaEwdZjCoy/iuqH/4eK20f+8clHp5Bh8crJfjrd63xFenPM7dverWI6kM/pXk/pZMKebYnMHO8XuW5g8ajTtYkOqNc7krEJuf+8j/KxvcvuXGlPBWyMUbn0Nj18zgdb1g6s1PiHEPztrWjVUSp2QeHXr1o0uXbrwxhtvAGCaJrGxsdxxxx1MnDix3Nu47bbbuOCCC7jiiitO+rnT6cTp/OO2f15eHrGxsVK4VaJDeSXc+PpTNDo8k5vnmjx5jcIb/CgPXDqIF2dcx0WLdhFUqPn6kmjuuOwrujQIq+6QxSnIKnDy789+RbsepPeiEnpv1XzexyC5WRTNmr7GpCEtTymJfvPX3SzfNIbRs/KwmPD5iAD6dJzBjb0a/uOy61Kyefbze2m+dyUjF5u8dbEiK7wrfc57hHaxwczZcIDdO1/FUCsY+5MmOgumDlLkRDegUeOneejiNthOMcETlUMSjFJSNp1Ia82SXZm8u3ANquAdvN5dXPobtNunSYyFry9QYHSiXZtJ3DWwGT42S3WHDJQ2a//vqZ/ia3udG2aahBXAa5crSmwDGDtkIsPayUUfIWqyKnnGqzq4XC7Wrl3LgAEDyqYZhsGAAQNYvnz5Ka3j4MGD5OeXVh3Kzc1l8eLFNGv2181SP/300wQHB5cNsbGxZ/YlxD969set+AXMYvQik9/aKJSjGQ9cOoj2sSEUGiNZ2hKapIPLzGDuprTqDlecgrXJ2Vz/5nt4Sv7D5d8Xc952zauXKvY0ak2/bu/z0MWtTvnO5c29G1Jk3sXnFyriDkPMjiLmLn2dtJziv12uyOXh8RnfYnhXcOUSk1k9FDl123LDsGe4tW8jejQK538j2jF53Mu4HJP5bISD31sqbvtB023VPnYlX8e4t2f+43aEqApSNv3B6fEye8MBRr4+k/e/vxZ95F4GLN3Fwx9pIvI0L15uMHtIXVTgkzx87RtMHNKyxiRdAAnh/rx363V41MN8eKWVpEjFfTM0kVk/8+WCm/jfnK14zRp7jVwIUQ61KvHKzMzE6/WeUC0wMjKSjIyMU1pHcnIyvXr1ol27dvTq1Ys77rjjb59BmzRpErm5uWVDamrqGX0H8feW7c4kPfl5uq00sXrht/MV9eIm0SEuFKUUvdp2Ia++FbcF4vfCxh3zpHGUGsw0NR8sTeKZaQ8Q5HqL26d5qZ8Fz49WZAZfyA0Xv8b15yeUa502i8FDlw9CW9oxt5Ni5BITi30ej3/79896PjVnK/72V7nhR01yJGxv50fXDo9xYauo4+ZrFR3M57dfQf3IqazsFsc7g0ubsR/zTQnekke5/e2n+XXHoXLvCyEqkpRNkJJVxDM/bWfU88/y9W8j8T3yCIN/TuGRTzXxBzXvDTb4cJQ/R8ImMHrAdKZNuIxW0TWz6l5kkA8f3HIFhuNFZg/1ZWkrxYQ5mk5rk9i691rGvfu9XPQR4ixQtU+T1gBdu3Zlw4YNpzy/w+HA4XBUXkCiTF6Jm6dnfkOwaxMXrtN82t/ALB7M/UPalc1zUaso1m1oyOYGO+m60ySt4c8kpt9YYwvTc9mBnGImfbkECp8gIe0I1/6i2R0N04cYOF3jeOKaf9Em5vT+3zrFh9Gg0X2s8bmO87a7GbIIFvb9HwsSmzOw5YnPa/664xD7dj9Bp40u6ubC09cqsExiwgVNTrp+H5uF50d1YsbqKcxY8CLPXLOAm77T3P+p5t0h3zD1u82savUU9w5qfsrPlglRkc7Vsim3yM1PW9L5cf02XNmfonw30vSAycD1JjFZsK8uvHaJIr9+AIXFV3JJ5ysY0y3+b58drSmCfW18eGN/Js+awopuE9kZncYNC0waZRTz2cWTuPWttfxr2C0MaVOvukMVQpymCj8SlZRUXhPf4eHhWCwWDh48eNz0gwcPEhUV9RdLidpAa83kWRsJdLzF+B80O+vDgaaBDO09gYjAP04uWtQLJJ/BrGymaJ4Kbksq87ac2t1OUTW01ny5JpUJbz1FScHdDJ6fxfU/m8ztpPhmmD8Ov+d5b8K/TzvpOuaBIa0pLLqGTwcYdN2psWft56050ylwHt+wzpFCFy/PmoatYBuD12qm9TXwmAN4evRF/5g0jeoSx+PjHqfQci9TRlvZ1EBx9yyT9pt2sHn3tVz79kx2Hyo4o+8hhPh7eSVuvtuYxs0fL2X8K/9mzooRqPS76b1uHQ+85+HaX0xSIxRPj1Z8fnkYGXXv5OLuXzP9P/dw/fkJtSLpOsZhtfDMlZ0Y1PN9Uuv245kxiqAi+M+nJvVzvmL6/LHcO30lOUXS2bIQtVGFHI1M0+SJJ56gfv36BAQEsHfvXgAeeeQRpk6dWhGbAMBut9OpUycWLlx43LYXLlxI9+7dK2w7oupNXZpEVvpEei11E1II0y9SKMfDjO3e4Lj5lFJ0bdmLw7EKFEQma1ZuXVw9QYsTbM/IY8w7c5mzeCwxWTO5/2OTuMOa56402Ni2OW2bfsKHNw8+Lpk+XSF+dq4bdDVFdSNZ31AxboGJw/E+j8xcU1b91DQ1/5nxO34+U7nxJ5NNDRTpjUMZ3u9eEsJP7PT9ZFrXD+azO8cSEvIOv/aty4cDDAat1YyeWYQufJQHp97H+0v2yjMYQlSgfZmFvL9kL2Pfnc8NL93JV7+NoGj/zZy37Tdu/aSAidM1LVM0M3sYPHmTwcIeLSHqOUZf8DVf3X0zo7vG4bDWnOe4ymtMt3geHPME+dZ/8/bVVjY0VNzxnabP0gMcyBjPuNde4YdN6VLVXohapkKqGj755JN8/PHHPPfcc9x0001l01u3bs0rr7zC+PHjT3ldBQUF7N69u+x9UlISGzZsICwsjLi4OO655x7GjRtH586d6dq1K6+88gqFhYVcf/31FfFVRDWYtzWDBUufoMH+dPpu1rxxscLjGcqLoy44aYMLg1pFsX17HNtik+i6Q/NT9Bz2Hr6KhhEB1RC9AMgtdvPSvO1s3/4SWFYy7Bdon6RZ3Erxa28LJSXjuXf4GHo1iajQ7V7WoT4zVz3At/3v5v5P4MJfvfzWexJTl07lxl4NefnnnXjyHuXin03sbvjmQkVg4KOM6RZXru0E+dh4e2wPPvz9Q34qeYanxyzh+h81D36imdF7MYvWrePnDQ/w0GX9z/hOnhDnoqwCJyv2HmHZnky27VmJjzkffHbhOOzi/D2ajns0dXMhxx+WtVCsaw5GYDCFRX1p2/BqrumeQOO6Z1cZ0Ck+lM/uuIGHZ7ZlyfmPsbZJCjfM07RI8fLJwI+Z8fNcvlv7MI8MP4+YUL/qDlcIcQoqpDn5xo0b8+6779K/f38CAwPZuHEjDRs2ZPv27XTv3p3s7OxTXtdvv/1Gv379Tpg+btw4PvroIwDeeOMNnn/+eTIyMmjfvj2vvfYa3bp1O9OvcUqkKeOK9ev2Q7w58zFCC37jrlkmc7oqNrduytgh75zQ6MExpqm59NmXaHHgA679xeTpm230bD+T2/o2ruLoRbHLyyfL9/Hjsi/w8/uKduvdDF2lyQqCT/sbuMLiiYz5L49e2oEgH1ulxJB6pIjb332GqNxvuOtbk+m9DZKaxXLAcwf1ba/QdMsBRizTPH+FQXbA1bw74V7qBp7YQfOp2pCaw39nzMLf/hpt1joZtrL02bUZAxRY25DQaCL3DW5FiJ+9Ar+lOEaOwSdXm/aL1pr03BI2pOawKukIW3avw+JcgM1vF6Yzl8g0Rbu9mnZJGh83HAqGtY0VmxorPHX8cBZ2Jjx6DMM6NuKC5nVrVAuFlUFrzcx1B/h47vv4+H3F0F81XXdq1jVUfHcBeN0X0KPzndzSpyF+9nPu0X0hql2V9+Pl6+vL9u3biY+PPy7xSkxMpGvXrhQUnD3PQNSmwq2m+3J1Kl8teIqgoiXcNdNkdVPFr32D6dzqI/498K+b+Ad4aNZm9u4dzaPvmbx+sSI7bhJf3HltFUUuXB6TGatT+GrRtwT5fkZ4UjFXLjUJKIFZ3Q12tbFTVDKe24ddSf8Wp9Y5+Zn4cXM60+beTuNd2xm9uLQbguXNFT22afps0XzWz2BvQmtuv/xNzm8cfsbbK3Z5ee6nbWzf/hQW10auna+JyYQFHRQruhk4i67gkt7juLpb7a7uVBPJMfjkavJ+yS9xs/lALhtSc9iYfIhDGYvwVesxHKmY7jzC06BVsqZViqZOPngV7I6GdY0NdidoLL4hlBS1JzDici5q34xBraII9q2cCzk1WXpuMZNnrqQk+3/Ysw4wboFJYBHMPN9gTys7hcXXcP2g0VzSLhqlTq17DiHEmavyxKtTp07cfffdXHPNNcclXo8//jgLFixgyZIlZ7qJGqMmF261hdtr8vzcbSQmPkRY+k5umqtZ11ixoL8fMdFv878Rnf+xT6ffd2fy7ndXM2JOKrn+inm9WvDMjZ8SGybVLSpTodPDjNWpfPf7DwT4TCPoQC5XLNHUy4bfWygW9ATT1Zcene/i1r6NqvRK9Nu/7WHZmjupcyCJq37TBBVDrh9M66vIjm7M4F6vM6ZbfIVuc+XeLJ6e+R2BtreI3lnEyCUmHgvM6G1wqKEvBSVjuWbA5VzWoT6WU+ynTPw9OQafXE3YLx6vyb6sIrZn5LEjI5/taVkcObwCi2czPj7JeIxMrNle4tOhSZqmcZomtBBMYF8UbIlXbI9TOMMtWD3RFLh70KjhMPq3rEevJuH4O+RujtaaHzan8+4P0wn0+5j2a90MXqPJDoDpfRT59UPxWCZw66CB9GoSLgmYEFWgyhOv2bNnM27cOCZNmsTjjz/OY489xo4dO/jkk0+YM2cOAwcOPNNN1Bg1oXCrzZIyC3ngi5+x66dota6A4Ss0C9orVnf3JyLydV4Y2e2UOtL1eE0uf/ZZWu3/jNGLTJ652U6/DjO5uXejKvgW557D+U4+XraPJWunE+j/PSGpRQxboYnLhDWNFT/2BIulKYF172bSxV2Iq1M9CfCXa1L5Yv6b+AYsQLmcaLuD4sILuWHIHQxrF10p23R6vLy/JImFy9/G6juXC37X9NqiORgKX/dS5NcPpsgznqv6XsSl7aOxSfPzZ0SOwSdXlfslu9DF3sxCkjIL2ZdZyN5D+WRlJeIuWo+fbQ84DmJ6CrFna6KzIO6QpmGGpn5maYteRXbYHa3YHQ176ymKIgxsOoqikjYERQylW5MGdG9Uh9bRwafcsfq5JqfIxUvztrNz5wtoYw1DlkDXnZrd9eDLfgrlH4f2u51/D+5B5wZh1R2uEGe1Kk+8AJYsWcLjjz/Oxo0bKSgooGPHjjz66KNceOGFFbH6GkMK/dNT7PLy9m+7WbrmLWz2BVz9o6ZROnzez2B/o3o0b/YyE4e0Llch+8BXG0hJHsOj75u8eqkiN3YyX9w+qhK/xblFa8361Bw+W7aHlKSPsPstIXanh4tXm4TnwbpGih+7g+HbAGvQv7nrom60iw2p7rDJKXIxf+tBDuQUExvmx4WtIivt+bI/O5BTzFPfryMv40U83u1cvBQ67dYkRcK3PQyKooIoLBnFJT0vZVSXWHkW4zTJMfjkKmq/aK3JK/GQllNMWk4xB44NR4rIyk6iJH8jDrUXu+MQHms2ylWCJd8kLEcRk6WJOwTxh0rvOAOU2CAlApIjFbuiFUciNNrfD6MkmmJPa4Ii+tOpYRzdG4XTNiZYLkyU046MfJ76fgU672VUwT5G/qpplAFrGyl+7AEWnwY4Qu7klv5dJQETopJUS+J1rpBCv3ycHi/frD3A9N9+IMj3PRokOhnxu0muP7x3scJr7cqwfo+dVhWwRTsPM/WH0Vw5O43DIYqfe7TmuZs+ktadzlCB08O36w8wa8UK/Dyf4mIvHbbABRs0fk74vaXi1y5gsybg9b2ZOy7qSfdGdao77Bpjzb4jvPzTcixFr2MWpXDpEmidojkQBt+dp8hq4EtR4VC6tBnFtT0SpHpsOckx+OROd78cKXTx4vwdpGfnUJi7A3fJbmwcwG7PAlsuXlWI4XRjKdQE5inqZWvqHYF6RzRR2eDjLl2PqeBgCKTUVSRHKFIjoDgMtI8vFlcEJa54rAG9aBLXjvaxIbSPDSEh3F+qwlUArTULEg/y5k8/E2R7i4ADOYxYqql/pLRGwk89wGJPwOt/O//q35neUgVRiAoliVclkkL/1OQWufl63X6++30OgT7T8E/PZ+Qik8gcmNtZsa6zFaf7dv47etRpN7/t9pqMePYp2qZMZ8TvJs/f5OCCDjO5sVfDiv0y5wDT1Kzad4RZa5LZtXs6fv6/4ThUyAXrNe33aop84Lc2ijXtweJtTUDEbdzUry2d40OlAD8JrTWLdh7mjbm/4We+h7fwAINWQpddmsxAmNfJYHdzhXK2wS/8Rq45vw19mkZItapTIMfgkzvd/ZJf4ub6l27Fq5bTKA3C8zTheRCRWzoOKTi+w8/MQMgIU6SHQXqo4mAouII0pp8DqzcItzMaNy0JrNODptHRNK8XSPOoIBpFBNSqjoxrI4/XZOa6A3z+y0wCfKYRnFrA5b9rorNhdRPFvPPA4heN07iesf0u4KLWUfLsqRAVoEoSr9DQUz/hOnLkyOlsokaSQv+vmaZmXUo2n6/Yy769n+EXsISg1GIuW6aJyYINCYpv+4JBJ5o1v5/7B7c44+pW985Yz4GUMUyeqnl5uEFh7ONMmzCiYr7QOWD3oQJmrd/P7xt+Jsj2HS5zPy23QZ8tpX3m7ImC+R0NshrYcBd2o0Gjm7i5bzMa1w2s7tBrBa01v+44xPsLV2Epfhe3dy991kCPxNLD7rKWiqXtweIfSb7rMnp1uIgRHesTX+fUOnc+F8kx+OTOZL+MeHoiXbZ/x5A1mqxAyAxWZAZBZhAcDlbkBoDpDx5fK1YdiHbVodhdH4tPC4JC29Mgog4JEf4khPvTLDKQOgFn3kG6OH1Oj5cvVqbwzeJZBPtNJyS1gMuOJmCJsfBjVwNX3SAKXSMZ3O1SRnaOJdjv3GslsqYqcXuZviqFn9aux1mwEqUshNTpxYB2LRnRMeas7z6hNqqSxOvjjz8ue52VlcWTTz7JoEGD6N69OwDLly9n3rx5PPLII9x9992ns4kaSQr945mmZm1KNj9uTmd94gICrfNw6WTaJkK/jaUtVq1tpJh7Hhj+MSi/f/Pg8N40i6qYE/dfth/kw7mjuWpWBmlhioXnteWlWz6kXrBvhaz/bJR6pIh5WzOYv3491pIZaPs2IveZ9Nli0nx/6YPvK5orlrU7mhC4R3Bhl8Fc2TmGcDmhOm2r9x3hnV82UnR4CtqxmUY7NBeuM6mbC7ujYGF7g4MJBsrZAiPgaoZ27sjQtvWq5Pm02kSOwSd3Jvtl5Csf4rG9iEM7sHkCMN0hlLgjwBKH3b8ZIcGNqB/mR3SILw3C/WkY7k/9EF+s8jxWjVbk8jB9VSrf/z6LAJ8Z+GYUMHSlSdM02F8Hvu9mkNXAQXFBP1o0H8v1PRuddZ1Q1zZJmYXc88l3BFhewXGwgMYHNKYBu2IU7gh/8ouv4V9DrmRIm3rVHar4kyqvajhixAj69evH7bffftz0N954g59//plvv/32TDdRY0ihD/uzi/h9dyZLdmRwYP9cAm2/41L7idkH3bZrWqdoihywuJViVRuw2huA363cOrA73RvVqdCqaU6Pl5HPPUa75G+4bJnJczfZ6ddeWjf8M601iel5zN96kBVbl2F1f4fp2ENAuofu2zSddmmsJmxMUCxpDXn17XiL2xAcdQNXdW9F7yZSBa4i7TlcwKfLktiQ+CUBfvNwHP6jSqfHgDVNFctaKUrq+uIq7Eqd6NFc1LYRF7SoK0kYcgz+K2eyXz78PYn8Eg/RIb5Eh/gQE+JHZLBD+qA7S7g8Jt+uP8Dni34myPgEXXiQgatKqz5n+8PPHRRbWyms7hY4Qsdz1fkd6dcsQhLrKrbncAF3TX0fH9sUrvtO0/BgaefhFhPq5ENGCEzrb+AMq0dw5OM8cVlnQv3t1R22oBoSr4CAADZs2EDjxo2Pm757927at28vHSjXYm6vybb0PNan5LBuXxapB5bjYy5F+e5G5RfRMEXRYY9J0/1gGrC5gWJZC0V2vA1PUWci6l/P2J4t6JpQea0p3TtjPQdSr2Hy+yavDVMcqT+RGXeNrbTt1QYer8nqfdnM25LG5h3z8bMsxGOkEL5f03WHpu0+jd0D++rCktYGe5uA1duQYssIBnToxfAO0XLXsJIVuTzM3pDGN8uW4euZhsvYS6Pd0GezSdxhyPErrYq4sSl4QwJwF3YiOHIUg9o1ZmCLyHO2atC5dgw+VbJfxD/xmpq5WzL46LeV+Lim4jb30H099N6isXphdVPFbx1AB4eR7xxO7w7DGNklVhqsqgK5xW7GvPEZ/jzPv6drnDZ4/2KFJTAMjRddkMvQpdA+SbOimWLuBQZFxTfzwIir6dkkvLrDP+dVeeIVHx/PnXfeyb333nvc9BdffJHXXnuN5OTkM91EjXE2F245RS62Z+SzPT2PHQfz2Ze2G2feEvwd2/HaMzDynESnK1qkalonawJKSqulbY1XrGqqyIozsLgTKFFDGdB5AFd0qpqqacc6U77sx1SK7Iqf+jXm0bHTaBp5bj2HVOzysnjXYeZvPsC+fbPwdyzD7T1IXBJ03VlajRANO2NgVVODpASNzVaXgpIBdGg5nMs7xdIqOkgay6hiWms2H8jlmzWpbNg2m2Cf+XhKMum4Fbpv14QVwJEAWNlMsaEpmKH+uArb4xM2gt4tm9KveV0ankOtw53Nx+AzIftFnCqtS5/H/nDJNjL3v4fyXUfsLi8XrjOpf6S0+f95HQ3SG1rRxW3xj7iOkee14oLmdaW5/0qgtea2T34nO+dWbpruwTTg/SsMUPdxy0VDKHF7+fz3jaii5/A7dJDxc01KbDBlmMJt70Cb1g/yn4uayx3qalTliddHH33EjTfeyODBg+nWrRsAK1euZO7cubz33ntcd911Z7qJGqO2F24lbi+pR4rYl1VEclYh+7IK2Z95mPyslVh1Ig7HAdyWI1gLXAQeUSQc1DRJ0yRkgN0LLgvsrVd6Z2tXHJghvlCSgNs+lPNb92RImyiaVHHC4zU1lz33Ko3T3+OG+SZP/stCx+Zfc/9Fzas0jupwpNDFwm0H+XnrXrLSvsbXby3eohya7VF03WnS4BC4LaX/X6uaKg7GKWxmDLmuPjRrdBFD29WnZ+NwqVJSQ7g8Jr/uOMTMNbs5kjYNu/9aVG4B7XdAtx2a8HzI8S+9Mr2xkaK4rg3D2YBiNYA2TftxQfO6dGsYdlYXwLX9GFxZZL+I05GeW8yny5P5fd03BPn/gOVILv3Wazrv0rissKyFYnkbUIGhFDsvokubEVzRJY5GEfIsWEWZtjKZuUvHc8HiNFona164RqHsj/DuTVcQ7Ftas0FrzXcb0/jgx/fxcXzJyLmlF1NndVdsb+dHCZN45qoLpeGralItzcmvXLmS1157jW3btgHQokUL7rzzzrJE7GxRkws3rTVZhS4ycktIzy0hPbe4dJxTzOHsNErytqLMXfja0sGejYcCrIVuHHmK6CyIO6yJO6yJzgLL0V/FoWDYFa3YWV+RVk9jBjqwuOtR5O5EVMxQejWN5vzG4dXeF9FTPySyeddIHpri5eP+Bntjx/P1f+4+K59NSj1SxPzEg/y2eSuu3K+x+m3ByCmi7a7SOvuROVDkKO3geHVTRX60BYuzIflcRJcWvbmwVRSd4kOlGeEaLqfIxYLEg8zbvIec9C/xCViHzsuj3U7oslMTlQMuK2yJU6xvrDgQBzZrBEXFHQiOvITzGsfTo3EdWkQFnVV/BzX5GFydZL+IM1Hi9vLdhjRmrliLteQj3MYuWm6DvptLGwDaXwd+bWewt4nC6mmCx3cMQzt1Zli7aEL85Dmj03Uor4QbX3+G+rlfcud3mleHGxwMHsWbt95HdMiJ1f0P5pUwccYivIWTSUjM58olJklR8OlQhds1mBEX3MmYbnHnTA2ImkL68apEVV24ub0m2UUusgqODoVOMgtcZBU4j04rpKgwFVdxCqYrFYflIDZrNtqWj0cVYXG6UUUa/wJFRC7Uy9ZEZpd2fBn2p0fvCh2QXBdSIxQpEYpD4RozyIqFYFzFDTDt5xEb3YOODcLoEBdS407mthzI5ZnPx3HRop0EFmm+GRbD7cNnVOqzZVXlz41jLNu6GptrNvjsxPegm067ShvHCCourY62polifROFK9yBLm6O1/dSerbqyIWtImkWGSgH41oqr8TNwm0H+WlTEllpX+Lnuw6PK5v4ZEXH3ZoWqaUNpKSGw4aGim3xipIIK1Z3fYrcnYmqP4QeTetxfqNw4uv41erfgSQYJyf7RVSUxLQ8vli5j8Ttn+Pr/yvWrAJ6btJ03alRGtY1Vixu++cGgMYwoktj+jaLkKqI5TThsxVkZt7IvR952ZSgWNwzluuHfkq/5nX/chnT1Hy4bB8/L30Sw7uc8XM0oQXw8QBFVlxd7EEPM3l4D+LqyLN5VaXKE6+UlJS//TwuLu5MN1FjnEnhtvtQAYfyS8gr9pBX4iav+OhQ4iGv2E1uURHO4oO4nYcw3ZmY3iMonYvdmofFkg/WQryWYrTpwuL0YDg1lhJFYBEEF0JogaZO/v+xd9/xUZVZA8d/905N74UUQif0XkUQpIgNbNhFdNe1F1xddV3bq2tZ14669oK9gB3EQu8l9N4SSCG9TJJp93n/mCQkgkogySTkfD+fy8zcmcycGWBOzr3Pcx6IKvFdhjvqvn6ZHbIjIDtCq7ksDVN4gk1o5gB0ZywVrnaYAgeSENeb1IQwUtuE0jsxrNl3zlFKccEzM4nNeZzbvlY8fK1G+w4f8fgFffwd2nGpbo7x45Zs1m35mRDTj3gs+4nI8DJ4J/TZo7C7ITMSVnbR2NQJCA3B5eiDPeICTu/VhbHd42RS9EmozOlh8c48ftmSzY49PxJiXojXkk5wlpe+exR99vjmhXl02JkAm6qGBXsj7OiVbalUQ0lMOp2BHWIZ1C6CLrEhzeogyp+RAuPo5HMRDa3C5eW7jVl8sXwdmuN9DNtWkncZjFnvG8ZeFARLumus7QamwEjKK0+nV7cLuXBgW5kvfAzmbz/E61/dyLA1WxiyXfHfq00kxL/BM5cMPaaf35JZwoMff47F9DzDl7o5fb1ibQeNr8eA4R7NKYNu4/pRHWXdrybQ5IWXrut/+B/M6/We6Es0GyeS3C5+5RdszocxTC68mhMMDyaXgeY20F2guzQCnRBUWbU5FYGVEFoBoQ5FWDmEloPdfeRzl9qhMATyQzTyQ32XBSEcXvjSbkbX7ZhcEThdcXj0DgQE9yYupgPtogJpFx1EanwI7aKDWuwRqxm/7mLpxgu58w0nXw/R2dhhEh/d9ShWc8t4P06Pl6W78vlhQyY793xNiG0hLpVF0j7f0LLu6b6zGjvbwMquOrs6KCy2aByOgcQkns/4nu0Ykxrb7Itk0XCUUmzOLOHXbYdYsnUjRuk3WAO3YVQWk3BQo+c+RY90XyOcSgtsTdbYnKKxP0FhhNkwuRIod/ciPG4C/dsnMzAlgj7J4c06UUuBcXTyuYjGtD27lM/XZLBy47eE2b/HU36I/lvglC2K8HLf2fZFPXR2dAErbXHpkzit3zjpkPs7PF6D8577jGDnw9z/nuLt8Tr7Eibzv5sfqNcC5JVuL49/u5E9u/+FrXAPU+cZhJbD56fo7Olhp9w5javGTubcPokyvaARNXnhtX79+jq33W4369at45lnnuGxxx7j/PPPP9GXaDZOJLn95Z3llO+7lttnGwQ5Qf+dT95pBoe9arOBI0CjKAhKAqE4UKM4CMoDQNkVXpuOslgwYUP3BOF1R1DpiUYzJWMN7kxYaHsSIoJoE2YnITyAlKhAUqKCCLaZG+DTaF4yCsq567W/ctrqdXTOVLwzJZIrx33B+B7x/g7tdzmcHhbsyGXOhgNk7P+SkMBluF2H6LRbY/B2g07ZvjMXm9tqrOyqkZkCFi2B0spT6dThHCb0SmBEp2gCrM33F2XRdPLLnCzbk8/iHbns2LcYu7EAzb4Hysppl+ErxFIP+M6WukywKwG2J2nsTNKojNaxGDFUVHbBHDKGLsnd6ZMcTu+kcDpEBzWbs2JSYBydfC6iKXi8Bot25vH56l0cyngfa9AqrIcqGLbFNxTR5vHNO13UUyM3xYTuTMUadjlnDujPxF5tTsrfPY7HzOX7mbv8ci74Jh+7Cz64MISzh3/OxYOOb4TYL9tyeG72BwQHvEvvtW7OWqU4FA6fjtQpjw/HYVzPdePHMb57XLP5Lj+ZNJs5Xt999x3/+c9/mD9/fmO9RJM7keQ2/dM0dh64jD47tJqiymkDZVEYVg3DqmOYTeiaBZNhRfPaUV47XiMYpycc9FhM1gSsgSmEBscSE2InKthGVLCVqCArMSE22oQFEBtia7Vd6i57+Vus2f/gH58aPHqlRnjbN5hx5XB/h1WHy2Mwf/shvlq7j8yMTwgOXIm3opDUXb7OdW3zfGcn0jporOwCRYkmTJ72ONQ4+nc7nfHd4xiQEtFq/47FsTtQWM7SXfks2ZlNesZPBOnLMewH0EorSMzUSD2gSM1QRFQNS06PgW1JGjsTNApjFSowEJOzDeXu7gRHjaZ723b0SQqnb3I48WF2v7wnKTCOTj4X0dSKy918tzGLb1enoco+BPtWotI9nLpZ0XOfwmP2zTte2l2jMs6Op3wgbdpOZfKATq26m26Z08MVzz5FYt5Mbv5W8fglGu6If/DBLVedUFFUUunmmbnb2Lb1KWAN5yyE/nsUu+Lhi5E6RkQ0DuNKLjp1PJP7JTbrkQ0tTbMpvHbt2kWfPn1wOBx//uAW4kSS26PfbmHNxs8wW0KwWGMwB7QhyB5GeJCNULuF0AALoXaz7zLAQqjdQliAmbAAK5FBVjlNfAxmLt/PnJUXctO7ZSzpprGixyjevOMFQu3+XWzWMBRr0gv5ck0GW3fMJtT+M57KPHpvh8HbFW0KfYX4mk4aq7tqOOItUNkFt3USI3oOZnyPOFLjpTmGOH5KKXYdKmPVvkJW7ckj/cAirMZyTPa9GK5SInKoKcSS8n0/U26FPW00drWB3W00yqNBN4diOJNxqj5Exp1Kt4Q4uieE0iMhjKSIgEb/NyoFxtHJ5yL8aW+eg1lrD/Br2gLC9a9w6/vosBNGbjJIyYWSgMPzwbTgCJzO0fTqdgnnD0yme5vWNR/sv3O2snrHJdz8novtSRrzR7Tn+vPeZXjHhlkIeUtmCY/NXoCp8iUozeCCBYoumbCrDXw7RMfRJoiyismMGnAhUwYlk3iU7omifpq88CopKalzWylFVlYWDz30ENu2bSMtLe1EX6LZkOTWvBU6XFz3wu0M2TyfU7YqZkwNZtLwL7loYLJf4ilwuPhsdQbfr/yFUP1L3KTTaSecutmgbVUyWtlVY21nDWeMDaO8O6aQCxjduw8TesT7vU2/OLllF1eyen8Bq/cVsmXvelT5rwQE7MStFWAp9NI2GzplKjpm+dYQAygM8hVhuxI00uPAFaGhmYLB1YYKTxcCwkfQKbFDVTEWSufYkAadZynfwUcnn4toDgxDsWpfAV+uyWDLjq+JCPoFT8Uh+myDEZt9jX8yI2BRT51tXRVWPQm3NplR/ScwuW+i386kN5Xs4kpufvkueu75hfOXGjx2jUZoxMu8fs1pDfo6Sil+3X6Il+f8QqB6BXN+PhNXKXruV2SHw3eDdA521FHO3gRETeOiIT0Y2y2uxcyJb26aRXMNpRTJycl8/PHHDBs27ERfotmQ5Nb8/fWdxVQcvI5/vq944mIdPflZ3v7L+CaNYc3+Qt5fsosD+97FFryU0AOVjEmD7ukKt9m3AO7SHhquGDve8j4ERF3M6b1SGd89jtjQkzvxiOarzOkhLb2I9QeK2JB+iEPZCwhgPaaADAxPMfY8RftsRadM6Jjta9oBUBQI++I09sfBvliN0iiFCgzA7IqhwtUBPWAwCfG96ZYQRtf4ULrFhxATYjuuo9zyHXx08rmI5qbS7eWnrTnMXrOXwsyPCAhZiTnfwdDNiiHbfXNNtyX5mnJkddAxObtgC7ucMwcO5oye8QSdhPPB7v50Nfsyr+aet7z81FdjTbchPHrNC3SOa5yFjw1D8e3GLN75eQHBvI2n4gCnr4Yh2xQeEyzrprG0FxAaiaNyFJ07Xsg5fZMZ2iGy1Q4FPR5NXngtWLCgzm1d14mJiaFTp06YzSfXfxxJbs3f1+sz+Wz+hVzzUSGbUzQW9RvCyze/Wq9OQcdDKcX87bm8/ssadMcruLVd9N0Eozf4OlJuaquxoJdGfooJraI7WsilTOzfl7N6tZFOhKLZyi6uZP2BItZnFLEpPZ3S/AUEmTej7AdRznICCiAlR9HuELTLUcRUDYBwmn1rA+6L09gXp5EXpfCEmTGrMLzONmS5J/HjPZfWu4uqfAcfnXwuojnLL3PyzfpMvl2zEXP5J+iBmwnPcHHKZuizV6E03/pgS3poONrYUBX9iE+ayuRB3TilU/RJMdViW3YJD717PUPWpzFii+K/00y0T5zJ4xf2bfTXVkqxcGce7yxYi7vwf3jM2+myHcasN4gr8i2QvaS7zvbOCoslnlLnaLp1PpdxPdswrEOUzAf7E01eeC1cuJDhw4cfUWR5PB6WLl3KyJEjT/Qlmg1Jbs1fucvDFU/fw4Bd3zN+reLZawKYOHgWVw5NaZTXU0rxy7ZDzJjzK8HqLTwVBxi/EgZtV5TbYUEvjdW9wGpqi4MLGdP/NM7tkyDDCEWLZBiKvfkONhwoYmtWKdsOHqA0bwkBpi2YbJl4KcZcaJCQ6yvE2uUokvLAVJVpDoXB2+N0HJE38dn0G+v9+vIdfHTyuYiWYtehMmatO8D8tGVEmGbhMe0hZZfi1M0GHbN9a44uS9VY1QNUaBhe50h6dLuc8we0o3tCy/23fc0bP1NWdAv/ekvx0Sidbcln8uot/0dsSNOOctmSWcIHy/eyZdtnBAf9hCm/hOGbYeBORYAL9sTBsm46uzsqzNZoKhz9iEw4j1Hd2jMmNVaWBziKJi+8TCYTWVlZxMbWXWk7Pz+f2NhYWcdLNLlbPlhJfsbV/OsdxVMX6rgSHueDG89t8NdJyyjiP98ux1T+HMqRzjlLoNd+RWYEfD1UJ6+dBXfFUBJTpnH5sM4MSIloVZOIReuglCK7pJLNB0vYklXClgP55B5ajsmzHrs9HY8pH73MRViBRttcxapuEJv4Ni9fOaTeryXfwUcnn4toaQxDsWJvAbPWHWDTtu+JDpqH25VNr6r5YDElvgM1i3pobOoGVnMCbi5i9ICxTOqbSFwLGpa/eGcer351NWMW76FzpuLVK+wM6v4Jt4/t4reYKlxevt+YxawV6/CWfIweuJmgTDdDtykG7FJYPXAw0ncmclNH8EYEYFR0xmsdQ7cOwxnWMZqhHSIJD5QRO36Z45WTk0NMTEyd/Tt27GDgwIFHNN9oySS5tQw/bs7mgx+ncPkXueyL0/h5cF+eu/7NBjtSU1zh5vHvtrB/9xMo1nPWIhi4S7E3Dr4epuNoE0xZ5cWcMXQSFw1MJlKGEopWqLjCzbYsXzG2PbuUfVk7cJUsx6zlMWLwP7n19M71fk75Dj46+VxES1bp9jJvSw5frtlLQebnhIQuQy8sYfAWGLpNEeSE7YnwU1+dghQLHsdAQuOncsXwrozsHNOs16YyDMVFL3yGrewh/jVTMeMsjQMx0/jgjjuazTy2jIJyvt+YxS/r12Cq/Bxs2wnM8dB3t6Lfbl9TFIcNNrTX2JSikZ2oMNnCcFV0gYDx9OrYjyEdoujfNrzRp3U0R01WeFUvjPzVV19xxhlnYLMd/rC9Xi8bNmyga9euzJkz53hfotmR5NYyOD1eLn3qIXrv/ZJzVxj85y82xvT9kr+O7HDCzz1vSw4vfP0xIQFvMWCVmzPWKHLD4JORGmUJEZR7p3LJqIlM6psoHYKE+A3DUGQUlhNgMR1XIxn5Dj46+VzEySK3tGo+2NqtmB2fYglaT8hBJ6M2QN+9CofNdxZsZS+w2Nvg8J7PxCETuWhAMmGB/l065mi+WHOA2Qsv57zvDhHggo/OD2fi8E+4dPDxLZbc2PbnO/huYxaLN2/BU/IV1qBNqLJSuuzR6L3XoHOmb+h4fghsSvEVYgUJYDKH4qlsh9s0lKTEUxnQLpJ+bSNIjQ856Rt11Of794RK7bCwMMA3zCQkJISAgMNnE6xWK0OHDuWvf/3ribyEEMfFZjbRoeP57LB8QcAiCMl0M4+VJ1R4OT1e/v3tFnbteJhox1amfWkQUgEfjNZJT7VTXnklV466kEl9Epv10Tch/EnXNVKigvwdhhCimYoJsXHNiPZcM6I9O3NO5ct1B5lfvpzvT/+Mr9hLry0wer3ijLWwIyGTn/q9zK/rXuf7RafRqdPV/HVUx0brElhflW4v7/z0FbEleXQ7AE9dpFNuXMdFA5L8HdrvSokK4sbTOnHjaZ0odJzBgh25/LzlIGna1+zuthqPJRNbrpdu+xU99itO3aTQgfyQInYkrmdH4gYOVv6PH3Jt/Lg0HoerO0ERY0hNbk/PxDB6JITRPjropGiYcjwaZKjhww8/zN///neCgk7+ZCpHFVuORTtzeePbS7joq0xywzR+HNGNf097j/bR9f93mlvq5MZ3fsRmPEzvNeWcs1Kxvp3GrLHgdY9j3PBbmTq8nXT+EaKRyXfw0cnnIk5mhqFYvjefWav3s2/vTGyBCwnMqmR0mqL3XkVZAPzYX2NzTw3N1ZuQ2Ov525heDEiJ9GvcL8/fxZINl3L1J2UUhGh8P64NU8/6kDGpcX6N63h4vAYbDhazbHc+q3bupTTve4ICNuLRcgg8ZNApU9H5oKJTFlg94DLBnnjYkaSxu41GWRRgD0R3xlPh7oI9dASdk7vQIyGUnolhdIoNrneX2+aiyed4tSaS3FoOj9dgyn+eJDV9JhcvMHjiOgvDen5R73klu3PLuP3tTwi0Psul33jpmAUzR+tkdo7AGvIAD58/gqQI6VAoRFOQ7+Cjk89FtBYOp4dZ6w4ya+liAo0Pcam9DEnzLd1iMmBxd41F/cFsaY838Ab+evpgTusS0+SNrfLLnFz9wuN0PPQZf5tj8OhVGubI//DO3848KZpsOT1e0tKLWLYnn9W79uDI/4UQ22Y8tiz0kkoSsjS6HFR0PaiIrmr14LDB3jiNvfGwJ9635iOBAeiuOCpd7TAF9Ccmph+pCWF0iQuha1wISREBzX4UUZMUXv379+fnn38mIiKCfv36/eE/orVr1x7PSzRLktxaln/N2sCuPZfywOsGL56jk5v4Dz67/apj/vmtWSXc9fb/CLK+zfWfK2xumHGehtt8KpNH/5NLByefFF+gQrQU8h18dPK5iNZGKcWqfYW8t2QHuRmvoweupMM2gzNWG0SXwqrOGnOGgcnWDm/Ajdw+cTiD2zfdGbAHZ29kW8YV3PaOi03tNBYO7co/Ln2HXklhTRZDU/J4DbZll7IuvZA1+wrIOLgEq2c5loD9eIwirAWK5EPQIVvRPkcRV+T7uUoL7IuF9FiNjBiNQ1EKd7gJsxaCcsZR7u6ALWQQSfHd6BofQue4EDpEB5EY3nwKsiaZ4zVp0qSaZhqTJ08+3qcRolGd0zeJGXsT2JFwgCHbFXNiZ7Pr0Pl0ig3+05/ddaiMO99+mxDT29zxoaLMDs9dpuE1ruPRK66hWxv55UYIIYTwB03TGNw+ksHth3KopC/vL9/P4vKZ7E/9gfCMcs5bovjXe7Cm0z6+G3Y3L3zRAWvkndx5Rn96JDRu8bM3z8GGLf+jz14PwZXwyzBok3DXSVt0AZhNOj0Tw+iZGMaVw9oB/ckru4516UVsPFjMlgM5bAlezK4OG7DYD+JRhVgKvSQe0mifo+h6QDFmvcJsAHgoCC4kPaaIjJjtZMTMIbtYcSDLwgIVgnJFU+Fpi8Xei8jo3nSIDadDTBAdYoLpEBNEqL35NVmpJkMN60mOKrYshqG46Kmn6JL+HpcsMHjsbxZG9Pjz4YZ5ZU6mzphJsOm/3PaRojgI3rnAhKbdx4vTLiAmpPW1SxWiOZDv4KOTz0UI3zDEj1am8+3Srwmxf0hoRhnnLVUkFvgWZZ47ApR7KJ263MbdE7s12hpUN7y3nPzCv3DPm15+6quxrvtQ/vO3l2RaAnCotJLNmSVsPljMpox88g4txeRJw2Y/iMdcgO5wEljoW/OxbS4k5ypii6B69lduKGRGamRG+S5zI8ETqqGsdszuCJzOeDx6Z+yhA2gbl0yH6GDaRgXSLiqItpGBBFgbfi6+3+Z4uVwuDh06hGEYdfa3bds8W2YeD0luLc+DszeyZ9fF3PeG4tlJOnlt7uHL26/83ce7PAZTX5+H4bqT6z/y4tXh9Sk6VutjvHrt2c1m3Q0hWiP5Dj46+VyEOMzlMZiddpCPf/mSINuHhKeXcskCRUgFfD9II62fiYryS7j09CuZMjC5QYesrdlfwFMf3czQDWmM2KJ4ZpqJXh0/5r6zezbYa5xsispd7MgpY3tOKTtzStmbuQ9H0Qrs+k7Mtiw8ehGmEjdhhRoJBYqEfEgoULQpoOoMmW/IYlZk3aLMEa7wBJkx6UFornCcrjgMvT32kN7Ex3QgJTKQtlGBpEQFkRIZSHig5bimjzR54bVjxw6uvfZali5dWme/UgpN0/B6vSf6Es2GJLeWZ+XeAl6ddTHnf32AQ+Eac0/pxmNT3/3d4YaPfLOFnXumcdYPpXTIVvz3Cg3d/i9e/+tFBEvRJYRfyXfw0cnnIsSRnB4vH61IZ/aijwkM/JiuGzycu8Kg0gLvjtNwxLXBCLqXJy4aTnLkiZ+NUkpx8ctzURXTeeAtxcejdHamnM3rtz1KWEDzHf7WHCmlyC1zsiPbV5DtyC5lf/YOnCXrsJv2YLHm4DYXYapwYimBuEJIyFck5isSCiDccfi5SgIgOwJywjXfZYRGaajCE6yDJQCTJwyPK5ZKox19e17Og+f0qFesTbaOV7Vp06ZhNpv59ttvadOmjTQbEM3KwJQIylxjWJ46kwuWGHx3+i6+35h11OGGS3blsWnTU/TYXUbP/Yr/TNHxGlfy0tTzpegSQgghWhCb2cTVp7TnooF38cbC81nieIGt3eczYYHitq8Uqztl8c2Y27jllUlcMu56Lh50Yg2zftiUjbXyRcYshqJg2J9q5YwB10vRdRw0TSM2xE5siJ0RnaOr9vZGqQvILXOyJ9dRtZWxNzef3fnr2JW4lQDrAZQ1H8Mow1TmIbBUI74Q4goV8YWKHukQWVZ9zslLpaWM7IgycsIz+WpsGkpd3qjvq0F+k0xLS2PNmjWkpqY2xNMJ0aB0XaNb6tmka+9jnw/hWR5+8a44ovAqqXTz6OdfE26s5IIlii+Ha7gDB/LQ5TfInC4hhBCihQqymbltXFcuHPQ0//56NfNHPcrS7geY9qPBP95VvDl+Nt8tWsfC7Q/y5EUDCDmO5gxOj5cXf/iVCNdehm1TvHyWhsd9BVcMTWmEd9R61S7IhnaIqnXPqbg8BukFDnbXKsoycw+wKXIDm727CbBmg7UQD2WYHW4spRrxRdVFGQRoVlKiGnceXoMUXt27dycvL68hnkqIRnFm7wRe353AvtiDDN2mmBsxm12HJtcZbvjcvJ1EBs7g6k8U++NgR+9Axg18iJ6JJ28XIiGEEKK1SAwPYMZVp7Jgx0xenP0uL1z1LmMWernlG8WvvfezaMTfuOKl6Txx+Xn17lz83tL9RFlf5fwfFftioSQ5jCvHXIbV3DIXBW6JrGadTrEhdIoNqbW3D3AWFS4v6QXl7M93kF5Qzr58Bxl5uewo2sh21w4CLJm4SyNoFxXUqDE2SOH15JNPcvfdd/Pvf/+bXr16YbHUPVIg482Fvw1sF8lzrtEsT/2QScsNvjt9Z53hhjtySlmz/nV6ZFbSpgAenaoRZL+fqcPa+TdwIYQQQjSoUV1i6H/rbTz69WgWD7uXje0OcM2Pis4HXbwx6Un+/uZurj/nes7pk3BMz1fgcPHJwm9IKc+n2wH4zxQNLDdzZq/4Rn4n4lgFWE10jQ+ha3zIb+4Zg9trkFlUwf78cno3csv/BinDx44dy/Llyzn99NOJjY0lIiKCiIgIwsPDiYiIaIiXEOKEmHSNrt3O4WAHgwCXb7jhTxtXAL4JnA/NXoc96EcuWGzwY38Nk96XBy8c32wW5xNCCCFEwwmxW3hyygCmnvU+2TFn8+SVGkqDuz5QxDq/4P3v7+OlX3ZyLD3onvtpBzFh73PJAoP17TSM8ERuP+dM6XnQQlhMOilRQYzsEtNoSwxUa5AzXr/++mtDPI0QjWpi70Te3t2G9Ogshm5T/DjkK3Ydmsy27BK04hcYuVrh1WHFYJ1BXf9J20Ye5yuEEEII/zq9Wxxd4x/irpn9efuixzj7Ry9//9zgvTGrWGG9gT2HnuLxC/tgMx99/ad16YWs3fgGPbIqSMiHd86C+Ni76ddWTjyIIzVI4TVq1KiGeBohGtXAdpG86BrDiq4fcuZqxXejd/Lp6gx+Xr+WeGMLp21QvDVeR3edz01jpFGMEEII0RokRQTyzg0X8fCs9swddxsH1pVw9c8G8/IzWWG+jqvf+DcvXTGKqOC6jbZcHoP7v1hBQNA8piwymNdPw2Tuw/SzR/jpnYjmrkEKrw0bNhx1v6Zp2O122rZti80mXeGEf5l0jU7dziXT+ICgJRCV5eaL7F/pHj2Ti2YrDsRATqdArhx9Q6OsbC6EEEKI5slmNvHYhYN5Y+FMFnruJDNyJ3+Zq4grKueLiXdy+Yy/898rJ9Mj4fAcoKfmbCOM/zJmocJthlVDTAzv/RCJ4QF+fCeiOWuQOV59+/alX79+R2x9+/YlNTWVsLAwpk6dSmVlZUO8nBDHbVL/ZAxbAjvbwKRlBpFtZxCzp4jOWfD+eI1A+51M7CmTYYUQQojWRtM0/jqqE1ed+Ro5cafx9MUaHbIVf/3UQ4jpSe548yXeWbIXt9fgrcV7WZb2OhEHMzllq+L90zWsXMl1o45cI1SIag1SeM2aNYvOnTvz2muvkZaWRlpaGq+99hpdu3blww8/5M033+SXX37h/vvvb4iXE+K49WsbgQr7C7NG6KQehPPmKa7+SfFTXw09sAv3X3CuTIYVQgghWrFx3eO4/8rHKQ+4kmcu09AN+PsHihg+ZM7Su+h+/xd8ufBpQvmRv8w1mN9LoyK+PdMv/BsWk7SPF79PU8fSruVPDB48mP/7v/9jwoQJdfbPnTuXf/3rX6xcuZLZs2dz5513snv37hN9Ob8qKSkhLCyM4uJiaZPfQm3LLuGpmVfQccsuzlmhWN1Z46cxAUwa+ikXD+7o7/CEEH9AvoOPTj4XIRreodJK/v7eLNyex7jgOy/dMuDX3hqrOmsk58EFSwz2xsE3Z9uYOOxLpgzp4O+QhR/U5/u3QeZ4bdy4kZSUI1fmTklJYePGjYBvOGJWVlZDvJwQJyQ1PpRrJ73Bu5a/83j/zQR4Ejhv6NNcJEWXEEIIIarEhth5428X8+hXbfn2zOms3FXG5GUGp69XuEy+Imz9ECv9ur0hRZc4Jg1SeKWmpvLEE0/w2muvYbX6+t+73W6eeOIJUlN93eEOHjxIXFxcQ7ycECfslM6xnHLbexSXuwkNMMvwQiGEEEIcwWrWeeSCU1ix53ve+/4V/tf5G0wuB4bZTCD9uPj0RzmjV6K/wxQtRIMUXjNmzODcc88lKSmJ3r17A76zYF6vl2+//RaAPXv2cOONNzbEywnRYMICLf4OQQghhBDN3JAOUQy5+X5ySv7OgcJywgOtdIgOkgO3ol4aZI4XQGlpKR988AE7duwAoGvXrlx22WWEhIQ0xNM3GzKOXggh/Ee+g49OPhchhPCPJp/jBRASEsL111/fUE8nhBBCCCGEECeNBiu8ALZs2UJ6ejoul6vO/nPPPbchX0YIIYQQQgghWpQGKbz27NnDeeedx8aNG9E0jerRi9XjXr1eb0O8jBBCCCGEEEK0SA2yytttt91G+/btOXToEIGBgWzevJmFCxcycOBA5s+f3xAvIYQQQgghhBAtVoOc8Vq2bBm//PIL0dHR6LqOruuMGDGCxx9/nFtvvZV169Y1xMsIIYQQQgghRIvUIGe8vF5vTffC6OhoMjMzAd8Cytu3b2+IlxBCCCGEEEKIFqtBznj17NmT9evX0759e4YMGcJTTz2F1Wrltddeo0MHWclbCCGEEEII0bo1SOF1//3343A4AHjkkUc4++yzOfXUU4mKiuKTTz5piJcQQgghhBBCiBarQQqvCRMm1Fzv1KkT27Zto6CggIiICFnRWwghhBBCCNHqNeg6XrVFRkY21lMLIYQQQgghRItyQoXXNddcc0yPe+utt07kZYQQQgghhBCiRTuhwuudd94hJSWFfv361Sya3BK0a9eO0NBQdF0nIiKCX3/91d8hCSGEEEIIIU5iJ1R43XDDDXz00Ufs3buXadOmccUVV7SYIYZLly4lODjY32EIIYQQQgghWoETWsdrxowZZGVlcffdd/PNN9+QnJzMlClTmDt3bos6AyaEEEIIIYQQjemEF1C22WxceumlzJs3jy1bttCjRw9uvPFG2rVrR1lZWb2fb+HChZxzzjkkJCSgaRqzZ88+4jEzZsygXbt22O12hgwZwsqVK+v1GpqmMWrUKAYNGsQHH3xQ7xiFEEIIIYQQoj4atKuhrutomoZSCq/Xe1zP4XA46NOnD9dccw3nn3/+Efd/8sknTJ8+nVdffZUhQ4bw3HPPMWHCBLZv305sbCwAffv2xePxHPGzP/74IwkJCSxevJjExESysrIYO3YsvXr1onfv3scVrxBCCCGEEEL8GU2d4JhAp9PJl19+yVtvvcXixYs5++yzmTZtGmeccQa6fmIn1DRNY9asWUyePLlm35AhQxg0aBAvvfQSAIZhkJyczC233MI999xT79e466676NGjB1dfffVR73c6nTidzprbxcXFtG3bloyMDEJDQ+v9ekIIIY5fSUkJycnJFBUVERYW5u9w/EZykxBCNA/1ykvqBNxwww0qIiJC9e7dWz333HMqNzf3RJ7uCICaNWtWzW2n06lMJlOdfUopddVVV6lzzz33mJ6zrKxMlZSUKKWUKi0tVf3791crV6783cc/+OCDCpBNNtlkk60ZbRkZGfXOKScTyU2yySabbM1rO5a8dEJnvHRdp23btvTr1w9N0373cV9++eVxPf9vz3hlZmaSmJjI0qVLGTZsWM3j7r77bhYsWMCKFSv+9Dn37NnDeeedB4DX6+Wvf/0rt9122+8+/rdHFQ3DoKCggKioqD98z7+nuipuaUclJe6m0xJjBom7qbXWuJVSlJaWkpCQcMKjKloyyU0+LTHulhgzSNxNTeJuOk2Zl05ojtdVV111XF/w/tShQwfWr19/zI+32WzYbLY6+8LDw084jtDQ0BbzD7I2ibvptMSYQeJuaq0x7tY8xLCa5Ka6WmLcLTFmkLibmsTddJoiL53wAspNKTo6GpPJRE5OTp39OTk5xMfHN2ksQgghhBBCCHGsWtQ4DavVyoABA/j5559r9hmGwc8//1xn6KEQQgghhBBCNCcN2k6+IZSVlbFr166a23v37iUtLY3IyEjatm3L9OnTmTp1KgMHDmTw4ME899xzOBwOpk2b5seoj53NZuPBBx88YohIcydxN52WGDNI3E1N4hYNqaX+vbTEuFtizCBxNzWJu+k0Zcwn3E6+oc2fP5/Ro0cfsX/q1Kk1Qxtfeukl/vOf/5CdnU3fvn154YUXGDJkSBNHKoQQQgghhBDHptkVXkIIIYQQQghxsmlRc7yEEEIIIYQQoiWSwksIIYQQQgghGpkUXkIIIYQQQgjRyKTwEkIIIYQQQohGJoWXEEIIIYQQQjQyKbyEEEIIIYQQopFJ4SWEEEIIIYQQjUwKLyGEEEIIIYRoZFJ4CSGEEEIIIUQjk8JLCCGEEEIIIRpZiyq8Fi5cyDnnnENCQgKapjF79uw695eVlXHzzTeTlJREQEAA3bt359VXX63zmMrKSm666SaioqIIDg7mggsuICcnpwnfhRBCCCGEEKK1aVGFl8PhoE+fPsyYMeOo90+fPp05c+Ywc+ZMtm7dyu23387NN9/M119/XfOYO+64g2+++YbPPvuMBQsWkJmZyfnnn99Ub0EIIYQQQgjRCmlKKeXvII6HpmnMmjWLyZMn1+zr2bMnF198Mf/6179q9g0YMICJEyfy6KOPUlxcTExMDB9++CEXXnghANu2baNbt24sW7aMoUOHNvXbEEIIIYQQQrQCZn8H0JCGDx/O119/zTXXXENCQgLz589nx44dPPvsswCsWbMGt9vN2LFja34mNTWVtm3b/m7h5XQ6cTqdNbcNw6CgoICoqCg0TWv8NyWEEKKGUorS0lISEhLQ9RY1aKNBSW4SQojmoT556aQqvF588UWuu+46kpKSMJvN6LrO66+/zsiRIwHIzs7GarUSHh5e5+fi4uLIzs4+6nM+/vjjPPzww40duhBCiHrIyMggKSnJ32H4jeQmIYRoXo4lL510hdfy5cv5+uuvSUlJYeHChdx0000kJCTUOctVH/feey/Tp0+vuV1cXEzbtm3JyMggNDS0oUIX4qhufH4It73n5akLdVS4Gd1roHkNNI8CL2he0DwaFg/Y3GD1gM0DVjfY3AprzXXfZvGC3VVrf9Wl3QWm3xl0bGhQboMKG1RYwWGH0gCN0kAorrosCQSnXaFsGl67CU2zYfKG4HVHUO6OwasnYw3qSnJMIh1igugQE0zHmCCSIwIxm/78rEV2cQUbDhSTW1qJAuJC7PRKCiM+LKBhP3DR7JWUlJCcnExISIi/Q/EryU3ieLjd5dzx2Gnc8o3BfddqRHp1cBlobtDdGgFOCHBBkBMCnIpAFwRUQoATgpyKACe+fU6wu3//dSotUG6Hcqsvf5RbNSrsvjzisEFJoEZJIFQEKgwbKLsJw2TBpOxonkAMTwgV7mg8WhKmwC5EhifSJiyA+DA7bSMDaR8dRHJkIDazqek+PCF+R33y0klTeFVUVHDfffcxa9YszjrrLAB69+5NWloaTz/9NGPHjiU+Ph6Xy0VRUVGds145OTnEx8cf9XltNhs2m+2I/aGhoZLcRKOz2E0EmyDQrrGg8EkAusQFc26fBMpdXircXsor3ThdZbhcxbhdxZR7ijHcZXiNUpTXgVeVg1EBWiUm3YmmudB0N+gulOZB6R68mhvN8KC5vehuA90NmlvD6oTAqi3IqQishKBKiCqH9ocUYQ4ILQezAVA9vMmg3FpBXlgFuaG55IXtIDdMIzcMCryQ5zCzdk8YXmcs5e4U9IC+xEd2pUNcKO2jg4gLtWPSNLJLKlmzP5+0vQuwGUuwBBzAaXKgKQjwBvLhohS85olM6H8KFw1MIjr4yP+n4uTV2ofTSW4Sx8PtNGG1mQg2acTYLSzNfpz20UGcnhpLcbmTioo8nJVZOJz5eDyFGN5CDKMEXXOgm8rR9EqUyYlXd2EoN7rXi+Y2MLkUuDUsrqp8UQmBVTmjOoeEOyGwVBFcAWEO34G/w3lD4dVclAS6KA4qoSgoh7ywXeSGauSFgsOpOFimkZ5vZd3OYAxXDGXOtihbHyKiutE1PoyeiWH0SgwjJSqw1X8/CP84ln93J03h5Xa7cbvdR4ytNJlMGIYB+BptWCwWfv75Zy644AIAtm/fTnp6OsOGDWvymIU4ZrX+L7ePDuLmMZ3r/RQer0GF20tFdcFWdVnh8uJweiit9FBa6aak+rLcjaOiiPLKbIoqc/F6CvB6ilBGISa9GN1cBqZyPKYKNI8Lk9NAr1RYyzWiSyCmWBFTAqkZilM3KwKrpqMYmotDYbkcjM7jQNQWDkbPIbcMVhRprNxtR/cGAhpuSzkuVzmdMqHnPkXqAUV0ie858kOcbE0uYk3nDSzcaGPu0tEM6nMtN5zWicgg64l/3kIIcRJSGGhVoxuq00qn2GDuP7v77/+MUjg9BiWVbkorPZRUVF1W3S6tdFNSUZ03ynBWZJNbcQiPOw+vpwjDKEKpUkx6ObqpAmWqwKtXoJQLU6UXzamwVGiEO3wFWZhDEeGADtmKwdsVoRW1otcqyQutJDMyn4NR2zgYPY/8CsWmYhNbd4XyYUUypZ7ehMWcyoD2cZzSMYp+bSOwmlvvfFDRvLSowqusrIxdu3bV3N67dy9paWlERkbStm1bRo0axV133UVAQAApKSksWLCA9957j2eeeQaAsLAwrr32WqZPn05kZCShoaHccsstDBs2TDoaiubpKMP/NI7vSJ7ZpBNi0gmxW04sJKUodXrIL3ORX+Ykr8xJXpnLd9vhJK8wlwPFm9lbuRudTCyWAgyL7+ybucRLaKFGYr4iMQ+GbVPEFh9+k2V2B8WBDrwmCC+D0AowgH3xsKqLRmakhqFBm0JFz32KkZsVxYEVfDdoDmkhPzEt7TymjJ3GpYPaoutyxFMIIWpTyjh8jqnqyp99U2qaht1iwm4xEXucI3wNQ1Hm8lDkcJPncB6ZP0pLKC7dS15lOm5XJqhczJYClKUEryrHVOHB5ICIEogrUiTmQ789ijPWqKph8h7yQgrY3aaQXQkbyXDPZHWFmbQNCRRWDCaizQTG90jijJ7xMjpC+FWLKrxWr17N6NGja25Xj2+fOnUq77zzDh9//DH33nsvl19+OQUFBaSkpPDYY49x/fXX1/zMs88+i67rXHDBBTidTiZMmMDLL7/c5O9FiGNRU5LUyoz+HkGhaRqhdguhdgvto4N+51G+/6cOp4eDRRUcLKxgf76DXYdKyczeTlrxWjZquzFbc/DqxZgdLqwlGrGFEFyhMHuhKFijMFzhjjGhqWRKXH0JDB+MgSK7eCXb+qzC7cpk4HqYssigbJWLj077lO+X/cycdffz5CUjSQiXOWBCCFFNKeNwYqmqvJoip+j64bzRNirwdx51eORRhctLZnEFWUWVZBZVcLCogsyCUvIKt5NdsokN+PKHx1SEyeHCXqzRIVvRKRPOW2oQ6AKv5mZnwn7Wd8hgN58zxxHErF8GEBB9CVOGdOGMHvFyJkw0uRZVeJ122mn80bJj8fHxvP3223/4HHa7nRkzZvzuIsxCNCtHO+PVgk7kBNnMdIkLoUtc7cOkvYGLKHS42HmojL15ZezJc3AgJ5f9BRtQrnyUcmG2JhEQ2Z3+HZMY0SmG1PiQmrNYXmMya9ML+WjZTlbxMisGrmL0Urjhe8X6dvl8Pe4Orn/lKu67+K8M7RDll/cuhBDNjVLq8HG8mjNezS+pBFhNdIwJpmNM8G/uGQBAaaWbvXkO9uQ62HWojO0H9pMW8QtbemzBsGVCeTmxWRp99irOXmkQtAiKgspY2m0h67ot5PNfo/jf9xcxftBEpo1oR+gJjgQR4li1qMJLiNbmaOmwOSbJ4xERZGVw+0gGt4+s2tMNGHlMP2vSNQa1i2RQuyHsz+/JM3M3sHjov1nebQ/T5hrcNhNemvw+z3y8n2nnPMDEXm0a7X0IIURLUXuOV7WWdDCvWojdQu+kcHonhVft6YpS48gqrmTjwWJW7ytgk30p8xLmMW/sLkyFFfTZCcO3KM5cDfti8/hu0Kus2vIOP6+8gInDL+HaEe3lDJhodFJ4CdGMVZ/grZMnW2CSbEwpUUE8f9kwFu54i2dnf8BLV7zB+T8Y/ONjgxnnLOCd7/+B23iCc/sk+DtUIYTwr6pmY1BrjtdJklM0TSMhPICE8AAm9IgHulPouIolu/OYu2Evq6wfsG7QKqy55Zy+VnHTd4qC4Ao+OfUDlqV9w09rb+a+C85iQEqEv9+KOIlJaS9Es/ab9lNI3fV7RnaJ4Z1bbiIk+AW+OjOIRT01bv5aEXtoNR989xDL9+T7O0QhhPCro57xOomzSkSQlbN7J/DiFafwzvQXuXjUV3gSHmPu6PY8dI3G1mSNG35QXPplMVbnv3niw7t46ZedGMbvT2sR4kRI4SVEc3bUOV4nb5I8UWEBFv43bTTtE19j2Snh/NpH42/fK+yVS3j8kxfZl+fwd4hCCOE3R5vjdRLXXXUEWE2c2yeB92+YzH2Xf0RM/KvMH96JR6/wfQD3va/oun8ZKzdM46b3l1Pp9vo5YnEyksJLiJZAzngdM5Ou8ej5fWmX8grLTglmQ3uNW74yCDZ9yj8+/AG31/jzJxFCiJNQna6GVdmkNeaUbm1Ceemqkdx96Xu4Qh7ggwsD+WaIxqXzDU6bX0BJ8Q1c//ZPlLs8/g5VnGSk8BKiGTtaE0854fXnNE3jwUm9CQr9L1+dYaIwGC77TmHmcZ6ft93f4QkhhF/UXserWmseRdE7KZyPbp7CkO4fsim1N8+dp9N/l+L8b5y4K//Bje8vwiMH60QDksJLiGbsiCEhtM6jk8fDpGs8e+kw3J47eOssjba50C2tgtVp/2FnTqm/wxNCiCanUEeZ49W6mU06t4ztyo3nv0RB6Jk8fbFGh2w4a44Lj+PvPPrNRn+HKE4iUngJ0Zwdpathaz46WV9BNjOPXHwBXm0YXw7XmbRcYZiW83+zF//hmoBCCHFSUrW6GlZdSkrxGdE5mgev/CeVlsk8e75Oz/2KoUsq2L37Xj5fc8Df4YmThBReQrQA2u9cF3+uZ2IYA/v9nR29reSFwnm/gO54ll+3H/J3aEII0aRqN9eoLrgkpxzWOymcey+9i3LbaN6YoHH6ekX4wX189OOLHCgs93d44iRQr8LLMAx+/fVXHnnkEa699louvfRSbr31Vt5++20yMjIaK0YhWi05J9Mwbh7TmbKKa/hgtE7fvQpVeoD//bhAznoJIVoVpQ63k68uwWQURV0DUiK4fOI9FCam8Gsvjat/Upit33L/50slZ4gTdkyFV0VFBY8++ijJycmceeaZ/PDDDxQVFWEymdi1axcPPvgg7du358wzz2T58uWNHbMQrUZNgpRTXick0GrmqrHnURkfzp54OHexwuJ9mZ+2ylkvIUQrJWe8ftd5/ZKIS/o/Fp5qotIK5/6iMEqflJwhTtgxFV5dunRhw4YNvP7665SUlLBs2TK++OILZs6cyffff096ejq7d+/m1FNP5ZJLLuH1119v7LiFaBXUUTLiybzYZWOa1DeBMvdf+HKETvcMMBce4o2f5ssRTCFEq6FqjaOQb74/9uCkPjgrpvHuOJ3+uxWmwkxe+u5bWZJEnJBjKrx+/PFHPv30U84880wsFstRH5OSksK9997Lzp07GTNmTIMGKURr9dvuUyAToY+X2aRz9enjcUdFsqsNTFwJdvc7rNxb4O/QhBCiSSjj8FBDTav6FVByylGF2C1MHX8xlbHRbE2Ci+cbhFpeZ9bag/4OTbRgx1R4devW7Zif0GKx0LFjx+MOSAhxFNJOvkGc0yeBIs8VfDdIo/c+hdO9n3cXbfZ3WEII0SQUtdbxqi7AJKv8rkl9E3CZb+OL0zRSciEoq4QP538nZ73Ecat3V8MNGzYcddu4cSM7d+7E6XQ2RpxCtEqHR8EdToxyxuv4mXSNScPG40gMJD8ERqyD/OxXySiQblVCiFZAKVBQu2yQnPL7NE3j+vEjMAUmsaGdxrnLFSGWmfywKdvfoYkWqt6FV9++fenXr98RW9++fUlNTSUsLIypU6dSWVnZGPEK0UodHnMoRydPzJSBSTgcE5jbX+fUTQrDtoF3l+zxd1hCCNHolKo646UdPpwnGeWPjeoSg8t6Hd8PhfY5YM4v5LMly/wdlmih6l14zZo1i86dO/Paa6+RlpZGWloar732Gl27duXDDz/kzTff5JdffuH+++9vjHj/VGlpKbfffjspKSkEBAQwfPhwVq1aVXO/UooHHniANm3aEBAQwNixY9m5c6dfYhXi2MkZr4YSYrfQp+el7Onm+82j+xbF2s3v4XB6/B2aEEI0KqUUmqo+lFfdTt6fETV/mqZxxcjhqLBo9sbBmHUK5XiPzZnF/g5NtED1Lrwee+wxnn/+ea699lp69epFr169uPbaa3n22Wf573//y+WXX86LL77IrFmzGiPeP/WXv/yFefPm8f7777Nx40bGjx/P2LFjOXjQNxnyqaee4oUXXuDVV19lxYoVBAUFMWHCBDlDJ5qn6hNdted4SZI8YVef0h6jYgCLemiMX2tgDfyZWetkwrQQ4uSmqgYZqjrzhiWp/JkJPeIpcp7HvH6+DodufRfvLt7r77BEC1Tvwmvjxo2kpKQcsT8lJYWNGzcCvuGIWVlZJx5dPVVUVPDFF1/w1FNPMXLkSDp16sRDDz1Ep06deOWVV1BK8dxzz3H//fczadIkevfuzXvvvUdmZiazZ89u8niF+DNHT4eSJE9USlQQwfHXsrwvRJZB6MEKZi37UVrLCyFOarUXUK4mB/P+nMWkc/rAs8lpb6bSAj23KHbteo/SSre/QxMtTL0Lr9TUVJ544glcLlfNPrfbzRNPPEFqaioABw8eJC4uruGiPEYejwev14vdbq+zPyAggMWLF7N3716ys7MZO3ZszX1hYWEMGTKEZctkvK5oftRRFlCWJNkwrjylGxZrMluS4Yw1Cqv2Mct25/s7LCGEaDxKoeHLKTVzvCSnHJNLhyTXjJQ4PU0RGLRQmmyIeqt34TVjxgy+/fZbkpKSGDt2LGPHjiUpKYlvv/2WV155BYA9e/Zw4403NniwfyYkJIRhw4bxf//3f2RmZuL1epk5cybLli0jKyuL7Gzff5DfFoVxcXE19/2W0+mkpKSkziZEU9N+57o4fiM6RVOsruCn/jrdDoCnMpt3Fm/wd1hCHBPJTeJ4KGVA9RwvJe016iM2xE5I/NWs7A0RDrAfquC7VSv9HZZoYepdeA0fPpy9e/fyyCOP0Lt3b3r37s0jjzzC3r17GTp0KABXXnkld911V4MHeyzef/99lFIkJiZis9l44YUXuPTSS9H1er9VAB5//HHCwsJqtuTk5AaOWIg/IGe8Go2ua0wafirONkHkhcBpaxWFuf/jQKG0lhfNn+QmcTwU6ogyS3LKsTt/QGfMAXHsiYeRGxTespmyHImol+OqRkJCQrj++ut55plneOaZZ/jb3/5GSEhIQ8d2XDp27MiCBQsoKysjIyODlStX4na76dChA/Hx8QDk5OTU+ZmcnJya+37r3nvvpbi4uGbLyMho9PcgxG/VPeMlWbKhXDAgiZKyiczrrzNii8Jl28x7S6W1vGj+JDeJ41Ezx6tOcw1xrMZ0iyXfMZFfe+v036Xwmnfy5ZoD/g5LtCDHVXi9//77jBgxgoSEBPbv3w/As88+y1dffdWgwZ2IoKAg2rRpQ2FhIXPnzmXSpEm0b9+e+Ph4fv7555rHlZSUsGLFCoYNG3bU57HZbISGhtbZhGgq1ZOga/d8kKOTDSfYZqZnr4s40EUDBb22KJZvfpcKl9ffoQnxhyQ3ieOhVFVXQw4fxJOccuxsZhP9ekzkYEcdrw7tdyqWr/9cGjOJY1bvwuuVV15h+vTpTJw4kcLCQrxe3y8oERERPPfccw0dX73NnTuXOXPmsHfvXubNm8fo0aNJTU1l2rRpaJrG7bffzqOPPsrXX3/Nxo0bueqqq0hISGDy5Mn+Dl2I36XJ0clGc9XwdnicA1jSXWPCWgNb4M98uDLd32EJIUSDq1nHS9rJH7cL+iehObuxqovG6PUGZvP3rNlf6O+wRAtR78LrxRdf5PXXX+ef//wnZrO5Zv/AgQNr2sn7U3FxMTfddBOpqalcddVVjBgxgrlz52KxWAC4++67ueWWW7juuusYNGgQZWVlzJkz54hOiEI0CzUH0WovoCxJsiF1iAnGGn8tywdAZAkk7XLy3eJP5KyXEOIkZMgcrxPUNzmcYtPFLO6lkZQPRlkBn6/Y5u+wRAtR78Jr79699OvX74j9NpsNh8PRIEGdiClTprB7926cTidZWVm89NJLhIWF1dyvaRqPPPII2dnZVFZW8tNPP9GlSxc/RizE76s+KqkkMTaqG07vhdfUhWXdNM5fahAU+BkfrNjv77CEEKJBVQ+J8+WUqqGG/gunRdI0jbMG9MEbFUxuKAzdBBn7X8fh9Pg7NNEC1Lvwat++PWlpaUfsnzNnDt26dWuImIQQ1arOeNVpriFZssENSInAEnETPw31nfWK21XJd0veosDh+vMfFkKIFqJmqGGtfTKKov4u6J9EUcmpzO+lMXyLQtnX8v3GLH+HJVqAehde06dP56abbuKTTz5BKcXKlSt57LHHuPfee7n77rsbI0YhWj0Zj9/4bh0/EIvWnQW9NKYsNAi0z+bJ79b7OywhhGgwSlUNNdSoW32JeokLtROddBFbumkEuiByv5s5K37yd1iiBah34fWXv/yFJ598kvvvv5/y8nIuu+wyXnnlFZ5//nkuueSSxohRiFZLO0pilIOTjaN/2whCE/7OLyN0NGD4UoMD+//Nst35/g5NCCEahKpaPbnuGS+/hdOiXTyoLRYthY0pGqdtVLgrP2ZPbpm/wxLN3HG1k7/88svZuXMnZWVlZGdnc+DAAa699tqGjk0IUaVOkvRbFCe/f53TF4/jLD4apTNmg8JctoOnPnubvDKnv0MTQogTVn3GS2m12slLVjkuY1LjyK6czK+9oUc6uIwMPlu1z99hiWbuuAqvaoGBgcTGxjZULEKI35IzXk0qPszOOSOvp6BdBOvbaVz/jYHd+jZ3zPwZl8fwd3hCCHGC1BEjKSSnHB+rWWdknxEUJ1tx2KDnVkjb9A5Oj3TEFb/P/OcPgX79+h3z5Mu1a9eeUEBCiD8mE6Eb19XD27Fw60N8NvF2pr/vZcr3Bp+fex93fBjK85cPx2w6oeNVQgjhN8o4vICydDU8cVMGJnP/hv4s7r6c0zYqtvVdyFdpmUwZmOzv0EQzdUy/QUyePJlJkyYxadIkJkyYwO7du7HZbJx22mmcdtpp2O12du/ezYQJExo7XiFaJ8mMTcakazxz6al4Kv/KK5M12uXAmXNcFBXdys3vL6LcJS2DhRAtk6LWAsrVXXMlvxy3rvEhWKIuY2VPiCoF2yEHHy9cVNO2X4jfOqbC68EHH6zZcnNzufXWW1m2bBnPPPMMzzzzDEuXLuX2228nJyenseMVopXSjnJNNJaoYBsPXzaVMv1snrlAp9d+xdk/VFJWfAtTX/mc3TKBWgjRQv02m8goihNz7chemANj2NUGzlxhEKy9ycKdef4OSzRT9R4z89lnn3HVVVcdsf+KK67giy++aJCghBA+NWPx6yzk5Y9IWp++yeHcffHdlFvH8t8LdTpnKq78zI2p8hHueeNeXl2wm0q3jOUXQrQcShm+roZ1ligRJ2J011hKjal8NVynewao0ixemStnvcTR1bvwCggIYMmSJUfsX7JkCXa7vUGCEkL4xuIfLSFKB6qmM7xjNHde/C8K7Bfyn8s1dAX3vqfonP4Li9dP4bJnX+HjlelSgAkhWgSFqlnCqyaTSEo5IbquMeXUsThjw9gXC2ctBav7VeZtkVFg4kj1Lrxuv/12brjhBm699VZmzpzJzJkzueWWW7jpppu44447GiNGIVolpWp10at9dFKSZJMa3jGa5/5yF07TP3jjEhvz+mtcPt/g2g/Lict7mR+WXcDlT/8f//5uC5sOFstRTiFEs1WTV+qc8ZKkcqIuHJBEmfsqZg/X6bNPQclBXvtxDl5D8oGo65i6GtZ2zz330KFDB55//nlmzpwJQLdu3Xj77beZMmVKgwcoRGtVPSTEd/3wfkmRTa9jTDAf3XIFj3/Xj7WOJ1jZK41J8+HWrxW5oWXMGfgZG+yfs2NLO8q0M+jXbQzDO0YzqH0kwbZ6f80KIUTjUFXNNYDDc7z8GdDJwWLSuXrsOXw+fybbE/O59BeDDy94hfeXjeLqU9r7OzzRjBzXbwRTpkyRIkuIRla90CUgZ7yagQCriUfO6836jJd54pvFzD39Jb45NZ1Ra+GSBQamX2FL2z0sS32FLeoVdu6I4FVnF8yBp5GS1JeeieH0TAylS1wIdovJ329HCNEKVZ/xkjleDe+c3gm8O/9WPh3zEP96XxG1t4zZjjeZ2Ot+4kJlKo7wOabCSyklXW+EaGIKo9ZCl7X7UMn/RX/qkxzOhzecxYIdg3ln8QYWDHqDRadsJjrdYNhWxTU/GpgUHArLZ3PKcnYkriCrRJF+wMYcbyROZxK63hV7eF8SYpJpHx1EUkQg8WF24kPtRAdbZa0wIUSjULUXUFZyxqsh6brGP849nRe/+JD5vXZw+XyD/0ydxUOzxvHyVSPl92gBHGPh1aNHDx544AHOP/98rFbr7z5u586dPPPMM6SkpHDPPfc0WJBCtEq/MzZcvrv9T9M0Tusay2ldx7IzZwhfrD3IUtfPFCT8wNeW/dgPGfRIV/TYrxi1UaEr8OiVHIjOJCM6i+yIVWRHfsCBHMWeEBO6yY7VE4LhDsXpjUQztcEc0JbA4I6EBkUTEWwjPNBCRKCViOrLICuhdgtBNhNBVjO6Lv8whBB/TCmFhvrNGS/57mgoQzpE8Vnb+5hvvYa+e7yc+7PixzEP8/mad7lIFlUWHGPh9eKLL/KPf/yDG2+8kXHjxjFw4EASEhKw2+0UFhayZcsWFi9ezObNm7n55pu54YYbGjtuIU56dc54ybCQZqtzXAj3TExFndGVDQcu59fth1hh3cHSoLls7L+FCnMephIPbQ5B+xxFYp6i536IcFT/5XqotJRREFxGQUg2hcFQEAKFwRpFwZBvU+RbwbBqeGwmlG5BVzY0rx3lDQDDjuENBC0ITQ/BZApBt4RitkRgsUVis0cRGOAb3mgz69gtJuwWvc5tm9m3r/rS9xgTVrOORdewmHQsZh2zrmE16VLkCdFCKarayQOSTRrHvWf14eoXruCtCe8z/UuD5anZzPzxfwztcB/JkYH+Dk/42TEVXqeffjqrV69m8eLFfPLJJ3zwwQfs37+fiooKoqOj6devH1dddRWXX345ERERjR2zEK1CnTletckpr2ZJ0zT6JIfTJzkcxnahwjWRtemFbDpYzMYDReyzrSU9bjV22wEMawFe5cBa5sFeohFVCpGlishSiCpVdM6EyDKF1VP7FRRg4NHdlAWUUxoA5TaosGpUWqHSChVVl5VWjYqq26VWKLCApis0E2g6YNLwmjQwaRi6htekg6ajY0YzTKBMaFVb9XUMMygTChO+1GFCxwyaBTQzumZG06xoevWlFV2zoOs2NN2CyWTFpFnRTWZ03UZCm17cPr6HH/6mhGjFlK+dvMwbbjxRwTZuOvdqPpi7kEU99jFtnuKpK7/kHx8N4L3rJ8lQ8lauXs01RowYwYgRIxorliY1Y8YM/vOf/5CdnU2fPn148cUXGTx4sL/DEqJGna6GdeZ4iZYgwGrilE7RnNIpumrPABzOaezNc7Av38G+PAd788o5mJdNbtk2vO592E152MxFaOYSDJMDQ7nA5UW5wOyEoEoIroCQCgiuUAQ6IcAFdheElynfdTcEOBV2F1h/d3mxI4exGhq4zOA2gdvsu+4xgVcHrwk8evV1rdZ136VHr3psrdt1r2sYmu+2oUNaB42Djn+DFF5CNCmljFpdDX0kpzS8M3rG8/Pmh/hl5LV0Pujhym8VH53/fzw/L5U7z+jm7/CEH7XKPseffPIJ06dP59VXX2XIkCE899xzTJgwge3btxMbG+vv8IQAqsfi+9ROjHJ0suUKspnpmRhGz8SwWnv7ABMwDEW+w0VOSSU5JZVkl1SSV+qisNxFUbmLgnInuY48siqz8Lpz0L0FWE2lmPRydFMFuu5E051ouguluzF0DxhuNK+ByWugvArDAOUFrWqzeMHqAUvVZvUcvm31KEwGmL1gNsDkpea2yai67gG7ASZDYap+XNVj615X6AboCnQDCoJ1Ktr//nxhIUTjqF6aRNU+7SVJpVE8OLk/lz9/K6+f+yz3fKDov7KSda57Wdb5TYZ1jPJ3eMJPWmXh9cwzz/DXv/6VadOmAfDqq6/y3Xff8dZbbzVqU5C0jCKyiysw6zpmk2/ehEnXsJi0mn0199Xc1jCbal3XdSwmTbrjtAK153jJROiTn65rxITYiAmx/aYwOzq318Dh9FBa6cHh8uBweihzeqsuq25Xeihzeahweal0e6l0G75Lj0GF20Op243XXYbhKcXrKQejHOV1gFGJWavErLvQNDe65kbX3WiaBx0vmuZB07zomhdN8wK+S00zQPOiYaA0L5qmUHhBM1Ca8m34rlvN0l65uVixJx9DgaUqL1XnJ7N++LZZ17FWX6/KUTLXr+VRHB7CXp1f5G+xcQTbzDx0yYU8OnMd749ZwDXzDLa13cmzX7xE6k33EhEkB59ao1ZXeLlcLtasWcO9995bs0/XdcaOHcuyZcuOeLzT6cTpdNbcLikpOe7XfmvxXopybkdpRtXcCR0wgaH75k0oHVU1h0Ips++6MmEoM4Yyo5QZAxNeZQYsvk2zgGZF0y0ozYpG1dwK3Y5uCUIzh2CxBGG3WGpNpNfrXv/NZbDN7NvsZkKqLoNtZumc1tQM46i7peYW4FuwMzzQSnhgwydvpRROj4Hba+DxKtxeA1et6+6ay8PXPYaBy6PwGFX7PQq3YeD2+B7jMRQer4HHUHgNRVuZZH5CGjI3PfDRp8QFflGVg8w1l0qZUZgwDPPhXIS5KidZMJQvB+m6FaVZAd98PnQbmmYD3Y5usqOZ7Oh6ACaTFbvVjN1swmbRsZvrNnqxVTV1sVfdd/gxJgJtJkJsZkLsFoLtZgItJslHx0FVL6Bc64yX5JTG069tBGOH38mydVtY0fUQ131v8PRVn3LPp8N49epxchC9FWp1hVdeXh5er5e4uLg6++Pi4ti2bdsRj3/88cd5+OGHG+S1PYaBpySP6781fHMfqudP6LXnUWh1507UeszRHw9eXfvNHAzf9ep5Gm4TeM3gMCkcvrnyKJOG0jWUSfdtuo6uTOjKhGZY0AwrymvHMOx4jEA83iBcRhDoYWimCHRrLAFBiUSHBhEVZCU62EpUsI2YYBtJkQEkRwQSZGt1/7walHQ1FP6iaVpNZ0PRPDVkbgo27SNxZxYjthg1ecRjOpyLPKZa8/hq5RhPnTyl+R73m5zlqfUcXh3QodysKK/ORWZfgxdl0sGko9AxVTV50ZQZqjevFcMIwOMNwG0E+/KRKQLNFIlujcZiiyE4IJCoIBvRwVaig21Eh/guo4JsxIfZiQi0tPpfdBVVCyjX2iejKBrX30Z2YNnOR5gz5iY6fOBhyg8Gs8/+F+8v78JVw9r5OzzRxOQ34z9x7733Mn369JrbJSUlJCcf31oMbq8iPxgW9tTqzJmomS/hPfK6xVM9v6JqvsVv5lroCsxV99V5Di/8cd+c6q/dw7PvXSZwWcBpqe6WBuU2jYqa61Bh8+1z2KEsEIrtikKbznabGR0bJm8gXlcUDlc8XlMHLCG9aB8bS/eEUHokhNI9IZRQu+W4Pr/WpvYcrzoLKEuOFKLVa8jcpAwXuWGwq412eF5frXxkdUOA4cs1v81T5pp8dJQ5gUdfivC3r1516ctF1U1eXGZfPqq+7rT4OniW2325qLqjZ/X1cjt4LYpcG+TYTXjNFszKjuYJwusNwemOolK1xxzUi/ioBFKiAukaH0LPxDA6RAe1nk5zqtYCynLGq0nousbTFw9m6kt/4/WzX+aejxQdNpfxQ+VDDGr3At3ahPo7RNGEjqvw2r17N2+//Ta7d+/m+eefJzY2lh9++IG2bdvSo0fz7lIVHR2NyWQiJyenzv6cnBzi4+OPeLzNZsNmszXIa8eG2Cg7dAWburvBcKNwgXKDcqOUG/AAHvSq+ROa5kWrmTvhAc1Aw+ubnV4zb8IAqi6rbivNwFAGmjLQDAPNq9C8BpoXMBS6t+rHPRpm728n1IPNpQh04euY5oTASogqOdxBLdDp6552mO8Jy21OCoNKOBSew6HwLRwK/5VDYZBTDtk5duaviKO0ohOmsDMY0rkDp3eLo3/b8FZ/BPL31O5qWLe5hnxeQrR2DZmbSrVBWMJ1lvbxoJSTw7nIXTWXz5eT0LzoHO7Oomneqpzjm99n1MpFRtX8Ps2r0JRCUwYYCs0AzaMO5yKP5mv0Uqu5i9VdfV3V3La5IdDl6+oZW+jLR4Eu32XdZRcAPBiah5LACoqCCikK0igIgZyIpeSEQ3mZYkOBiS27Qvm8MpFiVyohUSMZ1jmFEZ2j6ZscjukkHcZY94Cez8n5TpuX2FA7d58/hf/NXs4Xw9dy0SKDx9uu5IGPPua9m68lwCqjC1qLehdeCxYsYOLEiZxyyiksXLiQxx57jNjYWNavX8+bb77J559/3hhxNhir1cqAAQP4+eefmTx5MgCGYfDzzz9z8803N+prP3ZeL6DXHz5Gqeq5EL65Eh6vb56Et2qf2+u77vZWz6VQVfcZuA2Ft2qfy2Pg8hhUerw43QZOj29SvdNj4PT4Jtk7PV6crnK87mIcbgdeTxleTykedwlebzHKKEGjDJNegW6qQNMrUboLw+TCUC50lwfdaaBXagSXQ1i5by2i2CJIzVCM2qiwu33vq9JSzv7YvexK2MeWlJ9ZZ5jZsLEDhZzPxAFDuXJYO8IC5ExYbbXX8VLHdORYCCHqb969lwGX1dmnVN08Uz0/z+Uxaubr1b6/Zj5gVd7y1JoXWPsx1bmoutGL0+Ol0unB7S7D7S7F6S7B6ynD8Dp8m1GOMsoxjHKgDLOpHF2vAJPTl490F0q50d1eNI8BToWlUiPMAWEOCHcowhzQNlcxaIcipLL6HXrICylgX1whe+M3scf9BSvcFlata0eB92xG9R3BpYPa0jbq5JqLWH1AT+Z4Nb3RqbEs7nEv2wOnsWtfKX/9VvG/y2bw5A/DeGjSH/9uKE4e9S687rnnHh599FGmT59OSEhIzf4xY8bw0ksvNWhwjWX69OlMnTqVgQMHMnjwYJ577jkcDkdNl0N/0jStqrMUgP+PgHi8Bg6Xl7LqDmlONyWVHorL3eSVOckrc5FXUkFpWSaZjn3srdwHKgOLNQ+vuRjcFZiLFYm50C5bMWS74qxVCo/uYl3H7Szu8QTLNwYwZ9lkJg6/hOtGdsDSWoZ8/AmFOvocL0mSQohGpmkaVrOG9U8GrTclj9fA4fRSUun25SSnh9JKN6WVvutF5W7yy1zklRRTUrqP/Ip0PO4slJGLxZKPshbj9TowlXsIKNZod0jRPhsmrFGELla4zE42td3Bku7PsH7Hi6xcO4K27a/lrondSAgP8PfbbxCqalXIOnO8JKk0mbsnpnLpS/fx0Rn3cd+7ij5r3KSVP8XmQS/RI+HPu9mKlq/ehdfGjRv58MMPj9gfGxtLXl5egwTV2C6++GJyc3N54IEHyM7Opm/fvsyZM+eIhhsCzCadsAD9GM5G9au5Vun2cqCwnPSCcvbllbP1YD4Z2UvYm7KaRaN243UVkbRf49TNBnfMVhwKczBr+Ecs2/Ad89bfzdOXjaVTbMgfvFYroQ53NZSJ0EKI1s5s0gkL1AkLPJbREcNqrnm8BlnFlaQXlLM/vyo3Ze9nU/RCtnTfgrIdBEcZbTM0Bu0wuOVbKLe5mDPgVzYGLuBvM6Zw6diruXRwcosvUqq7Gkoa8Q+b2cR/Lh/LPW/8yBenLGDKQoP1qWt47MufmXnjedKpsxWod+EVHh5OVlYW7du3r7N/3bp1JCYmNlhgje3mm29u9KGFrZXdYqJTbEit4qk9MJBKt5e1+wtZvCuPdfr3fNbxO9yVWZy+Ev72g8G2pCI+OuOf3P7GZu6/5AaGdmjdCwzWWUBZzngJIcRxMZt0kiMDSY4M5JRO1XtTgQnklzlZf6CIxTty2Wabx8GOc/hEZdB/I5y5SjF2nZd3xn3MnEVLWJ/+JI+d37tFN+Ko09VQyVBDf+gYE8yIQXewKnAlmZsdXPiLYvaZL/DNhiFM6ttyfo8Wx6fe3x6XXHIJ//jHP8jOzkbTNAzDYMmSJfz973/nqquuaowYxUnCbjExvFM0d5+RyofT7+COCz4nJukV5p/SmX9f5huTf9dMRYTnQx7/6L9sOljs75D9SqlaCygftb+hEEKIExEVbGNMahwPnNuTD6ffwb2XfUb3zh+zrtupPHGNzuYUjVu/VgxYk0Fm5t+Y/vFqVEuedFtnHS8fGUXR9P42qgOlrhv4+DSd7hlgzcvl3Z++we09+vqd4uRR78Lr3//+N6mpqSQnJ1NWVkb37t0ZOXIkw4cP5/7772+MGMVJSNM0+rWN4IUrR3H3Je/iDLmT1y4xsy9OY/pnikB9Nve+/y55Zc4/f7KTlKo9wFDyohBCNLpubUJ5cFJvXr7lRdolvMn8kcn8b6LG6WmKUxeWUnTo7zz3005/h3ncFEfpaij5pcnZLSZuPPMsPJFxbEzRuHiBQaD1LT5bfcDfoYlGVu/Cy2q18vrrr7Nnzx6+/fZbZs6cybZt23j//fcxmfzfDEK0PL2Twnn3pqsJC3uBL8+ycDAKbv5CERjwGg/MWu3v8PxGGbXPeB0mSVIIIRpXZJCVJy8eyqXj3iErbgQzztU4dbOiw5Z8Vqx5hNX7Cvwd4nE5aldDv0bUeo3vHofLciuzR0FyHoSll/HJ/Nm4PHLW62R23AOVk5OTOfPMM7ngggtwOBwUFhY2ZFyilQmwmnjxilGYLY/y3iQdqwfGLDQozXmCpbtaRtOWxnB4jpdea5+kSSGEaAoTe7XhhvMepTiyN5+O0LlosYHuXcu/v/wer9Eyhxwe2dXQX5G0bpqmcevEEZis7VjbUePcFYpQ+8d8uyHT36GJRlTvwuv222/nzTffBMDr9TJq1Cj69+9PcnIy8+fPb+j4RCtiNes8e+V43JUX8cFojZGbFR7nbl6Ys7hlj6k/TorDCyjXGXUoSVIIIZrMaV1jGTn0MXb2DmJfHFz6kyJI/x+z1x30d2j1VrerYfUZL0kq/jK8UzRG4N+YMwTa5oI1t5iPFvzaKn/naS3qXXh9/vnn9OnTB4BvvvmGPXv2sG3bNu644w7++c9/NniAonWJDrZxwehp5LcL4UAUnLUULJWvsy6jyN+hNb2qroYy6EAIIfzrr6d2wGnczqenaXTKAtuhAj5c8FOL+wW5eu6w70/patgcTB01BD0knm1JcPZygwDeYXErHulzsqt34ZWXl0d8fDwA33//PVOmTKFLly5cc801bNy4scEDFK3PxYOSKa28kq+H6vTbrXCzl5lLdvk7rCZX09VQqzsGv6WvIyOEEC2NSde4fsLpqLB4tiX6Fl0OUh+zcm/LmutVnVfUb8cbCr8Z2y2OIu9Uvh+kk3oA3JVZvLt4s7/DEo2k3oVXXFwcW7Zswev1MmfOHMaNGwdAeXm5NNcQDcJi0hk/5AwK2loot0PvzbB/74dUuLz+Dq1JVR9JrX1ksu41IYQQTWV011jKvJcxr79Gz/0Kp5HOJyv3+jus+lFHLk4iB/P8S9c1LhpxGuVtAsgPgWHroTTnbTKLKvwdmmgE9S68pk2bxpQpU+jZsyeapjF27FgAVqxYQWpqaoMHKFqniwYk4y7vz4quGkO3KQIDV7JgR66/w2pSNQtd/iYnSo4UQoimp+saZw4ZQ1GyjXIbdNuhsXdvy+pCVzPUsM46XsLfzuuXSGXZaH7uo3PqZoUesI6PV2X4OyzRCOpdeD300EO88cYbXHfddSxZsgSbzQaAyWTinnvuafAAResUE2LDGnERa7potCkEb3khcze2rvUtai+gXJskSSGE8I8ze7XBKO/Omk4aQ7cZhAQsZtmefH+HdcyUUr528rX2ycE8/wuymenS9Qq2dgeLBxJ3e1m0djYeWVD5pHNc7eQvvPBC7rjjDpKSkmr2TZ06lUmTJjVYYEKM690NZ4yNCit03K+xP31ui5vIfEKqhoQomeMlhBDNQkJ4ACroAlZ30UjJBbcnlwXbW85ojJoFlKuTC3Iwr7m4fGg7zN4urO6iMW6tQbDta35tQf+2xLExH88PORwOFixYQHp6Oi6Xq859t956a4MEJsTILjH8uKQdW5O30XO/Ym/nFezOLaNTbIi/Q2sSNQtdQp1xIVJ3CSGE/5zSvRfLvDqGZhCTCZvNK4Hu/g7rmKjqA3qAzPFqXnomhuG2XcX8Pv/kH5+CKi3gk2UbGdc9zt+hiQZU78Jr3bp1nHnmmZSXl+NwOIiMjCQvL4/AwEBiY2Ol8BINpl1UIOXGCDa2286lCwy+tqazZFd+6ym8ah+ZrEVSpBBC+M/wjlGsXt+GPfEZ9NynOBQ7jwLHZUQGWf0d2jEwZKhhM3b+sAF8uzSUnPBiTtkAC6xvc7DoFBLDA/wdmmgg9R5qeMcdd3DOOedQWFhIQEAAy5cvZ//+/QwYMICnn366MWIUrZSmabRLOY0DiQqrB/QiF2v2tp7T7jVtf397h2RJIYTwm95J4ZRW9mVTikaPdIU5YE+LaStffcYLjepDe3Iwrxk5p08CpY6x/NxHZ9hWBfYNfCJNNk4q9S680tLSuPPOO9F1HZPJhNPpJDk5maeeeor77ruvMWIUrdjA9lEYIXacZkjM0cjKWurvkJqMUoe7GmrSTl4IIZoFq1knJGYcuxI0IsvA8JSw4UCRv8M6JgpV64BeVTaRg3nNRqDVTI/Ui9iZCiYDknYbLJYmGyeVehdeFosFXff9WGxsLOnp6QCEhYWRkSFVuWhYfZLCMTnbsDceOmUqTJ5VFJW7/vwHTwJKqaN3NZQcKYQQftUjOZmKaN+XcWAebEnf7+eIjk3N+pDSTr7ZumxoO8yerqzprDF2nUGw9RvmS5ONk0a9C69+/fqxatUqAEaNGsUDDzzABx98wO23307Pnj0bPEDRunWND8Hh6sKuNhqdshSWgP1sOFDs77CaiFG78VQNTdKkEEL4VZ+kMMzmMPJCoGOWorTgFwyj+XfdrbNMSXVXQ0kpzUqPhDDctstZ0NvXOdPryOeT5Zv9HZZoIPUuvP7973/Tpk0bAB577DEiIiK44YYbyM3N5bXXXmvwAOtj7dq1jBs3jvDwcKKiorjuuusoKyur85j09HTOOuusmmYgd911Fx6Px08Riz9jMelYQ0exK0Ejrgg8qqjFDOk4UTVHJoHaxyQlSQohhH/1SQ7HXZHM7gSNTlkQaN7CvnyHv8P6U9VNm+qe8ZKk0tycN3QQ7phgDoXB8A1QeuhtMosq/B2WaAD1LrwGDhzI6NGjAd9Qwzlz5lBSUsKaNWvo06dPgwd4rDIzMxk7diydOnVixYoVzJkzh82bN3P11VfXPMbr9XLWWWfhcrlYunQp7777Lu+88w4PPPCA3+IWfy61bWfKon3X7fkGG9Iz/RtQE6kZani49y8gw0KEEMLf4kPtVNCPXW00OmQpDGsW61vCQcGjLKAsmp9z+iTgKBvDL310hm9VaAHrpcnGSeK4FlBujr799lssFgszZsyga9euDBo0iFdffZUvvviCXbt2AfDjjz+yZcsWZs6cSd++fZk4cSL/93//x4wZM45Yj0w0H32Sw8AWREkApGRDYc5Cf4fUJJQyfltzAXLGSwgh/E3TNKLjRpIRrwh0gVZezvqM5j8MvnZXw8PrePkxIHFUQTYz3bpewvZUsHigzR4vC9Z+jcsjTTZaunoXXjk5OVx55ZUkJCRgNpsxmUx1Nn9xOp1Yrdaaxh8AAQG+dQ8WL14MwLJly+jVqxdxcYcXo5swYQIlJSVs3nz08bNOp5OSkpI6m2havRLD0ZwJ7I3T6JCtsGgbOVRS6e+wGp2qOix5ZFdDyZJCtHaSm/yvd3I0rnDf7z2heRq7Mrb5OaI/V7eroY9klObpsqHtsHg7s7qzxsTVBsH2z5mddtDfYYkTVO/C6+qrr2bt2rX861//4vPPP+fLL7+ss/nLmDFjyM7O5j//+Q8ul4vCwkLuueceALKysgDIzs6uU3QBNbezs7OP+ryPP/44YWFhNVtycnIjvgtxNB2ig3C4U9nTxjeJ2WQ/wMaDzf/I4omqPuN1BMmSQrR6kpv8r1diGBYiyQ6HDtmKyuJFeJt5g43qZUp85IxXc9YzMQwj6FrmDYakfAjILuWjBT82+39j4o/Vu/BavHgxH3zwATfccAOTJ09m0qRJdbaGds8996Bp2h9u27Zto0ePHrz77rv897//JTAwkPj4eNq3b09cXFyds2D1de+991JcXFyzScv8pqfrGgERo9gb51szxeMtaRWFF9RuJy/reAkhDpPc5H89E8NwVSazp41vNEaAdSd7csv+/Af9qOaMV50FlCWrNFd/GTMEU2Aim9pqnLvMIFh/l7mbj36iQLQM9a5IkpOTa7qtNYU777yTrVu3/uHWoUMHAC677DKys7M5ePAg+fn5PPTQQ+Tm5tbcHx8fT05OTp3nr74dHx9/1Ne32WyEhobW2UTT65acgqOmwYZic8bJ32BDGVULKP9mvyaHJ4Vo9SQ3+V9MiA2n1o/d8Rrtc8CwZTf75U5q5njVJiml2Tqtawzl+rV8N1SjUzZY8vN5Y+53MterBat34fXcc89xzz33sG/fvkYI50gxMTGkpqb+4Wa1Wuv8TFxcHMHBwXzyySfY7XbGjRsHwLBhw9i4cSOHDh2qeey8efMIDQ2le/fuTfJ+xPHplRiGZg2i1A4pOVB4aJG/Q2p0tY9MSldDIYRofqLjh3MwTmF3g+aoaAGjMQ7PHa7OK5JTmi9N05g6eiRGRDRbkuGS+QaB1td4f3nLWLBbHOmYCq+IiAgiIyOJjIzkkksuYf78+XTs2JGQkJCa/dWbP7300kusXbuWHTt2MGPGDG6++WYef/xxwsPDARg/fjzdu3fnyiuvZP369cydO5f777+fm266CZvN5tfYxR/rmRiG5kxgT7xvSIdV20jOSd5go+4cL0mNQgjR3PRK+m2DjR1+juiPVZ/x8v1ZPcdL8ktzdlavNjjNt/PFaI3EPIjd7eCbRW9xqPTk/h3oZGU+lgc999xzjRxGw1i5ciUPPvggZWVlpKam8r///Y8rr7yy5n6TycS3337LDTfcwLBhwwgKCmLq1Kk88sgjfoxaHIvDDTZ2MHJTVYONA8XEdbf7O7RGU7urYW2SI4UQonnolRTG6o0RZIfn0iFbsTxyAV5jMia9eX5RV4+kqNNO3p8BiT+l6xp3n3saz3zSjQW9tnLJAoOnp37JvZ+exhvXjJHCuYU5psJr6tSpjR1Hg3jvvff+9DEpKSl8//33TRCNaEiHG2x8w3nLFF5PCWvTCxnbPe7Pf7gFqx4NUvtrVb5jhRCieehV02Ajj47ZivX9drAjp5RubZr3nLs67eQlpzR7A1IiiE66i0XWv9Bvj5fJ8xQ/jHuYd5d24OpT2vs7PFEPxzzHyzAMnnzySU455RQGDRrEPffcQ0VFRWPGJkQdfdq3pyzOlyFCcgw27Nnq54gal1JGrSOTh0kHKiGEaB6ig2049SFsT9TomAWGJZNV+wr8HdbvqhlqKAsotzgPTupPeeXVvD5Rp98eRcKufL5f9AgLduT6OzRRD8dceD322GPcd999BAcHk5iYyPPPP89NN93UmLEJUcfg9pFYtCgyI6FbBjiL51Hp9vo7rMZT1T3U92et2V6SJIUQotlo13YEmQkKixf0Ihcrduf8+Q/5iTrKMiVyMK9liAyyMv38qbhCu/LNYI2rfjEILlvJs5//l7Xphf4OTxyjYy683nvvPV5++WXmzp3L7Nmz+eabb/jggw8wDGlpKZrGwJQIKiq6sKWtRvd0RUDAFlbvO3m/bKrPeP12jpcQQojmY2jHaFRwIOU26HBAI+vAj0267E59qFoH9KojlIN5LceoLjH07vUom/qHsaaTxi1fGQR5v+aRmf9m0U4589USHHPhlZ6ezplnnllze+zYsWiaRmbmyb+ekmgeQuwW9OAz2JQCyXng9uby87bme2TxRCkUWnVqVLXPeEmWFEKI5mJI+yiobMeWZI3eew2CLMvYdLDE32H9jtrLlEguaYn+PqEbYTHPMHeslV1tNO76RBFR8T0vfnEXr8zfjWE0z6Jf+Bxz4eXxeLDb63aQs1gsuN3uBg9KiN8zvFtvShLMeHRI3A/rt/3cbI8sniilfqeroX/CEUIIcRQpUYFU6GNZ00mj6wHwmDP4aWvzPChYdwFlaSffEum6xtMXD8Ye/DRfnW1jUzuNOz836Lx/FUvSpnLFq3PZdajU32GK33FMXQ3B95/16quvrrPeVWVlJddffz1BQUE1+7788suGjVCIWsZ2j2V1Wlu2tN3DoB2KnJQ5pGVcSb+2Ef4OrcEpjKpLkDleQgjRPGmaRr/UkWzWX8SkICLDy2JjKbeP7dzsipqavFIrrOYVoTgWNrOJV6aO4c6PX+LHcXeSHlPCFb8abNhbwBdjp3PPm6fSPfUmbjq9C3GhJ++yOy3RMZ/xmjp1KrGxsYSFhdVsV1xxBQkJCXX2CdGYurcJpYyzWdRDo+c+hYt9fLLyJF3BvfrIpHQ1FEKIZm189zisJLItCU7dDIHez1h/oNjfYR2hZiQFIF0NWzarWef5y4YzoPu7bE7tyVMX6cQXKv75jqL3rkVsT7+c62fczZ2frGV9RpG/wxVVjvmM19tvv92YcQhxTDRN4/T+p7N04ys4rS56boE05zvklj5JTIjtz5+gBalprvGb/ZIkhRCieRnULpJS77n82vsV/va9wWxtH+8t2UXfSwb6O7Q6fHOHAY3qa3IwrwXTdY3bxnVlSfuXeWbWx7x++ev0WOtm0grFxDUe5vb/ifW2n3ny485UmC5iVO9BnNWrDZ3jQvwdeqt1zGe8hGguLhiQhLd8IL/01jhjjcIa9Asv/bzD32E1OFWnr8ZvR+ULIYRoLnRdY3T/CeSlmCm3w8A0xYG9r7I/3+Hv0OpS6ogDenIwr+U7pVM0M2+7ge4dP2R91+E8fo3Ogp4aZ65WPPi6wYRFO4g59G+Wbb2IB967mnOfepm7Pkvjq7SDZBZVnLRz5ZujYz7jJURz0SYsgLYdbmCVbTnj1il6r/OwvuIp1qa/QP+TaK6XwqgpsrRa34mSJIUQovm5fEhbFq44jR8GzOPcFYrVfZfx4JfLePsvpzebuV6quuSqvYCy36IRDSnAauK+s3pw8JQXmfHzVlY7Z7Bu0Cri9ng5fb3BHbMVTrOLtA5b2dB+G+naDD7NDOXDue1xqL7ExY2gV3I03duE0jE2mJTIQMwmOT/T0KTwEi3SLWO7c+drp/LFKYuYsshgQ9c1PPjxF7x5wxXEhpwcE0mVqtX2t05qlDQphBDNTVSwjb69/sqmoF84fb2Hc35V/DjmMd5f3omrhrXzd3hArbwCNR02mklNKBpIYngA/76gP7mlL/Pp6gx+dX7FZ+2+x+3Jot0ejYE7DK75UWFSkBdaxI7ENHa3WU+6812WlplYtTkMwxWDw52Cyd6H2KgetIsJJjEigMTwABLCA0iMCCDUbvH3W22RpPASLVLbqEBGD/s7SwNXk77dwd++Urx88bNc93ooL007m6SIQH+HeMLqdjU8TJKkEEI0T7eO68rU567infHvMP1Lg7UdD/Gt8xGigh7jrN5t/B1ezRwvQ6udWySpnIxiQmzcNLoTN4yazpr0aXybdpAtxjcc6LgYr+UgtlwPXdMVnTIVF+1S2N1gYJAbnsfBqHwORm3jYNSP5Dlgfb5Gms2K2ROMcofjdEfjUkmY7R2wBqUQExJGdLCN6GArUcE2ooNtRAVbiQi0Emo3ExpgwSJnzwApvEQL9tdTO7Bo0z9499yHuPUjg2mzDN6Z/DB/e2U/10y4lPP7Jzab4R3HperIpI/M8RJCiOYu1G7h5klX8c538/lhwF6u/dHg+ckreO/7e9ie809uHt0Jq9l/v4BWdzWsM9RQkspJTdc1BrWLZFC7SJTqyfacUpbsymfljr2sDv6ezb23YViz0B0VhORpJOcpEvNg0A7FWUWqphmE21RBfkgFuWF55IXtIi8UCkI0ioIgPwBybeC1m9E1K2ZPAHiD8HqDcXuDcHlD8Gph6KYodFscVmssYYGhhAX4CrNAm5kgq4kAa/WlicBa14NsZgIsvstAqwmbWW+xv99J4SVaLItJ54Wp5/K3V3N44aKXufkLxa0fGrw94R2+mv8TMxfdyNRRA5nQI54Aq8nf4dZb9UKXRyyg3EK/bIQQojU4vVsc6/Y/zTrLNELLS7httsEHp61mnX0qF26czvXjRjChRzwmvem/y6vPeMk6Xq2TpmmkxoeSGh/KtSPaYxij2V9QzoYDRWw4UMyu9K2si1nNJm03ZmsOHlMx5jIXJodGRAlElyhiiiEpV9FvN4Q61G+69LmosLooDiyjKCiXsgANhx3fZgOH/fDtchs4rHDAomFYNAyTCV0zoxsmdGVBMyxgWFCGDcOw4TXseAw7HhWA2xuI0gJADwA9EE0PBHMwuikYq9mO3WzDbrZgM5uwWXRsJt13afYVbVazjsVUvWl1bndvE0r3hNBG+zuQwku0aDEhNp6/9hqmv23hpUtf4Nx5iju/MFjTKYMfht7LlwvieP27MbRvfyandolheMdokiICWkTxopRxxHorda8JIYRoju6ckMr9jpdYcNot5IYVc+WvBju2F/HJ6Q/y8bwkXvzuEk7rO5yJPePpkRDWdEXYUdfxkqzSWum6RvvoINpHBzGpbyLQHbiAQoeL3bll7M4tY0+ug4MFxeQWbudg+Q4wMrBa8sFShFevwORyoVcamCrBUqERXg5hDkWYA4IrIbZIEVTpux7o9A1pPDoPLpOTSis4LVBphUoLVFq1Wtepud9l1nCbwVW1uas2320Nh0nhMIEygTJpvktdxzBpaJoJXenoSker2lAmNHc4p/T6rxReQvyR5MhA3rjxGv75WRfmjn2ERTk5XLhIcf9M2BuXzeIeH7PP9hHf5oXxxU9dKVP9iIwZSI/ESDrEBJEcGUjbyEDahAX45Qjk76m93kptkiOFEKJ50zSNR88fwKvz32Ox8342ddzI1LmKf72v2JhygF/7PM36HVY2bOhIgfsU2iadSu+2EfRKDKNHQihRwY2zLmWddbyqm2s0yiuJliwiyMrAoEgGtoustXcoAE6Pl+ziSg4WVpBb5iSvzEVemZO8kkrKyg6RVbafDOcBvJ5sTFopZt2BZioHUyWG7sLAhdntQXMb6G4DzamheTQsbrC7wO4Gu0tVXR7eF1FWa78bLB6F1QMWD5iNY31nXt+fGrgs4NF9xZrH5NvemFLY6EOBW0zh9dhjj/Hdd9+RlpaG1WqlqKjoiMesWrWKe+65hzVr1qBpGoMHD+app56iT58+NY/ZsGEDN910E6tWrSImJoZbbrmFu+++uwnfiWgMYQEWXrpqNPO2dOeNHz7mvSkfYT1UyimbFRcvMLD+ArmhRWxpu5K9cavIcv+PDeVmNu8KRnOH43LFUOZJBnsn7IHtiA4NJjrYRkyIb5JoZJCVULuF0AAzYQEWQu0WQuzmRm21erTuUyCFlxBCtASapnHD6M6c2uU1Hvt8Fu9f9AqBWWWcscrg1q+h0uJkc8pWNrTbRqb2BktKbKxYF4Ojsj0VqhP20N4kRcfSLiqQxIgAYkPsxIbaiA2xExVkRT+OA4VK1vESJ8hmNpESFURKVNCfPrbS7aWkwk1JpZviCg8llW7f7Qo3JZUeiquuO52VeCoLKXMWUeQuwvAUYXhLUIYDpcpAVWDSK9H1SnTdBboLNC9K82BoXsCL7vWiGQqT1wCvQjMMNC91NrwaugdMXg2rB8xe32bxKiweMKqGHjamFlN4uVwuLrroIoYNG8abb755xP1lZWWcccYZnHvuubz88st4PB4efPBBJkyYQEZGBhaLhZKSEsaPH8/YsWN59dVX2bhxI9dccw3h4eFcd911fnhXoqGN6x7HmNRbmbt5Cl8uW8bP4R/y45id2HM99Niv6JqhGLZVYfUCuCkOLCQnvJBD4fvICV9FXqhGSRBUFCr223X22Mzo2DB57eC1YxgBeLxBuL0hOL3BePVITJZoNGsUJkskgbYAAo+YIOqbJBpYdb32fXaLiQCL79Ju0Wuu28z64a6Gvz3jJccnhRCixeiZGMbMW6YyZ9MEPvj1O2ad9Sku4wCddmn02au44lcDixfcpkr2x2SwP+4AWZGLyQmHwjJFXq6FdQSieYPwekJxuiOp9MagLClYAtsSEhRFWICF8EALEYHWqutWIgJ9+8ICLIRUHSysXser9jIlUniJxmKv+p0mNvTElvnxGooKt5dyl4dypxenx8Dpqbp0H77uOsp+3z4Dl9uDx12JclXgcTtQHgdOTzkVXieGUYFhlBNm2Bt9SaIWU3g9/PDDALzzzjtHvX/btm0UFBTwyCOPkJycDMCDDz5I79692b9/P506deKDDz7A5XLx1ltvYbVa6dGjB2lpaTzzzDNSeJ1ETLrGmb3acGav8zlUcibztuawYvtu1oR8zYa+W3FbczGVVRJSoBFX5Bt/HFek6J4O4WW1J4r6jqKU2Z2U2UuqJoRqvgmhVRNDy+wa5XYot4LTCh4zuM0KlxmKzBqGScMwm1AmHR0zumFGq5o0qgwryrCglBWvYcNQNjyGFY+y4zbsRAfso/fRFpOXJCmEEC2KSdc4q3cbzux1LVuzpvDlmnS2at+yv9NivNYMTEVu2hzSaJ+jaJ/tO0AY6Kr+aRclAS6KgosoCM6kKBgKg6EwWKM4EJx2KLEpiiwau61mMJkxGzY0rx3lDcRrBOHxBuH0BhMevImJVXlF1SygLElFNG8mXSPYZibYZoYQf0dzYlpM4fVnunbtSlRUFG+++Sb33XcfXq+XN998k27dutGuXTsAli1bxsiRI7FarTU/N2HCBJ588kkKCwuJiIg44nmdTidOp7PmdnFxMQAlJSWN+4ZEg7AD53SL4JxuAzGMARwsqmBbVglbs0rZl7OPbUWb2eTag8mUjWYpwqOXo7vd6JW+ccfB5RBWDkEViiAXBFRCYDlEFygCnRDohKDKYxtf7DIfnjDqNPvGFzst4DJVTRC1VE0KtYDL5JscmpBhUOYFV6UXw1kOQHlZKSUlJ8ci0ULUV/V3r1JHOyrRekhuarmSguHWUW1h1I1kFk1jxZ58Nh8s4kDAKvZEr8bc7yCGuQjlrcRU5iWkRCOiDMIdivAyCD+kSNkL4WVg8f722d14dHDYSymzgyMAym2+A4YVNt9BwuA8RV68hrvCheEsx+GQnCLEiahXXlItzNtvv63CwsKOet/GjRtVx44dla7rStd11bVrV7Vv376a+8eNG6euu+66Oj+zefNmBagtW7Yc9TkffPBBhW84tGyyySabbM1ky8jIaLC80hJJbpJNNtlka17bseQlv57xuueee3jyySf/8DFbt24lNTX1T5+roqKCa6+9llNOOYWPPvoIr9fL008/zVlnncWqVasICAg4rhjvvfdepk+fXnPbMAwKCgqIioo6rhasJSUlJCcnk5GRQWho47WrbGgSd9NpiTGDxN3UWmvcSilKS0tJSEhohOhaDslNPi0x7pYYM0jcTU3ibjpNmZf8WnjdeeedXH311X/4mA4dOhzTc3344Yfs27ePZcuWoet6zb6IiAi++uorLrnkEuLj48nJyanzc9W34+Pjj/q8NpsNm61uW9fw8PBjiumPhIaGtph/kLVJ3E2nJcYMEndTa41xh4WFNXA0LY/kprpaYtwtMWaQuJuaxN10miIv+bXwiomJISYmpkGeq7y8HF3X6xzpq75tGL4JOMOGDeOf//wnbrcbi8UCwLx58+jatetR53cJIYQQQgghRENo3FXCGlB6ejppaWmkp6fj9XpJS0sjLS2NsrIyAMaNG0dhYSE33XQTW7duZfPmzUybNg2z2czo0aMBuOyyy7BarVx77bVs3ryZTz75hOeff77OcA0hhBBCCCGEaGgtpqvhAw88wLvvvltzu1+/fgD8+uuvnHbaaaSmpvLNN9/w8MMPM2zYMHRdp1+/fsyZM4c2bdoAvtOAP/74IzfddBMDBgwgOjqaBx54oElbydtsNh588MEjhog0dxJ302mJMYPE3dQkbtGQWurfS0uMuyXGDBJ3U5O4m05Txqwp1cp78gohhBBCCCFEI2sxQw2FEEIIIYQQoqWSwksIIYQQQgghGpkUXkIIIYQQQgjRyKTwEkIIIYQQQohGJoWXEEIIIYQQQjQyKbyEEEIIIYQQopFJ4SWEEEIIIYQQjUwKLyGEEEIIIYRoZFJ4CSGEEEIIIUQjk8JLCCGEEEIIIRqZFF5CCCGEEEII0cik8BJCCCGEEEKIRiaFlxBCCCGEEEI0Mim8hBBCCCGEEKKRmRv6CZ1OJzabraGfttkwDIPMzExCQkLQNM3f4QghRKuilKK0tJSEhAR0XY4dVpPcJIQQ/lGfvHTChdcPP/zAxx9/zKJFi8jIyMAwDIKCgujXrx/jx49n2rRpJCQknOjLNBuZmZkkJyf7OwwhhGjVMjIySEpK8ncYzYbkJiGE8K9jyUuaUkodz5PPmjWLf/zjH5SWlnLmmWcyePBgEhISCAgIoKCggE2bNrFo0SKWLVvG1Vdfzf/93/8RExNzXG+kOSkuLiY8PJyMjAxCQ0P9HY4QQrQqJSUlJCcnU1RURFhYmL/DaTYkNwkhhH/UJy8dd+E1bNgw7r//fiZOnPiHp9UOHjzIiy++SFxcHHfcccfxvFSzUlJSQlhYGMXFxZLchBCiicl38NHJ5yKEEP5Rn+/f4x5quGzZsmN6XGJiIk888cTxvowQQgghhBBCtHgyM1kIIYQQQgghGlmDdDWcPn36UfdrmobdbqdTp05MmjSJyMjIhng5IYQQQgghhGhRGqTwWrduHWvXrsXr9dK1a1cAduzYgclkIjU1lZdffpk777yTxYsX071794Z4SSGEEEIIIYRoMRpkqOGkSZMYO3YsmZmZrFmzhjVr1nDgwAHGjRvHpZdeysGDBxk5cuRJ0VxDCCGEEEIIIerruLsa1paYmMi8efOOOJu1efNmxo8fz8GDB1m7di3jx48nLy/vRF/Or6RzlBBC+I98Bx+dfC5CCOEf9fn+bZAzXsXFxRw6dOiI/bm5uZSUlAAQHh6Oy+VqiJcTQgghhBBCiBalwYYaXnPNNcyaNYsDBw5w4MABZs2axbXXXsvkyZMBWLlyJV26dGmIlxNCCCGEEEKIFqVBmmv873//44477uCSSy7B4/H4nthsZurUqTz77LMApKam8sYbbzTEywkhhBBCCCFEi9Igc7yqlZWV8f/s3Wd4FOXXgPF7Zmt6IRAIJCH0XqR3EJBiAUFBQVFE/FuwYcVeXxUVFcWGFVDBRlMBERGk19A7AUJJICF1k63zvB8CCIJKSbIp53ddw+7Oljk7CXNyZp6yd+9eAGrUqEFwcHBhfXSJIe3ohRDCf+QYfG6yX4QQwj+KvY/XSSkpKRw5coTatWsTHBxMIdZ0QgghhBBCCFFqFUrhlZ6eTvfu3alTpw59+/blyJEjAIwYMYKHHnqoMDYhhBBCCCGEEKVWoRReDz74IBaLhQMHDhAYGHhq/eDBg5k7d25hbEIIIYQQQgghSq1CGVzj119/Zd68eVSrVu2M9bVr12b//v2FsQkhhBBCCCGEKLUK5YqXw+E440rXScePH8dmsxXGJoQg2+khz+31dxhCCCGEEEJcsEIpvDp16sSkSZNOPdY0DcMwGDt2LN26dSuMTYhyLM/t5YGvl3P7W3cyfNxwRn76K+m5Ln+HJYQQQgghxHkrlKaGY8eOpXv37qxZswa3282jjz7Kli1bOH78OEuXLi2MTYhySinFQ1NXkn38bjps9BDggt86ruPeL55n8t0DMemav0MUQgghhBDiPxXKFa9GjRqxc+dOOnbsSL9+/XA4HAwYMID169dTs2bNwtiEKKd+2ZRC7rEX6bbIQ6ctisb7FbfMMtB9Y5m8fJ+/wxNCCCGEEOK8FMoVL4CwsDCefPLJwvo4IVBK8emCRQRnJ9Nhm+Kda3RSIjRenuSjym4Hs52TuKH1U9gtJn+HKoQQQgghxL+66MJr48aN5/3aJk2aXOxmRDm2eFcawcYX9F2p2BsNjpgIgtyV+bPhVq5ZafBRrdn8sO5WhraJ93eoQgghhBBC/KuLLryaNWuGpmkopdC0v/rZKKUAzljn8/kuIURRXn3yRyJe9wGa7lOMv0Yjz3czgeG1+KPlvXTZDGHJeUz3/syNre5Cl75eQgghhBCiBLvoPl5JSUns3buXpKQkfvjhBxISEnj//fdJTEwkMTGR999/n5o1a/LDDz8UZryinNh8KAsj82O6rYajYZBTLYhBnftwR7cmWMwJrKuhcdUqRYDpG37fftTf4QohhBBCCPGvLvqKV3z8X827rr/+esaPH0/fvn1PrWvSpAmxsbE8/fTT9O/f/5KCFOXPZ4t34TVvpd12xeTLdfLy+9G/eVWsJp23zLcxv9UzPDZNoWWmM3nxKno0uNrfIQshhBBCCPGPCmVUw02bNpGQkHDW+oSEBLZu3VoYmxDlyNFsJ/uTvqDpJoXbAsl1TFzeajA2swlN0xjSqQ2+iAgOVISea8DI/Zgth7P8HbYQQgghhBD/qFAKr/r16/PKK6/gdrtPrXO73bzyyivUr1+/MDYhypHJK/ZjC15Cz/WKBU01jLz2DDltAI2rm1YhM38gP7fSuWy3wqP28NniHX6MWAghhBBCiH9XKMPJf/jhh1x99dVUq1bt1AiGGzduRNM0Zs+eXRibEOWE0+Pjj7UzqZ3iIdAFa5tCjfiRRAZZT73GZjbRo9VVrNo6idwAJ60TYZXvI47mNKFSiN1/wQshhBCiXNiX5mDDwUw8PkVCVBDNYsMxyUBf4j8USuHVunVr9u7dy1dffcX27dsBGDx4MEOGDCEoKKgwNiHKiamrDhAe+AN9VxusqqNhMeoyvFOds143tG0cf6zqyq+XzeOqVYpVrVfx6eK9jLmygR+iFkIIIUR5sCs1hxdnLMWX9SkE7MOnGVg9keS6L6dby+u4rWMCQbZCmyZXlDGF9psRFBTEHXfcUVgfJ8ohp8fH93/OJu54DlWPw+dXgi38DupEh5z12qhgG7Vr3cKWgPlcvdJHw40+1ud/QFqXN4gKtvkheiGEEOWNUopjOS4CrCZC7BZ/hyOK2MLtRxn/wxtYrT9xxXJotkdh9cK+6DwWN5rCioBpLFg9mOu7DWFwy1jMpkLp0SPKkIv+jVixYsV5vzYvL48tW7Zc7KYKVWZmJi1btqRZs2Y0atSIiRMn+jskccI3qw4QEfAV1y5VrKupodurc1fPNv/4+ju71cJwtuXXyzT6rlbo9oV8uHBXMUYshBCivEpKc3Dje9/zwIfDGPHWHYz5fhUur8xbWlYt3H6U96c/RWTez4z5UlHziGJmW50vu+ukh2gMW2Bw12QXcamTmbt8MIPfmcQfO2S6G3Gmiy68br75Znr16sV3332Hw+E452u2bt3KE088Qc2aNVm7du1FB1mYQkJCWLx4MYmJiaxcuZL/+7//Iz093d9hlXvZTg8/Lv6R0IM5VEuHX9qDOWQUrapH/uN7alQMJq7GXay9TEdX0GSDwaYt73I4M78YIxdCCFHeZOV7uPfTb1DOZ+n952Y6blxJ8qE7eH7mBn+HJorA1sPZvPPjOEJzlzL6R4PEGhofDbGypcb17Iu/j/md6vHScI0d1TTumqMY8mMWwVmv89lPw7jl43ks2JaK12f4+2uIEuCimxpu3bqVDz74gKeeeoohQ4ZQp04dYmJisNvtZGRksH37dnJzc7n22mv59ddfady4cWHGfdFMJhOBgYEAuFwulFIopfwclRg/fwehgZMZPF2xunbB1a67r2j7n++7r0d9HvqoA3Na/knf1YoNjZbwf7PX8N7NnYohaiGEEOXRhAU7CQuYwG1TFRYvhOwDi9fNsjZPs2zPl7SvGeXvEEUhyXV5GfPV9wRqsxg1q+BvlAXdg6lccTyvXNcKu8XE5kM38Na8dSzs8Ba/t9jN9QsVj08zWFvrAHPbPsiXcyvy3owOVIy5ilrRkYQHWrCYdHyGwu0zcHsLFpvZRKOqobSrWYFAq/QTK4s0VQhVx5o1a1iyZAn79+8nPz+fqKgomjdvTrdu3YiM/OcrFueyePFiXn/9ddauXcuRI0eYPn36WRMwT5gwgddff52UlBSaNm3Ku+++S+vWrc97G5mZmXTp0oVdu3bx+uuvc88995z3e7OzswkLCyMrK4vQ0NDzfp/4Z3uO5fLYxEdovHcx1y43ePlWjfCo9/ho+OXn9f4nf9zA3oM3M2qShx3VNBZ2qs5d/SbRvpYkPiHKGjkGn5vsl+JzNNvJyHefpun+X7huqcGjt5mofVhx988Gr9yooSq+zOS7r/V3mKKQPDR1DUdSRzBiqhunFb4aaKNS1AeMu6Et+t9GMVy6O41xPy0gkPcIOpLO4EWKypmwrxKsqa2xp6qGJxQMiwl0Hc1QYBgnbhWGRceshZOb14UebW/ljs41sEg/sRLvQo6/hVJOt2zZkpYtWxbGR+FwOGjatCm33XYbAwYMOOv5adOmMXr0aD788EPatGnD22+/Ta9evdixYweVKlUCoFmzZni93rPe++uvvxITE0N4eDgbNmwgNTWVAQMGcN111xEdHV0o8YsLYxiKp777E922iH4rFD+30tB8bRlz9T/37fq7h3vVZ9g7N/JVt8ncO1uxuOl+3pz5JsW4mAAA/1dJREFUPc3vHUmA1VSE0QshhChvvlp5gOCQBfRea7CwiUaYpzmZcXvYVymDK5fDz1d8wKqkLrROuLATz6LkmbH+EEcPPUPHFR4ic+GVWzR07RleG9T6rKILoEOtKNreN4gf1rVnym/f8+mQqViP5tJ+i6L3WkXw0pPXOv6p2aHB4Yg05l82neUb57J823NMGN6LsAAZuKWsKJQrXkVF07Szrni1adOGVq1a8d577wFgGAaxsbHce++9PP744xe8jbvvvpvLL7+c66677pzPu1wuXC7XqcfZ2dnExsbKWcVC8umSJP5YczNXzzlO5QzFhzdZaFxz6gUPCz9lxX7mrbiJfr+kUTELPhxqoW7cZJ7r37SIIhdC+INc2Skguck/3F6D/q9/QPX0Cdz7k+L52zRi4j5j9/5EYo6P5/6Zipdu1giIHscnt/f2d7jiEuxPd3D/R68Qk/kj985SvH+lxuGoq3jrjueIqxD4n+/Pd/uYveEwP65YjpY3Az1gJ7ozH1MOWDxg8YFPB48JlA6GCexORfM90HGLYk8V+LqfCYOn+PzO6wiWIepLrAvJS6Xq+qXb7Wbt2rX06NHj1Dpd1+nRowfLly8/r89ITU0lJycHgKysLBYvXkzdunX/8fWvvPIKYWFhp5bY2NhL+xLilK2Hs/lp8XgqJmXQeL/i0z46Ttcd3N/z7Hm7/suNreNwmR/luys0QvOg3TIvu3c9IyMKCSHKJMlN/jFn8xGi7N/Sd40iMUHDYqrOyC4NGNipN46YEA5HQs9V4Mz4kG1Hsv0drrhIHp/BI1//hs36IyPmKf5soJEVU5Vhfe4/r6ILIMBqYlCrWKbeO4i37vyM/u2nU7/R91hrfY679vs4ao7FVett9NoTsdWdRFi9KTjiX2Vx68a8cpNGdCaM+M6HiZd5eOpSDKPEXicRF6BUFV5paWn4fL6zmgVGR0eTkpJyXp+xf/9+OnXqRNOmTenUqRP33nvvvw78MWbMGLKysk4tycnJl/QdRIGsfA+Pf/U9dm0ewxYYzG+u4Quuw6MDb7yoDqUmXePVwd1wu/swqbtOz0RFUPpuxv/4DgfS84rgGwghhP9IbvKPr5esw8g9Rs0UmN9CQwsYTv0qoQxqGUtm3kBmt9ZptUPh0XYy8Y+t/g5XXKTX527DarzIkF8UOQGwsIuJilVfpl+zqhf1eZXD7FzfMpYn+tbn41ta8+WILnx555V8MbInnwxvz4c3t2D8jc2Zcvc1PHbjR+SaRvDWDRoRuXD1PB95GWP4bGlSIX9L4Q+lqvAqDK1btyYxMZENGzawceNG/ve///3r6202G6GhoWcs4tL4DMXD05YTZH2LkbMURyJhZVsrteo8R9saFS76c2tUDKZfl1GkxVdkWX2N//1iYDVN56HJ08lzn93nTwghSivJTcVvQ3Im5vzPuXwtHI4Ed8VQBnfsCBRc3ejeqj/HaljJDoT2a+HIgfdlepNSaPHOY2ze/CINEvOokQITr9Jwee7l+WsvK5btN40N553b78Tpu5H3r9ZptlcRsyuLuUveZn/6uadvEqVHoRdeTqezsD/ylKioKEwmE6mpqWesT01NpXLlykW2XVF4lFK8MGszzszH6PObj9A8+OIaDcP0FGOubHTJn39L++qEVHiF37paSAuF22coTPqr3Df5Dzwyh4YQQoiL9Pmfu/DqO2i9QzGnpU6e81quaPhXC5xh7aqTl9ODOS11um5SGLZ1fLp4jx8jFhfqWI6LN374EqtjE9cuV0ztouM1t+eFGwcXax+r6lFBPHzdnbhCGjK3pcYNiwxM5t946rs/ZAqkUq5QCi/DMHjxxRepWrUqwcHB7N27F4Cnn36aTz/9tDA2AYDVaqVFixYsWLDgjG0vWLCAdu3aFdp2RNH5YNEekvaM5rJVWbTYrXj/ah2PdxBv3nQlNvOlj0CoaRpjB7ci3/son/bTCMuD637yku94hMe+XSVtpIUQQlywo9lODu77guabFE4rHK5ppnvrAWcM9R0ZZKVRg5vZ2VDD0KDhJoONWyaSlefxY+TifHl9Bg9NXUyA/XP+97NBYoLGgXphXNHxSRpVDSv2eDrVrkiNWk+xtrWFzGC4bj6Q/xqzNhwu9lhE4SmUwuull17iiy++YOzYsVit1lPrGzVqxCeffHJBn5Wbm0tiYiKJiYkAJCUlkZiYyIEDBwAYPXo0EydO5Msvv2Tbtm3cddddOBwOhg8fXhhfRRShjxbtYdnqh4nfeZje6xSf9NLIC27Dw9eNIiY8oNC2E2wz8+awfuR7hvD2QI1ah+GK31wcPXYfj3+/Gp8UX4KCztN7juWyKzUHp8fn73CEECXYlBX7sQYvpXui4vemGr78dgxpHXfW60Z2roWR15oFTTX6rFWYgn7n/YW7/BCxuFCv/LwVI/8JBs5VoGDGFRq2kJe4rUN1v8X0+JUNcOT/jy976jQ6oAhKTefLedPId0vOKq0KpfCaNGkSH3/8MUOHDsVk+uuqRdOmTdm+ffsFfdaaNWto3rw5zZs3BwoKrebNm/PMM88AMHjwYN544w2eeeYZmjVrRmJiInPnzpV5uEowpRTv/LaLJavvo/qeXQxZZPBtR51jVetwY+8Xi2Si45oVg3l26N3kmfoyvr9Oi12Ky3/P5VDKXTzw1RLcXml2WF6lZjsZ8/16bnjtUZ77cjAvTBnEzW/cx0PfrCD5uAzEIoQ4k8Pl5Y+1M4je6yHQBWuaQkLNkUQEWc96bVyFQKLj7mR1Cw2LFxon+li/cTwpWUXXDUNcuh/XHWTnzjE0W+2gwQHFB/00XK5beG1QBzTt7Pm6ikuwzczdVw3AF1GVtbU0blxoEBAwmY8X7/VbTOLSFErhdejQIWrVqnXWesMw8Hgu7BJ7165dUUqdtXzxxRenXjNq1Cj279+Py+Vi5cqVtGlz/pPtiuLl9Rk8O2MTazfcTe1tSQz9w+C7Dhp76sfQte24ix4h6Hy0iI/kvgGPkh3YiXeu1Wm5S9F3bj7pGffyv8/nS/OPcmjG+kPc+d7T7Eu+iea7f2HAnL0M+mkfbbYsJPnwbTz44dPM23J+I6QKIcqHySv2Exr4HdesMFhZV8Nq1OHWTrX/8fUPXtEAI78Dv7TS6Ltagf1P3pq3pRgjFhdizb7jfDVvLGFH9nDlasUXPXRc1o48M2QkFYJt/g6PKxpEowIfZnYXiMyBmpvdLFk9niNZMnBLaVQohVeDBg34888/z1r//fffn7pyJcqfbKeHkV8sYm/y7TRbd4jrlyq+6qqzu34CnVt/xO2dahR5DN3qVeLO/s+THtKdcQN1GiQrrp/lIs/xELe9/zl7juUWeQzC/5RSvL9wF1MW/o9qaT/z8BcGfdYq0kM1DkZpdNiqePJzRfTxOUz+5S6+XnnA3yELIUoAh8vLz8t+IHJ/HpUzYX4bCKxwD/Uq//MokjUqBlOr7j1sbGbCa4IuyxWH9r/M2v0ZxRe4OC87U3N48et3CfT8xsi5it+baBytHsPAHk9zWVyEv8MDCvquP9GvLZqvFXNaaly73MBk+53Xfk70d2jiIhRK4fXMM88watQoXnvtNQzD4Mcff2TkyJG8/PLLp5oIivJl86Eshr33Nfm593HlnFyuWKeY2EtnX83a9Oz4frEUXSf1aBDNQ9c/Q6b9al4frFE5A+752ofV+zaPffKidFQt4wxD8dysDczfPJzGW5O4+xfFqjoar99mYkGb5iy8rC1v32phaX2NUT8p4vfu5OfF9zNj/SF/hy6E8LMvlu0jLPBbrl2mWFFXw2SpxagrWv3n+x7oWZe83Kv4povO5RsVWv52Xps+S0bXLUEOZ+bzyBefEWCaxv0/KHZU01je0U71Gq8zqFXJmpC8fpVQEmqPZv1lJlwW6LpMkXboNdYdkGK+tCmUwqtfv37Mnj2b3377jaCgIJ555hm2bdvG7Nmz6dmzZ2FsQpQSSik+W5LEc18+ic37GrdP81LriOL1QTqHK3fguh4TuLltfLHH1aFWFM/e9BjZphG8NUQjJwAe/UpRJW023/92Cw9NXUWuS+b6KmtcXh/3fL2YxIO3023ZcQYtMZjaWWNNm2pUjf6CcXd9wgf3fkDVip+xol0VfmivcdNCg8rJO/lu3hMs35Pu768ghPCTo9lOfl32OdF78qmUCb+2hcDI+2lSLfw/3xsdaueqTrdzrEYoO2Ng2DyFWX+Hd3/bWeRxi/92KDOfuyZOwWb+iHu/U6SGw/S+ZuzBb/DMNU38Hd45PdSrHnl5A5jaWafLZoXh3snYWb/JaM2lTKHN49WpUyfmz5/P0aNHycvLY8mSJVxxxRWF9fGiFEg+nsdtn/3BwtXDqZ6ykCcmKzxmeGOoRk7AjTwy5DX6Ny+6Pl3/pXlcBB/fdTeG5UW+GmhjeT2Nu39WdF5yiMOpIxg2/kMW7Tzmt/hE4cp2erjtk184lHUfA+blcXmiYmIfjUPVm9O29Ye8eUNLqoQFUCHYxps3tKBpo/fZ3bAqP7XSuO1XA3vOGl7/7m0ZcEOIcmrsnC0EBM9k8GKDP5po6KYG3Ner5Xm/f3iHBBzqIb65oqClRdO1TtZueIaVe+WEjj8lH8/j7o+/xK69xT3fKtxm+LK/jjI9x7s3dcFsKvQpbgtFVLCNqzvdQnpCMPsqweAFCov3HaZL64xSpWT+dolSxWcoPl2SxL0fPI8jaxRXLDjKiF8NFjTTmHKdHezP8c4do2kR7//20jHhAUy+sz+VK01kedvqjL9Go9lexV1feQnP+oBPZt3C/V8t42iOjEBVmh3NdnLrR9PI9Yzh1ulumu5VTBigkVahO1f1GcvIzmc2ddU0jdE965BQ6y0SW4axpo7G3bMNTKYfeHDSNBxyNVSIcmXl3nQO7XuD1qsMTAYsbqdRrfpjFzSfk8Wk8+zAHviMjnzTVeealQpL7hZe++5TjmZLjvGHHSk5jJr4GXZtPKO+VRgafHi9jlc9wgfDryLAeunziRalYe2qk+O5k2+6a9Q5DCGHjjP5t68lR5UiF114RUREEBkZeV6LKLuW7Erj+nemsXDtUGoc/YnHvjSoeUTx5kCddc3q0qDGFCbfdR2xkYH+DvWUAKuJcTe04rruEzkSPpCxw3SORGo88r1Bl6X7SD06krvfe4LxC3aR55aDWWmz+2guIz76BEO9wt3TfFRLg/GDId1+HSMHP/OPV101TePpqxsSGPYav3Y3kxYKd/2oQH+Th6YuluYcQpQTeW4vL38/E92zgV7rFD901PE5e/NwnwtvgtY0Npyu7R4luU4EiQkad802sJi+4N4vZkh+KWZLdqXx2GevEaje575pBVe6Phyk49Ue4aORN55zeoCSxmrWue+qvqjg6qyoqzHkD0VgwFdM+F2asJYWmlLqov6a+PLLL0/dT09P56WXXqJXr160a9cOgOXLlzNv3jyefvppHnzwwcKJtgTIzs4mLCyMrKwsQkP/eVSjsm730Rxe+3kt+enjUM7dDPpdUecw/NZMY2k7E878m3ng2lvpWreSv0P9V8nH83hs2iJMzlcJPXSMYQsUZgNmtNXZ29CCI28Ig7rdwHUtqmE1ywXikm7F3nRemfouNvO33Pt9wdnMT6/VyDFu54VhI2hc7b/PVuc4PQyb8AMB6gUemKLYW1lj5pVBtKz9JQ/1blAM30L8GzkGn5vsl8Lz7IxN7D4wjOFTneTZ4Nv+IXS9bOpFDwrlMxTDP/0NV96D3PmND68JPhtkJizsXd67uXOJbdpWlkxddYDpC14gyLGcUbMMjkTCpP46PjWGj0ZeXyKGjT9fSilGfPYHjox7eOJzxcy2GptqdWbc/8aVqJPc5cmFHH8vuvA63cCBA+nWrRujRo06Y/17773Hb7/9xowZMy51EyVGeU9uO1JyePe3LaQdnICyrKPnEkWHbYp9leCb7hpGYD3iajzFY30bExZg8Xe458UwFN+uSear374kKHAarVcb9FyvyAyCbztrZMaFkOsczDUdr+WGVnElvilCeTVj/SEmz3ue4Pyl3DfD4GgYTOmvk+8bzVu33UB8haDz/qx9aQ7u++hdKngm8dA0xbwWGmsvi2dIj0+5qklMEX4L8V/K+zH4n8h+KRy/b0/lk5n303TrRvqsUfzfMA1z8Mt8eVd/TPrFT6R73OHm1glTCNDe4OGvCk7ozOprIzJ8POOGdsAixVeRyHf7eHr6eo4eHEPUgcPc9qvB+poac3paMEzP8v7wq4gsBVe6/m730Rye/PRBmu5cxpWrFS/fplMx6gMmDOvs79DKpWIvvIKDg0lMTDxrEuXdu3fTrFkzcnPLzlxJ5TW5rT+Qwcd/bCXzyAR8lg10WK3otlGRa4epXTQy48LIM+7ikf5X0qZGBX+He1GO5bh47ZfNJCe9Dea19P4T2u5QHI6AmW01jicEkOe4li6tBnNj61gqhdr9HbKg4Ozfuwt2sXTDw1Q6vIc75hhsqq4xq7cZeJZ3h19FxZALP5u5ZFca7377FLGpixnxq+Kj3jqHqnTgmaGvn9eVM1E0yusx+L/Ifrl0B9LzuOejcVTM+4aHvjf4upvOrrjOvDbyDRKizv/EzT/ZfTSXBz95m3DP1zz0nWJbrMZPfWyEhb3DuCEdsFvkpF5h2pWaw2NfzcRmfp0uiz102ayY00JjfesQQiPf5M0b2pXqff7czE3s2D+Ue7/0sDVOY1GHBEZc/XmJb2lUFhV74RUfH899993HQw89dMb6N998k/Hjx7N///5L3USJUZ6Sm9trMGfzEb7+czUW12e42U3HNdBtoyLPBj+11tnbwExe/iAGXX4zg1rGXtIZwZJi6+FsXp+zBm/GOLyuvfReCa13KtJCYXZrnYO1TRj5LYmqNoKb2tejTUIkmlb6v3dp5PT4ePz7VRw69jBNNmRw3VLF/GYaa9oFYw99nbeHdiDQar7oz/9iaRKLlo+i6bokuicqXhusk2e/iQ/velAKbz8pT8fgCyH75dLkub0M+2AmJs/TPDxFsbOqxtyeEXRvNYVbOyQU2nZW7E3ntW9eJcTzC6O/N9heTWNmXzO66XnG39K3VDV5K6m8PoOJfybx+/JxmPSFjJiliMqGT3prZEcnUL/eWB7v2wC9lP+9kpXnYdg7rxGb/jX3/KQYe71ORshdfP3AnZeU98SFK/bC64svvuD222+nT58+tGnTBoCVK1cyd+5cJk6cyK233nqpmygxykNy23sslx/WHWTx+l8Is/+InplBj7WKFrsUOYEwq43O/npmXHm9ad/iNu7oUpNgW9n7T75kVxrvz1+FnvcBHt8euq+C9lsVbgssbqixqimYrdXIMwbSufnl9G9WlbgK0r66uKRmOxk1aTaGeok+v3vosE0xpZtOSs2qxNZ6g6evaXzJJwKUUoz5YSOpB0fSe24uCSmKsTdrmCxP8+n/ri+Tv/clXXk4Bl8M2S8Xz+sz+N+kJeTn3MvQH90EOeHDG81UjHyf8Td1LPQTa8t2pzF22lhCPT9z/48GR8Ph8/46Xu8dPH3DMJrH+X8E4NJq6+Fsnv/hN6zGa8Ruz2XwYoOUSPjsag2P72qG972fq5uWnebiU1cd4JelN3HV3DQqZSk+HmKjYY2vePKqhv4OrVwp9sILCgqt8ePHs23bNgDq16/Pfffdd6oQKyvKanLLcXr4eeMRZq1ORDm+Qtm2USXJR++1BnHH4EAUzGmpk5ZgIT+vD51aDWd4+wTCAktHP65LsSrpOB/+nogr/X285m003KLokaiIcMDWWFjUWCctXkdz1YOgG+l12WVc0aAylcPkikhR2ZCcyVPffILF/DkjZhnEpMPEvhp54S3o1OkFRnQsvDPUbq/BLZ8sQHc9yIipPnw6fDbIhD3gDT667QoZdKWYldVj8KWS/XJxlFKM+X49B4/cSe95uTQ8oBg7RMNtHc2Xo24tspMry/ek88q0dwjUv+eeHxW6AR9dreGxt6Rr22cZ2alGqb8iU5yOO9y8MXcLSbveQGnrGTJXUTMFfmqtsbGFFafvUV6/6RpqVAz2d6iFyjAUN3/0C17HIzzxhWJOS43Eei146ub3LmjqA3Fp/FJ4lRdlKbll5XtYsC2VORv3cfzItwQErcCc5qDDZkXrnQqzD9bW0ljQAlR4JLmuAfRs04+hbeMItZf9guvvthzOYtLSJHbumkRA8CJCDuVz+QZotE/hNcHa2hrLG2jkV7Zi5NdFBfajXYOW9GwQTb3KIdIcsZDMWH+IL+e9hN27mPumG3hMMLG/Rr5xLf8bcB89G0QX+jaPO9wMm/A1wdpYHvxacSQSvutnJSpyAm8N6SB/IBWjsnQMLkyyXy6cUopnZ2xi375RtF6RRpdNijev08kOuJaxIx6nVqWi/SN9e0o2j3z5FYHW97h+jkH9ZPiuo86eRsEY5sd59roe1IkOKdIYSrt8t4/JK/YxZ+kX2AJn0Gq1j15rFSkR8EUfDZ+1KQ0bPMnDveuX6v5c/2bPsVwe/+QR6u9bzKDFBi8P0/AFPctX91wvg7YUk2IvvA4cOPCvz8fFxV3qJkqM0p7cDmfms2jnMX7bvIes1G+xB62DrBya7IJ22xRROXCwAvzRRGdPHTB7a0HQCIZ0asUVDaJl2FsK2lV/v+4gP634nWDtR9zaPmrshk5bDBJSIc8KiTU11taCzKomzN548oyuVI/vToc6FWlfM4po6R90wXyGYuycrWzY8ShRh/Zxx1yDPVXg+ytN5Hnu57VbbijSP1L2Hsvl/k/eI9iYxKNTFVtjNeb0CiSu2se8dG1zKb6KSWk/BhcV2S8XxjAUT8/YSPL+UTRfnU7vdYoJV2ocq9iZR274P1pWL545SI9mO7l/8u+YvS9Qa1MO1y012F0FpvbUQG9F44aPcs/ldcpF65ILke/28dXK/fy0dBpBgd9TebeTQX8amH3wQwedA/VtODyjeOq6/rSIL/vzyY5fsItVm2/k5u8deE3wzbVBXFZvCqN71fN3aOVCsRdeuq7/69l8n893qZsoMUpbcnN5fazZl8GincdYu2MVVvdctMBdmDLyaLYT2uxQVMiBzEBYWVdjTUPQgyPJcfakWf2BXN8qjgYxJf97+oNSivXJmUxfm8yG7TMJC1iA13mUuns0Wu4yqJkCPg22xWmsr6GRHKtQIUEYebXwWbpSp0YHWlaPoHlsBLGRAXJF7F+kZjt5dOof5LuepuVqB1evUixoqrG2XSA++8u8c3O3Ypn8ckNyJs9Mfo1w1ywe+t5gVR2NxV1DqFbtAym+iklpOwYXF9kv58/tNXj8+3UcS3mAliuP02O9YmIfjaOVmzLsyrfo3ahyscbj9Rm8s2AXq9e9hMmzllvmKaoch59bayQ2N+HJu5qe7UdwS7vq5X46k9RsJ1NW7GfJum8JDJxBxSQn1y4ziMqCRY01FnXQ8OZdQZc2d3NH5xpl9irX37m9BjeMn4I9/1WemKKY3l5jS51WPD7kHZrFhvs7vDKv2AuvDRs2nPHY4/Gwfv16xo0bx8svv8yAAQMudRMlRklPbk6Pjw3JmaxMOs6avfvJOTaX4ID1eEgl6qCi+V5FkyRFsBPSQ2BVHY31dYHwYNyOpoRXuYmBrerStW4l6btyATw+g8U7jzFr/QH27Z9FqH05HnWEmP3QcpeiwQGFzQs5dtgar7EpXiO1qoLAIDRnNfJ9TYiIvpwmcTE0qhZG/cqhRIfaynUx5vEZpGY7+XnjEeYs+xw9YDo3zCnYl5O6a6THx1G11ms8fU3jYm1OsXjnMd7+7nmichdy3wyDFfU0FncNplrVD3hpwGVSfBWxkn4M9hfZL+cnK9/DqC8XYrjH0GlxPp02Kz7qq5EW3ZwhfV736zx9y/ek88qPMwmxTaDW5oKCwmWB6e11kuvacOZdRccWNzGsXXy5Gv3QMBSr9x3nq+W7SD0wET1wNbG7fFy5uqDgWl5f47e2YNLqU6HqIzx+ZVNiwgP8HXax23gwk/+bMpr6u1cycKnBK0M1HPaH+fq+YTLKYRErMX28fv75Z15//XX++OOPotpEsStpye1ojpONyVlsOJjJ2j17cGbOJci+FY9+lIA0H3WTFY2TFDVSwQD2VoH1NTR2JoAWGoLLcRnhlW+gd9OadK8fXWomPS7JPD6D1UnH+XVrKonbfidY/w2f7QDmDBe1D0Cj/Yrah8FsgMMGu2I0dsXAvioazigdixaGz1UFp1GHwPD21IipTt3KIdSICqJ6VBCVQkp3QaaU4liui8OZTg5l5HM4M59DmfkcycjFmZuKNy8Zn+cImpGGxZKOJ3A3VXa7GbzYwGuCT67UcFk7cmWPJxnaJt4v32HOpiN8+tMzRGQv476ZBqvraCzsFkSFiuMZO6iNnLQoQiXtGFxSyH75b7uP5vDwpJnYLa8x8BcfdQ8qPrhSI7NCC4b1HUufxlX8HSJOj4/3ft/NirVvowcspuNKxeUbFJnB8EsrnaS6Joz89sTED+f61jVpm1ChzJ7s2XsslxnrD7EkcS7B5p9w+Q7RJhG6blJYvLCinsbvrcFkqoM14l7uveKycj8i5Du/7WLNliFcPyOXICd8eqOVhKpf8PLA5v4OrUwrMYXX7t27adq0KQ6Ho6g2Uez8mdwy89xsPpTNhoOZbDxwjGMpf2JlPWZ7Ml6VSXCqot5BRf0DiupHQVdwPBi2xGkk1oDMqjoWVYVcV2tiYq+hR6NqXF6vEiHlcKCM4qKUYmdqLsv2pLF89yGOHZ5DsHUdXsshrOk+4o9AncOK2ocUofkF70kLhf0VNQ5Ugv2VNPIiFEagDbMvHJ8nCpc3FnNAfUIjG1G9Yjgx4QFEh9qIDrVTOcxOxWCbX/vi5bt9HM4qKKgKFieHM/NJyziGM2srPvdeAkwpmC3HUeYs8OWj53sw5WnY8zRC8iE0TxGaByF5Bfsn0Al/NtJY1kEnx3UHYwbfROsE/7bbn7v5CBNnP0dEzhLun2GwvqbG/J5WzPZXefeWHjLUfBGRAuPcZL/8u5mJh/j8l3exmWdx+0xFuAPGD9BwWnpwZ/8n6V6/8AfluRRJaQ7emLuJjENv49M30mUVdNpSMJ3JwiYa6xqC2VYZh/ca2jTuTa+GlWlcNaxUF2GGodhwMJPftqWyausyrJ6f8dl2UiXJoPMmg3qHICsQfmumsamxhsnTEFvkHYzq2YIW8eW74DrJ6zMY+v4McD7FmC8VixtrrGpRgxuu+NivV3PLumIvvLKzs894rJTiyJEjPPfcc2zfvp3ExMRL3USJURzJzenxsSs1lx2pOexIyWZXSirZaSswq63Y7Ml49ONYMnzEpkCtI4qaRxSVsgreezQMtsVqbI3TOF5ZodnC8ObXgqC+tKp7GV3qVqJptTAZJMNPvD6DLYezWbonjfX7jpCWspAA0wY020EMVy5hxyD+qCLuKMQdKxjsBMCrw9FwSInQSIkouE0LA18I+ALMmLQAdE8wPm8YTm8F0KthtlXBYq9CQGBFwoMCCAu0EBZgIdhmxm7RsZlNp25tFh2LSUepgv+/Cgruo/AZiny3j3yPj3y3j7wT97OdHjIcbo7n5uN2HMGTfwif+wgmDmO3pKOZM/GacjG73Gj5Piy5GlHZGlFZiorZEJWtiMqCQPeZ+yjHDtmBkBMI2YEayVGwpR6YqE9kzMM8f22LYunPdT7mbk5h4qwXCc37g/tmGuyvBNOuNmHoT/Du8AFUCpFBVAqbFBjnJvvl3BwuLy/O3sLBfU8TcnQ3I+cq0kPgk/4abuNmXhh6N42rldxhtzcezOSteetxpn2MYd1Isw0FV8BC82F3Ffijsc7hBA2LisHh6Ua92n3pVKcSrRMqEFlCjpP/RCnF/vQ8VuxNZ/nuVJKT5xFsXoLbdJCKyYpWOxXN9ipMBiQmaCxpBNnVbDhzu1K7zq3c1qmGjPp4DnuP5fLIxKeJOzqPO+Yqxl+jkRLej3F3PEVCVJC/wyuTSsTgGkopYmNjmTp1Ku3atbvUTZQYhZXcPD6DQxn5JKU72Jd2ckkj5/gGNO8WAq0H8FrT0Fz52DOgWhrEHlNUT1XEpIMOOC2wtzLsqaKxpwrkVNIwmyNw5dVCD7ycejVa0CahAq0TIkv8Abi8UkpxMCOfdQcyWH8gk+0HduLKXkGAZTeaLQWfysacaRCdDpUzFJUzCm6jM8Fy2pg1ufaCq5vHQzQyQgruZwdqOOyQbwdlVSgrGBYThlnHMOnomNGVCc0woSkzqJPF+GmHBE0VPNY9GJoPhRfN50PzGeheA5NTobk0ApwQmldwpSoyByrkKKKyISIHTKd9nMMGx8IgLVQjLRSOhRUUkJnB4A3SUDYzZmVD99nAF4jPF4zPXYvY6tdyS4c6JfKs5h87jvLWD68TyM/c/73CYYePB2i4fSN5fuhwmlQL93eIZYoUGOcm++Vsy/ek83/f/0hIwARarXDTe51iSQONhV0seNWjjLvlWqpFlI5J77enZDPxj13s3/sZtuAVBB5x0mWTotkehaZgZ1VYXUdnX3WFHhCKN68myn45daq3oXHVMBpWDaV2pRC/NoM+muNky6FsNh3KYsvBVI6n/IFNX4NhO4Apy0nCQY3mewzqHCx4/c6qsLKezv5aGiZfDfL06+jVogODW8USHih/0/ybb1cn89Pi4XRcepiWuxWv3azhszzDlHuuLzcDjhSnYi+8Fi1adMZjXdepWLEitWrVwmwuW81tLiW5vT5vO1sPHSXn+HoM9w4CLIfRrOn49BxMDjfWbI3oTKiarog9pohNg4ATVwMcNjhQEQ5U1NgTo5EZpVBBAejuKjjc9QiI6ErD+Jq0qRFJi/hI6atVihmG4lBmPttTcth+JJtthzM5mpaIN38bAeaDmKxpeE1Z4HZiyYGQXIjMhcgcRWQuRJy4Dc4Hu+eft+MxgccM7hOLTz9RZwEaf93XjYLPsXrA+i8DlObaITsAMkIKCqv00BO3IeAMBl+gjq4HYPYE4/NE4PJGo1vjsQfVp3JELNUiAqkUYiMyyEZkkJUKwVYig6xEBFoxlfDmM4nJmTw15WMCLF9w1w+KABd80F/DbenKDb2fpH/zqv4OscyQAuPcZL/8Jdvp4Y0529i18xVM7g0M/6Wg9cCX3XXS4itTNe5Vnu13Wan8AzQt18WM9Yf4ZfVyAn3T8FmSqJBs0GqXounegkGcMoJga5zGlniNY9EKI9iO2RNFvqsm5oDmRFRoQnzFcBIqBFEtMoBKIQVN1EMDzJfUf9jh8nI0x8WxHBcp2U72pznYeyyHY+m7cOZuxKrtwmQ/hNeXhS1DEZcC9ZMLukjYPZBvhc3xGmtqaxyN07D6qpHl7kLzhtdwXYtqNIwJLdX9m4uTUopHpq3hUOrtjJjmxmuCLwfZqFrpE14f1EL2YyEr9sJr8eLFtG/f/qwiy+v1smzZMjp37nypmygxLiW5DR7/DWnqJVrthCrH/7p6EZVVcAULCq5iHY6E5IoaBypqHKkAnggNLIHo7krku+Mxh3SkVmxjmlQLo2lsODUrBpf4P0zFpTMMxdEcF/vSHexPd5CUlseBtFwysg/gduzG8CZjM6VhNmeiLDn4TC6U4Ubz+NDdCs0DFhfY3WD1/rVYvGD1Kkw+QCu43qVO/DopDQwNXBYNl4UzFysoGxg2DcNixqRsmHx2lDcEtycSr1YNq70moWF1qBoRQZWwAKpGBFA1vGCpGGIrU7+3SWkO7v1sGkHWcQz62UedQ/BFD430+GrUqP0Gj/dtJINuFAIpMM5N9kvBMfL7dQeZPH8aIUGTabrWw1UrFbuqwje9NXzeHtxwxaNc16Kav0O9ZEopNh7MYs7mFFZuWUag8QvKvhtrmoc6yYqG+xU1jxS0OHCb4EAlSKqscSBKIy0CPCFgBNiweINRvmC83jA8vgro5srolghM5jDM1nCstggsFvuJZugFzc8Nw4vXnYXHlYbXk4nhzcTwpmHWjmExZ6KZc/GacsHjxJRrEJGpUTlDUS0NElL+akLvsMGOqhpb4zX2V1MYoQFo+QnkmfrSukE7etaPpnlcRJnKE8Upz+3lpve+xup+lcenKFbU01jWPprOrT7l9k41/B1emVLshZfJZOLIkSNUqlTpjPXp6elUqlRJ5vE64e4pa3DsHsYDP6hT/XRSIuBIpEZWmMIXrKOsAZg94Thd1VCWJkRWbE3tKlHUrRxC3egQqkcFyUzk4pwMQ5GR5yYl28nRbBeZ+W4y8zxk5XtOu3XgcmZi+HIwfA6U14Fh5KEMB0q5OXkKQEMD7eTvmQnNFIzJFIpmCcVsDsNqCyPYFkCF4IKrUxFBViqcuDpVMcRGTLi9XA5fm5br4r5Jv4P3aVquzKPPWsXvTTSWd7Dh057gtaFXElehdDRtKqmkwDi38r5f1h/I4JWZS7B73yAwNY2hvxuEOmBaZ53keiEo6xO8PKg78RXKXh8XpRR7juXy69ZUVu0+QFbqHELsG/GYUzBne6iSqpGQqkhIUcQc/6uZuleH4yGQGQRZQRqZwQVzejptGk5LwYlgl7WgNcTpNAU2T0GLHLsbAtyKICdE5EJ4bsHAJRG5f7W4MID0UDgcqZFUuaAAdFQA7IFozqo4jLZUq9adtrUq0a5GBWpUDC7O3Vem7T6aw2OfPEd0xhzuna2YfLnO3rjLuPPaN+lat9J/f4A4L37p45WamkrFihXPWL9z505atmx51uAbpdmlJLc35u0gccdNKE1h9oTjdkfjoTrW4CZUqlCbhIohVI8KJCEqiFqVgsvlH65ClHZur8ELszaRtPdJIg/v5fZ5isMVYNJVGl5fX27uO5prmsroUhervBcY/6S87pdtR7IZNy+RvKPj8RlbGbiwYMqO1bU1fuoCeLrSu9Mj3Nq+eqke8e9CuL0GGw8WzOe55WA6KSmrMXvXYbfvw2vORHO5sGRDhcyC/rjhDgjLhXCHIiyvoKCyuc/sn3suhgZOa0GB5rBDRrBGZhBkBENmsMbRMHCHKnxBVsxGCModhcNTh8DwztSJrU6jmJOtdoKk6VsRmrPpCFPm3kPDrdvpt1wx7jqd9MABvHH7Y9SqJEVuYSi2wuvkxMgzZ86kd+/e2Gx/Tejn8/nYuHEjdevWZe7cuRe7iRLnUpLbyr3pLN2TTkJUINUrBJEQFSQdRIUoo75ZdYBvf3sTqz6f22crKmYVND3MiKtMhSov80y/FvL//yKU1wLjv5S3/bI9JZv3fttG2sHxKPN6uq0oGG49JRym9NDxRVTFHjGGp65pI1eZgaw8D1uPZJOU5mBfuoOkY1lkpG/B50rCpFKxmTMxmbNQpjwM3YOheUH5TgympAo6/qKduAXDpKN0EyZlQTcsaIYNwxuK2xuBR1XEZK1GYFhD4ipWpHqFIGpUDCIhKpj4yMByUwCXJO/M38GarcPp+VsmDfcrXh+qka+P5tO7biaqHE3GXVSKrfAaPnw4AF9++SWDBg0iIOCvmcKtVivVq1dn5MiRREVFXewmSpzyltyEEBdv86EsnvhmOoHWt2i/zEPPRMWKuhq/ddVxuoZyx9XD6dWwsr/DLFXkGHxu5WG/KKVYsfc4ExdtJP/YRxiWTbRbrei+QZETAD+210mpbSffPZJRVw+gmzSlOi9Oj4+03IJBMdJz3Tjc3lNTh+S5vbh9qmDAJa2gGbqmQaDVRKDVTJDNRLDNTIjdQsUQG5VCbATJHIYljlKKe79aRvrxu7j1Ow82D3xwg46hv8AX/+snP7NLVOxNDZ9//nkefvhhgoLKXtvpvysPyU0IUXgcLi/PTN9A6sHnCEjfy+1zDQC+uEIjv1JVwqOf45lrW8mUD+dJjsHnVpb3i9PjY87mI0z9czVW9yd4fEl0XlNwhSvfCjPb6eyrbyE/rz99Owzj5nbx2Mylb8RCIYpSvtvHLR9+D57nuf8bRWYQTB5owR74Fh/f2k0Gf7oExV54lSdlObkJIYrOjPWH+GzORKxB39F3oaLddsWqOhpzump4nQO4tvvt3NAqTkbw+g9yDD63srhf9h7L5ZtVB1i18UdCA39Bz8ik5xposbtgEuSfW+nsr2vCnd+bLq1GMrxjAsFy5l6If5Sa7eR/H3xCgPE+D09V7K6iMatvABWjPmDcDa2kGehFKpbC67LLLmPBggVERETQvHnzf+0YuW7duovZRIlUFpObEKJ4pGQ5eerHleRnvEzQ0UMMn29g9sHUzhrHa4TiMj3Io/170Sw23N+hllhyDD63srJfsvI9zN18hNlrt+HNnIQWsJG4nQY91xtUPQ4HomB2G42M+ADyHVfSqeVNDO9QXfpLCnGe9hzL5eFP3iTEPY2Hv1WsrKuxqEsYsdU+4P8GNJWBTi7ChRx/L/rUUL9+/U4NptG/f/+L/RghhCg3KofZmXhrZ2Yk1uTTOZN4a9jXdF5mcPuvir3RWUzt8Tzjpk6javUneLB3IyqF2P0dshBFzuX18ceOY8xcm0TKwakEBq/AlJFDh83QfptCN2BVXY1JV4AKjyDPM4h+rfpxfYtYAqzSpFCIC1GzYjDPDL2Pl6ccZ8LVv3HfDAOXJYuV7Ubx7Iz3eb5/Yym+ipA0NbxAZeWsohDCv45mO3lx9nqOHRkLrh3cNF9RMwUWNtb4s72Gyu9Jm5Z3cUeXmtLx+TRyDD630rZfsp0e/thxjF83HeTggVmEBi7B4zxKk+3QcUvBJLup4bCgqc6uehomT32s4cMZ1K45PRtEY5b5LIW4JAt3HOWj6Y9R8dhK7vxZMf8yjbWtoqhd832evrqhFF8XwG99vNxuN0ePHsUwjDPWx8XFFdYm/K60JTchRMm2bHcaY2fNJcj0HhWTchjyh4HZgFltdPY2NJPnvJ7+XW9mcKtYmTwdOQb/k9KwX5KP57Fo5zEWbNlLduoPBAatw5eXSZ29Gm23G8Qfgxw7LKuvsbY+qLAw8vO7c1mjGxnStjoJUWV/AC8hitOcTUeY/MsjVEpZx//mKH5uqbHxsmjq1nmPJ65sIMXXeSr2wmvnzp2MGDGCZcuWnbFeKYWmafh8vkvdRIlRGpKbEKJ0cXsNPl+axM9LP8MWNJNWaw16r1U47PBdR43jCUE4vCMY0v0qrmlatVwPwCHH4HMrifsl2+lh+Z50/tx1jC27VhBgLEAL2I0pM4/Gu6DVLkV0JuTZYH0NjeUNNJzRdrx5zYmofDPXtKjD5fUqYbdIc0IhisrsDYf5Zu5DVD68kZHzDGa10djcrBJx8e/wYv8mMuDGeSj2wqtDhw6YzWYef/xxqlSpclaF3LRp00vdRIlREpObEKJsSM12Mm7eNvbseRssa+ixXNFpsyIlEr7rrOOqHEau91aGdr+Sq5vGlMsCTI7B51YS9kuGw82a/Rms3necjXt34c2eQ2DQFjyedKIPQtMkRaP9imAnHA+GNbU1EmtpuCraUPl1MYcMpk+Ly+jbuLIMliFEMZqx/hDfzX+QKslbGDHfYF5zjbVtw6lQ8W1eH9xSWlv8h2IvvIKCgli7di316tW71I8q8UpCchNClG07UnIYO2cdeWlvobw76bcYmiUpDkTBzA4a+ZXDcPhu4cbuV3F1k5hy1d9FjsHnVtz7xWco9hzLZePBLNbuz2DHvrWYXYuxB+7GYxwnJFVR/4CiSZIiJgN8GuyKgY0JOjurK7TQEDyOpgRXuo7ujerRo34lKoXKYDJC+Mv3aw8yfcFoKhzZzP/mKpY20FjcOYiAsLd4d2g7ufL8L4q98GrVqhVvvfUWHTt2vNSPKvEk6QshisuyPWmM+3kpdt97GLmHuXqZolmS4mAFmN5BIy8mlFz3EK7scDWDWsaWi0E45Bh8bkW5X7w+g6Q0B5sOZbHpUBbbD+wnP3MRwdYdKPthVH4uUSka9ZIV9ZMVlTML3pcSDhsSNDYnaDiiTVi8MeR5WlM17ip6NKxG17oVCbFbCjVWIcTF+2XTESb/PIawzNXcM8tgfU2NeT1tYHuN94d1IyxA/r+eS7EXXr///jtPPfUU//d//0fjxo2xWM78wZSl5ChJXwhRnJRSLNh2lA9++xO792OMvINctQwu26s4FAk/tdHIjLfjyOtDm8tuZliH6mV6GHo5Bp9bYewXw1AkZ+SxMzWXnak57EzN4VDKDrx5qwm27AXbYQx3LoFpUCNVUfMI1EgpaDpoaLCvEmyP1dhRTSO3kobJFIErryGBFa6iTZ36dKodRYMqodJnRIgSbNHOY0z44VmCHYu4f4bBzqoa319pwul5ggm39aNaRKC/Qyxxir3w0vWCZi5/79slg2sIIUThOFmAvf/bEgK8H2PkJ9N7JbTcqcgOgjktdJIa6Pjy2xJf638M71yXOtEh/g670Mkx+Nwudr8cysznzV93kJyyB0/OKoIse9FtKXjJxpzrIfy4RuwxReyxgiIrNL/gfekhsLeyxp4qGvujwR1lwmxUIt9Zn8DIXjStUYdW1SNoGR8pc20JUcqs3nec1775PwI8v3Dfj4rjIfBZf40c9/949eZbaFIt3N8hlijFXngtWrToX5/v0qXLpW6ixJCkL4Twp5MF2McLVmByT8RtJNFlnaLLJoWhwcImGmuag8WoixZ6G4M6tKRng+gy0zlajsHndrH75WiOk/sm3I5yr6f5XkW1YxCbpqiUVfC8ARwNh+SKGknRGvsqg7OChtkUhs9ZFS9NiIzuQssaVWlZPZKGMaFl5ndNiPJs86EsnpnyHla+4q4fFWYfvDdQw2H0495rH6Bng2h/h1hi+G0er/JAkr4QoiRQSrF6XwYTF20mM/UjlH0DTTYZ9Fpb0PRrbS2NRc3BiIzA4byGjq0GcEPr2FLfDFGOwed2sftFKcX1rz1Fs53T6bxZkVxRIzmqoNDKjlB4QyxYjDC87sp4VCMiK3ehUbUYGlULo3HVMKKCbUX4rYQQ/nQwI48HPv8GszaOoTMNqqXDu/10coNb0bXtU9zVpabM9YUfCq+NGzee+8M1DbvdTlxcHDZb2Tg4S9IXQpQ0u4/m8PEfu9m1+3NswYuostdLr7UFE9IejoBfL9M5UlvHcLYgsurtDGpbj/Y1K5TKvjZyDD63S9kvN30wm3yeJNAdhuGKxm3UJCCsDXFValMnOuTUEhkkQ7wLUd5k5Xu4b9I8PK4n6T3fQ4vdismX6xyqGU1oxVd4fXALAq1lf2Cnf+OXPl7/VvFaLBYGDx7MRx99hN0uZ1uFEKIoHM12Mmn5fv5YP4OQgNlo2Rlcvk7RZofCa4IlDTVWNQGrvTI53mvocNmVXNeyGlXDA/wd+nmTY/C5Xcp+mZl4iPRcN3Urh1A7OpiKwTY5iy2EOMXtNXji+1UcTXmABonZ9FuhWNxQY1EXK/m+p3hnWF/iKpTfQTeKvfCaOXMmjz32GI888gitW7cGYNWqVbz55ps8++yzeL1eHn/8cQYPHswbb7xxqZvzK0n6QoiSzuX1MWdTCt8sXYuW/yUey3Yab4EeiQYVcmBvNCxsonOspo7hbIi1wq1c26YJPRtEYzOX7IEQ5Bh8brJfhBBFSSnF+wt3sWLtY4Qc28EdvygORcGX1+g4nLfw6HXD6VKnor/D9ItiL7xat27Niy++SK9evc5YP2/ePJ5++mlWrVrFjBkzeOihh9izZ8+lbs6vJLkJIUqTzYey+HLpHnbs+prA0D8ISM2n60bFZbsVSoNVdTSWNwYjMpQ8Zzdq1xlM/8viaBEfUSKvesgx+NxkvwghisMfO44yYfpYzOoX7pqusHphQj8Nt7UVLS8bw4M96mAuZwPsFHvhFRAQwPr166lXr94Z67dv307z5s3Jz89n3759NGjQgLy8vEvdnF9JchNClEaZeW6mrz/EzFUrsPum4jHvoc5OxeUbCjpMHwuFRY01dtUFs7kqud6+tGrah37NqpaoYenlGHxusl+EEMUlKc3BY5O+QtfHM3COQYMDimmddJIahuG1Pse4IZ2oElZ6mrBfqmIvvJo3b07Tpk35+OOPsVoLOt96PB5GjhzJhg0bWL9+PUuXLuWmm24iKSnpUjfnV5LchBClmVKKTYeymLb6AInbfyA08DfIyaT9ZkX7rYpAN+yuAksa6KTUAM2oids8gC6XdeKaZjF+T6ZyDD432S9CiOKU6/LyyNTF5GaOoe6GHK5dptiYoDGjl0523nAeHXgz3epW8neYxaLYC69ly5ZxzTXXoOs6TZo0AWDTpk34fD5++ukn2rZty+TJk0lJSeGRRx651M35lSQ3IURZke/2MXfLEb5btRV3xhQI3kjEQQ+dtiia71HoCjbHayxtAJlxZpSrPnrIDXRp0ozejSoT44dBOeQYfG6yX4QQxU0pxad/7mXh8qexODZw508GPh0+vFrDZWtJ40aP80ivutgtJbvv8KXyyzxeOTk5fPXVV+zcuROAunXrMmTIEEJCSk4TlcIgyU0IURYlH89jZuIh5icuI8CYjse6h2pJBh23KBocKBgVcV0tjVX1NPJizKj8eqiggXRq2oI+jaoQG1k8I1rJMfjcZL8IIfwlMTmTl6Z+gsn8OdfPMaifDN930NnZJIhs7yO8MvgKGlUN83eYRUYmUC5CktyEEGWZUooth7OZmXiIpZvnEW6Zi0s7SJ1d0HGLQY1UcJkhsYbGmrqQU9WM5qqN134t7Zu2pU+jKiREBRVZfHIMPjfZL0IIf8rK8/D4t4twZD1JrU05DFyq2F0Fvuqr4Xb1omf7e7izS01MpXD+yP/it8Jr69atHDhwALfbfcb6a665prA24XeS3IQQ5YXPUKzcm870dcls3T2TsKA/cHqPUW83tNlhUOcweEywsbrGqrqQFWdCc9fEbb6a5g070KN+NE2rhRfqRM1yDD432S9CCH9TSvHlsn3MW/Icumc1I35WROTCpO46afEVcNueYuzgDsRXKLqTc/5Q7IXX3r17ufbaa9m0aROapnHyI08ORezz+S51EyWGJDchRHnk9PhYujuNnzccYkfSLEID/sRlpFBrL7TdblD3IBg6bInTWFVX43gsmLSq5Ho6klDzKro3rErHWlEEWC+trb8cg89N9osQoqTYlZrDM1O/R+Nt2i730DNRsbamxqyeGo7c67m2yzCGd0goM1e/ir3wuvrqqzGZTHzyySckJCSwatUq0tPTeeihh3jjjTfo1KnTpW6ixJDkJoQo79xeg2V70piz6Qibd/1EmH0RLg4Tv0/RZoei4X6FScHeyrC2lkZSgkILjCA/vzlhVQbStWEtLq9fiUoh9gvethyDz032ixCiJHF7Dd6ev5VNW57CmrGL2+cY6Aq+7KGRWyUah/Ux/u+6DtStXPrHgij2wisqKorff/+dJk2aEBYWxqpVq6hbty6///47Dz30EOvXr7/UTZQYktyEEOIvXp/ByqTj/LzxMIk7fyXMugC3+SAVDxq03KVotlcR5IK0kIIibEtN8EbZ2ZcxiEn333fBg3LIMfjcZL8IIUqitfuP8+p3n2Myf06PRQYdtp24+nW5hsvZhy6t7+CebjWxmUvvyIcXcvw1F8YGfT7fqdELo6KiOHz4MHXr1iU+Pp4dO3YUxiaEEEKUQGaTTodaUXSoFYXPaExi8m3M33qU5d4V/FrxF+Z2340tzUXz3YpWuxS91sMHfV2Ex0QW20iIQggh/KNFfCRf3Hs/r/7chcUdnmFl/f3c+qvBY18qvuo2h/Vbl3Hdpod5dkBXWlaP9He4Ra5QCq9GjRqxYcMGEhISaNOmDWPHjsVqtfLxxx9To0aNwtiEEEKIEs6ka7SIj6RFfCT0qUdS2vUs2JbKgi07WWL7kTWtN+HLz8YI0GhZs6O/wxVCCFEMgmxmXhzQktX7pvDmD5N47+ZPabfcx8h5BltjM/j2iqd49et2xNUYxRNXNSAq2ObvkItMoTQ1nDdvHg6HgwEDBrB7926uuuoqdu7cSYUKFZg2bRqXX355YcRaIkhzDiGEuHAZDjd/7DzK/M2H2bhvPW8OvY42NSpc8OfIMfjcZL8IIUoDp8fHu79tZePmZ9Ed27l1rqJCDsxqo7G1mZW07GHc1H0AQ9vEl5rBN0rEPF7Hjx8nIiLi1MiGZYUkNyGEuDQurw+zrl9UUpVj8LnJfhFClCZbDmfxf99/B7xHvQ1u+q1QZATDpB467qgYsvUHefbadlwWF+HvUP9TiSi8yipJbkII4T9yDD432S9CiNLG6zOYtCyJBcvG4tP/pN/v0CxJsaaWxk9dwe3qTI3ad/Jo73pEh174KLjFpdgKr9tuu+28XvfZZ59d7CZKHEluQgjhP3IMPjfZL0KI0upotpPXflpOWsqL2I+mcvMCg5B8mN5OZ0cTC+mZN3Blu4GM7JxAoLVQhqcoVMVWeOm6Tnx8PM2bN+ffPmb69OkXu4kiUb16dUJDQ9F1nYiICBYuXHje75XkJoQQ/iPH4HOT/SKEKO1W7E3n3ZmTQf+cxus8XLm6oPnhN101cmIiSMm/i7uu6Mq1zauil6D+X8VWeN1zzz188803xMfHM3z4cG666SYiI0v+UJDVq1dn8+bNBAcHX/B7JbkJIYT/yDH43GS/CCHKAo/P4Isle1m04v/w6ivo8ye03qnYGQPTLtfQ7LVwWEcx5qoWFzVAU1G4kOOvfikbmjBhAkeOHOHRRx9l9uzZxMbGMmjQIObNm/evV8CEEEIIIYQQ4nQWk87ILrV46+4PqFF9Ir93qc6rN2hYfPD0FEXXZbuxeh9k7NTHuO3zlWw7ku3vkC/IJRVeADabjRtvvJH58+ezdetWGjZsyN1330316tXJzc294M9bvHgxV199NTExMWiaxowZM856zYQJE6hevTp2u502bdqwatWqC9qGpml06dKFVq1a8dVXX11wjEIIIYQQQoiiUTHExv9d34HHh0zFVuVZpl4bzgd9NBruVzz5mUHLncvIy76dRz99lvunrmd/usPfIZ+XQu2hpus6mqahlMLn813UZzgcDpo2bcptt93GgAEDznp+2rRpjB49mg8//JA2bdrw9ttv06tXL3bs2EGlSpUAaNasGV6v96z3/vrrr8TExLBkyRKqVq3KkSNH6NGjB40bN6ZJkyYXFa8QQgghhBCi8NWvEspHdwzijx3d+PSXz5kw7Gvqbyjo/9Uj0cvMtr+yN3Qhd0y4hpaNr+e+7rVL9AiIlzycvMvl4scff+Szzz5jyZIlXHXVVQwfPpzevXuj65d2QU3TNKZPn07//v1PrWvTpg2tWrXivffeA8AwDGJjY7n33nt5/PHHL3gbjzzyCA0bNuTWW2895/MulwuXy3XqcVZWFnFxcSQnJ0s7eiGEKGbZ2dnExsaSmZlJWFiYv8PxG8lNQojyxuMz+G71fhaunIDXvJgW6xXdNihyAmFmG51jNQJIzRxA1xa9Gd4+gcgga7HEdUF5SV2Cu+66S0VERKgmTZqot99+Wx07duxSPu4sgJo+ffqpxy6XS5lMpjPWKaXUsGHD1DXXXHNen5mbm6uys7OVUkrl5OSoyy67TK1ateofX//ss88qQBZZZJFFlhK0JCcnX3BOKUskN8kiiyyylKzlfPLSJQ8nHxcXR/PmzdG0fx7W8ccff7yoz//7Fa/Dhw9TtWpVli1bRrt27U697tFHH2XRokWsXLnyPz9z7969XHvttQD4fD5GjhzJ/fff/4+v//tZRcMwOH78OBUqVPjX7/xPTlbFpe2spMRdfEpjzCBxF7fyGrdSipycHGJiYi65VUVpJrmpQGmMuzTGDBJ3cZO4i09x5qVL6uM1bNiwizrA+1ONGjXYsGHDeb/eZrNhs9nOWBceHn7JcYSGhpaaX8jTSdzFpzTGDBJ3cSuPcZfnJoYnSW46U2mMuzTGDBJ3cZO4i09x5KVLKry++OKLS3n7BYuKisJkMpGamnrG+tTUVCpXrlyssQghhBBCCCHE+SpV7TSsVistWrRgwYIFp9YZhsGCBQvOaHoohBBCCCGEECVJoQ4nXxhyc3PZvXv3qcdJSUkkJiYSGRlJXFwco0eP5pZbbqFly5a0bt2at99+G4fDwfDhw/0Y9fmz2Ww8++yzZzURKekk7uJTGmMGibu4SdyiMJXWn0tpjLs0xgwSd3GTuItPccZ8ycPJF7Y//viDbt26nbX+lltuOdW08b333uP1118nJSWFZs2aMX78eNq0aVPMkQohhBBCCCHE+SlxhZcQQgghhBBClDWlqo+XEEIIIYQQQpRGUngJIYQQQgghRBGTwksIIYQQQgghipgUXkIIIYQQQghRxKTwEkIIIYQQQogiJoWXEEIIIYQQQhQxKbyEEEIIIYQQoohJ4SWEEEIIIYQQRUwKLyGEEEIIIYQoYlJ4CSGEEEIIIUQRK1WF1+LFi7n66quJiYlB0zRmzJhxxvO5ubmMGjWKatWqERAQQIMGDfjwww/PeI3T6eSee+6hQoUKBAcHM3DgQFJTU4vxWwghhBBCCCHKm1JVeDkcDpo2bcqECRPO+fzo0aOZO3cuU6ZMYdu2bTzwwAOMGjWKWbNmnXrNgw8+yOzZs/nuu+9YtGgRhw8fZsCAAcX1FYQQQgghhBDlkKaUUv4O4mJomsb06dPp37//qXWNGjVi8ODBPP3006fWtWjRgj59+vDSSy+RlZVFxYoV+frrr7nuuusA2L59O/Xr12f58uW0bdu2uL+GEEIIIYQQohww+zuAwtS+fXtmzZrFbbfdRkxMDH/88Qc7d+7krbfeAmDt2rV4PB569Ohx6j316tUjLi7uHwsvl8uFy+U69dgwDI4fP06FChXQNK3ov5QQQohTlFLk5OQQExODrpeqRhuFSnKTEEKUDBeSl8pU4fXuu+9yxx13UK1aNcxmM7quM3HiRDp37gxASkoKVquV8PDwM94XHR1NSkrKOT/zlVde4fnnny/q0IUQQlyA5ORkqlWr5u8w/EZykxBClCznk5fKXOG1YsUKZs2aRXx8PIsXL+aee+4hJibmjKtcF2LMmDGMHj361OOsrCzi4uJITk4mNDS0sEIX4pzufqcN90/yMfY6HcLMaIYPzavQfAq8oBmgeTWsXrB6wOoFmxesbrB5VcFjT8FztpPPe9Sp19tO3NrdYPqHRscGkG8rWPJOLDkBGtmBkB0AOUEa2QHgtisMGxgBJtBsmHwhGJ5w8j0V8enVsAXVpUrFOGpUDKJGxWBqVgwiNiIQs+m/r1qkZOWz8WAWx3KcKCA6xE7jamFUDgso1P0tSr7s7GxiY2MJCQnxdyh+JblJlCVXTO3MI1/ksacqrG5twqd0dJ9CMxT4TuQ8Bfi0grznA4sPLN6CW/OJ27/uF+Q582nrrCduzUZBHrS5we6BAPdf+fFcsoJgf0WN3TEaqbEG2KM57BzAlR0vZ3CrOOwWU7HuK1HyXEheKjOFV35+Pk888QTTp0/nyiuvBKBJkyYkJibyxhtv0KNHDypXrozb7SYzM/OMq16pqalUrlz5nJ9rs9mw2WxnrQ8NDZXkJoqcxW4i2ASBdo1Fma8BUCc6mGuaxpDn9pHv8ZHn9OBy5+J2Z+JxZ5Hnzcbw5GIYORg+Bz6VB0Y+muZE111omhtNd4PuQWlelO7Fp3nQDC+ax4fuMdC8oLk1bC4IPLUoAl0Q7ISIPIg/pgjLgzBHQSKDk82bFE6Lk7RQJ8fC0kgL3U1aqMaxMMjxKNbmmNmwNwTDVYl8TyymgKZUiGxEQnQYCVFBRIfaMWkaKdlO1u07xt59P2NWKzFsKeBzoTTQtQB++DMOl/lKelzWietbViMq+Oz/p6LsKu/N6SQ3ibLEZDcRZDIRYVZYM25hqdEYgKhgK//rWhOX14fLa+DyGri9Bk6PB5c7D48nF683B7c3j3xvHoYvD58vD8Nwoox8DOVCKSdKudFwo+keNM0NesGidA+G7sGnezGUF83nQ/cqlFdhyYeobKiUqaiZAgM2KIJXmjhQMY2f205k09bv+HX9bdzU4wquaxGLSS/fxyRxfnmpzBReHo8Hj8dzVttKk8mEYRhAwUAbFouFBQsWMHDgQAB27NjBgQMHaNeuXbHHLMR5O+3/ckJUEKMur33BH+H1GeR7fOSfLNhO3Oa7fThcXnKcXnKcHrJP3uZ5cORnkudMJdN5DJ83HZ83E8PIxKxnoptzwZSH15SP5vWgu33oToU1T6NCDlTMUlTMgppHFG23K0KcJyPxkBZynEMVMjhcYQcHoxaQngEbj0Gi3YrJsAMaXi0PU7abOgc0Gu1TJKQqgk98RnaAh21xW1hVZytLNr7Dr8sup2WT27irW20ig6yXureFEEIUI4U67dTdXwkvLMDCyM41CmUbhqFw+wxcHgOXtyD/OVw+cl1eHC7vGbc5Ti9HsrI5lrGLzbmb2NRgM+6AZEJT3PRap7hrtuJAVAY/dh7Hr4u/4ps/7+G+vp3oVrdSuT8pJP5dqSq8cnNz2b1796nHSUlJJCYmEhkZSVxcHF26dOGRRx4hICCA+Ph4Fi1axKRJkxg3bhwAYWFhjBgxgtGjRxMZGUloaCj33nsv7dq1kxENRcl0juZ/Ghd3UDebdEJMOiF2y6WFpBQ5Li/puW7Sc12k5bpIy3UXPHa4OJaRxsGsrSQ596BzCIvlOMqcg085MOV4Cc3UqJquqJoOjfcpeq5XJ66YgVd34rAXNCcMzSuY7yLfqtgSpzG7tc6RSNAVxKRDs70G985WZAU6mdtiLpuC53PLhoHc0ONWbmwVhy5nH4UQovRQJ2/+OnYXZhGj6xp23XSiaeD55sGWwI2k5bpYujuNH1YtZUbkRGa2O8x1fyoe+FGxNTaFH7s8w+c/1WDiwvu4rUtTLq9XSa6AiXMqVYXXmjVr6Nat26nHJ9u333LLLXzxxRdMnTqVMWPGMHToUI4fP058fDwvv/wyd95556n3vPXWW+i6zsCBA3G5XPTq1Yv333+/2L+LEOfjVN112vHb3yfTNE0j1G4h1G4hISroH17VFQCHy8uhzHwOZeSzP93B7mO5JKfsIbHiGjaxG4s1Fa85E93pxpQDFbIgyAkmA46HQGa4whceAM4EsjzNCYlsi668rA9bxp4Gy3H7DtJ2PVy73CBvncE3nacxZ/l8fkl8htcHdyImXPqACSFESXfqitff8ltJKV2igm30a1aVfs0Gse5AT8bPXciPvcfz8/Fj3PiHwVNTFMvr7eW3Ng/w7c/VeXf2TVzTvi2DWsUSeoknO0XZUmrn8fKX7OxswsLCyMrKknb0osjd+lojHvvcx6tDNRY5Xgegb+PKvD+0hZ8ju3QZDje7juaSlJbL3jQHSUezyDy+FY/rOIbyYLLFExFZkzY1ouhYqyL1KoecuorlMxTrDmTwzfJdHNn/AYZlNT2WKjpuVSQmaEy/QsPlHMqYwf+jbY0Kfv6mojDJMfjcZL+I0qztpOa8+JGTffGKBdVHsMJoABT0af71wS5+ju7cFu88xntzZmFhIhEHshn0p6JiNmyNhYWXafiiQznsGEDbFlczvEMClcPs/g5ZFJELOf6WqiteQpQ35zrbd7FNDUuaiCArrRMiaZ0Qedra82vya9I1WlWPpFX1NuxPb8Rb8zawuN2rLG20hzt+NnhokmJCv68YO/UQI69+ij6NqxTNlxBCCHHJTm/dodRpTQ1LcL7rXKciHWvdxozEXnw9/xs+umkagYfz6bPG4J6ZiqNhWfzS6gt27fiae9b1pOVltzHq8lqX3NxflG7ld/ZJIUoBdarN+2lKbh7yi/gKQbw9pD0j+31KdvAIxt9sYl+0xiPfGlTOWMiXvzzGrA2H/R2mEEKIf6BQaCdGjD893/m7af1/0XWNAZdV46uHRjOgwwwyq47i62uiefIWnR3VNG5eoLjrSxcNkn9h586hDHnrI37dcu55Y0X5IIWXECXaiRR0eh8v/wRS4nWuU5HP772XoNB3md0nkCUNNe6ZrYhMX8Pkn59nxd50f4cohBDiH5xrVMPSwmY2MbBFNb4ddSdj+v1IpZhxzG9fmyfv0PmzocagPw2GTc2nYt77TP7pPp6dsQmPz/B32MIPpPASoiQ716iGJf0UoB+FBVj4+NauxFebyPIOEfzZSOOunxTB+Ut4Zdq77Etz+DtEIYQQf6NOXOpSfx9co5TlO03TaBEfyYe39OL1W76mYdyXrGjajOeG66SGazzynaLxls0c2DeCOyctxenx+TtkUcyk8BKiNJArXufNpGu8NKAZCfETWNI5mA0JGnfPNrBZv+WRr+fJWUYhhChhzpzH6y+lOd9Vjwri/wa25NXhHxMe+QYz+0TwRXedPmsUPebn4s6+l9s/mS/FVzkjhZcQJdi5xhwtZScA/ULTNJ7p15SA0Df56QoTDjvcPFth42Xemb/D3+EJIYQ4jQK0c87j5Z94ClONisF8cltvrm77DXvj2vLG9ToNDij6/+RCcz/Og1+vxGfIAOPlhRReQpRg2ll3SvcZwOJk0jXevrEdXt8DfHy1RvWjUGdjPqvWj2NXao6/wxNCCHHCGaMalrHCCwpOBg5pE8fjN7xOdsBVjB2sUSMFeixw4zz+CO8v3O3vEEUxkcJLiJLsHKMalrY27/4UZDPz3ODrMExtmdlWo/9yhWZayksz/kSmMBRCiJJB/cOQGiV5OPmL0SI+kpeHPYnLdBXj++m03KmI25bJslUvs1IGgCoXpPASohTQ/uG++G+NqoZxWdOH2dbMRnoIXL0IjNzxLNxx1N+hCSGEOEFTlNkrXqdrEBPKw9c/Sn5oE37oWDDiocWzild/mCn9vcqBCyq8DMNg4cKFvPDCC4wYMYIbb7yR++67j88//5zk5OSiilGIckuuyRSOUd3r4Mi/lWldNFrsUeA4wEe/LpKrXkIIUQKoM+6XwWrrb9rXiqJDi+fZ3iSYXTFw8zxFiHUCE6TJYZl3XoVXfn4+L730ErGxsfTt25c5c+aQmZmJyWRi9+7dPPvssyQkJNC3b19WrFhR1DELUW6c6mwsl7wuSaDVzM09BuKICWdvNAxYrLD5JvDbNrnqJYQQJYF2YoSNsjKq4X+5u1ttDP0xvr5Co3IGJGxxsnzNBI5k5fs7NFGEzqvwqlOnDhs3bmTixIlkZ2ezfPlyfvjhB6ZMmcIvv/zCgQMH2LNnD506deKGG25g4sSJRR23EOXC3+c0gbLX5r249GsWQ45nJN931ql3EPTMND76bbFc9RJCCD87vdw644pXWWxreIJJ13juup5otGROS41+KwysAb8zXkbeLdPOq/D69ddf+fbbb+nbty8Wi+Wcr4mPj2fMmDHs2rWLyy+/vFCDFKK80mQ4+UJjNunc2v0KfJGRJEVDr1WKAM/nrEo67u/QhBCiXDvRvevE/dP6ePklmuJTr3IotWqPZnULHQU0X2eQtHs8+9Ic/g5NFJHzKrzq169/3h9osVioWbPmRQckhDgHGU6+UFzdNIYc7xB+aq3RLEnhde/jyz+3+DssIYQo19Spf/4+iq8fgilm9/eoh5HXjdltdK5Yp9AsK3lvwTZ/hyWKiPlC37Bx48Zzrtc0DbvdTlxcHDab7ZIDE0KcPoFy2R7lqbiYdI2r2vVm4dpPSQt10GUtLNbfJ/l4S2IjA/0dnhBClFvaiX/K0xUvgEqhdlo0uZPEoEVcudqg+QbFWvdEjmY3olKo3d/hiUJ2wcPJN2vWjObNm5+1NGvWjHr16hEWFsYtt9yC0+ksiniFKKf+OgcofbwuzaCW1chx9OWXljodtip81k18uXSvv8MSQohy6WQ/W02dzHSnn2gsH/nuji61MBydmN9co3uiwhy4nEnL9/s7LFEELrjwmj59OrVr1+bjjz8mMTGRxMREPv74Y+rWrcvXX3/Np59+yu+//85TTz1VFPH+p5ycHB544AHi4+MJCAigffv2rF69+tTzSimeeeYZqlSpQkBAAD169GDXrl1+iVWI8ydXvApLiN3CZY0Hs6+ujtcEDbcoErd8gcPl9XdoQghR7pwxjqFGuRnV8HRRwTZq1x7B+iYaVi/E7vSxbN035LklL5U1F1x4vfzyy7zzzjuMGDGCxo0b07hxY0aMGMFbb73Fm2++ydChQ3n33XeZPn16UcT7n26//Xbmz5/P5MmT2bRpE1dccQU9evTg0KFDAIwdO5bx48fz4YcfsnLlSoKCgujVq5dcoRMl08kMVD4GeSo2t7avjpHXgkWNNXquU9gCf2f6+kP+DksIIcqd0694QdmfQPmfjOhcG5OnEUsbaPRdbRAU9DM/rD3o77BEIbvgwmvTpk3Ex8eftT4+Pp5NmzYBBc0Rjxw5cunRXaD8/Hx++OEHxo4dS+fOnalVqxbPPfcctWrV4oMPPkApxdtvv81TTz1Fv379aNKkCZMmTeLw4cPMmDGj2OMV4r+cO+eUo0xUROIrBBFSeQQrmkF4HkTsd/LTsl9kaHkhhChm6vQGhpr6Wx+v8pPvalUKITDqfyxsCRWzIeSggxlLF2AYkpfKkgsuvOrVq8err76K2+0+tc7j8fDqq69Sr149AA4dOkR0dHThRXmevF4vPp8Pu/3MzogBAQEsWbKEpKQkUlJS6NGjx6nnwsLCaNOmDcuXLy/ucIX4T+ocEyiXpzOARWlYh3pY9OokJmj0Wauwat+wfE+6v8MSQohyRf1tOMMzyoxylu9u79wEq7Uam+I1eq1RBJu+ZtHOY/4OSxSiCy68JkyYwE8//US1atXo0aMHPXr0oFq1avz000988MEHAOzdu5e777670IP9LyEhIbRr144XX3yRw4cP4/P5mDJlCsuXL+fIkSOkpKQAnFUURkdHn3ru71wuF9nZ2WcsQhQ37R/ui4vXsVYUmQxjfguNGimgctOYvGSdv8MS4rxIbhJlxolKq7yOani6djUrkK/dzLyWGnUOg+E4wqQ/E/0dlihEF1x4tW/fnqSkJF544QWaNGlCkyZNeOGFF0hKSqJt27YA3HzzzTzyyCOFHuz5mDx5Mkopqlatis1mY/z48dx4443o+gV/VQBeeeUVwsLCTi2xsbGFHLEQ/0KueBUZXde4rn1bPBVDORwJ3dZCzrEPOZiR5+/QhPhPkptEWXGqqaECVPnt4wUFozhe37ELzuhgUsKh8zrwZHzCrtQcf4cmCslFVSMhISHceeedjBs3jnHjxvG///2PkJCQwo7totSsWZNFixaRm5tLcnIyq1atwuPxUKNGDSpXrgxAamrqGe9JTU099dzfjRkzhqysrFNLcnJykX8HIf7uzCte5SwTFaGBLaqRnnMNc1rqtN6h8Og7mLRsj7/DEuI/SW4SZYU6s3Hh30Y1LH/5rl+zqjjyrmROS5222xVe8xY+XyJ5qay4qMJr8uTJdOzYkZiYGPbvL5hn4K233mLmzJmFGtylCAoKokqVKmRkZDBv3jz69etHQkIClStXZsGCBadel52dzcqVK2nXrt05P8dmsxEaGnrGIkRxOTXK0+mj7Za/PFRkgm1mWje5hsM1zTit0HyTYvOWj8h3+/wdmhD/SnKTKCvOGNXw700Ny2G+s1tMdGw+mP21TbjN0HizYteOz8lwuP/7zaLEu+DC64MPPmD06NH06dOHjIwMfL6CP1AiIiJ4++23Czu+CzZv3jzmzp1LUlIS8+fPp1u3btSrV4/hw4ejaRoPPPAAL730ErNmzWLTpk0MGzaMmJgY+vfv7+/QhfhHpyefcpiHitSwdvG4ctvye9OCiSuxL+HrVQf8HZYQQpQLp49q+Pf8Vh4LL4Cb28Vj5LVmYRONHusVtuDFfLNa8lJZcMGF17vvvsvEiRN58sknMZvNp9a3bNny1HDy/pSVlcU999xDvXr1GDZsGB07dmTevHlYLBYAHn30Ue69917uuOMOWrVqRW5uLnPnzj1rJEQhSoRTV7pOPwNYTjNREalRMZiQKsNY2wQCXRC918OsJd/JVS8hhCgGZ0zjcdbgGuUz30WH2qkSP5JVzSA4H6KSXPy6chZOj+Sl0u6CC6+kpCSaN29+1nqbzYbD4SiUoC7FoEGD2LNnDy6XiyNHjvDee+8RFhZ26nlN03jhhRdISUnB6XTy22+/UadOHT9GLMQ/OzmohiqfuafY3Nu9AVZVm9V1NK5ZYRAcOI2vVu73d1hCCFFuaKrgXGN5b2p40m2d6mKh5om8pAi1f8M30hqj1LvgwishIYHExMSz1s+dO5f69esXRkxCiJNOH2b3hPKciIpKi/gILBH3MLc9VMqCqjudzFn6KcelTb0QQhSpMydQ/ts8XuVYs9hw9OC7mNMeqmRA+D4HMxdLa4zS7oILr9GjR3PPPfcwbdo0lFKsWrWKl19+mTFjxvDoo48WRYxClHtnDCdfTpteFLVRV7RC1xqxsInGwKUGloBZvPqz/5tPCyFEWXaqqeE5Kq7y3rT+/t5tMZtrsryexnVLFaFBU/l0yV5/hyUuwQUXXrfffjuvvfYaTz31FHl5eQwZMoQPPviAd955hxtuuKEoYhSi3NLOmYiKP47y4LK4CCJiRrO4rYbVC+1W+Di0/yWW70n3d2hCCFFmnX3Fq/xOoPx3LatHYou4n7ntoUIOxG5zsWjFe+xP93/XHnFxLmo4+aFDh7Jr1y5yc3NJSUnh4MGDjBgxorBjE0KccOa8JqKoPH11M3z5fZjSTeeK9QpLzk5e+W4yabkuf4cmhBBlkkyg/O9G926FrprxS0uN6/80MFvn89yPy88clESUGhdVeJ0UGBhIpUqVCisWIcTfyRWvYlU5zE7fzvdwLCGSjdU1bp9jYLdP5KEpv+H2Gv4OTwghypyzRzU842G516hqGHXrPs6aVmZyAuGaBaByn+eTP5P8HZq4COb/fgk0b978vNvZrlu37pICEkL8u/Le5r2o3dq+Oku2Pc2PVzzI/V8phsw2+LbfUzzwdSTjh7bBbLqk81VCCCHOQTvV1UumT/m7R/o0YOjbt/Jpr0957DuDLTuOs9D1Ig2rvk77mlH+Dk9cgPP6C6J///7069ePfv360atXL/bs2YPNZqNr16507doVu93Onj176NWrV1HHK0T5JLmn2Jh0jTdu7ILTM5IJAzQSUuGK31xkZo5i1JQ/yXN7/R2iEEKUGSeveGln/iNOE2wz82D/m3CF1ueH9ho3LjKw563kjWnvkJic6e/wxAU4rytezz777Kn7t99+O/fddx8vvvjiWa9JTk4u3OiEECdIZ+PiVCHYxvNDbuXFyQcZ328O988wsHjzmd37Pm75YAyvDrmKmhWD/R2mEEKUHSeveJ3Z8lCc0LVuJdY3fYnV1ltISMnh3umKt67/gecnm3lk8D1y5auUuOA2M9999x3Dhg07a/1NN93EDz/8UChBCSEKnBrV8IyJvPwRSfnTLDachwY/TmZQD8Zdp9EgWTH8Ozea+wUe/eQJPly0B6dH5lMRQohL8a+jGkq+O8P9PeoSHPYas3pb2F1F4/7vFeGuaYyfNoZP/tyLYciAGyXdBRdeAQEBLF269Kz1S5cuxW63F0pQQghQhnHOGkvm8So+7WtG8dDgp8m2X8trN2mYfDBmkqLugQX8uWEwg976mKmrDkgBJoQQF+mMUQ05s/CSM41n0nWNd2/qRFDg63x/lZmdVTUe+laRcHApS9fcws0fzmXL4Sx/hyn+xXk1NTzdAw88wF133cW6deto3bo1ACtXruSzzz7j6aefLvQAhSivlDptFL3TJ1CWPFSs2teM4vXbx/DQpDp8Pngczdd4GPqHwaFNDn7s8C4/Lf+CrxdcRdsmA7mmWVUaxoRKh3AhhDhPZw6Lrs4c1VAOpWexmU28f0t37vtqPLP6PErymlyG/a7YvDud6T0e4v8mNSey2n3c3qUuTWPD/R2u+JsLLrwef/xxatSowTvvvMOUKVMAqF+/Pp9//jmDBg0q9ACFKK+UMqTNewlRs2Iw39x7E6/+3Iy1+WNZ22A9/RbB/TMVhyKz+a35VDYETGPr1prkaVfQon432teMolVCJMG2Cz7MCiFEuaH+Nm+KTKD83+wWEx8O68K4X79kjetJNifsYMRcgyc+h9+brmelZTjjvqlHvv1mrmzZnCsaRlMlLMDfYQsuovACGDRokBRZQhQxpU5raihXvPwuwGri+WubsiF5Am/MXsScnh/wU/4Buq2FIX8YaAthY/VdrK29m436+2zfFcX7+bUwB3cjoVpTGsWE0ahqKHWiQ7BbTP7+OkIIUaJoCunjdQF0XePh3vVZVvtjXvvxG96/6TMStrsZsNSg+wZYW2s7i5s9ycLVgcz7oyG+4GtpUacerRIiaREfQajd4u+vUC6dV+GllJKmM0IUM4Xx1+AaZ5wBlP+L/tQ0NpxJd13Dop3tmPznWn5v9zkLumyj6h6D1jsUt/1qYFJwKPIo22OPsaPaCvY5FEmH7MxZFonLFYtPb0BgRFPioqqQEBVEtYhAKofZqRxqJyrYKnOFCSHKjTOGk+fvEyhLvvsv7WtG8c39d/H50j7Md3zM67fNpdpeH1esM3joO8i1O1hbazWJtdaw2auxfVtFPsyrg7K3o1LlxtSPCaNe5RBqVAymangAVrPkn6J0XoVXw4YNeeaZZxgwYABWq/UfX7dr1y7GjRtHfHw8jz/+eKEFKUS59A+jE8k5EP/TNI2udSvRtW4fdqV25Id1h1jtnkNq/AKmm/cTethHvWSoe0jRbYNCB9ymfA5FHeJAxcMcjFpFajjsP6rYE2xC1wMweUPxecJx+yLQTVWwBsQSGFyTsOAKRARZCQ+0EBFoJeLkbZCVULuFIJuJIKsZXZdfDCFE6XOqqaE6md/kiteFCrSauadbLYa2eYnJy0eywDmDyQkz8TkyaLxb0WqXosvmgv18KDKF7bGpJEUvISNXseqYmTUbwjDcUeR7KoO5JtbQxkRXqEyVMDsVQ2x/LcEFt0HShP6indeee/fdd3nssce4++676dmzJy1btiQmJga73U5GRgZbt25lyZIlbNmyhVGjRnHXXXcVddxClHlnXPE6vamhX6IR/6R2dAiP96mH6l2XjQeHs3DHUdbatrE0/FfWt96ORz+KJdNHpTSN+KOK2GMFSTDQdfITvDhsORwLy+F48GGygiEzCDKDNDKDISVQccSmYVg0DIsZTbNgMqxoPjvKF4DPCMLjC0LTgtD1YEymEMzWMCyWcCy2cOy2SIICgrFbTNjMOnaLCbtFP+OxzVyw7uRtwWtMWM06Fl3DYtKxmHXMuobVpEuRJ4QoNCeveJ28ziJNDS9eeKCVe7vX5q6uD/HHjmHMTkxirf4DW5uuxONLp0IK1EtW1D2k6LJJYTYAPKSFpnEkIp2j4Ts4GraYY2GKrGOKjAAdr9WCrgWgewMxvKG4vWF4VQSaKRKTuQJmezTWwEoEBwYTGmAhLMBCiN1MgNVMoMVEoNVEgNVEoNV82n0TgRYzAVZTubvCdl6FV/fu3VmzZg1Llixh2rRpfPXVV+zfv5/8/HyioqJo3rw5w4YNY+jQoURERBR1zEKUC2f08TqdZKISSdM0msaGF4wi1aMO+e6rWHcgg82Hsth8MIP9QatJqrKeIFsyhiUdZeSjO3zYcjQqZUGlTEW4A6qmKRruh/BchfXUKPUnK3AfLrMLhx1y7eCwQ55dI9cOLgs4rSdvNZwWcFogxwrHzaDMCmUGZdZQJg2la6BrGLoOuo6GCV3paIYJTeloygzKBMqMUmaUYUYpCwoTYALM6JjRdDNQUBDqmgVNP3lrxaSfvLWi61Y03YZutqBrduIq1+XBKxr44SclhChJzh5c4y/S1PDimE06PRpE06NBNG5va1YlHWfZnjQ22DfxZ+R8VrXZjcecgeZwE5kOcccUlTMgPlXRaqciNP/kJxmAC4fNRWZQJllBh8kM1sixQ54dHHYNhx2cNsizGjgscMiqo8w6hm7CMJswKTOaYUZTFjAsKMOKoawYhh2vYUMpO5pmRdOsoNkL8oRuRzMFYDIFoOsB6OZATOZATOYAdIsds8WCzaxjNemYTQUnBU26dub9vz22mDRM+j8/Nps0zLpO1YgAqoYX3UAkF3StsGPHjnTs2LGoYilWEyZM4PXXXyclJYWmTZvy7rvvnhoeX4iS4IxRDWWUp1InwGqiQ60oOtSKOrGmJQ6Xl6Q0B/vSHexLc5CUlsehtBSO525lu2cPNvNxzKZMNLMDr8mBMtyYnD5MToXu1gh0QqALgp2KICcEOSHYWVC02Txg94DdDTaPwu4G0zlbq567CavbBF7zX7ceE3hO3prAY9bwmMDQwWcCn/4vyxnPa2c9v6m6xrHcl0AKLyEE/DV0798G15CEd+msZp2OtaPoWDsKqIfTM4Ath7PZkZLDtiNZ7D2ykzUVVmEiGYs1DZ8lC5+RjynPhzkfwh0FS5hDEZ5bcL/K8YIcFOhSBDr/PimwcWLxAuAyF5wQPLk4LeC2gNOi4bL+lWu8+l+5x2sCj0nDe2L9yVxk6AqvCdw65Omq4Byg9rdF11AnbtFAadqJ57QT9wtOPCpNK/hd0zQ0TAVFvjuCRs3G80CPOkX28yiXjTSnTZvG6NGj+fDDD2nTpg1vv/02vXr1YseOHVSqVMnf4QkBnBjU5sT9M6aTlERUagXZzDSqGkajqmGnrW0K9MIwFOkON6nZTlKznaRkO0nLcZOR5yYzz02GI59jjlTcrlQMzzGU7zg2UzZWkwOT7kAzuUFzg+7B0L0Ymgdl+NB9PnSfQvMaaF7QPIABmk/D7APLycVbcGv2gsWnTj0+9ZwXzD4wGWD1FNyaDFVw6yso8ky+k+tPW3zqrHUTrtJx1LD76acghChJTs91Z83jVfzhlHl2i4kW8RG0iD/ZQq0xMJCsfA/Jx/M4mJHHgeN5HMzIJzUrl+O5B0jJ34/XfRizlobFkoluykfpTnwmDz7NDV4vuscoSEHu008Cnr4o7Cfvu8HmhUDnX/nGbPyVZwpykyrISSfy0vk3SFR/u/1vPq3gpODY2/OwFPHgVuWy8Bo3bhwjR45k+PDhAHz44Yf8/PPPfPbZZ0U6KEhiciYpWfmYdR2zqaDfxMnLnSfXnXru1OMTl0pP3td1LCZNRpksB07v46XOOAEoP/uySNe1Ux2YzyzMzs3jM3C4vOQ4vTjcXhwuL7ku34nbE4+dXnLdXvLdPpweH06PUXDrNXC63fi8TnyeXAxvLj5vHoaRj/LlYRhOzJoLs+7CpLkxax5Muhtd84BmoGtewEDTvGiagYYPNAO0glsNA6UZoBkoTtxqBmgKAwOlKcxmKbxKipmJh/D41ImmN3/lGtNpecd84jmLrp/WLOev11lONe/567Unm/BIvhL/RqHO+Bv5zD5e8rtTXMICLISddWIQoO2pew6Xl2M5LjLzPWTle8g+cZuV7yHbWfA4M89FjstBhisTnzcLw5uNz5uNMhwolYuOE5Oej1l3oWku0D1omo+CM4MGaF6UZqA0H8aJ3GHgA2WAUgUnEg0Fxon+gapgKgKlKDipqE58jNLQDYWu/jrpp5+8VWc+PrnObQFTEfdhLneFl9vtZu3atYwZM+bUOl3X6dGjB8uXLz/r9S6XC5frVC94srOzL3rbny1JIjP1AZRmoCkTKB0oaLujKHislAmFqaBPhTKhlAlDmTFO9LMwMOFTZsBSsGgW0KxougWlWdGwoulWdN2ObglCM4dgsQRhP9Ee9mSH+jPu/+022GYuWOxmQk7cBtvMMnJacTOMc66WPCQALCad8EAr4YH/PNLsxVJK4fIaeHwGXp/C4zNwn3bfc+r2r/tew8DtVXiNE+u9Co9h4PEWvMZrKLw+A6+h8BmKuMjAQo+7PCnM3PThz1OJCJuGrnRQesHJHaWjKR1FwS3qrxwF+qn8VJCvTBjKBCfylULHpywoZcKHGQ0zioL+fmgW0APQ9CB0PRDdHIxuCcZsDsJuMWMzm7BZ9BN56mRuKrgfaDWdyk2n56kgq5kQu5kgm7nIz1aLwqdOL7X+Po+XXyIS/yTIZr7kEQ09PoM8t498t488t5d8jw+31yhYfMYZ911e4x+fc3sLnjeMglzjM/7KMz5D4TF8eH0+PIYXp+HD4/WglAef4UEZbpThQSk3GG4M5UEzPLizLAQU8Tyb5a7wSktLw+fzER0dfcb66Ohotm/fftbrX3nlFZ5//vlC2bbXMPBmp3HnTwY+vaAN69/7RXhP9oc48dzprzn360/0oTh1/6/1HjO4T7SL9ZnBYVI4TCfy54nO9cqkFyy6jq5M6MqEZljQDCvKZz/R+TEQry8ItxEEehiaKQLdWomAoKpEhQZRIchKVLCVCsEFQ41WiwwgNiJQhhu9RDKqofAXTdNOjWwoSqbCzE1BeioN1+VyxTqFoRXkGkMvyCPnvDWBoWunHv97H78zF69JO9Vfw20+LU+ZC85F5pkUeSfyFKaCgWBODgJT0FHfgu47kaOMghzl8wXgNQJwG0EYWhS6tSqWwFgigiNO5CYrlULsxJ7ITdUiAgmwyu92SaGUOpXrNP42uIYkvDLHYtIJC9AJCyifEzjLX8b/YcyYMYwePfrU4+zsbGJjYy/qszw+RXowLG6kYTbO7g9h9p19/2S715P9JE5/n9lXcGnUfFofilOf4fuv9rB/jZJ2kttUcJnVZYE8G+RbIc+mkX/qPuTbCtY57JAbCFl2RYZNZ4fNjI4Nky8Qn7sCDndlfKYaWEIak1CpEg1iQmkYE0qDmFCZLf08ndnuXYbXFUL8pTBzk1JeNlbXcNi003LSaX3zztV374x+fAW56nz6/ZlP9Nv4f/buOz6Kog3g+G+v36X3EEhI6L0XAZFeLaCo2BAUe0ewK/iqWFBUsIIiiL1hQwQB6b33IiUFSO+5frfz/nEQiaC0C2nz/XzWJJu93eeOeHPPzswzBo9vO0Nkp+yxGew4DH+3UXajcvyr7+dis0JhABRZwG0SHDEqpJo0oDOgc4fgcUdgc8Xg1jYgJKI9zeIiaBYXTOv4UOpFBsihbRXgn1UNkT1eUjV2XonXwYMHmTlzJgcPHmTKlClER0fz+++/k5CQQPPmzf0do19FRkai1WrJzMwssz8zM5PY2NhTjjcajRiNRr9cOzrISEnWLexs5gbVjcAFwg3C1/3pqwDjQaN4UBSvb+P41+NjXxW8vkGpx+dUCMVXPaZ0PsWJ8bBCRREqiqqieAWKV0XxAqrwTcPwAl6ldNK84aSvJrdvjSGzE8wu39eIouP7XL4qNmbXyc/MC3ixGZ3kBxSRFZpJVuhuskKXkBUCmTbIyDSxdF0MxfYGaEMG0rlhPfo0jaFdQqhs6P7FyVUNyxbXkK+XJNV0/mybDGED0LqbkBLsRKgeEE7f0JvStsmNEJ7j33tBeFDwHJ/j98+2yovC3/P9ON5GlX6veFEVr2/+Bl5QfW2VxiuOt08nNkqLwGi8voIuJveJdkn83T4db6NCrAKLCwLtghDryRU9fRM/XDoPWSE2skIzyArZRWbYEtIdH7HLpmHfoWC+sydQKHrRtF5nBraIpUejqBq3vlCFOT5H5+8f5Rwvqfo658Rr2bJlDBo0iG7durF8+XImTpxIdHQ027ZtY8aMGXz//fflEaffGAwG2rdvz+LFixk6dCgAqqqyePFiHnjggXK99sSrW+KrHvPvhDgxRtU3V8LjPWns6vG5FF5VHJ8z4Zs74T0+ptWtCrzH950YB+vweHG6feNgHW4vTo+K0+ObZO/0eHG6bHjdhdjcVryeEryeYjzuIrzeQoRahEIJWo0djdaOonEgNC5UrQtVuNC4PGicKhqHQqANQmwQXiyILoDGRwSX7fw7QXPobaREH+ZAXDK76y5mi6pj+4565HMNg9pfwoguiTW22/nfnLyOlzj74jySJEnn5Kt7B5zzY1RV4BXieHt08vyKv9uuE/P5Tv596dyN0vboeFvkPul7j3r85+Pfu1y43cUUO/PIc+eiuvJQvUWoaiGIYl8bpXGgaB2gtePWOFA8LrQOFY1ToHcoRBRBdKEgpgCapQp6bRcYPQAquUF57KuTz4662zmmg9lH4njrx4F0bzuY0d2TiA6ShWDK06lzvP4m0y6pujnnxOvJJ5/kpZde4tFHHyUoKKh0f+/evXn33Xf9Glx5efTRRxk5ciQdOnSgU6dOvP3221it1tIqhxVJUXxVDn1TKyp+DLrHq2J1eSk5USHN6abI4aHQ5ianxElOiYucIjvFJcc4Zk3msCMZxPG1IHSFCI8dXYGgTjYkZgg67xNcvkHg0bjYUn8fK5u/ytodZuavGcqgrjdw12X15OTo4wTi9HO8ZEskSVIF02gUNPjaqoqcC6iqApvbe7zCp5t8m5vc421TbomLXKuT/MJ8Mop2k2bbBxxFp8/BqysAm52AfIWkDEGzVMEdCwRaAftqH2F5i0/YceAz7tg8iD6X3M69PevLHrBy8l9VDWXmJVU355x47dixgy+//PKU/dHR0eTk5PglqPI2fPhwsrOzGT9+PBkZGbRp04b58+efUnBD8q1+fnaTINuWfudwe0vXgUjOsbHnaC5pGas4XHcjK3ocxOsqoE6KQvddKmN+EmSFWPmx61es3f4bC7c9zhs39aVBdNB/XKuGEH9XNSx7B1C2RJIkSeBLAE9UOIwJ/q+eqe4A2FwejuTbOZRdwu70YvYdSWZb+J/sabkFty6d0KMeuu2C0X+oFJmdzOn2Cxt3LmX4jrG8fesg6kYEXJwnVoOUmc/8j+ZNtndSdXPOiVdoaCjp6ekkJSWV2b9lyxZq167tt8DK2wMPPFDuQwtrKpNeS4PooJOSpySgAw63l80p+aw8kMMWzTy+q/8bbkc6fdbD3b+r7K1TwFcDn+GRj3fx7A33ckm9iIp8GhWuzALKssdLkiTpglkMOhrFBNEoJoiBLWoBjRCiHwezS1jxVw5LdGuYH/U1c3sd4NINMGqhyoFdBXw5eDwPTEvh5VvvoGWdM6+zJ529MqM7xD/neFVMTJJUXs653/yGG27giSeeICMjA0VRUFWVVatWMW7cOG699dbyiFGqJkx6LV0bRPL4wCZ8+egYxgz7nqg6H7C0W0NevkkhxAqPfS4I83zJK19NZufRwooOuUIJcdICyrLKkyRJUrlQFIUG0UHc1i2J2fffxIu3fUVi3Vms7dCMV25WCLbBI18KApnBU7M/Ii3PVtEhVysnVzVUFCHneEnV2jknXi+//DJNmjQhPj6ekpISmjVrxmWXXUbXrl159tlnyyNGqRpSFIW2CWFMHdGDx2/4FGfQWKbfoCM5RuHR7wQWzU889dmn5JQ4z3yyaqpM8yNbH0mSpIsiKTKAV6/rxBM3zsAZOJZpN2jJCYGHvhcEGD7h4Vk/43B7z3wi6eycVNVQ8I85XpJUzZxz4mUwGPjoo484dOgQc+fO5fPPP2fv3r189tlnaLUVXwxCqnpa1Qnl0/tHERIylTmX6zkaAQ/8ILCYpzP+x40VHV6FEapapjE6QQ69kCRJKn8t64Twyb23YjS9xhdDtDgMMOoXgVn3BlMW7qvo8KqVk4fVy6GGUnV23iV64uPjGTx4MMOGDcNqtZKfn+/PuKQaxmzQ8s4tPdDpX2L2EA0GD/RerlKc+SqrD1SNoi3l4e/GSHPSPtkSSZIkXQxBJj3vjOqPR4xj+hCFOjnQcIeDjVvf42iBvaLDqxb+WdXwZLK9k6qbc068HnnkEWbMmAGA1+ulR48etGvXjvj4eJYuXerv+KQaxKDT8NaI/rgd1/FFL4XLdgk8zoNMnb8SUQMXshL8vYBymVGHsh2SJEm6aIJNel668RpQOjKvo8JV61Q0lmW8u2hPRYdWLfyzqqHs8ZKqs3NOvL7//ntat24NwK+//sqhQ4fYu3cvY8aM4ZlnnvF7gFLNEhloZFiv28hNDOJIBFy+GvSOj9iSVlDRoV18xxsj9YwHSpIkSeWpSWwwjZqMY0tbDQJotV3lr/2fUmhzV3RoVV6ZqobIxEuq3s458crJySE2NhaAefPmcf3119OoUSNuv/12duzY4fcApZpneMd4ih0j+OUSDW0PCtwc5vNVByo6rIuutKqhUra2hiJbIkmSpIvu/t4NUe1dWNZSoc9WgTFwBd9vPlLRYVV5Zaoa8s9Rh7K9k6qXc068YmJi2L17N16vl/nz59OvXz8AbDabLK4h+YVeq6F/54HkJeixmaDVLkg5/CV2V82qInVieKXvv7KcvCRJUkWKDjYRHT+aDa0hxAYBx5ws2rqposOq8k6eSvDPqobyPqNU3Zxz4nXbbbdx/fXX06JFCxRFoW/fvgCsW7eOJk2a+D1AqWa6rn08bls71jVWuGSvwGJZz7L92RUd1kUljg8yFP9oeGRDJEmSVDGu61QfvSGG5GjouF+A9QeOySIbF+zEUEPfOl7yRqNUfZ1z4vX888/z8ccfc9ddd7Fq1SqMRiMAWq2WJ5980u8BSjVTVJARQ9h1bGqkUCsfvLZ8FuyoWUM6Tl5A+WSyIZIkSaoYXepFUGy/lLVNNLQ/INCY97Jwd2ZFh1WliX+s3CWXT5GqM935POjaa689Zd/IkSMvOBhJOlm/Vk1Z4DJiN9ipn6KwS7sAIdrXnDlOx4trCDnHS5IkqVIw6DTUTbyaA/yAeTlo85ys+SuNkV0TKzq0KuufVYvL9njJ9k6qXs4r8bJarSxbtozU1FRcLleZ3z300EN+CUySLmsUxR+rEtkTv5cWKYLDDddxMLuEBtFBFR3aRSGEr5y8gDLjDWXeJUmSVHG6N4pmTk4gReZiGqXCFuM8vOqlaDXyzfl8nFzV0Ne+yfZOqr7OOfHasmULgwcPxmazYbVaCQ8PJycnB4vFQnR0tEy8JL9JjLBgUy9lR+I+blym8oshlVUHcmtO4nXivt8/53hVRDCSJEkSAF0bRPD9ovrsqruNFimC/c03s/tYES3rhFR0aFXSP4canky2d1J1c85zvMaMGcOVV15Jfn4+ZrOZtWvXkpKSQvv27XnjjTfKI0aphlIUhcS6PTlSW2DwgKbAxabDNafAxok5XqdM85K3ACVJkipMrRAzLm0Pdico1E8Hjz6TLWn5FR1W1SUobehOLSYl2zupejnnxGvr1q2MHTsWjUaDVqvF6XQSHx/PpEmTePrpp8sjRqkG65AUgRpkwqmD2pkK6emrKzqki0aIv6saKrLKkyRJUqWRULsTGTECnQqaIhdbU2Tidb5O7vFS/pl5SVI1c86Jl16vR6PxPSw6OprU1FQAQkJCSEtL8290Uo3Xuk4oWmctDsdCg2MCrWcDBTbXmR9YDQghTl/VULZLkiRJFap1QhhqsBG3FmplKhw5traiQ6qyyrR1cvkUqZo758Srbdu2bNiwAYAePXowfvx4vvjiCx555BFatGjh9wClmq1xbBBWVyMO1FJokC7Qm1PYfqSwosO6SNTSqoYnk1WeJEmSKlbr+FB0rhgOx/huCiqutRQ73BUdVpUkTh1QX0q2d1J1c86J18svv0ytWrUAmDhxImFhYdx7771kZ2czffp0vwd4LjZv3ky/fv0IDQ0lIiKCu+66i5KSkjLHpKamcvnll5cWA3nsscfweDwVFLF0JnqtBkNwDw7EKcQUgEcUsP1IQUWHdVGcKLHr+6+s8iRJklRZNK0VjNXRgIPHbwoaTSnsOFpTbgr618lVDWWPl1TdnXPi1aFDB3r16gX4hhrOnz+foqIiNm3aROvWrf0e4Nk6duwYffv2pUGDBqxbt4758+eza9cuRo0aVXqM1+vl8ssvx+VysXr1aj799FNmzZrF+PHjKyxu6cyaJDSkJNL3vSlXZXvqsYoN6CIpHX6hUKbChmyHJEmSKpZJr0Ub1IMDtRTi8sBNbg0ajeFfQsiqhlLNcc6JV2U1d+5c9Ho97733Ho0bN6Zjx458+OGH/PDDDxw4cACAP/74g927d/P555/Tpk0bBg0axIsvvsh77713ynpkUuXROj4EjAEUmaFuBuRnLq/okC4KIdR/5lyAvAMoSZJUGTSMb0JhlO8d2pCvsiM1p4IjqsL+ZbShbO+k6uacE6/MzExGjBhBXFwcOp0OrVZbZqsoTqcTg8FQWvgDwGw2A7By5UoA1qxZQ8uWLYmJiSk9ZsCAARQVFbFr165/PW9RUVGZTbq4WtYORXHGcThGoV6GQK/sIKvIUdFhlTtxfPXkU6saypZIkmo62TZVvFZ1QhAWC3YD1MmCrMyVFR1SlVSmqqEsJy9Vc+eceI0aNYrNmzfz3HPP8f333zNnzpwyW0Xp3bs3GRkZvP7667hcLvLz83nyyScBSE9PByAjI6NM0gWU/pyRkXHa877yyiuEhISUbvHx8eX4LKTTqRcZgNXdhEO1oH66QGs6UiPG0p/o8TqFbIckqcaTbVPFa1E7BK3TV2CjfrpA69lcY6ru+tO/VfAF2dxJ1c85J14rV67kiy++4N5772Xo0KEMGTKkzOZvTz75JIqi/Oe2d+9emjdvzqeffsrkyZOxWCzExsaSlJRETExMmV6wc/XUU09RWFhYusmS+RefRqNgDuvB4RiF8BLweItqROLFyROO5TpekiSdRLZNFa9RjK/q7uFYhaQMgd6cxs6jsufxXP1XVUPZ4EnVje5cHxAfH19abe1iGDt2bJkCGadTr149AG666SZuuukmMjMzCQgIQFEU3nzzzdLfx8bGsn79+jKPzczMLP3d6RiNRoxG4wU+C+lCNY2vyx6r73tTrmBX2jGgUYXGVN6EenwB5X/sl0MvJEmSbVPF02s16IO6cyj2Dy7fIPCIfHYcLeTShpEVHVqV8p/reMnMS6pmzjnxevvtt3nyySeZNm0aiYmJ5RBSWVFRUURFRZ3TY04MH/zkk08wmUz069cPgC5dujBx4kSysrKIjo4GYOHChQQHB9OsWTP/Bi75VcvaIew9FECxqZi6mbAzaAXQs6LDKlcnSuwKWdVQkiSpUmoY35DDtpMKbKRlA/UrNqgq5uQ5XrKYlFTdnVXiFRYWVuYuu9VqpX79+lgsFvR6fZlj8/Ly/BvhOXj33Xfp2rUrgYGBLFy4kMcee4xXX32V0NBQAPr370+zZs0YMWIEkyZNIiMjg2effZb7779f3jms5FrUDuF7ZxyHYvdTL0Owv+EOMoscxASbKjq0clN2jpdsfSRJkiqblrVDOJRmwW6wEp8Je4NWApdUdFhVz/GMS47okKq7s0q83n777XIOwz/Wr1/PhAkTKCkpoUmTJkybNo0RI0aU/l6r1TJ37lzuvfdeunTpQkBAACNHjuSFF16owKils/F3gY39XLbzeIGNI4XENKvGiddJVQ1PJtslSZKkyqFl7VDmOmM4HHOIehmCA4lbyLe6CAswVHRoVYpcx0uqKc4q8Ro5cmR5x+EXs2fPPuMxdevWZd68eRchGsmf/i6w8StXrxF4PUVsTs2nb7OYMz+4CjsxyvDkxkcmXpIkSZVDw5hArK7GHKx1mC57BUssyWxOzadP0+rdNvlT2aqGZRs42d5J1c1Zl/tTVZXXXnuNbt260bFjR5588knsdnt5xiZJZbROSqIkxvcuHJSpsv3QngqOqHwJofoaIznZWJIkqVLSazWYw/qzv45CZBF43QWsO1xxUy6qojJVDf9RV162d1J1c9aJ18SJE3n66acJDAykdu3aTJkyhfvvv788Y5OkMjolhaNXIjgWDk3TwFm4EIfbW9FhlZ/j1UN9/z1ptpdshyRJkiqN9vWTsEX7Pk6FpsOOQ9srOKKq5eTiGrLHS6ruzjrxmj17Nu+//z4LFizgp59+4tdff+WLL75APV7yWpLKW4e6YdjtjdidoNAsVWA272Zjcn5Fh1VuTvR4/XOOlyRJklR5dK4XgU5EkRoFLVIFavF8uZDyOfjvcvKSVL2cdeKVmprK4MGDS3/u27cviqJw7NixcglMkv4pyKRHEziQnXUhPgfc3mwW782s6LDKje8u4PHWSJzc4yWbIkmSpMqiXUIYJfbWbK2n0PagQBewi6X7sis6rCqjzFBDWU9equbOOvHyeDyYTGUryOn1etxut9+DkqR/07VpK4ridHg0UDsFtu1dfFEX9L6YhPiXqoYVE44kSZJ0GmaDlvBaV7OzPoRaQSkuYeHOtIoOq+oQyB4vqcY46wWUhRCMGjWqzHpXDoeDe+65h4CAgNJ9c+bM8W+EknSSvs2i2bg1gd0Jh+i4X5BZdz5b00bQNiGsokPzO4F6/CvIOV6SJEmVV5/micwttlBkttJyP2zQfE6hrT0hFv2ZH1zDndzj9c/mTbZ3UnVz1j1eI0eOJDo6mpCQkNLtlltuIS4ursw+SSpPzWoFU8IVrGiu0CJZ4CKZb9anVHRY5UMcn3AsqxpKkiRVagOax+KytmdFc4XLdgqMAWv4edvRig6rSji5uIb4Z3EN2d5J1cxZ93jNnDmzPOOQpLOiKAp92vVh9Y4PcBpctNgNW52zyC5+jagg45lPUIWUFtf4x355B1CSJKlyCQ8wEFXnVjaxgss3QtBROz8453Njp7vQa8/6HneNdGJYPYDyj0ZPtndSdSPfDaQqZ1j7OnhtHfizlcLATQJDwJ+8u3h/RYfld6JMXY1/K7YrSZIkVQY3d2mM3liHbYkKQ9YIgg2f8tmaajoiw4/+s5z8xQ5GksqZTLykKqdWiJmEeveyoZ2CwQ2ttnjYt3cSm1OrV2l5gVra6CjyDqAkSVKl1rV+BG7DPfzaDRKzIPJwEb8s+5DUXFtFh1bpKacW8PXtl+2dVM3IxEuqkh7s2wzV2Z0fumm4cp1AEZuY8PUPZBU7Kjo0vzmxtsk/e7zkPUBJkqTKR1EUHh7cA8XUiMWtFW75U8VomstDs36h0CYrQP+bkysT/3NOl1w+RapuZOIlVUkJERZ6dRnHoeZmUqPh7p8FZv1b3PXRLxzJrx53F8tWNfybbIckSZIqp0vqRZBQbzwru2opMcPtP6qYdBMZOe1n0vKqR9vkbwLxrz1eklTdyMRLqrLu7F4Pr/YJPr1Kg94Dt/2oYuR/3P3Be/yw6UjVX99LiJOGGMo5XpIkSVXBU5e3xO15mPeHKYSVwK1zPBjEBO57fxIfrziE0+Ot6BArFSH+Wcvwb/JGo1TdyMRLqrL0Wg1TR16FW72XqdcpBDrgoS9Valln8fPS0Vwz5Rd+2nIUu6tqNnInGqNTx7zLlkiSJKmyCjLpmXL7TXjECN68QSHQDmM/U6mb/y3LNt3K0Nc/4u1F+zmcY63oUCsFwUlVDWU5eamaO+ty8pJUGUUFGZky+nbGzNTz7o1TuWqhYOwPKpsapPH7JU8xZ1kMH/3Wm6SkwXRvFEXX+pHUCTNXieRFCJW/2yPZ4yVJklRV1Amz8NYdDzP20wjev2kqvZer3P+b4FBsLr92nsqG3dNZvaE1duMVtG/QhM5J4TSPCyE+vGq0T/5UZh0vWVxDquZk4iVVefHhFmbcdzvPfNeIBX1fYEVmJteuEDz7ORyOyWBl869JNn7F3JwQfljUmBLRlvCoDjSvHU69qADiwy0khFuoFWJGq6k87/KljZFsiCRJkqqchAgLM+67jVfmtmFF1/+xslUyl68VPPyzoMRkZ13jdWxPWs9+jYZDhyJx2uMp9jYnILwz8ZGhxIdZiA+3EB9mplaImYhAAwHGavixTfxd1VD5xwwB2dxJ1U2V+T944sSJ/Pbbb2zduhWDwUBBQcEpx2zYsIEnn3ySTZs2oSgKnTp1YtKkSbRu3br0mO3bt3P//fezYcMGoqKiePDBB3n88ccv4jORykOIWc+7t/Zi4e5mfPz718y+/isMWcV02yUYvkzF8CdkBxewO2E9h2M2kO6exnabjl0HAlHcobhcUZR44sHUAJMlkcjgQCIDjUQFGYkMNBIeYCDYpCfYrCPErCfYpCfIpENXjgtjllY1hDK3AWXiJUmSVDUEm/S8cm0nNiR/xgfzl/Jb/w/50X2Elvvgkr2CPtsEKirHIjJIjskkOWYTx5yzyS6G9Aw9W0Qgwh2K2xOKwxOGS8SgGOtgsCQSERRQ2hYF/eNroElHsElHgFGHSafFbNBi0msx67XotUql6lUT/7FiciUKU5L8osokXi6Xi+uuu44uXbowY8aMU35fUlLCwIEDueqqq3j//ffxeDxMmDCBAQMGkJaWhl6vp6ioiP79+9O3b18+/PBDduzYwe23305oaCh33XVXBTwryd/6NYuhd5OHWLDreuasWcPi0C/5o/dfmLI9NE8RNE4TdNkjMHgB3BRa8skMzScrNJnM0A3kBCsUBYA9X5Bi0nDIqEODEa3XBF4TqmrG4w3A7Q3C6Q3EqwlHq49EMUSg1YdjMZqxGLSYDToCDL7GzmLQYjHojn8t+7sTDaFJr8Wk15R+b9Rp/q5q+M8eL3kPUJIkqUrpmBhOx3uuYceRPvyw8TA7+JK9rdfi9eQSfgzqpwsSswTtDwjMLt9jVJzkBTnJDsmlIFChyAIFAQqFAVBoAbdJkKuHbIMGr06L0OrQCQOKVw+qCeE1oQojXtW3eVQTHmHGI8womgCEJgiNPgiNNhiNPgSjwYRJr8Wg1WDUaTDoNBi0x78e3/Qnfnfy/n8cY/zHz6c7Vq/VoNP4EsAyVQ3/8brJ9k6qbqpM4vW///0PgFmzZp3293v37iUvL48XXniB+Ph4ACZMmECrVq1ISUmhQYMGfPHFF7hcLj755BMMBgPNmzdn69atvPnmmzLxqka0GoXBLWsxuOU1ZBUNZuGeTNbtO8imoF/Y3mYPbkM22hIHQXkKMQUQXSCIKRA0S4XQEnFSxRkv4KXE5KTEVITVBFaTgs0IVhPYjFBiUrCZwGYApwE8OnDrBC4dFOgUVK2CqtMitBo06NCoOhShR1H1CNWAUPUIYcCrGlGFEY9qwCNMuFUTkeZkWp2uMKNshyRJkqqklnVCaFmnDR5vK3YcLWT1wVy2Bm1hfcwKNmsO4jVmojjtaK2C0EKF6AJBVCGEWKFWniDUKgi2gaZM2+Brq1TFhc1ow2akdHPoFVx6cOrxfdWd+F7xfW/4e5/QgtAK3FpwawANCK2C0CigUVA1CkKroGo0aIUWRWjRCA2K74F/b6oOIXQIoUct/arHe/xnrzDgFXq8wgAYsQdkEnb8maj/qEYse7yk6qbKJF5n0rhxYyIiIpgxYwZPP/00Xq+XGTNm0LRpUxITEwFYs2YNl112GQaDofRxAwYM4LXXXiM/P5+wsLBTzut0OnE6naU/FxYWAlBUVFS+T0jyCxNwZdMwrmzaAVVtz9ECO3vTi9iTXkxyZjJ7C3ax03UIrTYDRV+AR2ND43ajcagoToVAG4TYIMAuCHCB2QEWG0TmCSxOsDghwAE69cyxuI43bo7jDV1pY6hVcB//2XXiqxZcOoW4NJUSL7gcXlSnbw0YW0kxRUWm8n3hJKmSOvHeW+WXi7hAsm2q+uqHaqnfPpoR7QcgRH+yipzsySgiJdfKkVwrOfmH2VW0C7c7Fa2mEEVXgtBa8SgOtG4PisuL4gbFrWBygdkJAU4wOwUWF5icYPSA3gYBHkG4GwxuMHjBeOJ79/mVt/ZowKMFt9Z3w9FzPFkr/V4Lbp1Setwp20mP92oVQq2CEq/A5hCo3r/XO7Nbi+XftFTpnVO7JKqYmTNnipCQkNP+bseOHaJ+/fpCo9EIjUYjGjduLJKTk0t/369fP3HXXXeVecyuXbsEIHbv3n3ac06YMEHg6/2Wm9zkJje5VZItLS3Nb+1KVSTbJrnJTW5yq1zb2bRLFdrj9eSTT/Laa6/95zF79uyhSZMmZzyX3W5n9OjRdOvWja+++gqv18sbb7zB5ZdfzoYNGzCbzecV41NPPcWjjz5a+rOqquTl5REREXFek1OLioqIj48nLS2N4ODg84qpIsi4L56qGDPIuC+2mhq3EILi4mLi4uLKIbqqQ7ZNPlUx7qoYM8i4LzYZ98VzMdulCk28xo4dy6hRo/7zmHr16p3Vub788kuSk5NZs2YNGo2mdF9YWBg///wzN9xwA7GxsWRmZpZ53ImfY2NjT3teo9GI0Wgssy80NPSsYvovwcHBVeYP8mQy7ounKsYMMu6LrSbGHRIS4udoqh7ZNpVVFeOuijGDjPtik3FfPBejXarQxCsqKoqoqCi/nMtms6HRaMrc6Tvxs6r6JuB06dKFZ555BrfbjV6vB2DhwoU0btz4tPO7JEmSJEmSJEmS/KH8FiHys9TUVLZu3Upqaiper5etW7eydetWSkpKAOjXrx/5+fncf//97Nmzh127dnHbbbeh0+no1asXADfddBMGg4HRo0eza9cuvvnmG6ZMmVJmuIYkSZIkSZIkSZK/VZmqhuPHj+fTTz8t/blt27YALFmyhJ49e9KkSRN+/fVX/ve//9GlSxc0Gg1t27Zl/vz51KpVC/B1A/7xxx/cf//9tG/fnsjISMaPH39RS8kbjUYmTJhwyhCRyk7GffFUxZhBxn2xybglf6qq/y5VMe6qGDPIuC82GffFczFjVoSo4TV5JUmSJEmSJEmSylmVGWooSZIkSZIkSZJUVcnES5IkSZIkSZIkqZzJxEuSJEmSJEmSJKmcycRLkiRJkiRJkiSpnMnES5IkSZIkSZIkqZzJxEuSJEmSJEmSJKmcycRLkiRJkiRJkiSpnMnES5IkSZIkSZIkqZzJxEuSJEmSJEmSJKmcycRLkiRJkiRJkiSpnMnES5IkSZIkSZIkqZzJxEuSJEmSJEmSJKmcycRLkiRJkiRJkiSpnMnES5IkSZIkSZIkqZzp/H1Cp9OJ0Wj092krDVVVOXbsGEFBQSiKUtHhSJIk1ShCCIqLi4mLi0OjkfcOT5BtkyRJUsU4l3bpghOv33//na+//poVK1aQlpaGqqoEBATQtm1b+vfvz2233UZcXNyFXqbSOHbsGPHx8RUdhiRJUo2WlpZGnTp1KjqMSkO2TZIkSRXrbNolRQghzufkP/74I0888QTFxcUMHjyYTp06ERcXh9lsJi8vj507d7JixQrWrFnDqFGjePHFF4mKijqvJ1KZFBYWEhoaSlpaGsHBwRUdjiRJUo1SVFREfHw8BQUFhISEVHQ4lYZsmyRJkirGubRL5514denShWeffZZBgwb9Z7fa0aNHeeedd4iJiWHMmDHnc6lKpaioiJCQEAoLC2XjJkmSdJHJ9+DTk6+LJElSxTiX99/zHmq4Zs2aszqudu3avPrqq+d7GUmSJEmSJEmSpCpPzkyWJEmSJEmSJEkqZ36pavjoo4+edr+iKJhMJho0aMCQIUMIDw/3x+UkSZIkSZIkSZKqFL8kXlu2bGHz5s14vV4aN24MwP79+9FqtTRp0oT333+fsWPHsnLlSpo1a+aPS0qSJEmSJEnSWfN6vbjd7ooOQ6qCDAaDX5Yw8UvidaI3a+bMmaWTygoLC7njjju49NJLufPOO7npppsYM2YMCxYs8MclJUmSJEmSJOmMhBBkZGRQUFBQ0aFIVZRGoyEpKQmDwXBB5znvqoYnq127NgsXLjylN2vXrl3079+fo0ePsnnzZvr3709OTs6FXq5CycpRkiRJFUe+B5+efF0k6d+lp6dTUFBAdHQ0FotFLjIunZMTC9Tr9XoSEhJO+fu5KFUNT1ZYWEhWVtYpiVd2djZFRUUAhIaG4nK5/HE5SZIkSZIkSTojr9dbmnRFRERUdDhSFRUVFcWxY8fweDzo9frzPo9fqhoOGTKE22+/nR9//JEjR45w5MgRfvzxR0aPHs3QoUMBWL9+PY0aNfLH5SRJkiRJkiTpjE7M6bJYLBUciVSVnRhi6PV6L+g8funxmjZtGmPGjOGGG27A4/H4TqzTMXLkSN566y0AmjRpwscff+yPy0mSJEmSJEnSWZPDC6UL4a+/H78kXoGBgXz00Ue89dZbHDp0CIB69eoRGBhYekybNm38cSlJkiRJkiRJkqQqx68LKGdkZJCenk7Dhg0JDAzED3U7JEmSJEmSJEkqJ0uXLkVRFFn18SLwS+KVm5tLnz59aNSoEYMHDyY9PR2A0aNHM3bsWH9cQpIkSZIkSZJqBEVR/nN7/vnnz+u8PXv25JFHHvFrrNLZ80viNWbMGPR6PampqWUmLw4fPpz58+f74xKSJEmSJEmSVCOkp6eXbm+//TbBwcFl9o0bN670WCFEaY0FqXLzS+L1xx9/8Nprr1GnTp0y+xs2bEhKSoo/LiFJkiRJkiRJF0RVBbklzgrdVPXMU3FiY2NLt5CQEBRFKf157969BAUF8fvvv9O+fXuMRiMrV65k1KhRpdXET3jkkUfo2bMnAKNGjWLZsmVMmTKltOcsOTm59NhNmzbRoUMHLBYLXbt2Zd++fX585SXwU3ENq9V62jKdeXl5GI1Gf1xCkihyuNFpFCwGv/zZSpIkSdIFEUKQU+Ii2KzDqNNWdDjSWci3uWj/0qIKjWHTs32JCLzwz8dPPvkkb7zxBvXq1SMsLOyMx0+ZMoX9+/fTokULXnjhBcC3PtWJ5OuZZ55h8uTJREVFcc8993D77bezatWqC45T+ptfPsF2796d2bNn8+KLLwK+camqqjJp0iR69erlj0tINZjN5eHp7zdwLOVjFMVFcNQIXh3ewy9vWpIkSZJ0Pg5kFfPMN7+jtc3BrYbTtuW9PDG4BVqNLFsuXRwvvPAC/fr1O+vjQ0JCMBgMWCwWYmNjT/n9xIkT6dGjB+BL6i6//HIcDgcmk8lvMdd0fkm8Jk2aRJ8+fdi4cSMul4vHH3+cXbt2kZeXJzNl6YIIIRj79TqK8u6j23Y3ZicsunQzD876H5/dN0w2cJIkSdJFV2hzM2bmbCy8x8CVcCwcdlk28OaC2Tw2qGlFhyfVEB06dPDr+Vq1alX6fa1atQDIysoiISHBr9epyfwyx6tFixbs37+fSy+9lCFDhmC1WrnmmmvYsmUL9evX98clpBpq3o4MrNkv0muZm+67BC1TBCN/UdF4J/HZmuSKDk+SJEmqgV6eu4NA0zTu/0GQmCkYvFHQYrOVLdtfYk96UUWHJ9UQAQEBZX7WaDSnLOXkdrvP+nx6vb70+xMLBquqegERSv/kt8kyISEhPPPMM/46nSQhhGDG4mUEFqXRbY9gylUaMsIUJs72UuuAlV8ds7mh07OY9HJcvSRJknRxHC2wc+jANNrv8xDogHGjtXTdI7hxmcqWZlt454+tvD/ysooOU/oXYRYDm57tW+ExlIeoqCh27txZZt/WrVvLJFQGgwGv11su15fO7LwTr+3bt5/1sSd3XUrS2Vr+Vw6B6iwGrxMcigFrXBgBrlhWNN/NVetUpjX4lR82j+LmznUrOlRJkiSphvhk5WG0Aavpt0Uwv71CaMmlHG62gbzNNnpugOUd3yc5pz2JkQFnPpl00Wk0SrWdI967d29ef/11Zs+eTZcuXfj888/ZuXMnbdu2LT0mMTGRdevWkZycTGBgIOHh4RUYcc1z3olXmzZtUBQFIURpdyRQ2sV58j6ZWUvn4+OlW/G4UmmdLJh6lYLNOwJLaAOWdniQHjshJM3Gj57fuLHjvWjkXC9JkiSpnBXa3Gzc9jktjqjovbC1lUJSrTs5cCiG3zp+z81LVJZ02cFHy/YzcVjbM59QkvxowIABPPfcczz++OM4HA5uv/12br31Vnbs2FF6zLhx4xg5ciTNmjXDbrdz+PDhCoy45jnvxOvkf6gtW7Ywbtw4HnvsMbp06QLAmjVrmDx5MpMmTbrwKKUaZ+fRQtSC6fTaAFkhUFwngOu7DCIu1Mz0n5PYXO8wV6wXfHnNV/y5dxh9m8VUdMiSJElSNffF+hSCghYweIPKiuYKWldrHunflM/XWdgR8BOOVS7abRdsck0n3zqFsIDyGVIm1SyjRo1i1KhRpT/37NnzlLlcJ/zvf//jf//737+eq1GjRqxZs6bMvsTExFPO16ZNm3+9hnT+zru4Rt26dUu3l19+malTp3L33XfTqlUrWrVqxd13383bb79dWmJeks7FJ8v/wqPbTZe9gt86arDZhzC0bW0ubRCJVXcbCzsqJGaBUpDLZ8vXV3S4kiRJUjXn9qr8vnYBoSl2wktgRXuIrH0/8eEWbu+WhNPajcVtFPpsFegsq/lyfWpFhyxJUiXjl6qGO3bsICkp6ZT9SUlJ7N692x+XkGqQrCIHKYdn0XqHwKWHtEZaenccjlGnRVEUbup+Cd6wUFKjoN9GUEums+tYYUWHLUmSJFVj83akE2b8hkEbBVvqKei1SdzRozkAMcEm4hNvY0tLBZMLah/w8Of6b3F5ZEU4SZL+5pfEq2nTprzyyiu4XK7SfS6Xi1deeYWmTeV6FtK5+WxtCsbAlfTbIljcWkG1deWmkwpoXNm6FgX2a/mto4Z2BwRucZBPlu+rwIglSZKk6kwIwefLNyGKc6iXCQvbKwjz7bSODy09ZvRljdB5m7K2icLgDSoW0y/M25FecUFLklTp+CXx+vDDD1mwYAF16tShb9++9O3blzp16rBgwQI+/PBDf1xCqiEcbi9LN/1M7EE3Fidsag31GtxJ+Enj5I06LX07XkFuooESM3TaCkeTp5FV7Ki4wCVJkqRqa3NqPkbnDPpuhCMR4IkM4abLupY5pmWdEETgHSxpD7EFYMwq5usVS+U8GUmSSvkl8erUqROHDh3ipZdeKp3jNXHiRA4dOkSnTp38cQmphvh6fSqhlh8YvEFlfSMFvdqY27o3OuW4my9JwFbSkz/aKfTaLsC8nhnLD1VAxJIkSVJ19/6fO3HzFx32++YdW53D6H+aok63XtYabUAs++Ng4AYVg3cWqw/mVkDEkiRVRn5JvMC3evZdd93Fm2++yZtvvsmdd955yorakvRfHG4v36/4laCjxdTOg4WdwBB6F41igk45NjLQSMMGI9nVQoMioPl2L1t2fEBOibMCIpckSZKqq61pBdiz36XneigMgKx6BgZdcjU67akfofo1i6XAfSO/ddbQPBVE8VHeX7BM9npJkgRcQOK1du3asz7WZrOxa9eu872UXxUUFNChQwfatGlDixYt+Oijjyo6JOm4r9anEmb+gqtXCTbVV9CaErm3X+d/Pf6eXg1QHZfwRzuFwRsEGtMSPlzy10WMWJIkSarupi7chlezk8t2Cn7qosFpu4qbL0k47bFajcLwHv2xx4aQHA1XrQSt632W7c++yFFLklQZnXfiNWLECAYMGMB3332H1Wo97TG7d+/m6aefpn79+mzatOm8g/SnoKAgli9fztatW1m3bh0vv/wyublyGEBFK3K4mbN8DsFHiqmTC793BV3QA3RM/PcV1etFBZJQ7142tdOgEdBqm8qOXe9yrMB+ESOXJEmSqqu1h3Jx5kyl11ooCICjjQwM6HorFsO/L4N6Xft4Chy38UN3DS1SBUphOh8sWIRXlb1eklTTnXfitXv3bi6//HKeffZZQkNDad68Of369ePKK6/k0ksvJTIyknbt2nH48GH++OMPbr31Vn/Gfd60Wi0WiwUAp9OJEEIOAagEpi7cR7DlM4YvE2xoqKAxJXJf/0vO+LiH+jZFtXXj9w6+Xi/0K3j5140XIWJJkiSpOvOqgkm/LEF49tB9p2BOVw0O2xBuuaTufz7OoNMwos9AnFFhHKgF1y8VGNSpfLsx7SJFLklSZXXeiZder+ehhx5i3759rFmzhjvvvJMWLVpQu3ZtevbsybRp0zh27BhfffUVLVu2POvzLl++nCuvvJK4uDgUReGnn3465Zj33nuPxMRETCYTnTt3Zv36c1tAt6CggNatW1OnTh0ee+wxIiMjz+nxkn8dzC5h6473aLTDTagVfrsMLOHj/rO364SECAuNmtzHjrY6rCYYsEJQmPUKqw/kXITIJUmSpOrqq/WpBPA21y0WpEVBZgMLQ3qM+s/erhOuaVubYu+9fN1HoV4GRCQX893ijymwuc74WEmSKJMDJCcnoygKW7duLbfrzZo1i9DQ0HI7/wl+Ka7RoUMHHnnkEd566y0+/PBDXnrpJYYNG0Z4+Jk/OP+T1WqldevWvPfee6f9/TfffMOjjz7KhAkT2Lx5M61bt2bAgAFkZWWVHnNi/tY/t2PHjgEQGhrKtm3bOHz4MF9++SWZmZnn98SlC6aqgue+W47GuIwha1V+66igeC/hqSv/fW7XP40b0JSSkhv5sqdCl70CbVEKb/78HXaXtxwjlyRJkqqr9EI73y6eSciRPJocha/6KLjUB7ip0+nndv2TTqth3FUDUIyNWdpS4cZlKnrLHF78ZUs5Ry5VJz179uSRRx45Zf/FShIqi/j4eNLT02nRokVFh3LB/FbV0F8GDRrESy+9xNVXX33a35+omHjbbbfRrFkzPvzwQywWC5988knpMVu3bmXnzp2nbHFxcWXOFRMTQ+vWrVmxYsW/xuN0OikqKiqzSf4zc3UyWtfLXPsHFFlge1s9rVuOIzHy7CtihgUYGN77JorqRLArAUYtUNHrP+C1eTvLMXJJkqSKI9um8iOE4Knv12IO+I5bFwlWNVVQA5N45IrLT1vJ8N9c2jCS4JjHWdpVQSOg7zKVrKMT+GNXRjlGL0kXh8t18XpvtVotsbGx6HRn7m2u7Cpd4vVfXC4XmzZtom/fvqX7NBoNffv2Zc2aNWd1jszMTIqLiwEoLCxk+fLlNG7c+F+Pf+WVVwgJCSnd4uPjL+xJSKV2Hyti7vKpRB3Op2WKYMYgDU7XnTzc79R1u87kxk4JOHWP831/hRArdFnt4cBf41m6L+vMD5YkSapiZNtUfj5dnYwofIHLFwu8GljYU0NUnefo2uDcpyU8e1UbHPZrmdlf4dLdgoDsND74dToZhY5yiFyqiUaNGsXQoUN54403qFWrFhEREdx///243e7SYxITE3n55Ze5/fbbCQoKIiEhgenTp5c5z44dO+jduzdms5mIiAjuuusuSkpKTrnOxIkTiYuLo3HjxqVDAL/99lu6d++O2WymY8eO7N+/nw0bNtChQwcCAwMZNGgQ2dl/V/bcsGED/fr1IzIykpCQEHr06MHmzZv/9Tn+c6jhqFGjUBTllG3p0qWA78bUuHHjqF27NgEBAXTu3Ln0dyfMmjWLhIQELBYLV1999UUrtFelEq+cnBy8Xi8xMWUXLYyJiSEj4+zuIKWkpNC9e3dat25N9+7defDBB/9zDtpTTz1FYWFh6ZaWJifH+kOh3c2TX3yPSVnArYtVFrZV8AY24vFhN57V+Pl/0moUXh3eC5drEJ/11tBvq8CSe4Cpc6aQmmsrh2cgSZJUcWTbVD42peTz27JJRCZn0fEvwScDNLgcI5hwVfvzOl9cqJlb+t9BSUxt1jRRGD1fxaD7mkc/m4fLo/o5eqmmWrJkCQcPHmTJkiV8+umnzJo1i1mzZpU5ZvLkyXTo0IEtW7Zw3333ce+997Jv3z7AN81nwIABhIWFsWHDBr777jsWLVrEAw88UOYcixcvZt++fSxcuJC5c+eW7p8wYQLPPvssmzdvRqfTcdNNN/H4448zZcoUVqxYwYEDBxg/fnzp8cXFxYwcOZKVK1eydu1aGjZsyODBg0s7Rs5kypQppKenl24PP/ww0dHRNGnSBIAHHniANWvW8PXXX7N9+3auu+46Bg4cyF9/+ZYcWrduHaNHj+aBBx5g69at9OrVi5deeumcX/fzUfX77M5Rp06dzmlyntFoxGg0ll9ANZBXFYz7Zg0Bhre4c44gPRzWXWKgYeLzXFIv4rzPWy8qkKt6PMCf6zexpkkW98xTmXTLj4z9rBmf3jf8vBI6SZKkyki2Tf6XXezk+a++JERdxqhFgt/bK9gj6vPgFaMIsejP+7zXtq/DHzvH80ev+0j6ysPIXwVfXPMCL/ySyEvXtPPjM5DOybQeUFIBo2ICo+HuZX49ZVhYGO+++y5arZYmTZpw+eWXs3jxYu68887SYwYPHsx9990HwBNPPMFbb73FkiVLaNy4MV9++SUOh4PZs2cTEOCb6vHuu+9y5ZVX8tprr5V2eAQEBPDxxx9jMBgAX08UwLhx4xgwYAAADz/8MDfeeCOLFy+mW7duAIwePbpMIti7d+8y8U+fPp3Q0FCWLVvGFVdcccbne6KnH2DOnDlMmzaNRYsWERsbS2pqKjNnziQ1NbV0itG4ceOYP38+M2fO5OWXX2bKlCkMHDiQxx9/HIBGjRqxevVq5s+ff/Yv+nnye4+Xw1F+3eeRkZFotdpTimFkZmYSGxtbbteV/EcIwQu/7MRR8ASDFnkJtsGsqxRU7bM8dfmFT5oc2TWRwIhXWNhLT04wjP5ZoNW8ykOfLcXtlXcXJUmSpFOVOD3cO/M3zMb3uftnwaFY2NTZTItmr9K7ScyZT/AfFEXh9es7Y3M+xPShCnG5cNlyFykpY/hw6QE/PQPpnJVkQfGxi7+VQ7LXvHlztFpt6c+1atUqU3QOoFWrVqXfK4pCbGxs6TF79uyhdevWpUkXQLdu3VBVtbRXDKBly5alSde/nftEknbyaLKYmJgy8WRmZnLnnXfSsGFDQkJCCA4OpqSkhNTU1HN63lu2bGHEiBG8++67pUnejh078Hq9NGrUiMDAwNJt2bJlHDx4sPT5du5ctohbly5dzuna58sviZeqqrz44ovUrl2bwMBADh06BMBzzz3HjBkz/HEJAAwGA+3bt2fx4sVlrr148eKL9oJJF+aDZQc5fPBR2q0vpP0BwftXanB7rmfyLZdj1GnPfIIzUBSFScM7Yvc8zowhCqFWuHauB7v1MZ74dj2qXMBSkiRJOonLo/LAZ8vR8zwjf1LRCPjiCg06wwuMG9DEL9cItRh45ZbrcaqDmDZY4bJdgiY7slm58RG+k+t7VYzAaAiKu/hbYPRZhxgcHExhYeEp+wsKCkp7fMC3xNPJFEVBVcvebD6bY87k5MTs386tKMpp9518rZEjR7J161amTJnC6tWr2bp1KxEREedUsCMjI4OrrrqKO+64g9GjR5fuLykpQavVsmnTJrZu3Vq67dmzhylTppz1+cuLX8ZevfTSS3z66adMmjSpTLdmixYtePvtt8u8IGdSUlLCgQN/3wE6fPgwW7duJTw8nISEBB599FFGjhxJhw4d6NSpE2+//TZWq5XbbrvNH09FKkfTlh1k9YZx1DtwjIGbBdMHKtgCOzPu2geICzX77TqBRh2Tbx3CmI8O8/awL3n8G0H/RU4W9n2IJ79/l1eu7YhWo/jtelLV5PaqpObZUFVBfLgFk/7CE39JkqoWh9vL/Z+vwmUbx9B5HmIL4PUbFdzee3nv5r7nVMXwTJrHhTBy8KN8s+AIn/XeyYg/VWZaDvPjn2MRTOb6DrJAykXl5+F+F0oIgc3lpcjhxu1R0WgU6tZrwPIli085dvPmzTRqdO6FyP5N06ZNmTVrFlartTS5WrVqFRqN5j8L0J2vVatW8f777zN48GAA0tLSyMk5+/VXHQ4HQ4YMoUmTJrz55ptlfte2bVu8Xi9ZWVl07979tI9v2rQp69atK7Nv7dq15/gszo9fEq/Zs2czffp0+vTpwz333FO6v3Xr1uzdu/eczrVx40Z69epV+vOjjz4K+LLjWbNmMXz4cLKzsxk/fjwZGRm0adOG+fPnn1JwQ6o8hBBMXXyAjVsfIfFgMjcuU/n2Ug3ZtRtxY78Xz6tS1JnUjwpkws338dIXBUwd+jtjflDxakpY3PteHvnibSbf2A2DrkrVlpH8JLPIwdt/7GH//i+wGPagKF7srvrEJ97KmAEtiQ+3VHSIkiRdBHaXl3s/XYrbPo7LFzhpkiaYfL2Ck+FMHjWKqCD/z6Eb0qY22cWvsFx3Pz9bU7htkcrMvrv4ZckD2JxvM6pbkt+vKVV+NpeH9PxiNCIHgQtFFXgUGHbTQD6a9gH33PcA991zF0ajkd9++42vvvqKX3/91W/Xv/nmm5kwYQIjR47k+eefJzs7mwcffJARI0aUy+frhg0b8tlnn9GhQweKiop47LHHMJvP/gb83XffTVpaGosXLy5TLTE8PJxGjRpx8803c+uttzJ58mTatm1LdnY2ixcvplWrVlx++eU89NBDdOvWjTfeeIMhQ4awYMGCizK/C/w01PDo0aM0aNDglP2qqpYpZ3k2evbsiRDilO3kSXkPPPAAKSkpOJ1O1q1bd8o4Tany8HhVJvy0g03b7qPRnsPcvFTlu24KB5vG0fOSyQxpU7vcrt2+bjgPXfM4RZbuTLlaQ4e/BIMX2MnNf5C7Zy6k0HZuf5tS1ffTlqPc9+4zHDoygrYH5nHN74e4bm4KnXct4cix23j4w+dZINfYkaRqL7vYyajp83DaH2XIb06apwjevFaDzTCQCTc/TL2owHK79h3d69G0yWR2t47h584Kty1Sqf/XAf5cexevzdsth8TXIEIIsoudpOceAfUowcVOauUIYvOgVi60C4rjm+9nsmvXVvr06Uvnzp359ttv+e677xg4cKDf4rBYLCxYsIC8vDw6duzItddeS58+fXj33Xf9do2TzZgxg/z8fNq1a8eIESN46KGHiI4++yGYy5YtIz09nWbNmlGrVq3SbfXq1QDMnDmTW2+9lbFjx9K4cWOGDh3Khg0bSEjwLYB+ySWX8NFHHzFlyhRat27NH3/8wbPPPlsuz/WfFCHEBf8f3r59e8aMGcMtt9xCUFAQ27Zto169erzwwgssXLjwPxcormqKiooICQmhsLCQ4ODgig6nUityuHn4i5W4rU/Qfm0JAzcLvuipIaVBIj06v8Md3etdlDgW7c7kg58nEmRfzMM/qhyOgW+u0OLxPMSkkTdRvxwbWKlyEELwwZL9rN1yP+Fpxxi5WKAqsLmBglsL7Q4KjG6Y0V+hILYJgy97m5s6J1R02NJpyPfg05Ovy9nbk17EE7O/xGx4h5t+VYnPgbeuVbDpr+Tpm56gfd3wco9BCMGr8/awZ88YkvYf4YblKr+3V9jUOYCAoNd486bLCDTKSrz+4HA4OHz4MElJSZhMpooOp5SqCo4W2PC4jqB3ugkvBo8OCgLAowdFhWArWJyQHwhOk56AgLpEB1We51CT/Nff0bm8//rl/+rx48czcuRIjh49iqqqzJkzh3379jF79uwydf6lmmPn0UKe/eonTPo3GDLfS8tkwfSBvuGFAy59mxGX1L1osfRtFoPZMJ43vjXzxvBfeWCO4L6vvHx49ds88fFBbh38MFe1jrto8UgXl6oKXvhlEwdT7qXzeit9tgkWt1ZY2UUDjtaowsjy7hu5bKWH+38TfNFzH/OUB7EY3mdo2/LrkZUk6eL7YdMRPl/wDgGaX7nvK4FGhdeHKzg01/K/EY/Sqk7oRYlDURSeurwZ71neY60Yy3TLAW7/Q6V2rpU5gx5i5LuP8MKN19E8LuTMJ5OqHLdXJTW3CEUcI9CqEmT3JVx2iwZVRBJgCsLuclEUnI7D7iG8BPJxU0IaOk0C4QFyKYmqyi89XgArVqzghRdeYNu2bZSUlNCuXTvGjx9P//79/XH6SkPeVfxvQghmrkpm3vLJ6DR/cscvghAbvDNUg9PYjRsHTqiwD7NbUvMZ/8UnWAwzuPUXQVwefNJfoTCuNlG1J/K/q9vJO4zVjNPj5fEvF5NV8BhXLHDT6rBg+iCFglr1iK7zP8YNaoZBq2Hir9vJSX+SBrvSuW6V4KMBGo7GXsIj10+iS/3zX1tO8j/5Hnx68nX5byVOD8/N2ULOsfEEZ6Rwx3zBsXD4dIgGt+cuXr31dhrHBlVIbF+sS+HnxRMxuVbywE8Cqwk+ulLBq+1Kv25Pc3u3JDSyINR5q2w9XnaXhyO5eWiULCIKweCG7BDw6k1YzLWJDjahURTfMMQSJyXWIxidDsKLITcYnPoAYsPjCZCfVy4qf/V4+S3xqilk4/bv0vJsjP9xHd6i54k+lMOtf6ocjYCZVyl4PDfxzI33075uWIXGeKzAzkOz52HgRS5d4aLXDsGqpgqLe2pxuu7m4SHD6dEoqkJjlPyjyOHm4Zk/Y3M9z80/eamTC1OHaigydmdQ98e587K/h7oKIXhz4X627XiI1lvSGLhJ8MYwDUWBV/P2XU/LghuViHwPPj35uvy71QdyeHXOzwQap3LZCjc9dgqWN1dY2kMPmgm8M+pyIgMrtgdh9cEcXvnmIyyGz7jlN0FiJnxzmYaUJsFoTM8yYViPcp13Vp1VpsSr0OYiuzATDYVE5YMCZIWBIJjI0FqEmE9dqDu72EmJNQWz3UWIFTLCwKuJICEqRhYJu4hk4lVBZON2Kq8qmLU6mbnLPkBv/o2r/hS0PyCY10FhcycTHuVJ3rj5ikrz4dXu8vLMnM1kH51AYGYyoxcIbEaYOUCDJySR8FrP8MxV7eQ46iosq8jBQzO/QHjfZPQclWAbTL1WoUi9mjsuv++0va6+xb13ceDwHfRbXESLFMGkmxQwPcqMe0fJu4uVhHwPPj35upyq0OZm4tztHE1+BcW5m9vmCcJLfHM5C2vHU6vORF64um2lWUoiJdfK2C8WYBYv03SrjSFrBHvj4dv+CngupUuHcdzTs36libeqqAyJlyoEmYUO7PZjaL12ogrBpYPcUBAiitoRkZj/4981vcCOw5lMcJEXkwsyIgAljqSoMNkbepFUeOIVFhZWukjameTl5Z3PJSol2biVtfKvHN6cu4gAzRSiDhVx8xIVhwE+GaDBGdqYpk1f5LGBTSvlXZmftx5l5rwPMRh/YNhCaJXs6/1aeJmC1zGQyzrdwx3dk7AY5AfuquRAVgmPf/oROuUj7v9OoAh47zqFAsconr3hNro3/PceTVUV3D17BSVFD3LLDy4CHfD+jVoiwt7i3RF9ZQNXCcj34NOTr8vf3F6Vr9an8sOSLzEHfEWXtV76bxHsj4MvByl4PFcwavAjXFkJ5/a6PCpvLNjDzp0T0Tm2ctvvgohi+KmLwr5WJqyOUdzWfxhXtoqT70dnqaITL6fby5G8IjSkY7KphFqhxATFQQootUmICD7jenFCCFJyi/F604jKEwgFcsMU9Lq61AkPOOvP49L5q/DE69NPPy39Pjc3l5deeokBAwbQpUsXANasWcOCBQt47rnnGDNmzPlcolKSjZvPgaxiXvttE47cyQj7Qa5bImh0DBa1UVjVRYvDPoJHrh5Fz8ZnXx60IqTl2Xjim2XoHK8SdDSbWxcLdCr82EXD4WYGrLYbub7XDVzbvk6lTB6lstYeyuWVr6diUb7joe8EJWb4+BoNhc4HeP3Wm2hZ58wT1Ysdbm55/0dM4nke+UJwLALmXGmhXaPZjB3Y/CI8C+m/yPfg05Ovi+/D6Z97s3h33nwCdR8SmlLIDcsFZhd82UNDZv1gvPrHee3GAZVmBMa/WXUgh9d++J4g84c03epiyFpBTjB801ODNTYMp+YBHhjYh+4NI+WH7jOoqMRLVU/M0cpCUYoIPz6fKz8InCYdBn08cWEWNGf57+dRVZKz8lBEBrF5YDdCUZCOIEtSuaw5J5VV4YnXyYYNG0avXr144IEHyux/9913WbRoET/99NOFXqLSqOmN276MYt5ZtIucI+8i9JvptxK67REcjoGveyt4LU2oW+9Znhjc8rRjlSsjVRV8uzGNLxfNwmL5lk4bVPptERQEwjfdFQoTgihxDOeqS6/mho4JmA1ymEdl9NOWo3y1YAJm2xoe+FklPQw+v1qL1f0E7912DXUjAs76XMk5Vu6b9j7h6kwe/1qwrKXCho61ub7fp1zRqvLdJa9Javp78L+pya+LEILFe7KYvmglJs80RPERbvhTUC8T3yiGSxU8rsEM7Xk/N3euW2V6imwuD2/9sY/tOyejKGu5crlv6Yv9cfB9TwWC4nDp7uT23j3o1zSmyjyvi+1iJ16qKsizuSgoLkSrycHgUAkrAY8GckJBEEhIUC0iAgznnDQ73F6O5KSj8xYQU+ArM+8wmokKSyDIVDU+c1VVlSrxCgwMZOvWracsonzgwAHatGlDSUnJhV6i0qipjduW1HymL91NQfq7qLrtdN0o6L1NUGSBry9TKEgIwa7ex2NDB9O5XtWsApdd7OS1eTs5evhNhHYLA1bCJft8la9+vkQhL9GM1Xo1PTsO58ZO8UQHyzlglYEQgncW7WfD9jFEp6Rw20KVbUkKcwfo8Cgv8P5tg87rbuDKv3J489vnqZP7J/fOE3zST8OROp15+ua3zqrnTCofNfU9+Exq4uvi8arM35XBrCWrsHinI6xHuXwVtE72JSff9VLA3IDg6HE8e1VH4kLNFR3yedl1rJCJP69GZ38bpSiN65f4ksqdCQq/dVHwhkZg5zaGdevDkDZxcnj8P1yMxEsVghKHhwKbC5crH62mGK3LS2gJ6L1QaAFrgIIQ0dQOD8N8Af9GhXY3uQVpmJw2wkogMxQ82lDiI2thlPP/yk2lSrzq1q3LQw89xNixY8vsnzx5MlOnTiUlJeVCL1Fp1KTGzeVR+X1nOl+u2IDe+QkuDtB9I/Tc7itG8WtnDYea6rDZhzO89y1c3yEebTW447b7WBGvz9uAp+BNvM7DDFwLHf8SZAf7nvORhlpUa0ci40czomtjOieFy6EeFcTh9vL0d2vIynmUdutKGLRJ8FtHha0dArGEvM7bN3W7oA8hs1YdZtGah2m1/QCDNwjeuE6DNeBG3rt3nEy8K0hNeg8+FzXpdcm3uvhqQyoL1s4jxPgdSmEuV6yBFimClCj4sZsGW2wYbu2DPHpFPzollf+CyOVNCMGiPVm8//t8gnTTMGUUMmSNSr0M+KsWzO2swRpnxm7rQ8smt3BrtyTqyyqIQPklXkIIrE4v+TYXTmcBWk0xQnVjckKQHXResBmhIBBQLJhNscQEm/3yOSmzyIHNnkxQsQezEzLCQSgxJEVHoNXIaRHloVIlXrNmzeKOO+5g0KBBdO7cGYB169Yxf/58PvroI0aNGnWhl6g0akLjdii7hB82H2Hllt8INs1BKSig7yZB+798PVy/XqIhubEOp20g3drfzl096lfL9a9W/pXDBwvXobF9gNtziN7roesegUsPy1sorGsNen0drOowerTtzdA2tUmIqNzzBqqTzCIHj8z+EaG+zJDffYt0zxigIadOIvUavMr4q1pccAMnhOCpH7Zz9OjdDFxQRKNjgjduVlCMz/Dx3cOr5d99ZVcT3oPPR3V/XVRVsD45j+/WJ5N8aDamwOVEJDsZuEmQlAmHY+DHrhocsSFY3bcyos8VDGldu9oNv/N4Vb7fdISvli4gRD8bbV4eV6xVaZYGuUGwqI2GPc1B62oCATczqF07Lm8VR3iAoaJDrzD+TrxUVZBndVFgLUJLPgIXRicE2sHgAVUBqwmKLaAoBhRNNLGhQX6dpiCEIDXPiteTQkS+QCMgK1xBo9ShblTwWc8bKw9Lly6lV69e5OfnExoayqxZs3jkkUcoKCgot2uOGjWKgoKCcp3aVKkSL/AlWlOnTmXPnj0ANG3alIceeqg0EasuqmvjVuxw89v2dH7dsBVh/QJh2E2twyoDNqkk5EBqFMzroCE3UY/NNojLOt7GbV2TCLFU/zHF6w/n8eHiLTjzPsCj20PLnYI+2wShVtgdD0tbachJ0KI4m0DAjQxs15b+zWKJDZE9IuVlW1oB//vyA/Taz7jjR9+/xbtDNFhNXRnU/WlGX5rkt2u5PCq3fvwnuMYw+hsPioCPh+swmd9g+u39ZNGVi6y6vgdfqOr6uqTl2fhh8xGWbllMsOYn3CKNFruh7xZBsB021VdY1F7BGx6G1TuSEb0HcWWruDNWiavqPF6V33ak8+mfSwlUZuJxHKPLNrh0l0DnhQ2NFFY1V7DHmPBY2xASewtXtG1Ez8bRVWb+tb/4K/ESQpBvc5FbXIBWyUXrUgmyg9kFQgGr0ZdwefUaUE1odRFEBQdc0FIko0aN4tNPP+WVV17hySefLN3/008/cfXVV/NXRi6KOEZMHni0kBeqoNPVJb4CKx3+M/Gy2+0UFxcTHV1+xdZqZOJVU1Snxq3Q7mbxnkx+336YvPRvMQesQ5dj5dIdgo77fdX9NjVQWNQeRGg4Vuc19Os8hJsvSSC4Bk7i3HWskNmrDvPXX59iDlxO0BE7fbZBsxSBRwcbGyqsbqZgjzEi7I0RliF0bdaefs1iaBIbJIcj+omviMbzGB2reeAnlWIzTL9GocR5A49cczf9msX4/Zp5Vhcj3v+SAGUSj30uSI2COVcaiQx/l7du6lbt7qpXZtXpPdifqtPrkppr4/ed6fy5fRsa2/copl1EpHroud3Xs2MzwLKWCuvagoF6eE23cWP3LgxuWataDHc/F6oqWLY/my/W7KYk8xM0pi3E/6XSc4dKYhYUm2B1U4WNzUCEBOGytsQSMYxezZvQt1kMdcKq/yiNC028hBAUOTxkFxaiUbLRuL2EF/vmbtkNUGwGj0EBYULRhBNssRBi1qP3Q/I/atQovvnmG0wmE4cOHSIsLAz4O/Fyuj2kZGejJcdX6dAAhcFaTIa61Ao1/+vnDpfLhcFQPr2g/0y8LoaqlHj5ZZxMamrqf/4+ISHBH5eR/OBYgZ1l+7NZtPMgRZnfYgzYDIXFdP4LuuwRRBbDkQj45jINBxuB3tMAAkZzU/eO9G8WU+3vIv6X5nEhvHZdGwptzfl+8xF+cy3ml/5z+F5Jof5f0H2XyrgfBDajnS31trGx4TbWeLVs2FIXm9qLpLq96dYoiq71I4mR84POmVcVTPp9F3v2jiH+6DFGLVLZVVfhx0EabM6xvHXntTSKCSqXa4cHGHhn1LWM+fgYU4Z9zthvBYMXuPi9/yM8++N0Xrq6rUy+JOk8CSHYdayIpfuyWL5zMzr7jyjmvQRnuOm4z3cj0OCGnYkK71wJBfEGvLYOxNW+k1GXNqZ1fGhFP4UKo9Eo9GoSTa8m0aTldeLzdSmstf1IesM/8DiyaLnX1ws2YAvkBRaxqeFqtjRYwwKHgT9WNcCt60fzBt3oUj+CS5IiasQolrMlhKDE6SGrqARFZKH1ugkrAaPbN3crJ9Q3lNBLGCGWIELMhnIZAdG3b18OHDjAK6+8wqRJk8r8zqDTUicikhmzf2Hq65NIOZxKTHQUt9xxCw+NeZ6YYBOKopCYmMjo0aP566+/+Omnn7jmmmvo2bMnjzzyCJ9//jljx44lLS2NwYMHM3v2bL777jsmTJhAYWEhI0aM4K233kKr9Q2V/Oyzz5gyZQr79u0jICCA3r178/bbb/9rj9Y/hxomJiaetvbDiX6gtLQ0xo4dyx9//IFGo6F79+5MmTKFxMREALxeL4899hiffPIJWq2W0aNHU5X6kPySeCUmJv7n3Xyv1+uPy0jnwenxsjE5n2X7s9m8bx161wI0lv1o8u102Qed9wnCSyA/ANY1VtjYHDQB4ZQ4+tOm3jVc1zGBZnFV++6pv4VY9Iy+NInbu41mS9owftyUxg7nzxxpvBi3I4vGBxU6/qXy8C/g0XjYnXCILfUPk+adwfdZgXz7R33c+t40rdeFDolhtI0PIz783+9MSb75XE98/Sdux3N0WW+nz7a/i2joTBOZfUcPwsp5DkO9qEBeuOU+JnxWxJSrf+XRH1TsS20s63Efz/74AS9d3UYmX5J0lgptbpb/lc3SvZkcTP6DIO0KvPo0wtI9dNonaHdAYHJDShT81EXD3sZgoC425VoGtOnB0La1iQ6SN7BOFh9u4alBTXH1a8zy/aP5cXMyG9Wv2N1uDUpBMS0OQMf9gn5bBA69g21Ju9ietJt93rc5cDiEGfZGaCz9adWgHW3jQ2mTEEqtkKpZCfJCeFWVQruHvJJiNOSC6iKkGCwucOh9hSzQ+OZuRYUEEmDQlmv7rdVqefnll7npppt46KGHqFOnTpnf796xjYfvvpeHxj7CkMt7sX/FVh6eOJHQsFBuHfVI6Y3eN954g/HjxzNhwgQAVqxYgc1mY+rUqXz99dcUFxdzzTXXcPXVVxMaGsq8efM4dOgQw4YNo1u3bgwfPhwAt9vNiy++SOPGjcnKyuLRRx9l1KhRzJs376yez4YNG0rzAq/Xy7XXXotery8994k1gVesWIFOp+Oll15i4MCBbN++HYPBwOTJk5k1axaffPIJTZs2ZfLkyfz444/07t3bL693efNL4rVly5YyP7vdbrZs2cKbb77JxIkT/XEJ6Sw53F62pRWw7nAemw4mU5wzn0DzFjwii1pHBW0PClomCwIdvom4a5oobG0MhATisrYhLO5mhnVoTM/G0XLuyhkoikK7hDDaJYTh9rZg+f67+GVLKtvEzxxqtga3mkFcCnT4S3DjMhWDB4rMxeyqu42ddbdzyPMWh9IC+c5RB5u3FeExvWmdUIsWdUJoGhtMTLCxRidjbq9KZpGD37ans3D1x2gMP3PjPEH9DPhgsEJ+XH2SEiYy/qoWfhnScTZax4fy6LVjefu7Et69agkP/6Ti0Raxqvs9PDPnAyZeI3u+JOl08q0uNiTnse5wHjsP7URY52O07MFrL6RZikKbQ4Kmab4h7odiYU5XDX81EOj10Vjt3WiadD3Xd6xLqzohNfp98WwYdBr6Nouhb7MYih3tmL8zg9+3H2Sd7kd2tduE25lHvcMK7f9Suf0PgVZATlABu+puYEfiRna4YNeBQD5zJOAUbYmK7UmbxBia1QqmSWwQUUHVo20aPnc4Ofac0p+F8JWFR6iArwdFI0ARvp/U0mZGi0ajcL4vQaQ5km+u+OacHnP11VfTpk0bJkyYwIwZM8r87s0336RPnz5MeP4V8gqPUD8pkb2HDjHjvRlce8M1ZOIbdda7d+8y1cdXrFiB2+3mgw8+oH79+gBce+21fPbZZ2RmZhIYGEizZs3o1asXS5YsKU28br/99tJz1KtXj6lTp9KxY0dKSkoIDDxzJc2oqKjS7x9++GHS09PZsGEDAN988w2qqvLxxx+X/o3NnDmT0NBQli5dSv/+/Xn77bd56qmnuOaaawD48MMPWbBgwTm9nhXJL4lX69atT9nXoUMH4uLieP3110tfHMn/soodbE8rZNuRArYc/At7wQICTLtxK1mYc1Q6pwlaHRYkZoEKHKwFv3dQ2JcISnAwTms7wmoNZ2Cr+vRpGlPjJt36i16roU/TGPo0jcHtbc+Gw3n8sTuTrZ7FZMYvYq4xFW2ei0apvpLHJxq7YlMxf8Xt5UDcPg5bv2N1kYb1O0PxOmvhUBtjCe1Kg7gEGscGUS8ygMTIAKKreKMnhCC7xMmxAgdH8+0cK7BztMD3Nb8kH7ftCB7XMTRqNgH6TDTmPSQdc3PDMkGJGSbdoGDT9GZY98e4uXPdix7/ZY2isF71DLPm2nnnqjU88ItA5y1mWc+7efTrqUy6vrO8aSHVaKoqSM61sv1IIZtT89l7aDOK40+MlgN4PflEpEPzVF/bFFkMTp1gV12FT/toOFIXDNoYSpyX0ShpKINb1aZL/YiLdnOlugky6bmuQzzXdYjH6ryUFX/l8MfOdHapc0lruBqP7ijGbC9NUgUtUgQ9dvoSjgJLEX/V3sWBuN0k279gaYGWlVtD8Dpr4RKNsYR0oX5cXRrHBpEYEUDdCAuxwaYqceNJCIHbK8i25ZBtz6rocM7aa6+9Ru/evRk3blyZ/Xv27GHIkCGEWgyoojYFRUdo3rUN7372GcZiBzZS8KqC9u3bn3JOi8VSmnQBxMTEkJiYWCaBiomJISvr79dp06ZNPP/882zbto38/HxUVQV8046aNWt21s9n+vTpzJgxg9WrV5cmY9u2bePAgQMEBZWdNuBwODh48CCFhYWkp6eXKdyn0+no0KFDlRluWK61kBs3blyaxUoXrsDmYufRIrYdKWB7ajY5GSswsAWdKQ23KCAwU9A2zXfXsG4maPD1au1KUPilMxTU1qBX4yhxdiIu/kr6tahD7ybRcrVzP9NrNXRtEEnXBpGIK5uxP/M2Vh/MYc2Bo2wy/sb+5ltw649hzPGSmA4NjwkGbVQJcgCoZIXkkBKdS2rUTlKi5/BXoWBfigm9JxSPOxKHJx6dpTlhYc1IjAohLtRMTLCRmGATsSEmogKNFToXz+7ycqzQl0j5NgfHCuykFxRiLdiD6jyISZuOwZAL+kK8ihXF6UZrFYQ4FIJtvjVQgmyCiGJfompxwpJWCiu7arE67uG562+s0LV5BrWshaK8xIxfJzBl6Coe+llF77XyR9/7ueuTV3h3ZB9Zal6qEYQQHCt0sONIAduOFLI7NZXi3KUE6HYjjMfAXkJspkKzVF/bFF3oe1xqFKxrorAjScEVqUfjTMKh9KZ5g54MbFmLTonhNXpOcXkIMOoY2CKWgS1i8apt2H6kgNUHc9nw12HWWuaxq+0O3Eom5hyVpGOChsdgyFoVixNAJTM0h9Sov9um/YWwN9WAzhOC6o7A7olDZ2pKSFgr4iPCqBViIibYRHSQqbSNupAKf+dCCEGR3UNmsYO07EKC3B4yCu2oRQ5U1YqCnWB9IKrqAXy9WooABV/vllB8m48GRaPBXzllpDnyvB532WWXMWDAAJ566ql/XaYpPMCIoA4erQEUCLGCTfEghAe3okcVokyp+RND/E5QFOW0+04kV1arlQEDBjBgwAC++OILoqKiSE1NZcCAAbhcrrN+LkuWLOHBBx/kq6++olWrVqX7S0pKaN++PV988cUpjzm5p6wq88v/AUVFRWV+FkKQnp7O888/T8OGDf1xiRrF4fbyV2YJ+zKL2ZdRxIGMTIpy1qAXuzGYjuBW8tDne2maAfXTBfXTBVHH/wkyQmFPvMK8Dgp5saA1BOO2N0AJuIKOjdvQo3E0reuEyAbtIlEUhcaxQTSODeK2bkl4vF3ZdayIVQdz2JJ8jO2WJfzVaBsYj4CjhJBshYQsQd0s6LVdEF7iu4Pj0trJCrWTEZZBevhOMsLmk5sHO7Ngu0mPVljQeALwekJxeCIQ2jrojHHojXEEWMIJCzARYtETYtYTaNRh0msw6rSlX416DXqtBiF8//8KfMMuBAKvKrC7vNjdXuwuL7bj3xc53ORbXeSWOLFac3Db0/C60tFxFJM+F42uAI+uCLxONHYv+hKF+sUQWSiILILIIkFUIYSW+G4S+Ai8iq9KVLEFCi0KC9op7GgKOtGMyIjH+ODqtuU+n+tsDGxRC3iBj355gbevWcZDP6kE/mrnuyvGMnra07xz2zVyDopUrRTYXOzNKGZ/ZjF7M4pJPnYIR+E6LLr9KKZjqO4izLmC9hmC+segfoZvWLuqCA7HwPpGCnvjFWzRGnREYrc3Jzj6avo3qUfPxtEkRQZU9FOsMbQahbYJYbRNCINeDXC4e7MltYA1h3LZnnKETQFL2Nd0B6rpKBqr1dc2ZQvis6H3NkGY1dc2eTQOskIcZIRlkRm2h4ywP8kOhf0ZsNusRaM1oPUEIjyBuD1heEQUGl0UWkMkemMMFnMMIRYTIWY9Jr0Wg07j27Sa0u/B14vqFcL3VRV4VIHN5cXq9FByYnPYsFpTcdmSUT3HMGhy0ekKCAsxMrzlA7icAoNGg8kDBjd83ngiWl8+gUvnqwjoMILQaRCqBY0+grAAE8EmXaUZZfLqq6/Spk0bGjduXLqvadOmrFq1qvTniAAjO7f9RWK9JPLCtUQV+oZMqmo+KTlFJEQEnfciy3v37iU3N5dXX32V+Ph4ADZu3HhO5zhw4ADXXnstTz/99Ckj4tq1a8c333xDdHT0v1YHrFWrFuvWreOyyy4DwOPxsGnTJtq1a3cez+ji80viFRoaesofpRCC+Ph4vv76a39cotpxe1WO5ts5nGslOce3peRkU5K3DcWzC7MhDY8hB8Vhx5QPLXIgPluQmCmonec7h93gGwu/pqnCwVgojlHQacNx2hqiWHrTrF5bOidF0CkpvEYvnliZ6LQaWseHHq/C1QAhunMk387m1Hy2pBaw37KPjLC17GxxAMWYgUctRl+gEpMLsfmC2Hxof0AQXeCbC+HjpNDiJC8on7zAo+QHQV6gQpHFt6aI3QTHDIKjRgVVr0HValE0WjRCi6JqUYQORehAaDgxrr2UInz7NG5UxYOq8aKqHjSqisatonGpKA6FUAfHe6oE4cUQUQQRxb4Ey+Q+cTKBRwM5wZATopAerrAjEbJDFAoDwWsRqCYdQqdDp5pQvGZUbwBWV1MSal/Nrd0a075u2EX5dzpbA1vEYtI/z9QfXuP14b/z4BzB6G+9fHz1S9z1fhYv3DyKVnVCKzpMSTprNpeHlFwbyTnW0vbpWNYR7MVb0Kv7MJqO4tbmoStxEZKv0PL4h/GkTF+hJoC8QDhYS2FuJw3JseCM0KJXo7E5mhMYMZBL6jWgU1I4bRNCMen9t6isdP5Mei1d6kfQpX4E0AhV7cWhHCtbUvPZmlbAwYD9HAtfx44WB1BMmaieInRFXiLyFGIKfG1TixTfGpcGz4mzqjj0bvIDreQFZZIfqJAfCFaTgtUEJSZfdUCrEY7qQWgVhObEVw1Co6Aovr8PIVRfd5R6vE0SAo1HRfEING7AA3oXRNohrATCSnzrO4aWCGJMtQhKgugCMGp8Cxw79b4bfE49CB0o6PEKC1pdCMFmIyFmfaW8Qd2yZUtuvvlmpk6dWrpv7NixdOzYkRdffJHhw4ezZs0aZkz/gDfemopbF0RmWDGKALNdoKpHOJwVTXzE+Y0YSUhIwGAw8M4773DPPfewc+dOXnzxxbN+vN1u58orr6Rt27bcddddZGRklP4uNjaWm2++mddff50hQ4bwwgsvUKdOHVJSUpgzZw6PP/44derU4eGHH+bVV1+lYcOGNGnShDfffLNcF2f2N78kXkuWLCnzs0ajISoqigYNGqDTyeE2J7y+YC+7j2ZizduC6tqPSX8UxZCLR1OCrsSFsUihUQHUzhUkZAvq5Pz9obXE5BuasSNR4acuCgVRAmGxoHHFYnU1wRLWk1Z169O5Xjjt64bLuVpVhKIoxIdbiA+3MKRNbaA5qno1Rwvs7M0oZm96EXuP5ZEasoWD9r2YdUfQGnJwa4vQOJ3oiiDYCmHFEF7s++CTlCFoXywIsnNScgbHpwcDHrwKuHW+u3wnNq/2eJ7FSV8BjQoGj+9v0eD+5zlPnBdcWl8vVX4g5AYrbItSyAlWyAkGV4DAa9GgGozoPYGo7hBcnmi0+gRMAU0JD6tPnXDf/LXwACPhAQYiAg2EBxgIsxgq9do8PRtHEzriOcZ/HsXkm2dz7/eCR74UvHvNdF747AC3DHyKoW1rV3SYknRaWcUO3vxjP0eyjuAo2oLGexCTIQNhyEdVS9CVeAkqVGiUL6idCwlZglp5oBUAgpxgSI1SWNpK4VCsgjVCQasPQjjq4BStCI/pQefEODonRdCydoic/1hFaDQKDaIDaRAdyHUd4oGWqOo1pOXb2JNezL6MYval53M0ZBfJtt2YdGnoDdm4dYVonA50VhW9XSG82JcEhRX7EqKkDEGAw9cTempbcir1+Fu/5hym7xSZoSAACgIVskMUUuoqdDJDbihojAqgAaFHFWa0uiDMBt9IEItBVyXmqL3wwgt8883fxTnatWvHt99+y/jx43nxxRepVasWL7zwAvfdfQfFDjeZ+Zl4NaBVISpPkB2aSVqOC4f73CuOR0VFMWvWLJ5++mmmTp1Ku3bteOONN7jqqqvO6vGZmZns3buXvXv3EhcXV+Z3QggsFgvLly/niSee4JprrqG4uJjatWvTp0+f0h6wsWPHkp6ezsiRI9FoNNx+++1cffXVFBYWnvPzqQh+WUB5+fLldO3a9ZQky+PxsHr16tLuwOrgQhapHD71K3LVl+i0H2LzBLXyfb0YkSeN1LQb4Fg4pEUppEYppEeAO0wBnQWNKxq7KxF9UHcaxDenVZ0QWseHUj8qsFJ/MJX8Q1UFWcVOknOtpORaOZxjIzWnhKKiZJzWA6ieIxi1Oeh0+Qh9CR6NA8XrQeP2+nqmXKB3KphcvkRK7znx1XeHUqf+Pbb9hBPj3B16BZcenDrfHUKnHlwGEEaBatTgNWhRFD1a1YTiCcTtCcdLHAZjPSwhTYkNj6JWiJnaYWZqh/q2qCBjtfq7PZxj5aFPvsZieIsbfvVSPwM+6a+QVyeBBg0n8eTgFvJDpx9Up4WC/el8X5d8q4t7pt6F17uRFimCWnmitHc92P73cQUWOBbhS7LSohSyIgWeYB06JRjVGYPD04igiF40T0igVZ1Q2sSHEhsih9rWBEIIsoudpOT5ekpTcm0cLbCTV5iNo+QQHmcyOpGJUZ+HoitCaJyoGieobrQeL8rx9klRQfEqoPraJ73X9xV8FQVV5fh2/HuPHoReIPQgdApCp6DqdWiEBcUbgNcTjMsdRmhQE26+pBd1EuoREGDGqNVgMmgxaDWVZghhebK5PBzLzUFRcogsAJ0XskNB1ZoJtNSuNlUqy5u/FlD2S+Kl1WpJT08/ZfG03NxcoqOjq9U6XhfS6N/3+UZKDtzKmB8E6eGQEaaQEQbp4QqFIQJPoAZFb0brDsPhrIPQtyI8qhONakX45gnFBJEYGSCrO0mnpaqCfJuLjCIHWUVOCuwuCmxuCu3u0q9FtiJcjgJUTzHCa0X1liBUO0K1IoSLv2dbKSiK73uBFo02EI02GK0uCJ0+GJ0xFLMhkIggk69XKsBAxPHeqaggI3GhJiyGmtfbnVPi5OHZi8Aznk5r7PTfIljURmFtVxMenuH1mweREGGp6DCrNJl4nd75vi5CCIa9+jQd9v9M722C9DDIDPMNBU4PA1uwwBuoQ6MEoLgisLsS0QW0p1Z0axrXCqFJbBCNYoOICzHJD2/Sv7K5PGQVOckucVJoc1Pk8LVJhXY3RXYPRQ43DrcXl9uLx+vC67aheqwIrw0AoejRaHQoig5Fo0ej0aMzBBFoNhBg1BJo1BNo1BJi1hMVZCI62Eh0kJGoICPC4/7XD8w1hcvjJTW3EA3pBBcJApy+0SkOsxZFE0ed8CD52fIM/JV4+eWTkRDitG+4ubm5BATIybIn1IsKYkt+AK/fJdC5w3C5onGThCGwFTERDUiKCiIx0kJSZAANogNr5AdX6fxpNAoRgUYiAo00jzvz8ZL/RQYamXnXIF78pTYb2z/DwdjDjP5DkJRh57MrnmPM9HWMHPwIV7WW/0BS5aAoCobANmzs+Bu72gWgcYXhcMWAtiGm4DbER8eTFGkhMSKAelEBJEYEVMq5L1LlZjHoSIzUkVgBBVQcHveZD6rmDDotSVFhHMk3UBx8BJfdQ1iJrwpxfkgayVkRxIZFyirXF8EFfbI/UY1EURRGjRqF0Wgs/Z3X62X79u107dr1wiKsRro3jESj+aK0EUuKDCDUIoteSFJ1YtBpePGaNny1/gO+X/QGr928iNG/Cp78VDCz71y+X7yJJbsnMmFIO/n/v1QpDO3chUL7j9Q93i7VjbDIoheSVM1oNQoJ4QFkFydSoh4jw2AjqgBiciE7JJfsAivFxjhiQszVahpAZXNBiVdISAjg6/EKCgrCbDaX/s5gMHDJJZdw5513XliE1UjnehF0rhdR0WFIknQR3NgpgZa1X+S5r9ow44YpdFvl5t7fBWuapPNHrzsYOfVW7rvyVgY0j63oUKUa7oZOCRUdgiRJF4GiKEQHm7AYEsjIzyUrIoeQIt+czkKLA6tymMNZUcSGhcu1KMvJBb2qM2fOBCAxMZFx48bJYYWSJEknaVE7hM8fvJXxP7ZibacJ7KiXzOj5Kk1me5nZ/1O+mL+YeVsnMGFoB7nkgyRJknRRBJp0JEZHcSTfQlHwUexOD+HFYHEKckKyyMovotAYR6zs/fI7vwzUnjBhgky6JEmSTiPAqGPyDR0Y1mc66cHX8satGg7EKTz6o0rPFWmkZ93J7VNe5ot1KXjVC651JEmSJElnpNNqqBsRSJAlCac+mIwI8GqgVh5YbA4crsMczsykwObCD3X4pOPOu8erXbt2LF68mLCwMNq2bfuf1Yw2b958vpeRJEmqFoa2rc0l9Z7iuR96sajXi6xuls6ohYKWnwo+6/Ut89f+wS/rx/DU0H60iQ+t6HAlSZKkak5RFCKDjASZanM0P5iC0HT0Ti/hxRDgFOQG5ZBXXEi+NZZaoYFy7qcfnHfiNWTIkNJiGkOHDvVXPJIkSdVWbIiJ6bf14KetDZj5+yzeGvk1vZer3Pu7YH9cPt/0mcDrX31DfNJTPDqwOdFBNbP0sSRJknTxGPVakqJCyCkxU6RmkhFRREgxxBSAzeCmIDiNozmBWMwxRAUb0WlkZdPz5Zd1vGoSuYaMJEn+kFXk4MVft1CQ/jLYDnLTIkFCFixprbCyiwaXbSDdO97FXZfVI0BOci4l34NPT74uknR6/7X+knQqp9vLsQIreDNQPC4iikHrhYJAsJsVhBpKaGAk4YEGNDVo7T5/rePl15TV5XJx5MgRUlNTy2ySJElSWdHBJt65uQv3Dp1JSeCTfDEsiJn9NXTeJxg300uz1N/ZuOd6bn7rdT5fm4Lbq1Z0yJIkSVI1kZycjKIobN26tcx+o15LYmQQ4cGJrFq3n+BmLUlRiwgtgZhcgcGdT5HtIIcys+X8r/Pgl8Rr//79dO/eHbPZTN26dUlKSiIpKYnExESSkpL8cQlJkqRqqWuDSL556Ba6t/mKA7FX8cZtWlY0Vxi+XOXez5wkZH7G/HXXMfytj/lxyxFZgEOSJKmGGDVqFIqioCgKer2epKQkHn/8cRwOxwWfOz4+nvT0dFq0aHHK7xRFIcRiIDYsDACHIZCMSLAbIKoIIvNVdN4s8ooPcSgrnyK7WyZgZ8kv41duu+02dDodc+fOpVatWv9ZaEOSJEkqy6DTcHeP+gxtO4G3FlzL+lZvsrbtFvqthnt/F6RFFvPdZVOYs2w2sxffzsg+A7iydZws8ytJklTNDRw4kJkzZ+J2u9m0aRMjR45EURRee+21CzqvVqslNva/15E80cbERdTGoRgoCcykOMBBaDHE5IPd4KEg8Bg5RXqyi6KICg4iyKSTecB/8EuP19atW5k2bRqDBg2iTZs2tG7duswmSZIknVlMsIlXr2vP86M+JCjifZZ2bcCLIxQKAhQenaNyw0+5ROZN5odl13PdmzP4ccsRPHIIoiRJUrVlNBqJjY0lPj6eoUOH0rdvXxYuXAiAqqq88sorJCUlYTabad26Nd9//33pY/Pz87n55puJiorCbDbTsGHD0jV4TzfUcN68eTRq1Aiz2UyvXr1ITk72xaDXUjcyiI/enc0V/W6kMFhPZhhoVfhhymf0bN8bxXuMnKJDHMrO5533p9G0aVNMJhNNmjTh/fffL72Gy+XigQceoFatWphMJurWrcsrr7xS/i9kJeGXHq9mzZqRk5Pjj1NJkiTVeI1jg/j49p6sPtiCt39bwdwB7/BbUQZXrhY8OkclOTqPH7u+xZzlM5i18FaGdhvI9R3iZREOSZKkamznzp2sXr2aunXrAvDKK6/w+eef8+GHH9KwYUOWL1/OLbfcQlRUFD169OC5555j9+7d/P7770RGRnLgwAHsdvtpz52WlsY111zD/fffz1133cXGjRsZO3ZsmWMMOg0GnZbw4CSyi4rIC8uh2AwI3/pfdoObT/+YzqsvvcmElyfRtXMXDuzdyd133UVAQAAjR45k6tSp/PLLL3z77bckJCSQlpZGWlpaeb90lYZfWunXXnuNxx9/nJdffpmWLVui1+vL/F5WWJIkSTp3XetH0uXBoSze05Xpi5byy6Dp/FKUzhWrYcxPKqlRBfx8yTss3jiDX5YNplv7m7m1a11Zhl6SJOkMVLsd56FDF/26xnr10JjNZ3383LlzCQwMxOPx4HQ60Wg0vPvuuzidTl5++WUWLVpEly5dAKhXrx4rV65k2rRp9OjRg9TUVNq2bUuHDh0ASExM/NfrfPDBB9SvX5/JkycD0LhxY3bs2HHaIY0hFgPB5ggKbMF4NUGoGsgKhdASeHfS+0x8fBx9BnRA6FRad+3GXfc9yIfTpjFy5EhSU1Np2LAhl156KYqilCaRNYVfEq++ffsC0KdPnzL7hRAoioLX6/XHZSRJkmocRVHo2yyGPk2vZ/GenkxbtIy5Az7iV+tRBq+GB38V5ARZmdfxBzYF/MS6zV2p3+Aubr+sIY1igio6fEmSpErJeegQycOuvejXTfzhe8zNm5/18b169eKDDz7AarXy1ltvodPpGDZsGLt27cJms9GvX78yx7tcLtq2bQvAvffey7Bhw9i8eTP9+/dn6NChdO3a9bTX2bNnD507dy6z70RCdzqKohAWYCAyKACNRo9bF8MRQxqH0tJ46JkJaJ59HhQQgMfrJSgomPRCOzffMoLBgwbSuHFjBg4cyBVXXEH//v3P+vWo6vySeC1ZssQfp5EkSZL+xd8J2HUs3tODjxavYl7fj/jFlcJlm+GmpSquVSqL2ixns2EF//u0GUrw7dzUrQ39msWg18oFLyVJkk4w1qtH4g/fn/nAcrjuuQgICKBBgwYAfPLJJ7Ru3ZoZM2aUViP87bffqF27dtlrGI0ADBo0iJSUFObNm8fChQvp06cP999/P2+88cZ5xa7RaE6pXujxeNAoUD8mkn2HfdUWJ77xIm3aNCPQDmYXeBSwBWmwOQ4RGR/Bso3b2bhyKSuXLeH666+nb9++ZeamVWd+Sbx69Ojhj9NIkiRJZ/B3AnY1G5J78vHS7Szv8CHLuuyg9XZBvy2CwRsE6xrvYlm7sXy1OIKP5l1Nrw5XckOneDkMUZIkCdCYzefU81QZaDQann76aR599FH279+P0WgkNTX1Pz+HR0VFMXLkSEaOHEn37t157LHHTpt4NW3alF9++aXMvrVr155yroyMjNIRbUBpcQ5FUWhSL564uDhyM0uo27AripqNEE4CbRBoB3IExSYbWGxc1r8Dl/brT/8rhjL86ivJy8sjPDz8wl6gKsAvidf27dtPu19RFEwmEwkJCaXZtyRJknThFEWhU1I4nZJ6ciCrPR8t/YvNjhnsaLuSOn95GLBJpdsXkBydwx/tZrB256es2tCRqDq3c0PnRnStH4FGlqOXJEmqUq677joee+wxpk2bxrhx4xgzZgyqqnLppZdSWFjIqlWrCA4OZuTIkYwfP5727dvTvHlznE4nc+fOpWnTpqc97z333MPkyZN57LHHuOOOO9i0aROzZs0qc0zPnj3Jzs5m0qRJXHvttcyfP5/ff/+9TC2H//3vfzz00ENER4ZzWe++ZOaVsG3TcgoLs7lv1Ehmv/8pcZFRNGnZBGuAhu+/m0VUdBQlwoDJ5cGs11brcvR+SbzatGnzny+SXq9n+PDhTJs2DZNJ3m2VJEnypwbRQbx2fTuyipoxe00Kq+3fkd7wN7S5RfTaKrhjgYpD72JZy9WsV1Yz/dc6vOEZQq92/bm2Qx1qh579RG9JkiSp4uh0Oh544AEmTZrE4cOHiYqK4pVXXuHQoUOEhobSrl07nn76aQAMBgNPPfUUycnJmM1munfvztdff33a8yYkJPDDDz8wZswY3nnnHTp16sTLL7/M7bffXnpM06ZNef/993n55Zd58cUXGTZsGOPGjWP69Omlx9xxxx1YLBZef/11HnvsMQICAmjeogUj77wPuzEUYgKZNHMmKYdT0Gq0tGvRnM8/eQ+XO5mMPCOqEkaoJYhQix5dNRwirwg/LDX9888/88QTT/DYY4/RqVMnANavX8/kyZOZMGECHo+HJ598kuHDh5/3uNLKoqioiJCQEAoLC2W1RkmSKiWnx8vvOzL4ZtU6tPbZuDV/0XKXoM9WQagN9taBJa00ZNfVIewtMUWM5NrOzenXLAajTlvR4f8n+R58evJ1kaTTczgcHD58mKSkJHnzv4J5VZV8m5uCkmK05CGEiwAbBDlAEWAzQrEZvHoNQg1Ab4ggPMBIoLHiF2X+r7+jc3n/9Uvi1alTJ1588UUGDBhQZv+CBQt47rnnWL9+PT/99BNjx47l4MGDF3q5CiUbN0mSqpKdRwuZveoABw58jjlwGUFHHfTYDq0OC1x6WNNEYU1LICQUq70PTRtdy9Xt4mlfN6zCG7rTke/BpydfF0k6PZl4VT5CCKxOD7klDrzuHNDY0LkEQTYwucGrgRIzWM2gKHpUEUKAOYRQiwGzvmJuDvor8fLLUMMdO3actg5/3bp12bFjB+Abjpienu6Py0mSJElnqUXtECZd354CW0t+3HKUed7lzIv6lp+UZJrsg57bVXrtgPSwfJa0msN+wxymHo6nyHsF3Vr3ZUib2rIsvSRJkuQ3iqIQaNITaNLj8gSQb3NRrBZREFKAEC4sDl8xjhArOPVuis05WJVc7HYDqhJGsCWQULMBg67qDUX0S+LVpEkTXn31VaZPn47BYADA7Xbz6quv0qRJEwCOHj1KTEyMPy4nSZIknaNQi4HbuiUxqmsiO44O4Zv1KWx3fcvB5n+iLSikyw4YtkpFvwz2JKSwsvkHbDZOY9v2Bjj019Kv7SVc1SaOWiFyPpgkSZLkHwadhphgE9FBRqzOMPKsTqzkYbeUoHGrBNohsggEArvRSbE5g2K7QnGJGUUbTojFTIi56swH80vi9d5773HVVVdRp04dWrVqBfh6wbxeL3PnzgXg0KFD3Hffff64nCRJknSeFEWhVZ1QWtUJxe5qwfxd9/LDuh0sM33Gsu47iUj10m234M75KqqisqX+PlY3m8gyjYEla5ujDbqBPq1aMLBFLHGyKIckSZLkByf3gnm8FgrsbgqtVop0eRQFOX1DEe0QUwCqIrCabJSYbRRYNeQXW9Dpwwk2mwg26Sp1EuaXOV4AxcXFfPHFF+zfvx+Axo0bc9NNNxEUVL2GqMhx9JIkVUdpeTZ+3nqUpVuXY1Z/xKNNJv6goNtulUbHwG6ADQ0V1jVVcMQYUa1NEYHX0qdVKwa1qEV8uOWixCnfg09Pvi6SdHon5uYkJiZiNsubRVWN3eWlwO7CaitCqxQCLozHhyLqvcfng5nAZgKh1SBUC1pdGMEWEyEm//WE2e12kpOTK0dxjZpENm6SJFVnQgh2HSvi561H2bBjLsHGP3B5jtFkv8Klu1Rq54HVCJsaKGxorGCL1YO9CV7z1fRo1Z5BLWqRFBlQbvHJ9+DTk6+LJJ2e1+tl//79REdHExERUdHhSOfJV5DDS4HNhcOZj05TzP/bu/+oKOt8D+DvZ2aYAUL5GT+GGH6IgoIogs2ieb175V5zTcv27lbXCuXUuZltuHo0Tq3r7nZcO9uejnXq1K02+0O3n1dI+3GNAPGSBWGAogYSJBYgFpefwgDzfO4fbrNN4CbBzDwj79c533Pi+3yd5913mPmcL/PM9xH7CPwGgWsGAYN6aRHW73upiUEHsftBZwhBoJ8vpvv5wGcCi7Du7m60trYiMTERPj4+Tsc8tvA6deoUWlpaMDQ05NS/evXqyTqFx7G4EdFUYVcFFU3foODTc2hs/G8E+B/GyGAnZp8BrPUCy9fAoA/waaKCyllAr9kHim0mbIabYU1ZiOzZEZh3XdCk3qiZ78Fj47wQXV5bWxu6uroQHh4Of39/Te7YSldO/duuiD0DQ7APd0OnGwBUO0xDgL8N0P/tk7AB06Umeh1gN0HRT4O/rx8CjAYYDbor/j1QVRWtra3w8fGBxWIZ9e/cvqthU1MT1qxZgxMnTkBRFHy7lvs2mN1un4zTEBGRG+l1ChYlhmFRYhgGh9PwYePXeLf2S9TIm2icexTDQxcw83MF1s9UbHoLGDIMoSbhFCpmncaneh2O18WgZ3gpkmcsR3ZKFG5IDIOfUdv3CSOiq09kZCQAoKOjw8NJaLKpIhgcVjEwpEK190OnDEFEhc8wYBoG9AKoCjBkuNRUPQAYIPCF0eAHP6MORv0PL8J0Ot2Yi67xmpRPvFatWgW9Xo8XX3wR8fHxqKysxDfffIMtW7bgz3/+M5YsWTLRU2gG/6pIRFPd0IiKo59/jfeOt6KhsRDTfI9geKQd8c2XFmGzWgG7AtRfB1TN1OGLeIGPTxh6L2YgzLwGy+bE4V9mhyN82vjvqcP34LFxXoh+mN1ux/DwsKdjkItcHBpBRXMnyj9rxTdth3CNbw3UgW5YzilIPSswdwIjeqDBrOBkrIKvrgNMCMWAbT6izcuwaGYkFsYF4xqTz6jHNhqN0OnGvlTR7ZcahoWFoaSkBGlpaQgMDERlZSWSkpJQUlKCLVu2oLq6eqKn0AwWNyKivxuxq6ho7sQ7tV/h1Jl3Md1YihG0IrxFkNkomPuFwDgCfBUCVM1UcHIGoAb548tv/h178x4c96YcfA8eG+eFiOjvBobsKGu4gP853oIvvyjENL+PYR9uR2SLgoVnBLNbBHoBGiOBqlk6nEoAdP7T0GnbjqKtK8d1Lrdfami32x27F4aFhaG1tRVJSUmIjY1FfX39ZJyCiIg0yKDXYXFiGBYnhsGupqHm3H+i6FQHqvC/uBD9Ht79tyb4tw8jvVGw9ITg5grgv1YMYDA6yG07IRIR0dTiZ9TjxtRI3JgaCdtIBj5u6sQHJ9txUi3BhdgPcNDwBaa32pHeCKz+WMXtR4BH7+3F4qQ4l+aalIVXamoqamtrER8fD6vVij/96U8wGo14/vnnkZCQMBmnICIijdPrFGTEhiAjNgRYkYzmr/8DxafPo6zuJMqmFeDoDXVQevpgmwYsSPwnT8clIqIpwGTQY+msa7F01rUQScXJ1nUoOnUelbpqlIYeQPk/fwZ71wB8dBHInh3h0iyTsvD6zW9+g/7+fgDAH/7wB9x0001YsmQJQkND8dprr03GKYiIyMvEh12De5Yk4J4lCfi//uU43NCBD+q+xOkvjrm8uBEREX2foihIjQ5EanQg8K+z0Nq1GsWnz6P0ZDOa2tpwfXyIa8/vqvt4dXZ2Ijg4+KrbspPX0RMRTYxtxA6DTgf9j9hmnu/BY+O8EBFNzNCICqNh/Pf6cvt3vMYSEuLaFSMREXknk4FbyhMRkbb8mEXXeE1o4ZWbm3tF41566aWJnIaIiIiIiMirTWjh9fLLLyM2Nhbp6elw0RWLLhEXF4fp06dDp9MhODgYpaWlno5ERERERERXsQktvDZs2IBXXnkFzc3NWL9+Pe68806vucTw6NGjCAgI8HQMIiIiIiKaAiZ0MeMzzzyDtrY2bNu2DQcPHkRMTAx++ctf4tChQ171CRgREREREZErTfhbZCaTCXfccQeKiopw6tQppKSk4P7770dcXBz6+vrG/XhHjhzBqlWrYDaboSgKCgsLR4155plnEBcXB19fX1itVlRWVo7rHIqiYOnSpVi4cCH27ds37oxERERERETjMam7Gup0OiiKAhGB3W7/UY/R39+PefPmITc3F7feeuuo46+99ho2b96M5557DlarFbt378by5ctRX1+P8PBwAMD8+fMxMjIy6t++//77MJvNKC8vR3R0NNra2pCdnY25c+ciLS3tR+UlIiIiIiL6IRO+j5fNZsP+/fvx0ksvoby8HDfddBPWr1+PG2+8ETrdxD5QUxQFBQUFuOWWWxx9VqsVCxcuxNNPPw0AUFUVMTEx+NWvfoX8/Pxxn2Pr1q1ISUnBunXrxjxus9lgs9kcP3d3d8NiseDcuXO8VwoRkZv19PQgJiYGXV1dCAwM9HQcj2FtIiLShnHVJZmADRs2SHBwsKSlpcnu3bvlwoULE3m4UQBIQUGB42ebzSZ6vd6pT0Tk7rvvltWrV1/RY/b19UlPT4+IiPT29sqCBQuksrLysuN37NghANjY2NjYNNTOnTs37ppyNWFtYmNjY9NWu5K6NKFPvHQ6HSwWC9LT06EoymXH7d+//0c9/vc/8WptbUV0dDSOHj2KrKwsx7ht27ahrKwMFRUVP/iYTU1NWLNmDQDAbrfj3nvvRV5e3mXHf/+viqqqorOzE6Ghof/w//lyvl0Ve9tfJZnbfbwxM8Dc7jZVc4sIent7YTabJ3xVhTdjbbrEG3N7Y2aAud2Nud3HnXVpQt/xuvvuu3/UG7wnJSQkoLa29orHm0wmmEwmp76goKAJ55g+fbrX/EJ+F3O7jzdmBpjb3aZi7ql8ieG3WJuceWNub8wMMLe7Mbf7uKMuTfgGyu4UFhYGvV6P8+fPO/WfP38ekZGRbs1CRERERER0pbzqOg2j0YiMjAwUFxc7+lRVRXFxsdOlh0RERERERFoyqdvJT4a+vj40NjY6fm5ubkZNTQ1CQkJgsViwefNm5OTkIDMzE9dffz12796N/v5+rF+/3oOpr5zJZMKOHTtGXSKidcztPt6YGWBud2Numkze+rx4Y25vzAwwt7sxt/u4M/OEt5OfbIcPH8ZPf/rTUf05OTmOSxuffvppPP7442hvb8f8+fPx1FNPwWq1ujkpERERERHRldHcwouIiIiIiOhq41Xf8SIiIiIiIvJGXHgRERERERG5GBdeRERERERELsaFl4scOXIEq1atgtlshqIoKCwsdDouIvjtb3+LqKgo+Pn5ITs7G2fOnPFM2L/ZtWsXFi5ciGnTpiE8PBy33HIL6uvrncYMDg5i48aNCA0NRUBAAH7+85+Puq+auz377LNIS0tz3PguKysL7733nuO4FjN/32OPPQZFUbBp0yZHn1Zz/+53v4OiKE4tOTnZcVyrub/66ivceeedCA0NhZ+fH+bOnYuqqirHcS2+JuPi4kbNtaIo2LhxIwDtzrXdbsf27dsRHx8PPz8/zJgxA48++ii++5ViLc73VMDa5D6sTe7jrXUJYG1yJ03UJiGXePfdd+WRRx6R/fv3CwApKChwOv7YY49JYGCgFBYWSm1traxevVri4+NlYGDAM4FFZPny5bJnzx6pq6uTmpoa+dnPfiYWi0X6+vocY+677z6JiYmR4uJiqaqqkp/85CeyaNEij2UWETlw4IC888470tDQIPX19fLwww+Lj4+P1NXVaTbzd1VWVkpcXJykpaVJXl6eo1+ruXfs2CEpKSnS1tbmaBcuXHAc12Luzs5OiY2NlXXr1klFRYU0NTXJoUOHpLGx0TFGi6/Jjo4Op3kuKioSAFJaWioi2pxrEZGdO3dKaGiovP3229Lc3CxvvPGGBAQEyJNPPukYo8X5ngpYm9yHtcl9vLEuibA2uZsWahMXXm7w/eKmqqpERkbK448/7ujr6uoSk8kkr7zyigcSjq2jo0MASFlZmYhcyujj4yNvvPGGY8zp06cFgHz00Ueeijmm4OBgefHFFzWfube3V2bOnClFRUWydOlSR3HTcu4dO3bIvHnzxjym1dwPPfSQ3HDDDZc97i2vyby8PJkxY4aoqqrZuRYRWblypeTm5jr13XrrrbJ27VoR8Z75vtqxNrkfa5NreGNdEmFtcjct1CZeaugBzc3NaG9vR3Z2tqMvMDAQVqsVH330kQeTOevu7gYAhISEAACOHTuG4eFhp9zJycmwWCyayW232/Hqq6+iv78fWVlZms+8ceNGrFy50ikfoP25PnPmDMxmMxISErB27Vq0tLQA0G7uAwcOIDMzE7/4xS8QHh6O9PR0vPDCC47j3vCaHBoawt69e5GbmwtFUTQ71wCwaNEiFBcXo6GhAQBQW1uL8vJyrFixAoB3zPdU5C3PC2uT63ljbfK2ugSwNrmbFmqTYVIehcalvb0dABAREeHUHxER4TjmaaqqYtOmTVi8eDFSU1MBXMptNBoRFBTkNFYLuU+cOIGsrCwMDg4iICAABQUFmDNnDmpqajSb+dVXX8Wnn36KTz75ZNQxLc+11WrFyy+/jKSkJLS1teH3v/89lixZgrq6Os3mbmpqwrPPPovNmzfj4YcfxieffIIHH3wQRqMROTk5XvGaLCwsRFdXF9atWwdA278j+fn56OnpQXJyMvR6Pex2O3bu3Im1a9cC8I73wKnIG54X1ibX88ba5I11CWBtcjct1CYuvGhMGzduRF1dHcrLyz0d5YokJSWhpqYG3d3dePPNN5GTk4OysjJPx7qsc+fOIS8vD0VFRfD19fV0nHH59i9DAJCWlgar1YrY2Fi8/vrr8PPz82Cyy1NVFZmZmfjjH/8IAEhPT0ddXR2ee+455OTkeDjdlfnLX/6CFStWwGw2ezrKD3r99dexb98+/PWvf0VKSgpqamqwadMmmM1mr5lv0ibWJtfy1trkjXUJYG1yNy3UJl5q6AGRkZEAMGqHl/PnzzuOedIDDzyAt99+G6Wlpbjuuusc/ZGRkRgaGkJXV5fTeC3kNhqNSExMREZGBnbt2oV58+bhySef1GzmY8eOoaOjAwsWLIDBYIDBYEBZWRmeeuopGAwGREREaDL3WIKCgjBr1iw0NjZqdr6joqIwZ84cp77Zs2c7LkXR+mvy7Nmz+OCDD3DPPfc4+rQ61wCwdetW5Ofn4/bbb8fcuXNx11134de//jV27doFQPvzPVVp/XlhbXK9q6U2eUNdAlib3E0LtYkLLw+Ij49HZGQkiouLHX09PT2oqKhAVlaWx3KJCB544AEUFBSgpKQE8fHxTsczMjLg4+PjlLu+vh4tLS0ezT0WVVVhs9k0m3nZsmU4ceIEampqHC0zMxNr1651/LcWc4+lr68Pn3/+OaKiojQ734sXLx61/XRDQwNiY2MBaPc1+a09e/YgPDwcK1eudPRpda4B4OLFi9DpnMuLXq+HqqoAtD/fU5VWnxfWJve5WmqTN9QlgLXJ3TRRmyZliw4apbe3V6qrq6W6uloAyBNPPCHV1dVy9uxZEbm0XWVQUJC89dZbcvz4cbn55ps9vj3ohg0bJDAwUA4fPuy0TejFixcdY+677z6xWCxSUlIiVVVVkpWVJVlZWR7LLCKSn58vZWVl0tzcLMePH5f8/HxRFEXef/99zWYey3d3jhLRbu4tW7bI4cOHpbm5WT788EPJzs6WsLAw6ejoEBFt5q6srBSDwSA7d+6UM2fOyL59+8Tf31/27t3rGKPF16SIiN1uF4vFIg899NCoY1qcaxGRnJwciY6OdmzZu3//fgkLC5Nt27Y5xmh1vq92rE3uw9rkPt5Yl0RYm9xNC7WJCy8XKS0tFQCjWk5Ojohc2rJy+/btEhERISaTSZYtWyb19fUezTxWXgCyZ88ex5iBgQG5//77JTg4WPz9/WXNmjXS1tbmudAikpubK7GxsWI0GuXaa6+VZcuWOQqbiDYzj+X7xU2ruW+77TaJiooSo9Eo0dHRcttttzndc0SruQ8ePCipqaliMpkkOTlZnn/+eafjWnxNiogcOnRIAIyZRatz3dPTI3l5eWKxWMTX11cSEhLkkUceEZvN5hij1fm+2rE2uQ9rk/t4a10SYW1yJy3UJkXkO7drJiIiIiIioknH73gRERERERG5GBdeRERERERELsaFFxERERERkYtx4UVERERERORiXHgRERERERG5GBdeRERERERELsaFFxERERERkYtx4UVERERERORiXHgRERERERG5GBdeRERERERELsaFFxERERERkYtx4UVERERERORi/w92ESn256TgqQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general_stab.size\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "H_general_fromUnNormModes = np.sum([\n",
+    "    psi_stab[:,r,np.newaxis]@lr_stab.T[:,r,np.newaxis].T/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + psi_stab[:,r,np.newaxis].conj()@lr_stab.T[:,r,np.newaxis].T.conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "H_general_fromFitModes = np.sum([\n",
+    "    psi_stab_normalized[:,r,np.newaxis]@psi_stab_normalized[drive_indices_input,r,np.newaxis].T/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + psi_stab_normalized[:,r,np.newaxis].conj()@psi_stab_normalized[drive_indices_input,r,np.newaxis].T.conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "H_general_fromFitResidues = np.sum([\n",
+    "    residues[r]/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + residues[r].conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_general_fromFitResidues.shape[1]*2,H_general_fromFitResidues.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_t,f_m,f_r,f_u in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromFitModes[:,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromFitResidues.reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromUnNormModes.reshape(omegas.size,-1).T\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_u)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas,np.angle(f_r)*180/np.pi,linewidth=1)\n",
+    "    ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_u),linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_r),linewidth=1)\n",
+    "ax[1].legend(['Truth','Unnormalized','Normalized','Residues'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c801ba95-827a-4b4d-9bdd-d1205b84c327",
+   "metadata": {},
+   "source": [
+    "The last demonstration will be to understand how residuals can be implemented.  To do this, we will truncate the frequency domain to remove the first mode.  We will then use residuals to try to fit the effect of this mode while only truly fitting the second two modes.  We will assume we have correctly identified the poles and participation factors from the first analysis, and only consider solving the shape problem here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036eaad3-1f6a-4cb8-a107-5ddc8a079dea",
+   "metadata": {},
+   "source": [
+    "We will update our function to solve for the $\\mathbf{P}$ matrix to also solve for residual terms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "fd95b767-a897-4fa4-b46f-23fb79831fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def P_modal_w_residuals(omegas, poles, participation_factors, lower_residuals = False, upper_residuals = False):\n",
+    "    '''Construct the mode shape coefficient matrix\n",
+    "\n",
+    "    Constructs the coefficients from the system poles,\n",
+    "    participation factors, and frequency lines.\n",
+    "\n",
+    "    Arguments should be passed as arrays and will be broadcast\n",
+    "    together.\n",
+    "    '''\n",
+    "    # Get the number of modes and number of inputs from the shape\n",
+    "    # of the participation factors.\n",
+    "    n_modes,n_inputs = participation_factors.shape\n",
+    "    n_freq, = omegas.shape\n",
+    "    # We want the output array to be n_input x n_freq*2 x n_modes*2\n",
+    "    # So let's adjust the terms so they have the right shapes\n",
+    "    # We want frequency lines to be the middle dimension\n",
+    "    omegas = omegas[np.newaxis,:,np.newaxis]\n",
+    "    # We want inputs to be the first dimension and modes the\n",
+    "    # last dimension\n",
+    "    participation_factors = participation_factors.T[:,np.newaxis,:]\n",
+    "    # Split up terms into real and imaginary parts\n",
+    "    pr = poles.real\n",
+    "    pi = poles.imag\n",
+    "    lr = participation_factors.real\n",
+    "    li = participation_factors.imag\n",
+    "    P_blocks = np.array([\n",
+    "        [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n",
+    "         (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n",
+    "        [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n",
+    "         (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n",
+    "    if lower_residuals:\n",
+    "        RL_block = np.concatenate([\n",
+    "            np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n",
+    "            np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n",
+    "    if upper_residuals: \n",
+    "        RU_block = np.concatenate([\n",
+    "            np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n",
+    "            np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n",
+    "    P = np.block([[P_blocks[0,0],P_blocks[0,1]],\n",
+    "                  [P_blocks[1,0],P_blocks[1,1]]])\n",
+    "    if lower_residuals:\n",
+    "        P = np.concatenate((P,RL_block),axis=-1)\n",
+    "    if upper_residuals:\n",
+    "        P = np.concatenate((P,RU_block),axis=-1)\n",
+    "    return P"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43af6e4a-d977-4586-af15-07d6fe168447",
+   "metadata": {},
+   "source": [
+    "We can then call the function with a subset of the modes that we aim to solve for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "917fedde-e85a-4b05-8c3e-f253ab9dc214",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  P: (2, 778, 12)\n",
+      "  H_LS: (2, 778, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_freq = 45\n",
+    "mode_indices = slice(1,None)\n",
+    "P = P_modal_w_residuals(omegas[omegas > min_freq],\n",
+    "                        poles[mode_indices],\n",
+    "                        lr_stab[mode_indices],\n",
+    "                        lower_residuals = True, upper_residuals = True)\n",
+    "\n",
+    "H_LS = H[...,omegas>min_freq].transpose(1,2,0)\n",
+    "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n",
+    "\n",
+    "print('Shapes:\\n  P: {:}\\n  H_LS: {:}'.format(P.shape,H_LS.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6151d42f-0920-4794-adb0-e66fd8d34f8d",
+   "metadata": {},
+   "source": [
+    "We can then solve the least squares problem over all frequency lines and inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "c6ee7e46-ed84-434a-aa91-5b438cc0e8d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n",
+    "                            H_LS.reshape(-1,H_LS.shape[-1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08ee6d5a-4f87-464b-a35b-53c94ab7282f",
+   "metadata": {},
+   "source": [
+    "Now at this point, we can reconstruct the portions of the FRFs from the modes and those from the residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "6aaeb549-5e36-4667-87e2-8f32afbfe4ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P_full = P.reshape(-1,P.shape[-1])\n",
+    "P_modes = P_full[...,:2*poles[mode_indices].size]\n",
+    "P_residuals = P_full[...,2*poles[mode_indices].size:]\n",
+    "\n",
+    "shapes_modes = shapes_LS[:2*poles[mode_indices].size]\n",
+    "shapes_residuals = shapes_LS[2*poles[mode_indices].size:]\n",
+    "\n",
+    "H_modes = (P_modes@shapes_modes).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_modes = H_modes[:H_modes.shape[0]//2] + 1j*H_modes[H_modes.shape[0]//2:]\n",
+    "\n",
+    "H_residuals = (P_residuals@shapes_residuals).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_residuals = H_residuals[:H_residuals.shape[0]//2] + 1j*H_residuals[H_residuals.shape[0]//2:]\n",
+    "\n",
+    "H_full = (P_full@shapes_LS).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_full = H_full[:H_full.shape[0]//2] + 1j*H_full[H_full.shape[0]//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bba9570d-2dcf-43bf-9c9f-b0ddb08213b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_full.shape[1]*2,H_full.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_t in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n",
+    "for ax,f_r,f_m,f_f in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_residuals.reshape(omegas[omegas>min_freq].size,-1).T,\n",
+    "                      H_modes.reshape(omegas[omegas>min_freq].size,-1).T,\n",
+    "                      H_full.reshape(omegas[omegas>min_freq].size,-1).T\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_f)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_r)*180/np.pi,linewidth=1)\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_m)*180/np.pi,linewidth=1)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_f),linewidth=2)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_r),linewidth=1)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_m),linewidth=1)\n",
+    "ax[1].legend(['Truth','Fit','Residual Contribution','Mode Contribution'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6f0d0b-7662-4165-b20b-9e2d1a111635",
+   "metadata": {},
+   "source": [
+    "We see that the fit matches the measured data very due to the inclusion of the residual terms.  If we were to just include the modal contributions, it would not fit as well, but by adding in residual contributions we can get a much better match."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "739c7f46-ba2e-4b8a-be4a-01b7fa034e55",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "In this document, we have extensively studied complex modes.  We first explored the real-modes case to understand the limitations of that approach.  We then derived the state space system of equations from the typical 2nd order differential equation to construct the eigenvalue equation to solve for the complex modes.\n",
+    "\n",
+    "We then related the modal parameters to the frequency response function, which allowed us to compute a frequency response function given the modal parameters.\n",
+    "\n",
+    "We finally spent a good deal of time discussing how to fit modal parameters to frequency response functions using the PolyMax approach, which is implemented in SDynPy as PolyPy."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 9dc22d6..dc47a19 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.11.0',
+    VERSION: '0.14.0',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/bibliography.html b/bibliography.html
index a08c561..b8f0c91 100644
--- a/bibliography.html
+++ b/bibliography.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Bibliography &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Bibliography &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
@@ -109,7 +109,7 @@ <h1>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to t
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99edfe2bf0>
+<jinja2.runtime.BlockReference object at 0x7f5a28f25c30>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/airplane_modal_test.html b/example_problems/airplane_modal_test.html
index d91f18a..dbb892a 100644
--- a/example_problems/airplane_modal_test.html
+++ b/example_problems/airplane_modal_test.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Airplane Modal Test &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Airplane Modal Test &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -102,8 +102,8 @@
 </ul>
 </li>
 <li class="toctree-l3"><a class="reference internal" href="#running-a-virtual-experiment-modal-testing">Running a Virtual Experiment: Modal Testing</a></li>
-<li class="toctree-l3"><a class="reference internal" href="#fitting-modes-using-polymax">Fitting Modes using PolyMax</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#running-polymax">Running PolyMax</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#fitting-modes-using-polypy">Fitting Modes using PolyPy</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#running-polypy">Running PolyPy</a></li>
 <li class="toctree-l4"><a class="reference internal" href="#comparing-test-and-finite-element-modes">Comparing Test and Finite Element Modes</a></li>
 </ul>
 </li>
@@ -217,9 +217,9 @@ <h1><a class="toc-backref" href="#id1">Airplane Modal Test</a><a class="headerli
 </ul>
 </li>
 <li><p><a class="reference internal" href="#running-a-virtual-experiment-modal-testing" id="id29">Running a Virtual Experiment: Modal Testing</a></p></li>
-<li><p><a class="reference internal" href="#fitting-modes-using-polymax" id="id30">Fitting Modes using PolyMax</a></p>
+<li><p><a class="reference internal" href="#fitting-modes-using-polypy" id="id30">Fitting Modes using PolyPy</a></p>
 <ul>
-<li><p><a class="reference internal" href="#running-polymax" id="id31">Running PolyMax</a></p></li>
+<li><p><a class="reference internal" href="#running-polypy" id="id31">Running PolyPy</a></p></li>
 <li><p><a class="reference internal" href="#comparing-test-and-finite-element-modes" id="id32">Comparing Test and Finite Element Modes</a></p></li>
 </ul>
 </li>
@@ -979,7 +979,7 @@ <h2><a class="toc-backref" href="#id19">Optimizing Instrumentation for Test</a><
 which is the number we just defined.  The second is the <code class="docutils literal notranslate"><span class="pre">shape_matrix</span></code>.
 Reading the documentation, this <code class="docutils literal notranslate"><span class="pre">shape_matrix</span></code> should have its first dimension
 corresponding to each sensor (here a sensor could be a channel or group of
-channels for a triaxial accelerometer) and its last dimension be correspond to
+channels for a triaxial accelerometer) and its last dimension correspond to
 each target mode.  We will therefore want to set up a matrix with shape
 (<code class="docutils literal notranslate"><span class="pre">candidate_node_ids.size</span></code> x 3 x <code class="docutils literal notranslate"><span class="pre">target_shapes.size</span></code>).  This way, the
 target shape dimension is last, and the group of channels corresponding to each
@@ -1731,23 +1731,23 @@ <h2><a class="toc-backref" href="#id29">Running a Virtual Experiment: Modal Test
 </div>
 <a class="reference internal image-reference" href="../_images/airplane_gui_plot.png"><img alt="Drive points used for a MIMO modal test." src="../_images/airplane_gui_plot.png" style="width: 600px;" /></a>
 </section>
-<section id="fitting-modes-using-polymax">
-<h2><a class="toc-backref" href="#id30">Fitting Modes using PolyMax</a><a class="headerlink" href="#fitting-modes-using-polymax" title="Permalink to this heading"></a></h2>
+<section id="fitting-modes-using-polypy">
+<h2><a class="toc-backref" href="#id30">Fitting Modes using PolyPy</a><a class="headerlink" href="#fitting-modes-using-polypy" title="Permalink to this heading"></a></h2>
 <p>Now that we have frequency response functions created, we can fit modes to them.
 SDynPy has two mode fitters implemented,
-<code class="xref py py-class docutils literal notranslate"><span class="pre">PolyMax</span></code> and
+<a class="reference internal" href="../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI" title="sdynpy.modal.sdynpy_polypy.PolyPy_GUI"><code class="xref py py-class docutils literal notranslate"><span class="pre">PolyPy</span></code></a> and
 <a class="reference internal" href="../_autosummary/sdynpy.modal.sdynpy_smac.SMAC_GUI.html#sdynpy.modal.sdynpy_smac.SMAC_GUI" title="sdynpy.modal.sdynpy_smac.SMAC_GUI"><code class="xref py py-class docutils literal notranslate"><span class="pre">SMAC</span></code></a>.  Both curve fitters can
 be used via graphical user interface or via Python commands if it is desirable
 to automate the curve fitting.  This example will use the
-<code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.PolyMax_GUI</span></code> approach.</p>
-<section id="running-polymax">
-<h3><a class="toc-backref" href="#id31">Running PolyMax</a><a class="headerlink" href="#running-polymax" title="Permalink to this heading"></a></h3>
-<p>We open the PolyMax GUI by initializing the
-<code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.PolyMax_GUI</span></code> class
+<a class="reference internal" href="../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI" title="sdynpy.modal.sdynpy_polypy.PolyPy_GUI"><code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.PolyPy_GUI</span></code></a> approach.</p>
+<section id="running-polypy">
+<h3><a class="toc-backref" href="#id31">Running PolyPy</a><a class="headerlink" href="#running-polypy" title="Permalink to this heading"></a></h3>
+<p>We open the PolyPy GUI by initializing the
+<a class="reference internal" href="../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI" title="sdynpy.modal.sdynpy_polypy.PolyPy_GUI"><code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.PolyPy_GUI</span></code></a> class
 with our frequency response function dataset <code class="docutils literal notranslate"><span class="pre">frf_sampled</span></code></p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now that we have FRFs we can go fit modes.  We will first look at using</span>
-<span class="c1"># PolyMax</span>
-<span class="n">pm</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">PolyMax_GUI</span><span class="p">(</span><span class="n">frf_sampled</span><span class="p">)</span>
+<span class="c1"># PolyPy</span>
+<span class="n">pm</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">PolyPy_GUI</span><span class="p">(</span><span class="n">frf_sampled</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>The initial screen shows mode indicator functions, as well as options for
@@ -1756,15 +1756,15 @@ <h3><a class="toc-backref" href="#id31">Running PolyMax</a><a class="headerlink"
 modes.  We can drag the frequency region on the figure to select the frequency
 range of interest, set the polynomial orders, and press the button to compute
 the stabilization curve.</p>
-<a class="reference internal image-reference" href="../_images/airplane_polymax_stabilization.png"><img alt="Setting up parameters to compute the stabilization plot in PolyMax" src="../_images/airplane_polymax_stabilization.png" style="width: 600px;" /></a>
+<a class="reference internal image-reference" href="../_images/airplane_polypy_stabilization.png"><img alt="Setting up parameters to compute the stabilization plot in PolyPy" src="../_images/airplane_polypy_stabilization.png" style="width: 600px;" /></a>
 <p>Once the stabilization plot is computed, stable poles can be selected by
 clicking on them in the stabilization plot.  Once all poles are selected,
 shapes can be computed.</p>
-<a class="reference internal image-reference" href="../_images/airplane_polymax_stabilization_selection.png"><img alt="Selecting poles on the stabilization plot in PolyMax" src="../_images/airplane_polymax_stabilization_selection.png" style="width: 600px;" /></a>
-<p>The final tab of the PolyMax implementation allows you to see how well the
+<a class="reference internal image-reference" href="../_images/airplane_polypy_stabilization_selection.png"><img alt="Selecting poles on the stabilization plot in PolyPy" src="../_images/airplane_polypy_stabilization_selection.png" style="width: 600px;" /></a>
+<p>The final tab of the PolyPy implementation allows you to see how well the
 modes fit to the measured frequency response data.  On this page, modes can be
 saved to a file.</p>
-<a class="reference internal image-reference" href="../_images/airplane_polymax_stabilization_resynthesis.png"><img alt="Visualizing the modal fits in PolyMax" src="../_images/airplane_polymax_stabilization_resynthesis.png" style="width: 600px;" /></a>
+<a class="reference internal image-reference" href="../_images/airplane_polypy_stabilization_resynthesis.png"><img alt="Visualizing the modal fits in PolyPy" src="../_images/airplane_polypy_stabilization_resynthesis.png" style="width: 600px;" /></a>
 </section>
 <section id="comparing-test-and-finite-element-modes">
 <h3><a class="toc-backref" href="#id32">Comparing Test and Finite Element Modes</a><a class="headerlink" href="#comparing-test-and-finite-element-modes" title="Permalink to this heading"></a></h3>
@@ -1774,15 +1774,15 @@ <h3><a class="toc-backref" href="#id32">Comparing Test and Finite Element Modes<
 print a shape comparison table obtained by the
 <a class="reference internal" href="../_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html#sdynpy.core.sdynpy_shape.shape_comparison_table" title="sdynpy.core.sdynpy_shape.shape_comparison_table"><code class="xref py py-func docutils literal notranslate"><span class="pre">sdpy.shape.shape_comparison_table</span></code></a>
 function.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># In the PolyMax GUI we saved the shapes to disk, so we will now load them.</span>
-<span class="n">test_shapes_polymax</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;shapes_polymax.npy&#39;</span><span class="p">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># In the PolyPy GUI we saved the shapes to disk, so we will now load them.</span>
+<span class="n">test_shapes_polypy</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;shapes_polypy.npy&#39;</span><span class="p">)</span>
 
 <span class="c1"># Let&#39;s compare the shapes to the finite element model shapes</span>
-<span class="n">mac</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shapes</span><span class="p">,</span><span class="n">test_shapes_polymax</span><span class="p">)</span>
+<span class="n">mac</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shapes</span><span class="p">,</span><span class="n">test_shapes_polypy</span><span class="p">)</span>
 <span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">matrix_plot</span><span class="p">(</span>
     <span class="n">mac</span><span class="p">,</span><span class="n">text_size</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
 <span class="n">shape_correspondences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">mac</span> <span class="o">&gt;</span> <span class="mf">0.9</span><span class="p">)</span>
-<span class="n">shape_1</span> <span class="o">=</span> <span class="n">test_shapes_polymax</span><span class="p">[</span><span class="n">shape_correspondences</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
+<span class="n">shape_1</span> <span class="o">=</span> <span class="n">test_shapes_polypy</span><span class="p">[</span><span class="n">shape_correspondences</span><span class="p">[</span><span class="mi">1</span><span class="p">]]</span>
 <span class="n">shape_2</span> <span class="o">=</span> <span class="n">test_shapes</span><span class="p">[</span><span class="n">shape_correspondences</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">shape_comparison_table</span><span class="p">(</span><span class="n">shape_1</span><span class="p">,</span> <span class="n">shape_2</span><span class="p">,</span>
                                         <span class="n">percent_error_format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">{:0.4f}</span><span class="s1">%&#39;</span><span class="p">))</span>
@@ -1871,14 +1871,14 @@ <h2><a class="toc-backref" href="#id33">SEREP Expansion</a><a class="headerlink"
 and global finite element geometry are handled automatically by SDynPy.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Perform the expansion using the finite element shapes in the bandwidth</span>
 <span class="n">expansion_basis</span> <span class="o">=</span> <span class="n">shapes_global</span><span class="p">[</span><span class="n">shapes_global</span><span class="o">.</span><span class="n">frequency</span> <span class="o">&lt;</span> <span class="n">shape_bandwidth</span><span class="p">]</span>
-<span class="n">expanded_shapes</span> <span class="o">=</span> <span class="n">test_shapes_polymax</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">geometry_global</span><span class="p">,</span>
+<span class="n">expanded_shapes</span> <span class="o">=</span> <span class="n">test_shapes_polypy</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">geometry_global</span><span class="p">,</span>
                                              <span class="n">expansion_basis</span><span class="p">)</span>
 <span class="c1"># We can then plot the expanded shapes on the original finite element geometry</span>
 <span class="n">geometry_global</span><span class="o">.</span><span class="n">plot_shape</span><span class="p">(</span><span class="n">expanded_shapes</span><span class="p">,</span><span class="n">plot_options</span><span class="p">)</span>
 
 <span class="c1"># Or overlay the geometries and shapes</span>
 <span class="n">expansion_comparison_geometry</span><span class="p">,</span><span class="n">expansion_comparison_shapes</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">overlay_shapes</span><span class="p">(</span>
-    <span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">geometry_global</span><span class="p">),(</span><span class="n">test_shapes_polymax</span><span class="p">,</span><span class="n">expanded_shapes</span><span class="p">),[</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">])</span>
+    <span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">geometry_global</span><span class="p">),(</span><span class="n">test_shapes_polypy</span><span class="p">,</span><span class="n">expanded_shapes</span><span class="p">),[</span><span class="mi">1</span><span class="p">,</span><span class="mi">7</span><span class="p">])</span>
 <span class="n">expansion_comparison_geometry</span><span class="o">.</span><span class="n">plot_shape</span><span class="p">(</span><span class="n">expansion_comparison_shapes</span><span class="p">,</span><span class="n">plot_options</span><span class="p">,</span>
                                          <span class="n">deformed_opacity</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span><span class="n">undeformed_opacity</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 </pre></div>
@@ -1899,7 +1899,7 @@ <h2><a class="toc-backref" href="#id33">SEREP Expansion</a><a class="headerlink"
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ef261660>
+<jinja2.runtime.BlockReference object at 0x7f5a297709d0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/craig_bampton_substructuring.html b/example_problems/craig_bampton_substructuring.html
index 4f6cef0..994eb27 100644
--- a/example_problems/craig_bampton_substructuring.html
+++ b/example_problems/craig_bampton_substructuring.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Substructuring using Craig-Bampton Superelements from Sierra/SD &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Substructuring using Craig-Bampton Superelements from Sierra/SD &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -514,7 +514,7 @@ <h2><a class="toc-backref" href="#id11">Summary</a><a class="headerlink" href="#
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eee32620>
+<jinja2.runtime.BlockReference object at 0x7f5a28e98220>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/mimo_random_vibration_control.html b/example_problems/mimo_random_vibration_control.html
index e378d58..9f70007 100644
--- a/example_problems/mimo_random_vibration_control.html
+++ b/example_problems/mimo_random_vibration_control.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>MIMO Random Vibration &mdash; SDynPy 0.11.0 documentation</title>
+  <title>MIMO Random Vibration &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -757,7 +757,7 @@ <h2><a class="toc-backref" href="#id33">Summary</a><a class="headerlink" href="#
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee520c10>
+<jinja2.runtime.BlockReference object at 0x7f5a29667f40>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/model_reduction/model_reduction.html b/example_problems/model_reduction/model_reduction.html
index 20fba05..ba22b37 100644
--- a/example_problems/model_reduction/model_reduction.html
+++ b/example_problems/model_reduction/model_reduction.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Model Reduction &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Model Reduction &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/plot_directive.css" />
@@ -1145,7 +1145,7 @@ <h2>Comparison of Results<a class="headerlink" href="#Comparison-of-Results" tit
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec3ffa60>
+<jinja2.runtime.BlockReference object at 0x7f5a2654b220>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/rattlesnake_demonstration.html b/example_problems/rattlesnake_demonstration.html
index a4b9fcd..c2028ec 100644
--- a/example_problems/rattlesnake_demonstration.html
+++ b/example_problems/rattlesnake_demonstration.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Rattlesnake Demonstration &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Rattlesnake Demonstration &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -501,7 +501,7 @@ <h2><a class="toc-backref" href="#id5">Creating a Test Geometry</a><a class="hea
 <section id="fitting-modes-to-the-frfs">
 <h2><a class="toc-backref" href="#id6">Fitting Modes to the FRFs</a><a class="headerlink" href="#fitting-modes-to-the-frfs" title="Permalink to this heading"></a></h2>
 <p>Now that we have data and geometry, we can perform experimental modal analysis.
-We will fit modes both with SMAC and PolyMax.  First we will do SMAC, so we can
+We will fit modes both with SMAC and PolyPy.  First we will do SMAC, so we can
 call <a class="reference internal" href="../_autosummary/sdynpy.modal.sdynpy_smac.SMAC_GUI.html#sdynpy.modal.sdynpy_smac.SMAC_GUI" title="sdynpy.modal.sdynpy_smac.SMAC_GUI"><code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.SMAC_GUI</span></code></a> in the
 IPython console to load the GUI.</p>
 <div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">In [12]: sdynpy.SMAC_GUI(frfs)</span>
@@ -530,25 +530,25 @@ <h2><a class="toc-backref" href="#id6">Fitting Modes to the FRFs</a><a class="he
 <a class="reference internal image-reference" href="../_images/sdynpy_demo_smac_mac.png"><img alt="MAC matrix between SMAC shapes" src="../_images/sdynpy_demo_smac_mac.png" style="width: 600px;" /></a>
 <p>Shapes are saved to the file <code class="code docutils literal notranslate"><span class="pre">'rattlesnake_test_shapes_smac.npy'</span></code> so they
 can be loaded into the script without re-running SMAC.</p>
-<p>Similarly, PolyMax can be run by calling the <code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.PolyMax_GUI</span></code>
+<p>Similarly, PolyPy can be run by calling the <a class="reference internal" href="../_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI" title="sdynpy.modal.sdynpy_polypy.PolyPy_GUI"><code class="xref py py-class docutils literal notranslate"><span class="pre">sdpy.PolyPy_GUI</span></code></a>
 class from the IPython console.</p>
-<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">In [13]: sdynpy.PolyMax_GUI(frfs)</span>
-<span class="go">Out[13]: &lt;sdynpy.modal.sdynpy_polymax.PolyMax_GUI at 0x14fff4c5d30&gt;</span>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">In [13]: sdynpy.PolyPy_GUI(frfs)</span>
+<span class="go">Out[13]: &lt;sdynpy.modal.sdynpy_polypy.PolyPy_GUI at 0x14fff4c5d30&gt;</span>
 </pre></div>
 </div>
-<p>The first page of the SDynPy PolyMax implementation allows users to select the
+<p>The first page of the SDynPy PolyPy implementation allows users to select the
 frequency range of the analysis.  Also selected is the number of poles to use
 in the polynomial fitter.</p>
-<a class="reference internal image-reference" href="../_images/sdynpy_demo_polymax_setup.png"><img alt="Initial setup for PolyMax frequency range" src="../_images/sdynpy_demo_polymax_setup.png" style="width: 600px;" /></a>
+<a class="reference internal image-reference" href="../_images/sdynpy_demo_polypy_setup.png"><img alt="Initial setup for PolyPy frequency range" src="../_images/sdynpy_demo_polypy_setup.png" style="width: 600px;" /></a>
 <p>Once the poles are solved, a stability plot is presented.  The user can click
 on individual icons on the stability plot to select given poles to use in the
 resynthesis.</p>
-<a class="reference internal image-reference" href="../_images/sdynpy_demo_polymax_stability_plot.png"><img alt="PolyMax stability plot" src="../_images/sdynpy_demo_polymax_stability_plot.png" style="width: 600px;" /></a>
+<a class="reference internal image-reference" href="../_images/sdynpy_demo_polypy_stability_plot.png"><img alt="PolyPy stability plot" src="../_images/sdynpy_demo_polypy_stability_plot.png" style="width: 600px;" /></a>
 <p>With the poles selected, shapes can be computed.  Resynthesized FRFs are plotted
 for the user to investigate.</p>
-<a class="reference internal image-reference" href="../_images/sdynpy_demo_polymax_resynthesize.png"><img alt="PolyMax resynthesis" src="../_images/sdynpy_demo_polymax_resynthesize.png" style="width: 600px;" /></a>
-<p>Shapes are saved to the file <code class="code docutils literal notranslate"><span class="pre">'rattlesnake_test_shapes_polymax.npy'</span></code> so they
-can be loaded into the script without re-running PolyMax.</p>
+<a class="reference internal image-reference" href="../_images/sdynpy_demo_polypy_resynthesize.png"><img alt="PolyPy resynthesis" src="../_images/sdynpy_demo_polypy_resynthesize.png" style="width: 600px;" /></a>
+<p>Shapes are saved to the file <code class="code docutils literal notranslate"><span class="pre">'rattlesnake_test_shapes_polypy.npy'</span></code> so they
+can be loaded into the script without re-running PolyPy.</p>
 </section>
 <section id="analyzing-modal-parameters">
 <h2><a class="toc-backref" href="#id7">Analyzing Modal Parameters</a><a class="headerlink" href="#analyzing-modal-parameters" title="Permalink to this heading"></a></h2>
@@ -557,7 +557,7 @@ <h2><a class="toc-backref" href="#id7">Analyzing Modal Parameters</a><a class="h
 <a class="reference internal" href="../_autosummary/sdynpy.core.sdynpy_array.SdynpyArray.html#sdynpy.core.sdynpy_array.SdynpyArray.load" title="sdynpy.core.sdynpy_array.SdynpyArray.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">sdpy.shape.load</span></code></a> method.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load the shape files from our curve fitting into the script</span>
 <span class="n">test_shapes_smac</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;rattlesnake_test_shapes_smac.npy&#39;</span><span class="p">)</span>
-<span class="n">test_shapes_polymax</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;rattlesnake_test_shapes_smac.npy&#39;</span><span class="p">)</span>
+<span class="n">test_shapes_polypy</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;rattlesnake_test_shapes_smac.npy&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>We also want to extract finite element shapes from the Exodus file using the
@@ -584,7 +584,7 @@ <h2><a class="toc-backref" href="#id7">Analyzing Modal Parameters</a><a class="h
 <span class="n">response_dofs</span> <span class="o">=</span> <span class="n">test_shapes_smac</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">coordinate</span>
 <span class="c1"># Can extract a shape matrix by indexing the shape arrays with a CoordinateArray</span>
 <span class="n">test_shape_matrix_smac</span> <span class="o">=</span> <span class="n">test_shapes_smac</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
-<span class="n">test_shape_matrix_polymax</span> <span class="o">=</span> <span class="n">test_shapes_smac</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
+<span class="n">test_shape_matrix_polypy</span> <span class="o">=</span> <span class="n">test_shapes_smac</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
 <span class="n">test_shape_matrix_fem</span> <span class="o">=</span> <span class="n">fem_shapes</span><span class="p">[</span><span class="n">response_dofs</span><span class="p">]</span><span class="o">.</span><span class="n">T</span>
 <span class="c1"># Create an actual ShapeArray object</span>
 <span class="n">fem_shapes_reduced</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape_array</span><span class="p">(</span><span class="n">response_dofs</span><span class="p">,</span>
@@ -599,11 +599,11 @@ <h2><a class="toc-backref" href="#id7">Analyzing Modal Parameters</a><a class="h
 module also contains a <a class="reference internal" href="../_autosummary/sdynpy.signal_processing.sdynpy_correlation.matrix_plot.html#sdynpy.signal_processing.sdynpy_correlation.matrix_plot" title="sdynpy.signal_processing.sdynpy_correlation.matrix_plot"><code class="xref py py-func docutils literal notranslate"><span class="pre">sdpy.correlation.matrix_plot</span></code></a>
 function that provides a nice visualization of the Modal Assurance Criterion matrix.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Look at correlation between shapes from different tests/analyses</span>
-<span class="n">mac_smac_polymax</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shape_matrix_smac</span><span class="p">,</span><span class="n">test_shape_matrix_polymax</span><span class="p">)</span>
+<span class="n">mac_smac_polypy</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shape_matrix_smac</span><span class="p">,</span><span class="n">test_shape_matrix_polypy</span><span class="p">)</span>
 <span class="n">mac_smac_fem</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shape_matrix_smac</span><span class="p">,</span><span class="n">test_shape_matrix_fem</span><span class="p">)</span>
-<span class="n">mac_polymax_fem</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shape_matrix_polymax</span><span class="p">,</span><span class="n">test_shape_matrix_fem</span><span class="p">)</span>
+<span class="n">mac_polypy_fem</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">mac</span><span class="p">(</span><span class="n">test_shape_matrix_polypy</span><span class="p">,</span><span class="n">test_shape_matrix_fem</span><span class="p">)</span>
 <span class="c1"># Plot the mac matrix between the</span>
-<span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">matrix_plot</span><span class="p">(</span><span class="n">mac_polymax_fem</span><span class="p">)</span>
+<span class="n">sdpy</span><span class="o">.</span><span class="n">correlation</span><span class="o">.</span><span class="n">matrix_plot</span><span class="p">(</span><span class="n">mac_polypy_fem</span><span class="p">)</span>
 </pre></div>
 </div>
 <a class="reference internal image-reference" href="../_images/sdynpy_demo_mac_test_fem.png"><img alt="MAC between test and FEM" src="../_images/sdynpy_demo_mac_test_fem.png" style="width: 600px;" /></a>
@@ -618,11 +618,11 @@ <h2><a class="toc-backref" href="#id7">Analyzing Modal Parameters</a><a class="h
 of the first mode to a <code class="code docutils literal notranslate"><span class="pre">.gif</span></code> file.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compare shapes by overlaying them</span>
 <span class="c1"># Find the best match in the FEM to each of the test shapes</span>
-<span class="n">fem_matches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">mac_polymax_fem</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">fem_matches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">mac_polypy_fem</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 <span class="c1"># Overlay the shapes and geometry</span>
 <span class="n">combined_geometry</span><span class="p">,</span><span class="n">combined_shapes</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">overlay_shapes</span><span class="p">(</span>
     <span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">test_geometry</span><span class="p">),</span>
-    <span class="p">(</span><span class="n">test_shapes_polymax</span><span class="p">,</span><span class="n">fem_shapes_reduced</span><span class="p">[</span><span class="n">fem_matches</span><span class="p">]),</span>
+    <span class="p">(</span><span class="n">test_shapes_polypy</span><span class="p">,</span><span class="n">fem_shapes_reduced</span><span class="p">[</span><span class="n">fem_matches</span><span class="p">]),</span>
     <span class="n">color_override</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">])</span>
 <span class="c1"># Plot the shapes to set up the animation</span>
 <span class="n">geometry_kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;view_up&#39;</span><span class="p">:[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="s1">&#39;line_width&#39;</span><span class="p">:</span><span class="mi">5</span><span class="p">,</span><span class="s1">&#39;node_size&#39;</span><span class="p">:</span><span class="mi">8</span><span class="p">}</span>
@@ -664,7 +664,7 @@ <h2><a class="toc-backref" href="#id8">Documentation Generation</a><a class="hea
                               <span class="n">title</span><span class="o">=</span><span class="s1">&#39;BARC Modal with Rattlesnake&#39;</span><span class="p">,</span>
                               <span class="n">subtitle</span><span class="o">=</span><span class="s1">&#39;A SDynPy Demonstration&#39;</span><span class="p">,</span>
                               <span class="n">geometry</span> <span class="o">=</span> <span class="n">test_geometry</span><span class="p">,</span>
-                              <span class="n">shapes</span> <span class="o">=</span> <span class="n">test_shapes_polymax</span><span class="p">,</span>
+                              <span class="n">shapes</span> <span class="o">=</span> <span class="n">test_shapes_polypy</span><span class="p">,</span>
                               <span class="n">frfs</span><span class="o">=</span><span class="n">frfs</span><span class="p">,</span>
                               <span class="n">geometry_plot_kwargs</span> <span class="o">=</span> <span class="n">geometry_kwargs</span><span class="p">,</span>
                               <span class="n">content_slide_layout_index</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
@@ -694,7 +694,7 @@ <h2><a class="toc-backref" href="#id8">Documentation Generation</a><a class="hea
 <span class="n">sdpy</span><span class="o">.</span><span class="n">doc</span><span class="o">.</span><span class="n">create_latex_summary</span><span class="p">(</span>
     <span class="n">figure_basename</span><span class="o">=</span><span class="s1">&#39;figures/rattlesnake_test.png&#39;</span><span class="p">,</span>
     <span class="n">geometry</span> <span class="o">=</span> <span class="n">test_geometry</span><span class="p">,</span>
-    <span class="n">shapes</span> <span class="o">=</span> <span class="n">test_shapes_polymax</span><span class="p">,</span>
+    <span class="n">shapes</span> <span class="o">=</span> <span class="n">test_shapes_polypy</span><span class="p">,</span>
     <span class="n">frfs</span> <span class="o">=</span> <span class="n">frfs</span><span class="p">,</span>
     <span class="n">output_file</span> <span class="o">=</span> <span class="s1">&#39;rattlesnake_test_memo_content.tex&#39;</span><span class="p">,</span>
     <span class="n">geometry_plot_kwargs</span> <span class="o">=</span> <span class="n">geometry_kwargs</span><span class="p">)</span>
@@ -751,7 +751,7 @@ <h2><a class="toc-backref" href="#id9">Exporting to Exodus</a><a class="headerli
 that multiply the FEM shapes to produce the test shapes.  We can use the
 Modal Assurance Criterion matrix to help identify which finite element mode
 shapes should be included in the expansion.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">test_shape_matrix_fem</span><span class="p">[:,:</span><span class="mi">14</span><span class="p">],</span><span class="n">test_shape_matrix_polymax</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">q</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">test_shape_matrix_fem</span><span class="p">[:,:</span><span class="mi">14</span><span class="p">],</span><span class="n">test_shape_matrix_polypy</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
 </pre></div>
 </div>
 <p>We then want to multiply this coefficient by the full finite element shapes to
@@ -775,7 +775,7 @@ <h2><a class="toc-backref" href="#id9">Exporting to Exodus</a><a class="headerli
 <span class="c1"># Linearly combine the existing shapes into new shapes</span>
 <span class="n">fexo_repack</span> <span class="o">=</span> <span class="n">fexo</span><span class="o">.</span><span class="n">repack</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
 <span class="c1"># Fill in the abscissa data (frequencies in this case)</span>
-<span class="n">fexo_repack</span><span class="o">.</span><span class="n">time</span> <span class="o">=</span> <span class="n">test_shapes_polymax</span><span class="o">.</span><span class="n">frequency</span>
+<span class="n">fexo_repack</span><span class="o">.</span><span class="n">time</span> <span class="o">=</span> <span class="n">test_shapes_polypy</span><span class="o">.</span><span class="n">frequency</span>
 </pre></div>
 </div>
 <p>If we want to plot the new shapes in SDynPy, we could load the shapes back into
@@ -783,11 +783,11 @@ <h2><a class="toc-backref" href="#id9">Exporting to Exodus</a><a class="headerli
 even combine with the finite element shapes for comparison.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot expanded shapes against finite element shapes</span>
 <span class="c1"># Load shapes into sdynpy ShapeArray object</span>
-<span class="n">expanded_shapes_polymax</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">from_exodus</span><span class="p">(</span><span class="n">fexo_repack</span><span class="p">)</span>
+<span class="n">expanded_shapes_polypy</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">from_exodus</span><span class="p">(</span><span class="n">fexo_repack</span><span class="p">)</span>
 <span class="c1"># combine the shapes</span>
 <span class="n">combined_expanded_geometry</span><span class="p">,</span><span class="n">combined_expanded_shapes</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">shape</span><span class="o">.</span><span class="n">overlay_shapes</span><span class="p">(</span>
     <span class="p">(</span><span class="n">fem_geometry</span><span class="p">,</span><span class="n">fem_geometry</span><span class="p">),</span>
-    <span class="p">(</span><span class="n">expanded_shapes_polymax</span><span class="p">,</span><span class="n">fem_shapes</span><span class="p">[</span><span class="n">fem_matches</span><span class="p">]),</span><span class="n">color_override</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">])</span>
+    <span class="p">(</span><span class="n">expanded_shapes_polypy</span><span class="p">,</span><span class="n">fem_shapes</span><span class="p">[</span><span class="n">fem_matches</span><span class="p">]),</span><span class="n">color_override</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">11</span><span class="p">])</span>
 <span class="c1"># Plot the shapes overlaid</span>
 <span class="n">plotter</span> <span class="o">=</span> <span class="n">combined_expanded_geometry</span><span class="o">.</span><span class="n">plot_shape</span><span class="p">(</span>
     <span class="n">combined_expanded_shapes</span><span class="p">,{</span><span class="s1">&#39;view_up&#39;</span><span class="p">:[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="s1">&#39;node_size&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">},</span>
@@ -799,11 +799,11 @@ <h2><a class="toc-backref" href="#id9">Exporting to Exodus</a><a class="headerli
 <p>We can also write out both the test data and expanded data to Exodus files
 to visualize in external software.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create an ExodusInMemory object from sdynpy objects</span>
-<span class="n">fexo_out</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">ExodusInMemory</span><span class="o">.</span><span class="n">from_sdynpy</span><span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">test_shapes_polymax</span><span class="p">)</span>
+<span class="n">fexo_out</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">ExodusInMemory</span><span class="o">.</span><span class="n">from_sdynpy</span><span class="p">(</span><span class="n">test_geometry</span><span class="p">,</span><span class="n">test_shapes_polypy</span><span class="p">)</span>
 <span class="c1"># Write it to a file</span>
 <span class="n">fexo_out</span><span class="o">.</span><span class="n">write_to_file</span><span class="p">(</span><span class="s1">&#39;rattlesnake_test_output.exo&#39;</span><span class="p">,</span><span class="n">clobber</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="c1"># Repeat with the expanded data</span>
-<span class="n">fexo_out</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">ExodusInMemory</span><span class="o">.</span><span class="n">from_sdynpy</span><span class="p">(</span><span class="n">fem_geometry</span><span class="p">,</span><span class="n">expanded_shapes_polymax</span><span class="p">)</span>
+<span class="n">fexo_out</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">ExodusInMemory</span><span class="o">.</span><span class="n">from_sdynpy</span><span class="p">(</span><span class="n">fem_geometry</span><span class="p">,</span><span class="n">expanded_shapes_polypy</span><span class="p">)</span>
 <span class="n">fexo_out</span><span class="o">.</span><span class="n">write_to_file</span><span class="p">(</span><span class="s1">&#39;rattlesnake_test_output_expanded.exo&#39;</span><span class="p">,</span><span class="n">clobber</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -824,7 +824,7 @@ <h2><a class="toc-backref" href="#id9">Exporting to Exodus</a><a class="headerli
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec2a51b0>
+<jinja2.runtime.BlockReference object at 0x7f5a273c4160>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/transient_fem_analysis.html b/example_problems/transient_fem_analysis.html
index c59489e..f36e9e5 100644
--- a/example_problems/transient_fem_analysis.html
+++ b/example_problems/transient_fem_analysis.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Analysis of Transient Finite Element Results &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Analysis of Transient Finite Element Results &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -989,7 +989,7 @@ <h2><a class="toc-backref" href="#id39">Summary</a><a class="headerlink" href="#
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec12d690>
+<jinja2.runtime.BlockReference object at 0x7f5a266884c0>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/example_problems/transmission_simulator.html b/example_problems/transmission_simulator.html
index 1f863b9..25a73ea 100644
--- a/example_problems/transmission_simulator.html
+++ b/example_problems/transmission_simulator.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Substructuring using the Transmission Simulator Method &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Substructuring using the Transmission Simulator Method &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
@@ -681,7 +681,7 @@ <h2><a class="toc-backref" href="#id27">Summary</a><a class="headerlink" href="#
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec193190>
+<jinja2.runtime.BlockReference object at 0x7f5a273bb100>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/examples.html b/examples.html
index 34a8964..b88cd4a 100644
--- a/examples.html
+++ b/examples.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>SDynpy Examples &mdash; SDynPy 0.11.0 documentation</title>
+  <title>SDynpy Examples &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
@@ -109,7 +109,7 @@ <h1>SDynpy Examples<a class="headerlink" href="#sdynpy-examples" title="Permalin
 <li class="toctree-l2"><a class="reference internal" href="example_problems/airplane_modal_test.html#creating-a-test-geometry">Creating a Test Geometry</a></li>
 <li class="toctree-l2"><a class="reference internal" href="example_problems/airplane_modal_test.html#running-a-virtual-experiment-rigid-body-checkouts">Running a Virtual Experiment: Rigid Body Checkouts</a></li>
 <li class="toctree-l2"><a class="reference internal" href="example_problems/airplane_modal_test.html#running-a-virtual-experiment-modal-testing">Running a Virtual Experiment: Modal Testing</a></li>
-<li class="toctree-l2"><a class="reference internal" href="example_problems/airplane_modal_test.html#fitting-modes-using-polymax">Fitting Modes using PolyMax</a></li>
+<li class="toctree-l2"><a class="reference internal" href="example_problems/airplane_modal_test.html#fitting-modes-using-polypy">Fitting Modes using PolyPy</a></li>
 <li class="toctree-l2"><a class="reference internal" href="example_problems/airplane_modal_test.html#serep-expansion">SEREP Expansion</a></li>
 </ul>
 </li>
@@ -181,7 +181,7 @@ <h1>SDynpy Examples<a class="headerlink" href="#sdynpy-examples" title="Permalin
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ec1b7670>
+<jinja2.runtime.BlockReference object at 0x7f5a268f7e50>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/genindex.html b/genindex.html
index a87fd03..7754c82 100644
--- a/genindex.html
+++ b/genindex.html
@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Index &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Index &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
@@ -162,6 +162,8 @@ <h2 id="_">_</h2>
         <li><a href="_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.__init__">(ImpulseResponseFunctionArray method)</a>
 </li>
         <li><a href="_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.__init__">(Matrix method)</a>
+</li>
+        <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.__init__">(ModalTest method)</a>
 </li>
         <li><a href="_autosummary/sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.html#sdynpy.core.sdynpy_data.ModeIndicatorFunctionArray.__init__">(ModeIndicatorFunctionArray method)</a>
 </li>
@@ -212,6 +214,8 @@ <h2 id="_">_</h2>
         <li><a href="_autosummary/sdynpy.fem.sdynpy_shaker.Shaker4DoF.html#sdynpy.fem.sdynpy_shaker.Shaker4DoF.__init__">(Shaker4DoF method)</a>
 </li>
         <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.__init__">(ShapeArray method)</a>
+</li>
+        <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable.__init__">(ShapeCommentTable method)</a>
 </li>
         <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.__init__">(ShapePlotter method)</a>
 </li>
@@ -261,6 +265,8 @@ <h2 id="A">A</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.AbscissaValueExtractor.html#sdynpy.core.sdynpy_data.AbscissaValueExtractor">AbscissaValueExtractor (class in sdynpy.core.sdynpy_data)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html#sdynpy.core.sdynpy_data.SpecificDataType.ACCELERATION">ACCELERATION (SpecificDataType attribute)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable.accept">accept() (ShapeCommentTable method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.acceptanceChanged">acceptanceChanged() (SignalProcessingGUI method)</a>
 </li>
@@ -356,6 +362,8 @@ <h2 id="A">A</h2>
 <h2 id="B">B</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.bandwidth_average">bandwidth_average() (PowerSpectralDensityArray method)</a>
+</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.beam">beam() (System class method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_beam.beamkm.html#sdynpy.fem.sdynpy_beam.beamkm">beamkm() (in module sdynpy.fem.sdynpy_beam)</a>
@@ -365,12 +373,10 @@ <h2 id="B">B</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.block_averaging_signals">block_averaging_signals() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.block_data_range_signals">block_data_range_signals() (SignalProcessingGUI method)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.block_sampling_signals">block_sampling_signals() (SignalProcessingGUI method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.buckling_eigenvalue">buckling_eigenvalue (Sdynpy_UFF_Dataset_55 property)</a>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.block_sampling_signals">block_sampling_signals() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_generator.burst_random.html#sdynpy.signal_processing.sdynpy_generator.burst_random">burst_random() (in module sdynpy.signal_processing.sdynpy_generator)</a>
 </li>
@@ -486,6 +492,8 @@ <h2 id="C">C</h2>
         <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC.compute_residues">(SMAC method)</a>
 </li>
       </ul></li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_resynthesized_frfs">compute_resynthesized_frfs() (ModalTest method)</a>
+</li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes.html#sdynpy.modal.sdynpy_modeshape.compute_shapes">compute_shapes() (in module sdynpy.modal.sdynpy_modeshape)</a>
 
       <ul>
@@ -499,6 +507,10 @@ <h2 id="C">C</h2>
 </li>
       </ul></li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference.html#sdynpy.modal.sdynpy_modeshape.compute_shapes_multireference">compute_shapes_multireference() (in module sdynpy.modal.sdynpy_modeshape)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities">compute_spectral_quantities() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.compute_spectral_quantities_SignalProcessingGUI">compute_spectral_quantities_SignalProcessingGUI() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.compute_stabilization">compute_stabilization() (PolyPy_GUI method)</a>
 </li>
@@ -601,6 +613,12 @@ <h2 id="C">C</h2>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone.html#sdynpy.signal_processing.sdynpy_geometry_fitting.create_cone">create_cone() (in module sdynpy.signal_processing.sdynpy_geometry_fitting)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_coordinate.create_coordinate_string.html#sdynpy.core.sdynpy_coordinate.create_coordinate_string">create_coordinate_string() (in module sdynpy.core.sdynpy_coordinate)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_latex">create_documentation_latex() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_pptx">create_documentation_pptx() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.create_documentation_word">create_documentation_word() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.create_frf_window">create_frf_window() (PolyPy_GUI method)</a>
 </li>
@@ -695,6 +713,8 @@ <h2 id="D">D</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.DecayedSineTable.html#sdynpy.core.sdynpy_data.DecayedSineTable">DecayedSineTable (class in sdynpy.core.sdynpy_data)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_camera.decomposeP.html#sdynpy.signal_processing.sdynpy_camera.decomposeP">decomposeP() (in module sdynpy.signal_processing.sdynpy_camera)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.define_spectral_processing_parameters">define_spectral_processing_parameters() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.DeflectionShapePlotter.html#sdynpy.core.sdynpy_geometry.DeflectionShapePlotter">DeflectionShapePlotter (class in sdynpy.core.sdynpy_geometry)</a>
 </li>
@@ -738,9 +758,15 @@ <h2 id="D">D</h2>
 <h2 id="E">E</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.edit_comments">edit_comments() (ShapeArray method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.edit_mode_comments">edit_mode_comments() (ModalTest method)</a>
+</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.eigensolution">eigensolution() (System method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.EIGENVALUE">EIGENVALUE (FunctionTypes attribute)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.eigenvalue">eigenvalue (Sdynpy_UFF_Dataset_55 property)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.EIGENVECTOR">EIGENVECTOR (FunctionTypes attribute)</a>
 </li>
@@ -766,12 +792,12 @@ <h2 id="E">E</h2>
       </ul></li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.error_summary">error_summary() (PowerSpectralDensityArray method)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html#sdynpy.core.sdynpy_data.SpecificDataType.EXCITATION_FORCE">EXCITATION_FORCE (SpecificDataType attribute)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus">Exodus (class in sdynpy.fem.sdynpy_exodus)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.ExodusError.html#sdynpy.fem.sdynpy_exodus.ExodusError">ExodusError</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.ExodusInMemory.html#sdynpy.fem.sdynpy_exodus.ExodusInMemory">ExodusInMemory (class in sdynpy.fem.sdynpy_exodus)</a>
@@ -825,6 +851,8 @@ <h2 id="F">F</h2>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_frf.fft2frf.html#sdynpy.signal_processing.sdynpy_frf.fft2frf">fft2frf() (in module sdynpy.signal_processing.sdynpy_frf)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_array.SdynpyArray.html#sdynpy.core.sdynpy_array.SdynpyArray.fields">fields (SdynpyArray property)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.doc.sdynpy_latex.figure.html#sdynpy.doc.sdynpy_latex.figure">figure() (in module sdynpy.doc.sdynpy_latex)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.find_end_of_ringdown">find_end_of_ringdown() (ImpulseResponseFunctionArray method)</a>
 </li>
@@ -845,6 +873,10 @@ <h2 id="F">F</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC.fit_frequency">fit_frequency() (SMAC method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud.html#sdynpy.signal_processing.sdynpy_geometry_fitting.fit_line_point_cloud">fit_line_point_cloud() (in module sdynpy.signal_processing.sdynpy_geometry_fitting)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_PolyPy">fit_modes_PolyPy() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.fit_modes_SMAC">fit_modes_SMAC() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC.fit_paraboloid">fit_paraboloid() (SMAC method)</a>
 </li>
@@ -893,11 +925,19 @@ <h2 id="F">F</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.frf_from_time_data.html#sdynpy.core.sdynpy_data.frf_from_time_data">frf_from_time_data() (in module sdynpy.core.sdynpy_data)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse.html#sdynpy.signal_processing.sdynpy_frf_inverse.frf_inverse">frf_inverse() (in module sdynpy.signal_processing.sdynpy_frf_inverse)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_lrm.frf_local_model.html#sdynpy.signal_processing.sdynpy_lrm.frf_local_model">frf_local_model() (in module sdynpy.signal_processing.sdynpy_lrm)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC.frf_sdof_complex">frf_sdof_complex() (SMAC method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC.frf_sdof_real">frf_sdof_real() (SMAC method)</a>
 </li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.from_excel_template">from_excel_template() (Geometry class method)</a>
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.from_excel_template.html#sdynpy.core.sdynpy_geometry.from_excel_template">(in module sdynpy.core.sdynpy_geometry)</a>
+</li>
+      </ul></li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.from_exodus">from_exodus() (Geometry class method)</a>
 
       <ul>
@@ -940,8 +980,16 @@ <h2 id="F">F</h2>
       </ul></li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.ExodusInMemory.html#sdynpy.fem.sdynpy_exodus.ExodusInMemory.from_sdynpy">from_sdynpy() (ExodusInMemory static method)</a>
 </li>
-      <li><a href="_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.from_time_data">from_time_data() (TransferFunctionArray static method)</a>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.CoherenceArray.html#sdynpy.core.sdynpy_data.CoherenceArray.from_time_data">from_time_data() (CoherenceArray static method)</a>
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.core.sdynpy_data.MultipleCoherenceArray.html#sdynpy.core.sdynpy_data.MultipleCoherenceArray.from_time_data">(MultipleCoherenceArray static method)</a>
+</li>
+        <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.from_time_data">(PowerSpectralDensityArray static method)</a>
 </li>
+        <li><a href="_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.from_time_data">(TransferFunctionArray static method)</a>
+</li>
+      </ul></li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.from_uff">from_uff() (Geometry class method)</a>
 
       <ul>
@@ -1040,6 +1088,10 @@ <h2 id="G">G</h2>
         <li><a href="_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html#sdynpy.core.sdynpy_data.SpecificDataType.GENERAL">(SpecificDataType attribute)</a>
 </li>
       </ul></li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_complex.html#sdynpy.modal.sdynpy_modeshape.generate_kernel_complex">generate_kernel_complex() (in module sdynpy.modal.sdynpy_modeshape)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modeshape.generate_kernel_real.html#sdynpy.modal.sdynpy_modeshape.generate_kernel_real">generate_kernel_real() (in module sdynpy.modal.sdynpy_modeshape)</a>
+</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.generate_time_history">generate_time_history() (PowerSpectralDensityArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry">Geometry (class in sdynpy.core.sdynpy_geometry)</a>
@@ -1063,6 +1115,8 @@ <h2 id="G">G</h2>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_coords">get_coords() (Exodus method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_displacements">get_displacements() (Exodus method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.get_drive_points">get_drive_points() (NDDataArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_elem_attr">get_elem_attr() (Exodus method)</a>
 </li>
@@ -1093,11 +1147,11 @@ <h2 id="G">G</h2>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_global_variable_values">get_global_variable_values() (Exodus method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.get_indices_by_coordinate">get_indices_by_coordinate() (System method)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_info_records">get_info_records() (Exodus method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_info_records">get_info_records() (Exodus method)</a>
+</li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_node_num_map">get_node_num_map() (Exodus method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_node_set_dist_factors">get_node_set_dist_factors() (Exodus method)</a>
@@ -1119,6 +1173,8 @@ <h2 id="G">G</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC.get_num_roots">get_num_roots() (SMAC method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_qa_records">get_qa_records() (Exodus method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.get_reciprocal_data">get_reciprocal_data() (NDDataArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.get_side_set_dist_factors">get_side_set_dist_factors() (Exodus method)</a>
 </li>
@@ -1202,20 +1258,14 @@ <h2 id="I">I</h2>
         <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.DataType.IMAGINARY">(CPSDPlot.DataType attribute)</a>
 </li>
       </ul></li>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_eigenvalue">imaginary_eigenvalue (Sdynpy_UFF_Dataset_55 property)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_a">imaginary_modal_a (Sdynpy_UFF_Dataset_55 property)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.imaginary_modal_b">imaginary_modal_b (Sdynpy_UFF_Dataset_55 property)</a>
-</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.imaginary_state">imaginary_state() (CPSDPlot method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.IMPULSE_RESPONSE_FUNCTION">IMPULSE_RESPONSE_FUNCTION (FunctionTypes attribute)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray.html#sdynpy.core.sdynpy_data.ImpulseResponseFunctionArray">ImpulseResponseFunctionArray (class in sdynpy.core.sdynpy_data)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.init_matrix_plot">init_matrix_plot() (CPSDPlot method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.initialize_ui">initialize_ui() (SignalProcessingGUI method)</a>
@@ -1389,6 +1439,10 @@ <h2 id="M">M</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.min">min() (NDDataArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.min_frequency">min_frequency (PolyPy_GUI property)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_a">modal_a (Sdynpy_UFF_Dataset_55 property)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_b">modal_b (Sdynpy_UFF_Dataset_55 property)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_hysteretic_damping">modal_hysteretic_damping (Sdynpy_UFF_Dataset_55 property)</a>
 </li>
@@ -1399,6 +1453,8 @@ <h2 id="M">M</h2>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_shaker.Shaker4DoF.html#sdynpy.fem.sdynpy_shaker.Shaker4DoF.modal_shop_100lbf">modal_shop_100lbf() (Shaker4DoF class method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.modal_viscous_damping">modal_viscous_damping (Sdynpy_UFF_Dataset_55 property)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest">ModalTest (class in sdynpy.modal.sdynpy_modal_test)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.MODE_INDICATOR_FUNCTION">MODE_INDICATOR_FUNCTION (FunctionTypes attribute)</a>
 </li>
@@ -1501,6 +1557,8 @@ <h2 id="M">M</h2>
         <li><a href="_autosummary/sdynpy.modal.html#module-sdynpy.modal">sdynpy.modal</a>
 </li>
         <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.html#module-sdynpy.modal.sdynpy_ccmif">sdynpy.modal.sdynpy_ccmif</a>
+</li>
+        <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.html#module-sdynpy.modal.sdynpy_modal_test">sdynpy.modal.sdynpy_modal_test</a>
 </li>
         <li><a href="_autosummary/sdynpy.modal.sdynpy_modeshape.html#module-sdynpy.modal.sdynpy_modeshape">sdynpy.modal.sdynpy_modeshape</a>
 </li>
@@ -1531,6 +1589,8 @@ <h2 id="M">M</h2>
         <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_harmonic.html#module-sdynpy.signal_processing.sdynpy_harmonic">sdynpy.signal_processing.sdynpy_harmonic</a>
 </li>
         <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_integration.html#module-sdynpy.signal_processing.sdynpy_integration">sdynpy.signal_processing.sdynpy_integration</a>
+</li>
+        <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_lrm.html#module-sdynpy.signal_processing.sdynpy_lrm">sdynpy.signal_processing.sdynpy_lrm</a>
 </li>
         <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_rotation.html#module-sdynpy.signal_processing.sdynpy_rotation">sdynpy.signal_processing.sdynpy_rotation</a>
 </li>
@@ -1578,17 +1638,21 @@ <h2 id="N">N</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.node_by_global_position">node_by_global_position() (Geometry method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.NodeArray.html#sdynpy.core.sdynpy_geometry.NodeArray">NodeArray (class in sdynpy.core.sdynpy_geometry)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.normalize">normalize() (ShapeArray method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs.html#sdynpy.signal_processing.sdynpy_cpsd.nth_octave_freqs">nth_octave_freqs() (in module sdynpy.signal_processing.sdynpy_cpsd)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.num_attr">num_attr() (Exodus method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.num_blks">num_blks (Exodus property)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.num_coordinate_columns">num_coordinate_columns (Matrix property)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.num_coordinate_rows">num_coordinate_rows (Matrix property)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_matrix.Matrix.html#sdynpy.core.sdynpy_matrix.Matrix.num_coordinate_rows">num_coordinate_rows (Matrix property)</a>
+</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.num_coordinates">num_coordinates (NDDataArray property)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.num_dimensions">num_dimensions (Exodus property)</a>
@@ -1622,6 +1686,8 @@ <h2 id="O">O</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_srs.octspace.html#sdynpy.signal_processing.sdynpy_srs.octspace">octspace() (in module sdynpy.signal_processing.sdynpy_srs)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_coordinate.CoordinateArray.html#sdynpy.core.sdynpy_coordinate.CoordinateArray.offset_node_ids">offset_node_ids() (CoordinateArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_srs.optimization_callback.html#sdynpy.signal_processing.sdynpy_srs.optimization_callback">optimization_callback() (in module sdynpy.signal_processing.sdynpy_srs)</a>
 </li>
@@ -1634,11 +1700,11 @@ <h2 id="O">O</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.ORBIT">ORBIT (FunctionTypes attribute)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html#sdynpy.core.sdynpy_data.SpecificDataType.ORDER">ORDER (SpecificDataType attribute)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.ORDER_FUNCTION">ORDER_FUNCTION (FunctionTypes attribute)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.FunctionTypes.html#sdynpy.core.sdynpy_data.FunctionTypes.ORDER_FUNCTION">ORDER_FUNCTION (FunctionTypes attribute)</a>
+</li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_correlation.orthog.html#sdynpy.signal_processing.sdynpy_correlation.orthog">orthog() (in module sdynpy.signal_processing.sdynpy_correlation)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_coordinate.outer_product.html#sdynpy.core.sdynpy_coordinate.outer_product">outer_product() (in module sdynpy.core.sdynpy_coordinate)</a>
@@ -1728,8 +1794,12 @@ <h2 id="P">P</h2>
 </li>
       </ul></li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.plot_ccmif">plot_ccmif() (ColoredCMIF method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_cmif">plot_cmif() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.plot_cmifs">plot_cmifs() (ColoredCMIF method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_coherence_image">plot_coherence_image() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_cond_num">plot_cond_num() (TransferFunctionArray method)</a>
 </li>
@@ -1738,18 +1808,48 @@ <h2 id="P">P</h2>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error.html#sdynpy.signal_processing.sdynpy_cpsd.plot_cpsd_error">plot_cpsd_error() (in module sdynpy.signal_processing.sdynpy_cpsd)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.plot_deflection_shape">plot_deflection_shape() (Geometry method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_deflection_shapes">plot_deflection_shapes() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frf_coherence">plot_drive_point_frf_coherence() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_drive_point_frfs">plot_drive_point_frfs() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_shaker.Shaker4DoF.html#sdynpy.fem.sdynpy_shaker.Shaker4DoF.plot_electrical_impedance">plot_electrical_impedance() (Shaker4DoF method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_figures_for_documentation">plot_figures_for_documentation() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.plot_frequency">plot_frequency() (ShapeArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.plot_frfs">plot_frfs() (ColoredCMIF method)</a>
 </li>
-      <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC_GUI.html#sdynpy.modal.sdynpy_smac.SMAC_GUI.plot_mac">plot_mac() (SMAC_GUI method)</a>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.plot_image">plot_image() (NDDataArray method)</a>
 </li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mac">plot_mac() (ModalTest method)</a>
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC_GUI.html#sdynpy.modal.sdynpy_smac.SMAC_GUI.plot_mac">(SMAC_GUI method)</a>
+</li>
+      </ul></li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_magnitude_coherence_phase">plot_magnitude_coherence_phase() (PowerSpectralDensityArray method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_mmif">plot_mmif() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_modeshape">plot_modeshape() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_nmif">plot_nmif() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_Stability.html#sdynpy.modal.sdynpy_polypy.PolyPy_Stability.plot_poles">plot_poles() (PolyPy_Stability method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_psmif">plot_psmif() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_qmif">plot_qmif() (ModalTest method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reciprocal_frfs">plot_reciprocal_frfs() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.plot_reference_autospectra">plot_reference_autospectra() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.plot_selected_function">plot_selected_function() (CPSDPlot method)</a>
 </li>
@@ -1763,8 +1863,6 @@ <h2 id="P">P</h2>
         <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC_GUI.html#sdynpy.modal.sdynpy_smac.SMAC_GUI.plot_shapes">(SMAC_GUI method)</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.plot_singular_values">plot_singular_values() (PowerSpectralDensityArray method)</a>
 
       <ul>
@@ -1776,6 +1874,8 @@ <h2 id="P">P</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy.html#sdynpy.modal.sdynpy_polypy.PolyPy.plot_stability">plot_stability() (PolyPy method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.plot_transient">plot_transient() (Geometry method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.TransferFunctionArray.html#sdynpy.core.sdynpy_data.TransferFunctionArray.plot_with_coherence">plot_with_coherence() (TransferFunctionArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.plotDeflection">plotDeflection() (SignalProcessingGUI method)</a>
 </li>
@@ -1912,6 +2012,8 @@ <h2 id="R">R</h2>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_tshaker.read_mat_time_history.html#sdynpy.fileio.sdynpy_tshaker.read_mat_time_history">read_mat_time_history() (in module sdynpy.fileio.sdynpy_tshaker)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_modal_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_modal_data">read_modal_data() (in module sdynpy.fileio.sdynpy_rattlesnake)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.read_modal_fit_data.html#sdynpy.modal.sdynpy_modal_test.read_modal_fit_data">read_modal_fit_data() (in module sdynpy.modal.sdynpy_modal_test)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_rattlesnake.read_random_spectral_data.html#sdynpy.fileio.sdynpy_rattlesnake.read_random_spectral_data">read_random_spectral_data() (in module sdynpy.fileio.sdynpy_rattlesnake)</a>
 </li>
@@ -1936,12 +2038,6 @@ <h2 id="R">R</h2>
 </li>
       </ul></li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.real_data_dtype">real_data_dtype() (ShapeArray static method)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_eigenvalue">real_eigenvalue (Sdynpy_UFF_Dataset_55 property)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_a">real_modal_a (Sdynpy_UFF_Dataset_55 property)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.real_modal_b">real_modal_b (Sdynpy_UFF_Dataset_55 property)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.real_state">real_state() (CPSDPlot method)</a>
 </li>
@@ -1953,8 +2049,6 @@ <h2 id="R">R</h2>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_beam.rect_beam_props.html#sdynpy.fem.sdynpy_beam.rect_beam_props">rect_beam_props() (in module sdynpy.fem.sdynpy_beam)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.ElementArray.html#sdynpy.core.sdynpy_geometry.ElementArray.reduce">reduce() (ElementArray method)</a>
 
       <ul>
@@ -1969,6 +2063,8 @@ <h2 id="R">R</h2>
         <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.TracelineArray.html#sdynpy.core.sdynpy_geometry.TracelineArray.reduce">(TracelineArray method)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.reduce_craig_bampton">reduce_craig_bampton() (System method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.reduce_dynamic">reduce_dynamic() (System method)</a>
@@ -1994,6 +2090,8 @@ <h2 id="R">R</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.remove_file">remove_file() (ColoredCMIF method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.remove_rigid_body_motion">remove_rigid_body_motion() (TimeHistoryArray method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_system.System.html#sdynpy.core.sdynpy_system.System.remove_transformation">remove_transformation() (System method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.ExodusInMemory.html#sdynpy.fem.sdynpy_exodus.ExodusInMemory.repack">repack() (ExodusInMemory method)</a>
 
@@ -2002,18 +2100,28 @@ <h2 id="R">R</h2>
 </li>
       </ul></li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.replace_file">replace_file() (ColoredCMIF method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.reset_shape">reset_shape() (ShapePlotter method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.reset_ui">reset_ui() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.reshape_to_matrix">reshape_to_matrix() (NDDataArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.response_coordinate">response_coordinate (NDDataArray property)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.response_indices">response_indices (ModalTest property)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.responsesChanged">responsesChanged() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.responseViewChanged">responseViewChanged() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.resynthesize">resynthesize() (ColoredCMIF method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_PolyPy">retrieve_modes_PolyPy() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_modes_SMAC">retrieve_modes_SMAC() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.retrieve_spectral_quantities_SignalProcessingGUI">retrieve_spectral_quantities_SignalProcessingGUI() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_shape.rigid_body_check.html#sdynpy.core.sdynpy_shape.rigid_body_check">rigid_body_check() (in module sdynpy.core.sdynpy_shape)</a>
 </li>
@@ -2370,6 +2478,13 @@ <h2 id="S">S</h2>
 
       <ul>
         <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.html#module-sdynpy.modal.sdynpy_ccmif">module</a>
+</li>
+      </ul></li>
+      <li>
+    sdynpy.modal.sdynpy_modal_test
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.html#module-sdynpy.modal.sdynpy_modal_test">module</a>
 </li>
       </ul></li>
       <li>
@@ -2475,6 +2590,13 @@ <h2 id="S">S</h2>
 
       <ul>
         <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_integration.html#module-sdynpy.signal_processing.sdynpy_integration">module</a>
+</li>
+      </ul></li>
+      <li>
+    sdynpy.signal_processing.sdynpy_lrm
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_lrm.html#module-sdynpy.signal_processing.sdynpy_lrm">module</a>
 </li>
       </ul></li>
       <li>
@@ -2491,8 +2613,6 @@ <h2 id="S">S</h2>
         <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_srs.html#module-sdynpy.signal_processing.sdynpy_srs">module</a>
 </li>
       </ul></li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151.Sdynpy_UFF_Dataset_151.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151.Sdynpy_UFF_Dataset_151">Sdynpy_UFF_Dataset_151 (class in sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164.Sdynpy_UFF_Dataset_164.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164.Sdynpy_UFF_Dataset_164">Sdynpy_UFF_Dataset_164 (class in sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_164)</a>
@@ -2501,6 +2621,8 @@ <h2 id="S">S</h2>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400.Sdynpy_UFF_Dataset_2400.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400.Sdynpy_UFF_Dataset_2400">Sdynpy_UFF_Dataset_2400 (class in sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2400)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411.Sdynpy_UFF_Dataset_2411.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411.Sdynpy_UFF_Dataset_2411">Sdynpy_UFF_Dataset_2411 (class in sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2411)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412.Sdynpy_UFF_Dataset_2412.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412.Sdynpy_UFF_Dataset_2412">Sdynpy_UFF_Dataset_2412 (class in sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_2412)</a>
@@ -2598,6 +2720,12 @@ <h2 id="S">S</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.sendToReference">sendToReference() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.sendToResponse">sendToResponse() (SignalProcessingGUI method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_autopower_spectra">set_autopower_spectra() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_channel_table">set_channel_table() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_coherence">set_coherence() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.PowerSpectralDensityArray.html#sdynpy.core.sdynpy_data.PowerSpectralDensityArray.set_coherence_phase">set_coherence_phase() (PowerSpectralDensityArray method)</a>
 </li>
@@ -2609,8 +2737,16 @@ <h2 id="S">S</h2>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.set_elem_variable_values">set_elem_variable_values() (Exodus method)</a>
 </li>
-      <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.set_geometry">set_geometry() (PolyPy_GUI method)</a>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_fit_modes">set_fit_modes() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_frfs">set_frfs() (ModalTest method)</a>
 </li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_geometry">set_geometry() (ModalTest method)</a>
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.modal.sdynpy_polypy.PolyPy_GUI.html#sdynpy.modal.sdynpy_polypy.PolyPy_GUI.set_geometry">(PolyPy_GUI method)</a>
+</li>
+      </ul></li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.set_global_variable_values">set_global_variable_values() (Exodus method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.set_imaginary">set_imaginary() (GUIPlot method)</a>
@@ -2634,10 +2770,18 @@ <h2 id="S">S</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.set_real">set_real() (GUIPlot method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.set_real_imag">set_real_imag() (GUIPlot method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_resynthesized_frfs">set_resynthesized_frfs() (ModalTest method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_rigid_body_shapes">set_rigid_body_shapes() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.set_time">set_time() (Exodus method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_time_histories">set_time_histories() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.set_times">set_times() (Exodus method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modal_test.ModalTest.html#sdynpy.modal.sdynpy_modal_test.ModalTest.set_units">set_units() (ModalTest method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.settext">settext() (CPSDPlot method)</a>
 </li>
@@ -2654,6 +2798,8 @@ <h2 id="S">S</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_shape.shape_comparison_table.html#sdynpy.core.sdynpy_shape.shape_comparison_table">shape_comparison_table() (in module sdynpy.core.sdynpy_shape)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray">ShapeArray (class in sdynpy.core.sdynpy_shape)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable">ShapeCommentTable (class in sdynpy.core.sdynpy_shape)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_cpsd.shaped_psd.html#sdynpy.signal_processing.sdynpy_cpsd.shaped_psd">shaped_psd() (in module sdynpy.signal_processing.sdynpy_cpsd)</a>
 </li>
@@ -2692,6 +2838,10 @@ <h2 id="S">S</h2>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_generator.sine.html#sdynpy.signal_processing.sdynpy_generator.sine">sine() (in module sdynpy.signal_processing.sdynpy_generator)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_signal">sine_signal() (TimeHistoryArray class method)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_generator.sine_sweep.html#sdynpy.signal_processing.sdynpy_generator.sine_sweep">sine_sweep() (in module sdynpy.signal_processing.sdynpy_generator)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.sine_sweep_signal">sine_sweep_signal() (TimeHistoryArray class method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.SMAC.html#sdynpy.modal.sdynpy_smac.SMAC">SMAC (class in sdynpy.modal.sdynpy_smac)</a>
 </li>
@@ -2723,6 +2873,8 @@ <h2 id="S">S</h2>
         <li><a href="_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray.srs">(TimeHistoryArray method)</a>
 </li>
       </ul></li>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_modeshape.stack_and_lstsq.html#sdynpy.modal.sdynpy_modeshape.stack_and_lstsq">stack_and_lstsq() (in module sdynpy.modal.sdynpy_modeshape)</a>
+</li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.startTimeChanged">startTimeChanged() (SignalProcessingGUI method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.CPSDPlot.html#sdynpy.core.sdynpy_data.CPSDPlot.state_changed">state_changed() (CPSDPlot method)</a>
@@ -2803,6 +2955,8 @@ <h2 id="S">S</h2>
 <h2 id="T">T</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.doc.sdynpy_latex.table.html#sdynpy.doc.sdynpy_latex.table">table() (in module sdynpy.doc.sdynpy_latex)</a>
+</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.SpecificDataType.html#sdynpy.core.sdynpy_data.SpecificDataType.TEMPERATURE">TEMPERATURE (SpecificDataType attribute)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_55.Sdynpy_UFF_Dataset_55.time">time (Sdynpy_UFF_Dataset_55 property)</a>
@@ -2824,6 +2978,8 @@ <h2 id="T">T</h2>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.TimeHistoryArray.html#sdynpy.core.sdynpy_data.TimeHistoryArray">TimeHistoryArray (class in sdynpy.core.sdynpy_data)</a>
 </li>
       <li><a href="_autosummary/sdynpy.fem.sdynpy_exodus.Exodus.html#sdynpy.fem.sdynpy_exodus.Exodus.title">title (Exodus property)</a>
+</li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeArray.html#sdynpy.core.sdynpy_shape.ShapeArray.to_complex">to_complex() (ShapeArray method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.NDDataArray.html#sdynpy.core.sdynpy_data.NDDataArray.to_imat_struct">to_imat_struct() (NDDataArray method)</a>
 </li>
@@ -2844,11 +3000,11 @@ <h2 id="T">T</h2>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_ccmif.ColoredCMIF.html#sdynpy.modal.sdynpy_ccmif.ColoredCMIF.toggle_auto_resynth">toggle_auto_resynth() (ColoredCMIF method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.ShapePlotter.html#sdynpy.core.sdynpy_geometry.ShapePlotter.toggle_undeformed">toggle_undeformed() (ShapePlotter method)</a>
-</li>
-      <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.toggleROI">toggleROI() (SignalProcessingGUI method)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.toggleROI">toggleROI() (SignalProcessingGUI method)</a>
+</li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_correlation.trac.html#sdynpy.signal_processing.sdynpy_correlation.trac">trac() (in module sdynpy.signal_processing.sdynpy_correlation)</a>
 </li>
       <li><a href="_autosummary/sdynpy.signal_processing.sdynpy_cpsd.trace.html#sdynpy.signal_processing.sdynpy_cpsd.trace">trace() (in module sdynpy.signal_processing.sdynpy_cpsd)</a>
@@ -2939,6 +3095,8 @@ <h2 id="U">U</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_shape.ShapeCommentTable.html#sdynpy.core.sdynpy_shape.ShapeCommentTable.update_mode">update_mode() (ShapeCommentTable method)</a>
+</li>
       <li><a href="_autosummary/sdynpy.core.sdynpy_data.GUIPlot.html#sdynpy.core.sdynpy_data.GUIPlot.update_ordinate_log">update_ordinate_log() (GUIPlot method)</a>
 </li>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_smac.AddRootDialog.html#sdynpy.modal.sdynpy_smac.AddRootDialog.update_plot_range">update_plot_range() (AddRootDialog method)</a>
@@ -3001,6 +3159,12 @@ <h2 id="W">W</h2>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="_autosummary/sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.html#sdynpy.modal.sdynpy_signal_processing_gui.SignalProcessingGUI.windowChanged">windowChanged() (SignalProcessingGUI method)</a>
 </li>
+      <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.Geometry.html#sdynpy.core.sdynpy_geometry.Geometry.write_excel_template">write_excel_template() (Geometry static method)</a>
+
+      <ul>
+        <li><a href="_autosummary/sdynpy.core.sdynpy_geometry.write_excel_template.html#sdynpy.core.sdynpy_geometry.write_excel_template">(in module sdynpy.core.sdynpy_geometry)</a>
+</li>
+      </ul></li>
       <li><a href="_autosummary/sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151.Sdynpy_UFF_Dataset_151.html#sdynpy.fileio.sdynpy_uff_datasets.sdynpy_uff_dataset_151.Sdynpy_UFF_Dataset_151.write_string">write_string() (Sdynpy_UFF_Dataset_151 method)</a>
 
       <ul>
@@ -3055,7 +3219,7 @@ <h2 id="Z">Z</h2>
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ebb74a90>
+<jinja2.runtime.BlockReference object at 0x7f5a2765d120>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/index.html b/index.html
index fdf064d..bd4e6c4 100644
--- a/index.html
+++ b/index.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Welcome to SDynPy’s documentation! &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Welcome to SDynPy’s documentation! &mdash; SDynPy 0.14.0 documentation</title>
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       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
@@ -149,6 +149,7 @@ <h2>Information<a class="headerlink" href="#information" title="Permalink to thi
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html">Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom</a></li>
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html">Modal Tutorial 04: Modal Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="modules.html">SDynPy Programming Interface</a><ul>
@@ -179,7 +180,7 @@ <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Pe
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99eee325c0>
+<jinja2.runtime.BlockReference object at 0x7f5a27e6db40>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/modal_tutorials.html b/modal_tutorials.html
index 583e56d..cb1720e 100644
--- a/modal_tutorials.html
+++ b/modal_tutorials.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorials &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorials &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
@@ -63,6 +63,7 @@
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html">Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom</a></li>
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html">Modal Tutorial 04: Modal Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="modules.html">SDynPy Programming Interface</a></li>
@@ -141,6 +142,17 @@ <h1>Modal Tutorials<a class="headerlink" href="#modal-tutorials" title="Permalin
 <li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l1"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#The-Second-Order,-Linear-Differential-System-of-Equations">The Second-Order, Linear Differential System of Equations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#Real-Modes----Damping-is-Hard">Real Modes – Damping is Hard</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#Complex-Modes----The-General-Solution">Complex Modes – The General Solution</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#An-Example-Problem">An Example Problem</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#Frequency-Response-Functions-and-Modal-Parameters">Frequency Response Functions and Modal Parameters</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#Solving-for-Modal-Parameters-from-FRFs">Solving for Modal Parameters from FRFs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#Fitting-Mode-Shapes-to-FRF-Data">Fitting Mode Shapes to FRF Data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html#Summary">Summary</a></li>
+</ul>
+</li>
 </ul>
 </div>
 </section>
@@ -156,7 +168,7 @@ <h1>Modal Tutorials<a class="headerlink" href="#modal-tutorials" title="Permalin
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee619870>
+<jinja2.runtime.BlockReference object at 0x7f5a27b3f520>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/modal_tutorials/Modal_01_What_is_Modal_Analysis/Modal_01_What_is_Modal_Analysis.html b/modal_tutorials/Modal_01_What_is_Modal_Analysis/Modal_01_What_is_Modal_Analysis.html
index f1be370..d5bbd82 100644
--- a/modal_tutorials/Modal_01_What_is_Modal_Analysis/Modal_01_What_is_Modal_Analysis.html
+++ b/modal_tutorials/Modal_01_What_is_Modal_Analysis/Modal_01_What_is_Modal_Analysis.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 01: What is modal analysis? &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 01: What is modal analysis? &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
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@@ -71,6 +71,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html">Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html">Modal Tutorial 04: Modal Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -409,7 +410,7 @@ <h2>Summary<a class="headerlink" href="#Summary" title="Permalink to this headin
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ee178f70>
+<jinja2.runtime.BlockReference object at 0x7f5a27e97820>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
   </div>
diff --git a/modal_tutorials/Modal_02_Basics_of_Vibrations/Modal_02_Basics_of_Vibrations.html b/modal_tutorials/Modal_02_Basics_of_Vibrations/Modal_02_Basics_of_Vibrations.html
index b20931c..be1ec10 100644
--- a/modal_tutorials/Modal_02_Basics_of_Vibrations/Modal_02_Basics_of_Vibrations.html
+++ b/modal_tutorials/Modal_02_Basics_of_Vibrations/Modal_02_Basics_of_Vibrations.html
@@ -4,7 +4,7 @@
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 02: Basics of Vibrations &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 02: Basics of Vibrations &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/plot_directive.css" />
@@ -85,6 +85,7 @@
 <li class="toctree-l2"><a class="reference internal" href="../Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html">Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html">Modal Tutorial 04: Modal Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -876,7 +877,7 @@ <h3>4. Investigate Damping Changes<a class="headerlink" href="#4.-Investigate-Da
   <hr/>
 
   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99edfbd240>
+<jinja2.runtime.BlockReference object at 0x7f5a263d0dc0>
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   </div>
diff --git a/modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html b/modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html
index 3abd3c6..de1d1ab 100644
--- a/modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html
+++ b/modal_tutorials/Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html
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-  <title>Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
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@@ -82,6 +82,7 @@
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 <li class="toctree-l2"><a class="reference internal" href="../Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html">Modal Tutorial 04: Modal Analysis</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -513,7 +514,7 @@ <h3>3. Identifying Drive Points<a class="headerlink" href="#3.-Identifying-Drive
   <hr/>
 
   <div role="contentinfo">
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+<jinja2.runtime.BlockReference object at 0x7f5a27516530>
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   </div>
diff --git a/modal_tutorials/Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html b/modal_tutorials/Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html
index e2dc7c6..5a4d16b 100644
--- a/modal_tutorials/Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 04: Modal Analysis &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 04: Modal Analysis &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
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@@ -92,6 +92,7 @@
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 </li>
 <li class="toctree-l2"><a class="reference internal" href="../Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -1216,7 +1217,7 @@ <h3>5. Compute the FRF matrix from modal quantities<a class="headerlink" href="#
   <hr/>
 
   <div role="contentinfo">
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+<jinja2.runtime.BlockReference object at 0x7f5a264279a0>
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diff --git a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html
index 5e2d5df..49399ba 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 05: Experimental Modal Analysis &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05: Experimental Modal Analysis &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
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@@ -72,6 +72,7 @@
 <li class="toctree-l3"><a class="reference internal" href="Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
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   <div role="contentinfo">
-<jinja2.runtime.BlockReference object at 0x7f99ef1dbd00>
+<jinja2.runtime.BlockReference object at 0x7f5a2760bee0>
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diff --git a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05a_Computing_FRF_from_Time_Data.html b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05a_Computing_FRF_from_Time_Data.html
index d323f79..b57b8b1 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05a_Computing_FRF_from_Time_Data.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 05 Part A: Computing Frequency Response from Time Data &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05 Part A: Computing Frequency Response from Time Data &mdash; SDynPy 0.14.0 documentation</title>
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@@ -102,6 +102,7 @@
 <li class="toctree-l3"><a class="reference internal" href="Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -856,7 +857,7 @@ <h3>2. Compute Frequency Response Functions<a class="headerlink" href="#2.-Compu
   <hr/>
 
   <div role="contentinfo">
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diff --git a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05b_Types_of_Excitation.html b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05b_Types_of_Excitation.html
index 78edf5c..d6b2ca1 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05b_Types_of_Excitation.html
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-  <title>Modal Tutorial 05 Part B: Types of Excitation for Experimental Modal Analysis &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05 Part B: Types of Excitation for Experimental Modal Analysis &mdash; SDynPy 0.14.0 documentation</title>
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@@ -87,6 +87,7 @@
 <li class="toctree-l3"><a class="reference internal" href="Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -948,7 +949,7 @@ <h1>Homework Problems<a class="headerlink" href="#Homework-Problems" title="Perm
   <hr/>
 
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diff --git a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05c_Selecting_Response_and_Measurement_DoFs.html b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05c_Selecting_Response_and_Measurement_DoFs.html
index 8fc2d44..16436c4 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05c_Selecting_Response_and_Measurement_DoFs.html
+++ b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05c_Selecting_Response_and_Measurement_DoFs.html
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-  <title>Modal Tutorial 05 Part C: Selecting Excitation and Response Degrees of Freedom &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05 Part C: Selecting Excitation and Response Degrees of Freedom &mdash; SDynPy 0.14.0 documentation</title>
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@@ -81,6 +81,7 @@
 <li class="toctree-l3"><a class="reference internal" href="Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -460,7 +461,7 @@ <h2>Computing Optimal Responses<a class="headerlink" href="#Computing-Optimal-Re
   <hr/>
 
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diff --git a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05d_Data_Quality_Checks.html b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05d_Data_Quality_Checks.html
index 244b830..24a62ff 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05d_Data_Quality_Checks.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 05 Part D: Data Quality Checks &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05 Part D: Data Quality Checks &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
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@@ -75,6 +75,7 @@
 <li class="toctree-l3"><a class="reference internal" href="Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -130,7 +131,7 @@ <h1>Modal Tutorial 05 Part D: Data Quality Checks<a class="headerlink" href="#Mo
   <hr/>
 
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index 2d7df1c..c6d5ffc 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05e_Fitting_Modes_to_FRF.html
+++ b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05e_Fitting_Modes_to_FRF.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 05 Part E: Fitting Modes to Data &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05 Part E: Fitting Modes to Data &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
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@@ -75,6 +75,7 @@
 <li class="toctree-l3"><a class="reference internal" href="Modal_05f_Comparing_Test_and_Analysis.html">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
@@ -130,7 +131,7 @@ <h1>Modal Tutorial 05 Part E: Fitting Modes to Data<a class="headerlink" href="#
   <hr/>
 
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diff --git a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05f_Comparing_Test_and_Analysis.html b/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05f_Comparing_Test_and_Analysis.html
index 58b1598..d5341f3 100644
--- a/modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05f_Comparing_Test_and_Analysis.html
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   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Modal Tutorial 05 Part F: Comparing Test and Analysis &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Modal Tutorial 05 Part F: Comparing Test and Analysis &mdash; SDynPy 0.14.0 documentation</title>
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       <link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
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-    <link rel="next" title="SDynPy Programming Interface" href="../../modules.html" />
+    <link rel="next" title="Modal Tutorial 06: Complex Modes" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html" />
     <link rel="prev" title="Modal Tutorial 05 Part E: Fitting Modes to Data" href="Modal_05e_Fitting_Modes_to_FRF.html" /> 
 </head>
 
@@ -75,6 +75,7 @@
 <li class="toctree-l3 current"><a class="current reference internal" href="#">Modal Tutorial 05 Part F: Comparing Test and Analysis</a></li>
 </ul>
 </li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_06_Complex_Modes/Modal_06_Complex_Modes.html">Modal Tutorial 06: Complex Modes</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
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diff --git a/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html b/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html
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--- /dev/null
+++ b/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html
@@ -0,0 +1,2994 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+    <link rel="prev" title="Modal Tutorial 05 Part F: Comparing Test and Analysis" href="../Modal_05_Experimental_Modal_Analysis/Modal_05f_Comparing_Test_and_Analysis.html" /> 
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+<li class="toctree-l1"><a class="reference internal" href="../../usage.html">Usage</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../sdynpy_showcase.html">SDynPy Showcase</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../examples.html">SDynpy Examples</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../../modal_tutorials.html">Modal Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../Modal_01_What_is_Modal_Analysis/Modal_01_What_is_Modal_Analysis.html">Modal Tutorial 01: What is modal analysis?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_02_Basics_of_Vibrations/Modal_02_Basics_of_Vibrations.html">Modal Tutorial 02: Basics of Vibrations</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_03_Multi_DoF_Vibrations/Modal_03_Multi_DoF_Vibrations.html">Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_04_Modal_Analysis/Modal_04_Modal_Analysis.html">Modal Tutorial 04: Modal Analysis</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../Modal_05_Experimental_Modal_Analysis/Modal_05_Experimental_Modal_Analysis.html">Modal Tutorial 05: Experimental Modal Analysis</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Modal Tutorial 06: Complex Modes</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#The-Second-Order,-Linear-Differential-System-of-Equations">The Second-Order, Linear Differential System of Equations</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Real-Modes----Damping-is-Hard">Real Modes – Damping is Hard</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Geometric-Interpretation-of-the-Eigenvalues-and-Eigenvectors">Geometric Interpretation of the Eigenvalues and Eigenvectors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#An-Infinite-Number-of-Mode-Shapes">An Infinite Number of Mode Shapes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Units-with-Scaled-Mode-Shapes">Units with Scaled Mode Shapes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Adding-Damping">Adding Damping</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#Complex-Modes----The-General-Solution">Complex Modes – The General Solution</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#An-Example-Problem">An Example Problem</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#Frequency-Response-Functions-and-Modal-Parameters">Frequency Response Functions and Modal Parameters</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Real-Modes">Real Modes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Complex-Modes">Complex Modes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#What-are-Real-Modes-in-the-Complex-Mode-Framework?">What are Real Modes in the Complex Mode Framework?</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Back-to-our-Example-Problem">Back to our Example Problem</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#Solving-for-Modal-Parameters-from-FRFs">Solving for Modal Parameters from FRFs</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Setting-up-the-System-of-Equations">Setting up the System of Equations</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Selecting-Coefficients-that-Minimize-Errors">Selecting Coefficients that Minimize Errors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Solving-the-Constrained-Problem">Solving the Constrained Problem</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Extracting-Poles-and-Participation-Factors">Extracting Poles and Participation Factors</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Stabilization-Diagrams">Stabilization Diagrams</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Back-to-the-Example-Problem">Back to the Example Problem</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#Fitting-Mode-Shapes-to-FRF-Data">Fitting Mode Shapes to FRF Data</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Fitting-Residuals-for-Out-of-Band-Modes">Fitting Residuals for Out-of-Band Modes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#A-Note-on-Types-of-FRFs">A Note on Types of FRFs</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Normalizing-Mode-Shapes">Normalizing Mode Shapes</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#id1">Back to the Example Problem</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#Summary">Summary</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../modules.html">SDynPy Programming Interface</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../bibliography.html">Bibliography</a></li>
+</ul>
+
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+      </div>
+    </nav>
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+      <li><a href="../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../modal_tutorials.html">Modal Tutorials</a></li>
+      <li class="breadcrumb-item active">Modal Tutorial 06: Complex Modes</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../../_sources/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Modal-Tutorial-06:-Complex-Modes">
+<h1>Modal Tutorial 06: Complex Modes<a class="headerlink" href="#Modal-Tutorial-06:-Complex-Modes" title="Permalink to this heading"></a></h1>
+<p>Complex modes can be unintuitive and difficult to understand compared to real normal modes. George Fox Lang wrote an article in Sound and Vibration magazine called “Matrix Madness and Complex Confusion… A Review of Complex Modes from Multiple Viewpoints.” He states:</p>
+<blockquote>
+<div><p>Complex Modes is one of those topics that every vibration practitioner understands – just not fully.</p>
+</div></blockquote>
+<p>The author of this document agrees with Lang; in fact, his own lack of knowledge of complex modes is what caused him to write this document, to teach himself what is going on!</p>
+<p>To better understand complex modes, we will start from the differential equations of motion and develop the theory of complex modes, discussing:</p>
+<ul class="simple">
+<li><p>Real Modes and their Limitations</p></li>
+<li><p>The state space transformation to derive the complex mode eigenvalue problem</p></li>
+<li><p>Computing natural frequency, damping, and complex mode shapes</p></li>
+<li><p>Computing FRFs from the modal parameters</p></li>
+</ul>
+<p>This document will largely follow the equations from the following sources:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://doi.org/10.1016/j.ymssp.2023.110621">An engineering interpretation of the complex modal mass in structural dynamics by Lopez and Brinckner</a></p></li>
+<li><p><a class="reference external" href="http://www.sandv.com/downloads/1211lang.pdf">Matrix Madness and Complex Confusion… A Review of Complex Modes from Multiple Viewpoints by Lang</a></p></li>
+<li><p><a class="reference external" href="https://content.iospress.com/articles/shock-and-vibration/sav00272">The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation? by Peeters, et al.</a></p></li>
+</ul>
+<section id="The-Second-Order,-Linear-Differential-System-of-Equations">
+<h2>The Second-Order, Linear Differential System of Equations<a class="headerlink" href="#The-Second-Order,-Linear-Differential-System-of-Equations" title="Permalink to this heading"></a></h2>
+<p>Most vibration engineers are familiar with the standard system of second-order, linear differential equations.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}\ddot{\mathbf{x}} + \mathbf{C}\dot{\mathbf{x}} + \mathbf{K}{\mathbf{x}} = \mathbf{F}\]</div>
+<p>where <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> is the system mass matrix, <span class="math notranslate nohighlight">\(\mathbf{C}\)</span> is the system damping matrix, <span class="math notranslate nohighlight">\(\mathbf{K}\)</span> is the system stiffness matrix, <span class="math notranslate nohighlight">\(\mathbf{F}\)</span> is the forcing applied to the system, and <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> is the system displacement response. <span class="math notranslate nohighlight">\(\dot{\mathbf{x}}\)</span> and <span class="math notranslate nohighlight">\(\ddot{\mathbf{x}}\)</span> represent the first and second derivatives of the response, velocity and acceleration, respectively.</p>
+<p>For a standard linear, time-invariant (LTI) system, the mass, stiffness matrices are not functions of time (i.e. the mass of the system doesn’t change). However, the forces and responses are generally a function of time.</p>
+</section>
+<section id="Real-Modes----Damping-is-Hard">
+<h2>Real Modes – Damping is Hard<a class="headerlink" href="#Real-Modes----Damping-is-Hard" title="Permalink to this heading"></a></h2>
+<p>Many times, when we are just starting out, we will simply ignore the damping in our part because it is “hard” or it doesn’t fit with our elegant mathematical tools.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}\ddot{\mathbf{x}} + \mathbf{K}{\mathbf{x}} = \mathbf{F}\]</div>
+<p>To try to understand the free vibration response of the this system, we can make the following assumptions:</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\mathbf{F} = \mathbf{0}\)</span> – There is no force on the system.</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathbf{x} = \mathbf{\phi}\cos(\omega t)\)</span> – The response of the system looks like some shape <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span> that will vibrate over time (represented mathematically by multiplying by a cosine function), and the vibration will have a frequency of <span class="math notranslate nohighlight">\(\omega\)</span> radians per second.</p></li>
+</ul>
+<p>Using the chain rule for differentiation, it follows that <span class="math notranslate nohighlight">\(\ddot{\mathbf{x}} = -\omega^2\mathbf{\phi}\cos(\omega t)\)</span></p>
+<p>We can substitute these equations into the previous equation to arrive at an equation in the form of the well-known <a class="reference external" href="https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem">Generalized Eigenvalue Problem</a>.</p>
+<p>First we make the substitutions.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}(-\omega^2\mathbf{\phi}\cos(\omega t)) + \mathbf{K}\mathbf{\phi}\cos(\omega t) = \mathbf{0}\]</div>
+<p>We can then recognize that the term <span class="math notranslate nohighlight">\(\cos(\omega t))\)</span> is in general nonzero so can be cancelled.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}(-\omega^2\mathbf{\phi}) + \mathbf{K}\mathbf{\phi} = \mathbf{0}\]</div>
+<p>We can then add <span class="math notranslate nohighlight">\(\omega^2\mathbf{M}\mathbf{\phi}\)</span> to both sides of the equation.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{K}\mathbf{\phi} = \omega^2\mathbf{M}\mathbf{\phi}\]</div>
+<p>This is now the same form of the <a class="reference external" href="https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem">Generalized Eigenvalue Problem</a>, which is <span class="math notranslate nohighlight">\(\mathbf{A}\mathbf{v} = \lambda\mathbf{B}\mathbf{v}\)</span>.</p>
+<p>Here it can help to further rearrange the equation by premultiplying by the inverse of the mass matrix <span class="math notranslate nohighlight">\(\mathbf{M}^{-1}\)</span>. Note that this is generally acceptable to do, because the mass matrix should generally be positive definite and therefore invertable (said another way, every degree of freedom in the system has mass associated with it).</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}^{-1}\mathbf{K}\mathbf{\phi} = \omega^2\mathbf{\phi}\]</div>
+<section id="Geometric-Interpretation-of-the-Eigenvalues-and-Eigenvectors">
+<h3>Geometric Interpretation of the Eigenvalues and Eigenvectors<a class="headerlink" href="#Geometric-Interpretation-of-the-Eigenvalues-and-Eigenvectors" title="Permalink to this heading"></a></h3>
+<p>We can gain intuition here by thinking about what the eigenvalue problem means <em>geometrically</em>. We have a vector <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span> that represents some shape. Looking at the right side of the equation, if we multiply that vector by a scalar <span class="math notranslate nohighlight">\(\omega^2\)</span>, it will simply scale the length of the vector, but not change its direction in which it’s pointing. In the context of a vibration shape, this means that it will scale the shape larger or smaller, but it won’t change the shape of
+vibration. You get a bigger or smaller vibration with the same shape.</p>
+<p>Now because of the equality, the left-hand side of the equation must be equivalent to the right-hand side of the equation, which means that the left-hand side must also be equivalent to simply scaling the original vector <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span>. However, we know from linear algebra that multiplying a vector by a matrix will in general change the length of the vector <em>and</em> where the vector is pointing. In the context of our vibration shape <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span>, this means that multiplying a
+vibration shape by a matrix will in general change not only the size of the vibration shape, but the shape itself.</p>
+<p>Let’s look at a brief example to illustrate this point.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="c1"># Set random seed so we can get repeatable results for the documentation.</span>
+<span class="c1"># Remove this line to allow the matrices and vectors to be random.</span>
+<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
+
+<span class="c1"># Create a vector to test</span>
+<span class="n">vector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">],</span>
+                   <span class="p">[</span><span class="mi">2</span><span class="p">]])</span>
+
+<span class="c1"># Now create a random scalar</span>
+<span class="n">scalar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Scalar:&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">scalar</span><span class="p">)</span>
+
+<span class="c1"># Now create a random matrix</span>
+<span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Matrix:&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">matrix</span><span class="p">)</span>
+
+<span class="c1"># Now multiply the vector by the scalar, and multiply the vector by the matrix</span>
+<span class="n">vector_scalar</span> <span class="o">=</span> <span class="n">scalar</span><span class="o">*</span><span class="n">vector</span>
+<span class="n">vector_matrix</span> <span class="o">=</span> <span class="n">matrix</span><span class="nd">@vector</span> <span class="c1"># Matrix multiplication is @ in numpy.</span>
+
+<span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="o">*</span><span class="n">vector</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="o">*</span><span class="n">vector_scalar</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Multiply Vector by Scalar&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="o">*</span><span class="n">vector</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="o">*</span><span class="n">vector_matrix</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Original Vector&#39;</span><span class="p">,</span><span class="s1">&#39;Modified Vector&#39;</span><span class="p">])</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Multiply Vector by Matrix&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">]);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Scalar:
+0.5507979025745755
+Matrix:
+[[0.70814782 0.29090474]
+ [0.51082761 0.89294695]]
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_4_1.png" />
+</div>
+</div>
+<p>Clearly, multiplying by a scalar has resulted in a vector with the same direction and a different length, whereas multiplying by a matrix has changed not only the length, but also the direction of the vector.</p>
+<p>The resulting direction of the vector will not only be dependent on the matrix that it is multiplied by, but also the original vector itself. Multiplying different vectors by the same matrix will generally result in a different final direction of the resulting vectors. We can investigate this by multiplying many different vectors by the matrix from the previous step.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here we make a 20x20 grid of 2x1 vectors that we can multiply by our matrix</span>
+<span class="n">vectors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mgrid</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="mi">3</span><span class="p">:</span><span class="mi">20</span><span class="n">j</span><span class="p">,</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="mi">3</span><span class="p">:</span><span class="mi">20</span><span class="n">j</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+
+<span class="n">scaled_vectors</span> <span class="o">=</span> <span class="n">matrix</span> <span class="o">@</span> <span class="n">vectors</span>
+
+<span class="c1"># Reshape for easier plotting</span>
+<span class="n">vectors_for_plotting</span> <span class="o">=</span> <span class="n">vectors</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+<span class="n">scaled_vectors_for_plotting</span> <span class="o">=</span> <span class="n">scaled_vectors</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="o">*</span><span class="n">vectors_for_plotting</span><span class="p">,</span> <span class="o">*</span><span class="n">vectors_for_plotting</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">quiver</span><span class="p">(</span><span class="o">*</span><span class="n">vectors_for_plotting</span><span class="p">,</span> <span class="o">*</span><span class="n">scaled_vectors_for_plotting</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">angles</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale_units</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Original Vector&#39;</span><span class="p">,</span><span class="s1">&#39;Multiplied Vector&#39;</span><span class="p">]);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_6_0.png" />
+</div>
+</div>
+<p>Clearly, we can see that in general, vectors in different directions will be modified by the matrix differently. However, looking closely at the above plot, we can see that there are vectors in particular directions that maintain their original direction after multiplication by the matrix (a change in length is acceptable). And if you recall, we are looking for exactly such a vector that when multiplied by a matrix retains its direction. Therefore, in order to solve the eigenvalue equation
+listed previously, we need to find these special directions. We can’t just use any shape <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span>. We have to find particular shapes <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span> that when multiplied by the system matrices <span class="math notranslate nohighlight">\(\mathbf{M}^{-1}\mathbf{K}\)</span> do not change shape but perhaps get bigger or smaller. Mathematically, we call these vectors the <em>eigenvectors</em> where the prefix <em>eigen</em> is German for <em>particular</em>. In structural dynamics, we call these particular shapes the <em>mode shapes</em>, and they have
+many special properties.</p>
+<p>Similarly, we call the scalar quantity <span class="math notranslate nohighlight">\(\omega^2\)</span> that gets multiplied by the eigenvector the <em>eigenvalue</em>. It is the particular value that goes along with the particular vector. We will generally have as many eigenvector/eigenvalue pairs as we have degrees of freedom in the system.</p>
+<p>We note that there are linear algebra tools available to solve for the eigenvalues and eigenvectors of a given matrix. For example, in Numpy, we can use <code class="docutils literal notranslate"><span class="pre">np.linalg.eig</span></code>. We can then plot the eigenvector directions on the above figure to show that they indeed align with the directions where the vectors are scaled but the direction does not change. Note also that there are two degrees of freedom in this problem, so we find two eigenvectors, or two directions where the direction of the vectors do
+not change.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">eigenvalues</span><span class="p">,</span> <span class="n">eigenvectors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">matrix</span><span class="p">)</span>
+<span class="c1"># Plot the positive and negative eigenvectors so it looks like a line through the origin</span>
+<span class="c1"># Also reshape them so they are easier to plot</span>
+<span class="c1"># Multiply by 4 so they take up the whole plot</span>
+<span class="n">eigenvectors_to_plot</span> <span class="o">=</span> <span class="mi">4</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">eigenvectors</span><span class="p">,</span><span class="o">-</span><span class="n">eigenvectors</span><span class="p">))</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="o">*</span><span class="n">eigenvectors_to_plot</span><span class="p">,</span><span class="s1">&#39;k--&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
+<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
+<span class="n">fig</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_8_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_8_0.png" />
+</div>
+</div>
+<p>We see that vectors that are along the directions represented by the eigenvectors do not change direction, only change scale.</p>
+<p>Without approximation or loss of generality, we can take <em>all</em> of the eigenvectors or mode shapes of a system as column vectors and <em>stack</em> them side-by-side to form a matrix. This matrix forms a coordinate transformation: in the same way we can transform between a local and global coordinate system, we can also transform between <em>physical</em> and <em>modal</em> degrees of freedom. Physical degrees of freedom are generally represented by some physical quantity (e.g., Point 1 moves 2.5 centimeters in the
+vertical direction). Modal degrees of freedom are generally represented by some <em>modal degree of freedom</em> or <em>modal quantity</em>. Motions are then represented by how much of each mode shape is present in the motion (e.g., the system moves with a shape that looks like mode shape 1 scaled up by a factor of 2, plus mode shape 2 scaled down by a factor of 1/2). We can represent this mathematically by saying physical motions <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> are represented by a linear combination of shapes
+<span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span>. We call the coefficient matrix <span class="math notranslate nohighlight">\(\mathbf{q}\)</span> the <em>modal coefficients</em> or <em>modal degrees of freedom</em>, because they describe how much of each mode shape in <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span> is present in <span class="math notranslate nohighlight">\(\mathbf{x}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{x} = \mathbf{\phi}\mathbf{q}\]</div>
+<p>We can substitute this transformation into our original undamped equations of motion to get equations of motion in terms of the modal degrees of freedom <span class="math notranslate nohighlight">\(\mathbf{q}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}\mathbf{\phi}\ddot{\mathbf{q}} + \mathbf{K}\mathbf{\phi}{\mathbf{q}} = \mathbf{F}\]</div>
+<p>We note that the mode shapes <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span> are invariant in time, (they are a function of the matrices <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> and <span class="math notranslate nohighlight">\(\mathbf{K}\)</span>, which are also invariant in time). Therefore taking the derivative of the physical response <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> is simply</p>
+<div class="math notranslate nohighlight">
+\[\ddot{\mathbf{x}} = \mathbf{\phi}\ddot{\mathbf{q}}\]</div>
+<p>While the transformed equation above is technically valid, we generally do not like this form, because it breaks the symmetry of the mass and stiffness matrices. For all real structures, the mass, stiffness, and damping matrices should be symmetric. This is a function of Netwton’s third law of equal and opposite reactions: if there is a force developing at degree of freedom 1 due to motion of degree of freedom 2, an equal and opposite force will develop on degree of freedom 2 due to degree of
+freedom 1 pushing back on it. Currently, however, in our transformed equations of motion, the new effective mass matrix <span class="math notranslate nohighlight">\(\mathbf{M}\mathbf{\phi}\)</span> is not symmetric, nor is the equivalent effective stiffness matrix. We therefore premultiply the equations of motion by <span class="math notranslate nohighlight">\(\mathbf{\phi}^T\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\ddot{\mathbf{q}} + \mathbf{\phi}^T\mathbf{K}\mathbf{\phi}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<p>In this final format, the effective mass and stiffness matrices <span class="math notranslate nohighlight">\(\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\)</span> and <span class="math notranslate nohighlight">\(\mathbf{\phi}^T\mathbf{K}\mathbf{\phi}\)</span> are symmetric. We will often make a substitution</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{M}}=\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\]</div>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{K}}=\mathbf{\phi}^T\mathbf{K}\mathbf{\phi}\]</div>
+<p>where <span class="math notranslate nohighlight">\(\tilde{\mathbf{M}}\)</span> and <span class="math notranslate nohighlight">\(\tilde{\mathbf{K}}\)</span> are referred to as the <em>modal mass</em> and <em>modal stiffness</em> matrices. The modal system of equations is then</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{M}}\ddot{\mathbf{q}} + \tilde{\mathbf{K}}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<p>The symmetry of the system matrices is not the only interesting property when we transform into modal coordinates. It turns out that the mode shapes <span class="math notranslate nohighlight">\(\mathbf{\phi}\)</span> <em>diagonalize</em> the mass and stiffness matrices. The modal mass and stiffness matrices, formed by pre- and post-multiplying the mass and stiffness matrices by the mode shapes, are diagonal. This physically means that there is no coupling between modal degrees of freedom; the response of mode 1 does not depend at all on the
+response of mode 2. They can be treated as entirely separate equations. Compare this to a physical mass and stiffness matrix, where there would be coupling between the degrees of freedom.</p>
+<p>We can think about this concept physically. In the physical domain, if I pluck the end of a beam, all of the degrees of freedom on the beam will eventually vibrate. This is because the physical degrees of freedom are all connected to one another through the mass and stiffness of a beam. In the modal domain, however, if I pluck “mode 1” (an admittedly hard thing to accomplish in practice, as you would need to deform the beam into its first mode shape and then let it go instantaneously), none of
+the other modes will be excited at all, because there is no coupling between them. Note that this still means that the entire beam will vibrate, because the mode shape of the beam contains all the beam degrees of freedom, so if the mode shape vibrates, the whole beam vibrates. In fact because the modal representation described above is exact, plucking the end of a beam will result in exactly the same beam motion regardless of whether it is represented by modal or physical degrees of freedom. The
+modal transformation is simply a different way to represent a dynamic system.</p>
+</section>
+<section id="An-Infinite-Number-of-Mode-Shapes">
+<h3>An Infinite Number of Mode Shapes<a class="headerlink" href="#An-Infinite-Number-of-Mode-Shapes" title="Permalink to this heading"></a></h3>
+<p>One last concept to describe before we move on is the concept of mode shape scaling. From the previous figure, we saw that each eigenvector of a matrix mostly represented a particular direction, rather than a specific vector. Any vector in that direction would be only scaled by the matrix instead of having its direction modified. Therefore, eigenvectors are only really unique up to a scale factor. An eigenvector times a scalar is still a valid eigenvector of a matrix, so you could argue there
+are an infinite number of eigenvectors of any matrix.</p>
+<p>Rather than allowing for any arbitrary scaling of eigenvectors, we will often scale them to some preferred normalization. One might, for example, scale an eigenvector such that it has a length of 1, i.e. make it a unit vector. Another might, for example, scale a specific degree of freedom to 1. The most common scaling on an eigenvector, particularly in the context of structural dynamics where an eigenvector represents a mode shape, is to scale the eigenvectors such that the modal mass matrix
+<span class="math notranslate nohighlight">\(\tilde{\mathbf{M}}\)</span> is equal to the identity matrix. This is known as <em>mass normalization</em>. We have already described how the modal mass matrix is diagonal. To mass-normalize a mode shape, we only need to scale the mode shape such that the diagonal entries obtained when computing the modal mass matrix end up being 1. To do this for the <span class="math notranslate nohighlight">\(r\)</span>th mode, we can simply compute the modal mass matrix with a single unscaled mode shape <span class="math notranslate nohighlight">\(\tilde{\mathbf{\phi}}_r\)</span>, then divide the mode
+shapes by the square-root of the diagonal entry of the modal mass matrix corresponding to that mode shape.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{\phi}_r = \frac{\tilde{\mathbf{\phi}}_r}{\sqrt{{\tilde{\mathbf{\phi}}_r}^T\mathbf{M}\tilde{\mathbf{\phi}}_r}}\]</div>
+<p>When we compute the modal stiffness using mass normalized modes, the entries on the diagonal of the modal stiffness matrix will be equal to the natural frequency squared. For example, for the <span class="math notranslate nohighlight">\(r\)</span>th mode shape:</p>
+<div class="math notranslate nohighlight">
+\[{\mathbf{\phi}_r}^T\mathbf{K}\mathbf{\phi}_r = {\omega_r}^2\]</div>
+</section>
+<section id="Units-with-Scaled-Mode-Shapes">
+<h3>Units with Scaled Mode Shapes<a class="headerlink" href="#Units-with-Scaled-Mode-Shapes" title="Permalink to this heading"></a></h3>
+<p>Note that when we mass-normalize the mode shapes, the mode shapes will have a unit of <span class="math notranslate nohighlight">\(\frac{1}{\sqrt{m}}\)</span>. The mass matrix has dimensions of <span class="math notranslate nohighlight">\(m\)</span> where <span class="math notranslate nohighlight">\(m\)</span> is the unit of mass, and to equal the dimensionless identity matrix when the modal mass matrix is assembled, the mode shape units from pre- and post-multiplying must cancel out the units of the mass matrix. The modal coefficient <span class="math notranslate nohighlight">\(\mathbf{q}\)</span> must then have units of <span class="math notranslate nohighlight">\({l}{\sqrt{m}}\)</span> where <span class="math notranslate nohighlight">\(l\)</span> is a length
+dimension, and one must recall that taking a derivative over time adds a <span class="math notranslate nohighlight">\(\frac{1}{t}\)</span> where <span class="math notranslate nohighlight">\(t\)</span> is the unit representing time.</p>
+<p>As an example, we will assume we have a meter/kilogram/newton/second unit system. The modal transformation equation must have consistent units.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{x} = \mathbf{\phi}\mathbf{q}\]</div>
+<div class="math notranslate nohighlight">
+\[\left[m\right] = \left[\frac{1}{\sqrt{kg}}\right]\left[m\sqrt{kg}\right]\]</div>
+<p>Similarly, the equations of motion must have consistent units.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\ddot{\mathbf{q}} + \mathbf{\phi}^T\mathbf{K}\mathbf{\phi}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<div class="math notranslate nohighlight">
+\[\left[\frac{1}{\sqrt{kg}}\right]\left[kg\right]\left[\frac{1}{\sqrt{kg}}\right]\left[\frac{m\sqrt{kg}}{s^2}\right] + \left[\frac{1}{\sqrt{kg}}\right]\left[\frac{N}{m}\right]\left[\frac{1}{\sqrt{kg}}\right]\left[m\sqrt{kg}\right] = \left[\frac{1}{\sqrt{kg}}\right]\left[N\right]\]</div>
+<p>We can substitute <span class="math notranslate nohighlight">\(\left[N\right] = \left[\frac{kg{\,}m}{s^2}\right]\)</span></p>
+<div class="math notranslate nohighlight">
+\[\left[\frac{1}{\sqrt{kg}}\right]\left[kg\right]\left[\frac{1}{\sqrt{kg}}\right]\left[\frac{m\sqrt{kg}}{s^2}\right] + \left[\frac{1}{\sqrt{kg}}\right]\left[\frac{kg{\,}m}{m{\,}s^2}\right]\left[\frac{1}{\sqrt{kg}}\right]\left[m\sqrt{kg}\right] = \left[\frac{1}{\sqrt{kg}}\right]\left[\frac{kg{\,}m}{s^2}\right]\]</div>
+<p>Then with a good deal of cancelling units</p>
+<div class="math notranslate nohighlight">
+\[\left[\frac{m\sqrt{kg}}{s^2}\right] + \left[\frac{m\sqrt{kg}}{s^2}\right] = \left[\frac{m\sqrt{kg}}{s^2}\right]\]</div>
+</section>
+<section id="Adding-Damping">
+<h3>Adding Damping<a class="headerlink" href="#Adding-Damping" title="Permalink to this heading"></a></h3>
+<p>Eventually, we will need to consider the damping values that we simply disregarded previously. If we bring back our original damped system of equations, and transform to modal space, we end up with the following equations of motion:</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}\ddot{\mathbf{x}} + \mathbf{C}\dot{\mathbf{x}} + \mathbf{K}{\mathbf{x}} = \mathbf{F}\]</div>
+<div class="math notranslate nohighlight">
+\[\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\ddot{\mathbf{q}} + \mathbf{\phi}^T\mathbf{C}\mathbf{\phi}\dot{\mathbf{q}} + \mathbf{\phi}^T\mathbf{K}\mathbf{\phi}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{M}}\ddot{\mathbf{q}} + \tilde{\mathbf{C}}\dot{\mathbf{q}} + \tilde{\mathbf{K}}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<p>Here the matrix <span class="math notranslate nohighlight">\(\tilde{\mathbf{C}} = \mathbf{\phi}^T\mathbf{C}\mathbf{\phi}\)</span> is referred to as the modal damping matrix.</p>
+<p>The important consideration that we must make to determine if the mode shapes that we have computed are still valid mode shapes is to understand if the modal damping matrix is also diagonalized by the mode shape matrix. If it is, then our mode shapes are still valid: they represent independent, uncoupled degrees of freedom in our system. However if the modal damping matrix is not diagonal, that means there is coupling between the degrees of freedom, and the degrees of freedom represented by the
+equation are not actually modal degrees of freedom.</p>
+<p>The mode shapes can diagonalize the damping matrix, but only in a very limited case. Since the mode shapes only decouple the mass and stiffness matrices from which they were computed, the damping matrix will only be decoupled completely if the physical damping matrix is a linear combination of the physical mass and stiffness matrices.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{C} = \alpha\mathbf{M} + \beta\mathbf{K}\]</div>
+<p>This is known as <em>proportional damping</em>, where the damping is proportional to the mass and stiffness of the system. Transforming to modal coordinates will diagonalize this matrix. If we do have proportional damping, and we use mass normalized modes for the modal transformation, the modal damping matrix will be diagonal, and the entry corresponding to the <span class="math notranslate nohighlight">\(r\)</span>th mode will be equal to</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{C}}_{rr} = {\mathbf{\phi}_r}^T\mathbf{C}\mathbf{\phi}_r = 2\zeta_r\omega_r = \alpha+\beta\omega_r^2\]</div>
+<p>where <span class="math notranslate nohighlight">\(\omega_r\)</span> is the natural frequency and <span class="math notranslate nohighlight">\(\zeta_r\)</span> is the fraction of critical damping for mode <span class="math notranslate nohighlight">\(r\)</span>.</p>
+<p>We only get a diagonal modal damping matrix in this case. For any other case, we will still have off-diagonal terms. Luckily for us, these diagonal terms are often small, so using the real mode shapes derived from the mass and stiffness of the system is not a terrible approximation. However, there are cases where the approximation is not good, and in those cases, we need to turn to a different toolset to help us analyze the system.</p>
+</section>
+</section>
+<section id="Complex-Modes----The-General-Solution">
+<h2>Complex Modes – The General Solution<a class="headerlink" href="#Complex-Modes----The-General-Solution" title="Permalink to this heading"></a></h2>
+<p>A more general solution to decoupling damped vibration problems requires us to rearange our equations of motion. Rather than treating the system of equations as <span class="math notranslate nohighlight">\(N\)</span> equations with a second order derivative, we will instead treat the system of equations as <span class="math notranslate nohighlight">\(2N\)</span> first-order equations using a simple introducton of a new variable <span class="math notranslate nohighlight">\(\mathbf{v} = \dot{\mathbf{x}}\)</span> to represent velocity. In this way, we can eliminate the double-derivative in the acceleration term
+(<span class="math notranslate nohighlight">\(\ddot{\mathbf{x}} = \dot{\mathbf{v}}\)</span>)</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}\dot{\mathbf{v}} + \mathbf{C}\dot{\mathbf{x}} + \mathbf{K}{\mathbf{x}} = \mathbf{F}\]</div>
+<p>However, we now need to add some relationship between <span class="math notranslate nohighlight">\(v\)</span> and <span class="math notranslate nohighlight">\(x\)</span>. We could simply write <span class="math notranslate nohighlight">\(\mathbf{v} = \dot{\mathbf{x}}\)</span> as we showed above. However, it will be useful to keep our matrices symmetric, so we will construct the equations in a slightly more complex way.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{M}\dot{\mathbf{x}} - \mathbf{M}\mathbf{v} = \mathbf{0}\]</div>
+<p>We can combine these two sets of equations into one set of equations of the form <span class="math notranslate nohighlight">\(\mathbf{A}\dot{\mathbf{z}}+\mathbf{B}\mathbf{z} = \mathbf{u}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\begin{bmatrix}\dot{\mathbf{v}}\\\dot{\mathbf{x}}\end{bmatrix} + \begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}{\mathbf{v}}\\{\mathbf{x}}\end{bmatrix} = \begin{bmatrix}\mathbf{0}\\\mathbf{F}\end{bmatrix}\end{split}\]</div>
+<p>Here we note that our coefficient matrices are both symmetric, the first term contains all of the derivatives, the second term contains no derivatives, and the right-hand side of the equation contains our input forces.</p>
+<p>We can perform a similar operation as previous to try to understand the free response of the system. We will make the following assumptions.</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\begin{bmatrix}\mathbf{0}\\\mathbf{F}\end{bmatrix} = \begin{bmatrix}\mathbf{0}\\\mathbf{0}\end{bmatrix}\)</span> – The force into the system is zero.</p></li>
+<li><p><span class="math notranslate nohighlight">\(\begin{bmatrix}\mathbf{v}\\\mathbf{x}\end{bmatrix} = \begin{bmatrix}\lambda\mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}e^{\lambda t}\)</span> – The response is a complex sinusoid.</p></li>
+</ul>
+<p>In this case, the first assumption is essentially identical to the first assumption in the real mode shape case. The second assumption is essentially a complex generalization of the second assumption made in the real mode shape case. In this generalization, instead of a “real” shape where all responses are either in-phase or 180<span class="math notranslate nohighlight">\(^\circ\)</span> out-of-phase, the shape can now be complex, meaning there can be any phasing between different values in the shape. The exponential <span class="math notranslate nohighlight">\(e\)</span> now
+represents an oscillatory portion and decaying or growing portion of the response (<span class="math notranslate nohighlight">\(\lambda\)</span> is in general a complex number. The imaginary portion of it will result in sinusoidal motion of the form <span class="math notranslate nohighlight">\(e^{j\theta} = \cos{\theta} + j\sin{\theta}\)</span> where <span class="math notranslate nohighlight">\(j = \sqrt{-1}\)</span>. The real part will result in exponential decay or exponential growth of the form <span class="math notranslate nohighlight">\(e^{x}\)</span>). The term <span class="math notranslate nohighlight">\(\lambda\)</span> appears on the first row of the second assumption due to the chain rule when differentiating
+<span class="math notranslate nohighlight">\(\mathbf{v} = \dot{x}\)</span>.</p>
+<p>Using the chain rule for differentiation, we can also say <span class="math notranslate nohighlight">\(\begin{bmatrix}\dot{\mathbf{v}}\\\dot{\mathbf{x}}\end{bmatrix} = \lambda\begin{bmatrix}\lambda\mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}e^{\lambda t}\)</span></p>
+<p>We can then apply these assumptions to our equations of motion:</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\left(\lambda \begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}e^{\lambda t}\right) + \begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}e^{\lambda t} = \begin{bmatrix}\mathbf{0}\\\mathbf{0}\end{bmatrix}\end{split}\]</div>
+<p>We can again recognize that the term <span class="math notranslate nohighlight">\(e^{\lambda t}\)</span> is in general nonzero and therefore can be cancelled.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\left(\lambda \begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}\right) + \begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = \begin{bmatrix}\mathbf{0}\\\mathbf{0}\end{bmatrix}\end{split}\]</div>
+<p>We can then subtract <span class="math notranslate nohighlight">\(\begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}\)</span> from both sides of the equation and we end up with</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\lambda \begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = - \begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}\end{split}\]</div>
+<p>While a little more complex, this equation is also a <a class="reference external" href="https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem">Generalized Eigenvalue Problem</a>.</p>
+<p>Transforming the eigenvalues and eigenvectors into natural frequencies and mode shapes is not as straightforward in the present case with complex modes. In general, we will solve for <span class="math notranslate nohighlight">\(2N\)</span> complex eigenvalues; however, due to the fact that the matrices are real, the eigenvalues will be complex conjugates of one another. We will often hear these values called <em>poles</em> because they are zeros of the denominator polynomials in the frequency response function formulation that we will discuss
+later.</p>
+<p>The eigenvalues <span class="math notranslate nohighlight">\(\lambda\)</span> are often expressed as a function of the critical damping ratio <span class="math notranslate nohighlight">\(\zeta\)</span> and the natural frequency <span class="math notranslate nohighlight">\(\omega\)</span>. For the <span class="math notranslate nohighlight">\(r\)</span>th mode,</p>
+<div class="math notranslate nohighlight">
+\[\lambda_r = -\zeta_r \omega_r + j\omega_r\sqrt{1-\zeta_r^2}\]</div>
+<p>The real part <span class="math notranslate nohighlight">\(-\zeta_r \omega_r\)</span> drives the exponential decay of the sinusoid. The imaginary part drives the oscillatory behavior. Because of this, the term <span class="math notranslate nohighlight">\(\omega_r\sqrt{1-\zeta_r}\)</span> is often referred to as the <em>damped natural frequency</em> <span class="math notranslate nohighlight">\(\omega_d\)</span></p>
+<div class="math notranslate nohighlight">
+\[\omega_{dr} = \omega_r\sqrt{1-\zeta_r^2}\]</div>
+<p>To recover the natural frequency <span class="math notranslate nohighlight">\(\omega_r\)</span>, we can simply take the magnitude of the eigenvalue.</p>
+<div class="math notranslate nohighlight">
+\[\left|\lambda_r\right| = \sqrt{\Re{(\lambda_r)}^2 + \Im{(\lambda_r)}^2} = \sqrt{(\zeta_r^2 \omega_r^2) + (\omega_r^2 (1-\zeta_r^2))} = \sqrt{\omega_r^2 + \omega_r^2\zeta_r^2 - \omega_r^2\zeta_r^2} = \omega_r\]</div>
+<p>To recover the damping ratio, we can divide the negative real part of the eigenvalue by the magnitude of the eigenvalue.</p>
+<div class="math notranslate nohighlight">
+\[- \frac{\Re{(\lambda_r)}}{\left|\lambda_r\right|} = \frac{\zeta_r\omega_r}{\omega_r} = \zeta_r\]</div>
+<p>Note that for complex modes, the damping is part of the solution to the free vibration problem and appears in the solution, whereas in the real mode case where damping was ignored, it was not.</p>
+<p>We will also solve for <span class="math notranslate nohighlight">\(2N\)</span> eigenvectors. These will also appear in complex conjugate pairs. They will be of the form</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}\end{split}\]</div>
+<p>We can extract the mode shape from the eigenvector simply by selecting the bottom partition of the vector.</p>
+<p>The equations to compute the modal mass and modal damping matrices are the same as those used in the case of real modes. However, in general, the modal mass and modal damping matrices are not diagonal and have complex elements.</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{M}}=\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\]</div>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{C}}=\mathbf{\phi}^T\mathbf{C}\mathbf{\phi}\]</div>
+<p>Recall that the eigenvalue problem we solved for was with respect to the generalized form <span class="math notranslate nohighlight">\(\mathbf{A}\dot{\mathbf{z}}+\mathbf{B}\mathbf{z} = \mathbf{u}\)</span>. Therefore the eigenvectors will diagonalize these matrices.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}^T\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = \begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}^T\mathbf{A}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = \tilde{\mathbf{A}}\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}^T\begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = \begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}^T\mathbf{B}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = \tilde{\mathbf{B}}\end{split}\]</div>
+<p>We often refer to these matrices <span class="math notranslate nohighlight">\(\tilde{\mathbf{A}}\)</span> and <span class="math notranslate nohighlight">\(\tilde{\mathbf{B}}\)</span> as the <em>Modal A</em> and <em>Modal B</em> matrices.</p>
+<p>Modal mass is defined in different ways for complex modes. Some references will treat the diagonal entries in Modal A <span class="math notranslate nohighlight">\(\tilde{\mathbf{A}}_{rr}\)</span> directly as the modal mass values. Other references define the modal mass for the <span class="math notranslate nohighlight">\(r\)</span>th mode <span class="math notranslate nohighlight">\(m_r\)</span> as:</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{A}}_{rr} = 2\lambda_r m_r + 2\zeta_r\omega_r m_r = 2j\omega_{dr} m_r\]</div>
+<div class="math notranslate nohighlight">
+\[m_r = \frac{\tilde{\mathbf{A}}_{rr}}{2j\omega_{dr}}\]</div>
+<p>It doesn’t really matter which definition is used as long as the definition is consistently used when reconstructing responses from modal parameters. This document will proceed with the modal mass defined as the diagonal terms of <span class="math notranslate nohighlight">\(\tilde{\mathbf{A}}\)</span>.</p>
+<p>We can also see that if we premultiply the eigenvalue equation by our eigenvectors, we get a relationship between the Modal A and Modal B matrices.</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{B}}_{rr} = -\lambda_r \tilde{\mathbf{A}}_{rr}\]</div>
+</section>
+<section id="An-Example-Problem">
+<h2>An Example Problem<a class="headerlink" href="#An-Example-Problem" title="Permalink to this heading"></a></h2>
+<p>Before we get too far, let’s illustrate some of these concepts with a small example problem. We will generate a 3-degree-of-freedom problem that we will simulate. We will explore undamped, proportionally damped, and generally damped cases.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">scipy.linalg</span> <span class="k">as</span> <span class="nn">la</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span><span class="p">,</span> <span class="n">HTML</span>
+
+<span class="k">def</span> <span class="nf">pretty_print_table</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
+    <span class="k">return</span> <span class="n">display</span><span class="p">(</span> <span class="n">HTML</span><span class="p">(</span> <span class="n">df</span><span class="o">.</span><span class="n">to_html</span><span class="p">()</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\\</span><span class="s2">n&quot;</span><span class="p">,</span><span class="s2">&quot;&lt;br&gt;&quot;</span><span class="p">)</span> <span class="p">)</span> <span class="p">)</span>
+
+<span class="c1"># Set up physical matrices</span>
+<span class="n">K</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span> <span class="mi">6000</span><span class="p">,</span> <span class="o">-</span><span class="mi">2000</span><span class="p">,</span>     <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="o">-</span><span class="mi">2000</span><span class="p">,</span>  <span class="mi">4000</span><span class="p">,</span> <span class="o">-</span><span class="mi">2000</span><span class="p">],</span>
+              <span class="p">[</span>    <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2000</span><span class="p">,</span>  <span class="mi">6000</span><span class="p">]])</span>
+<span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+              <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]])</span>
+<span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.36</span><span class="o">/</span><span class="mi">2</span>
+<span class="n">beta</span> <span class="o">=</span> <span class="mf">0.00154865</span><span class="o">/</span><span class="mi">2</span>
+<span class="n">C_proportional</span> <span class="o">=</span> <span class="n">alpha</span><span class="o">*</span><span class="n">M</span><span class="o">+</span><span class="n">beta</span><span class="o">*</span><span class="n">K</span>
+
+<span class="n">C_general</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">14</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
+                      <span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">],</span>
+                      <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">]])</span><span class="o">/</span><span class="mi">2</span>
+</pre></div>
+</div>
+</div>
+<p>For the complex mode cases, let’s construct the <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> and <span class="math notranslate nohighlight">\(\mathbf{B}\)</span> matrices. The <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> matrix will depend on damping, the <span class="math notranslate nohighlight">\(\mathbf{B}\)</span> matrix will not.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># For convenience, construct a matrix of zeros</span>
+<span class="n">Z</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+<span class="c1"># Assemble State Space Matrices</span>
+<span class="n">A_undamped</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">Z</span><span class="p">,</span> <span class="n">M</span><span class="p">],</span>
+                       <span class="p">[</span><span class="n">M</span><span class="p">,</span> <span class="n">Z</span><span class="p">]])</span>
+<span class="n">A_proportional</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">Z</span><span class="p">,</span> <span class="n">M</span><span class="p">],</span>
+                           <span class="p">[</span><span class="n">M</span><span class="p">,</span> <span class="n">C_proportional</span><span class="p">]])</span>
+<span class="n">A_general</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">Z</span><span class="p">,</span> <span class="n">M</span><span class="p">],</span>
+                      <span class="p">[</span><span class="n">M</span><span class="p">,</span> <span class="n">C_general</span><span class="p">]])</span>
+<span class="n">B</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="o">-</span><span class="n">M</span><span class="p">,</span> <span class="n">Z</span><span class="p">],</span>
+              <span class="p">[</span> <span class="n">Z</span><span class="p">,</span> <span class="n">K</span><span class="p">]])</span>
+</pre></div>
+</div>
+</div>
+<p>Now let’s compute the eigenvalues and eigenvectors. For the 2nd-order eigenvalue using the mass and stiffness matrices directly, we can use the <code class="docutils literal notranslate"><span class="pre">eigh</span></code> function which assumes Hermetian, positive definite matrices. For the complex mode cases, we will have to use the more general <code class="docutils literal notranslate"><span class="pre">eig</span></code> function.</p>
+<p>When using an eigenvalue solver, be wary of the order that matrices occur in the function call. For example, in the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html">scipy.linalg.eig documentation</a>, it states the following:</p>
+<blockquote>
+<div><p><strong>scipy.linalg.eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False)</strong></p>
+<p>Solve an ordinary or generalized eigenvalue problem of a square matrix.</p>
+<p>Find eigenvalues w and right or left eigenvectors of a general matrix:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">a</span>   <span class="n">vr</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>        <span class="n">b</span>   <span class="n">vr</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span>
+<span class="n">a</span><span class="o">.</span><span class="n">H</span> <span class="n">vl</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">w</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span> <span class="n">b</span><span class="o">.</span><span class="n">H</span> <span class="n">vl</span><span class="p">[:,</span><span class="n">i</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>where <code class="docutils literal notranslate"><span class="pre">.H</span></code> is the Hermitian conjugation.</p>
+</div></blockquote>
+<p>SciPy uses the definition <span class="math notranslate nohighlight">\(\mathbf{A}\mathbf{v} = \lambda\mathbf{B}\mathbf{v}\)</span> for its eigenvalue problem, and our equation we have defined as <span class="math notranslate nohighlight">\(- \mathbf{B}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix} = \lambda \mathbf{A}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}\)</span>. Note that what SciPy calls <code class="docutils literal notranslate"><span class="pre">a</span></code> in its function call is what we have defined as <span class="math notranslate nohighlight">\(-\mathbf{B}\)</span>, and what SciPy calls <code class="docutils literal notranslate"><span class="pre">b</span></code> is what we have defined as <span class="math notranslate nohighlight">\(\mathbf{A}\)</span>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute Eigenvalues and Eigenvectors</span>
+<span class="n">lam_2ndOrder</span><span class="p">,</span><span class="n">E_2ndOrder</span> <span class="o">=</span> <span class="n">la</span><span class="o">.</span><span class="n">eigh</span><span class="p">(</span><span class="n">K</span><span class="p">,</span><span class="n">M</span><span class="p">)</span>
+<span class="n">lam_undamped</span><span class="p">,</span> <span class="n">E_undamped</span> <span class="o">=</span> <span class="n">la</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="o">-</span><span class="n">B</span><span class="p">,</span><span class="n">A_undamped</span><span class="p">)</span>
+<span class="n">lam_proportional</span><span class="p">,</span> <span class="n">E_proportional</span> <span class="o">=</span> <span class="n">la</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="o">-</span><span class="n">B</span><span class="p">,</span><span class="n">A_proportional</span><span class="p">)</span>
+<span class="n">lam_general</span><span class="p">,</span> <span class="n">E_general</span> <span class="o">=</span> <span class="n">la</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="o">-</span><span class="n">B</span><span class="p">,</span><span class="n">A_general</span><span class="p">)</span>
+
+<span class="c1"># Since the half the complex eigenvalues and eigenvectors are complex conjugates of one another,</span>
+<span class="c1"># we will only keep those corresponding to the eigenvalues with imaginary part &gt; 0</span>
+<span class="n">keep_undamped</span> <span class="o">=</span> <span class="n">lam_undamped</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<span class="n">keep_proportional</span> <span class="o">=</span> <span class="n">lam_proportional</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mi">0</span>
+<span class="n">keep_general</span> <span class="o">=</span> <span class="n">lam_general</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mi">0</span>
+
+<span class="c1"># Now reduce to just those we want to keep</span>
+<span class="n">lam_undamped</span> <span class="o">=</span> <span class="n">lam_undamped</span><span class="p">[</span><span class="n">keep_undamped</span><span class="p">]</span>
+<span class="n">E_undamped</span> <span class="o">=</span> <span class="n">E_undamped</span><span class="p">[:,</span><span class="n">keep_undamped</span><span class="p">]</span>
+<span class="n">lam_proportional</span> <span class="o">=</span> <span class="n">lam_proportional</span><span class="p">[</span><span class="n">keep_proportional</span><span class="p">]</span>
+<span class="n">E_proportional</span> <span class="o">=</span> <span class="n">E_proportional</span><span class="p">[:,</span><span class="n">keep_proportional</span><span class="p">]</span>
+<span class="n">lam_general</span> <span class="o">=</span> <span class="n">lam_general</span><span class="p">[</span><span class="n">keep_general</span><span class="p">]</span>
+<span class="n">E_general</span> <span class="o">=</span> <span class="n">E_general</span><span class="p">[:,</span><span class="n">keep_general</span><span class="p">]</span>
+
+<span class="c1"># Let&#39;s also sort the eigenvalues so they are all ascending</span>
+<span class="k">for</span> <span class="n">evals</span><span class="p">,</span><span class="n">evects</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="n">lam_2ndOrder</span><span class="p">,</span> <span class="n">lam_undamped</span><span class="p">,</span> <span class="n">lam_proportional</span><span class="p">,</span> <span class="n">lam_general</span><span class="p">],</span>
+                        <span class="p">[</span><span class="n">E_2ndOrder</span><span class="p">,</span> <span class="n">E_undamped</span><span class="p">,</span> <span class="n">E_proportional</span><span class="p">,</span> <span class="n">E_general</span><span class="p">]):</span>
+    <span class="n">isort</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">evals</span><span class="p">))</span>
+    <span class="n">evals</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">evals</span><span class="p">[</span><span class="n">isort</span><span class="p">]</span>
+    <span class="n">evects</span><span class="p">[</span><span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">evects</span><span class="p">[:,</span><span class="n">isort</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Now we can extract the natural frequencies and damping ratios from the eigenvalues.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute natural frequencies</span>
+<span class="n">omega_2ndOrder</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">lam_2ndOrder</span><span class="p">)</span>
+<span class="n">omega_undamped</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam_undamped</span><span class="p">)</span>
+<span class="n">omega_proportional</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam_proportional</span><span class="p">)</span>
+<span class="n">omega_general</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam_general</span><span class="p">)</span>
+
+<span class="c1"># Compute damping ratios</span>
+<span class="n">zeta_undamped</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">lam_undamped</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam_undamped</span><span class="p">)</span>
+<span class="n">zeta_proportional</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">lam_proportional</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam_proportional</span><span class="p">)</span>
+<span class="n">zeta_general</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">lam_general</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lam_general</span><span class="p">)</span>
+
+<span class="c1"># Compute damped natural frequencies</span>
+<span class="n">omega_d_undamped</span> <span class="o">=</span> <span class="n">omega_undamped</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_undamped</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">omega_d_proportional</span> <span class="o">=</span> <span class="n">omega_proportional</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_proportional</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">omega_d_general</span> <span class="o">=</span> <span class="n">omega_general</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Let’s plot these datasets in a table.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Undamped&#39;</span><span class="p">,</span><span class="s1">&#39;Proportional Damping&#39;</span><span class="p">,</span><span class="s1">&#39;General Damping&#39;</span><span class="p">],</span>
+                  <span class="n">index</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Mass Matrix&#39;</span><span class="p">,</span><span class="s1">&#39;Stiffness Matrix&#39;</span><span class="p">,</span><span class="s1">&#39;Damping Matrix&#39;</span><span class="p">,</span><span class="s1">&#39;Poles&#39;</span><span class="p">,</span><span class="s1">&#39;Natural Frequencies&#39;</span><span class="p">,</span><span class="s1">&#39;Damping Ratios&#39;</span><span class="p">],</span>
+                  <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Mass Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;Undamped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Mass Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;Proportional Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Mass Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;General Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">M</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Stiffness Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;Undamped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Stiffness Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;Proportional Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Stiffness Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;General Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">K</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Damping Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;Undamped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">C_proportional</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Damping Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;Proportional Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">C_proportional</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Damping Matrix&#39;</span><span class="p">,</span> <span class="s1">&#39;General Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">C_general</span><span class="p">)</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Poles&#39;</span><span class="p">,</span> <span class="s1">&#39;Undamped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lam_undamped</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Poles&#39;</span><span class="p">,</span> <span class="s1">&#39;Proportional Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lam_proportional</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Poles&#39;</span><span class="p">,</span> <span class="s1">&#39;General Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lam_general</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Natural Frequencies&#39;</span><span class="p">,</span> <span class="s1">&#39;Undamped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">omega_undamped</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Natural Frequencies&#39;</span><span class="p">,</span> <span class="s1">&#39;Proportional Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">omega_proportional</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Natural Frequencies&#39;</span><span class="p">,</span> <span class="s1">&#39;General Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">omega_general</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Damping Ratios&#39;</span><span class="p">,</span> <span class="s1">&#39;Undamped&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">zeta_undamped</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Damping Ratios&#39;</span><span class="p">,</span> <span class="s1">&#39;Proportional Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">zeta_proportional</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="s1">&#39;Damping Ratios&#39;</span><span class="p">,</span> <span class="s1">&#39;General Damping&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">zeta_general</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">])</span>
+<span class="n">pretty_print_table</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area rendered_html docutils container">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Undamped</th>
+      <th>Proportional Damping</th>
+      <th>General Damping</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>Mass Matrix</th>
+      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>
+      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>
+      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>
+    </tr>
+    <tr>
+      <th>Stiffness Matrix</th>
+      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>
+      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>
+      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>
+    </tr>
+    <tr>
+      <th>Damping Matrix</th>
+      <td>[[0. 0. 0.]<br> [0. 0. 0.]<br> [0. 0. 0.]]</td>
+      <td>[[ 6.00595 -1.54865  0.     ]<br> [-1.54865  5.1373  -1.54865]<br> [ 0.      -1.54865  6.00595]]</td>
+      <td>[[ 7. -1.  0.]<br> [-1.  5. -1.]<br> [ 0. -1.  5.]]</td>
+    </tr>
+    <tr>
+      <th>Poles</th>
+      <td>[[0.+27.2518998j ]<br> [0.+54.77225575j]<br> [0.+59.92217695j]]</td>
+      <td>[[-0.62753244+27.24467371j]<br> [-1.5014875 +54.75167153j]<br> [-1.73017173+59.89719356j]]</td>
+      <td>[[-0.73753202+27.24242803j]<br> [-1.50037636+54.76028673j]<br> [-1.59542495+59.89042233j]]</td>
+    </tr>
+    <tr>
+      <th>Natural Frequencies</th>
+      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>
+      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>
+      <td>[[27.25240977]<br> [54.78083727]<br> [59.91166888]]</td>
+    </tr>
+    <tr>
+      <th>Damping Ratios</th>
+      <td>[[-0.]<br> [-0.]<br> [-0.]]</td>
+      <td>[[0.02302711]<br> [0.02741329]<br> [0.02887365]]</td>
+      <td>[[0.027063  ]<br> [0.02738871]<br> [0.02662962]]</td>
+    </tr>
+  </tbody>
+</table></div>
+</div>
+<p>We will now look at the eigenvectors. To start out, we will normalize the eigenvectors <strong>to a value that is NOT equal to unity modal mass or unity Modal A</strong>. This is useful while we are learning, because if we have unity modal mass, it allows us to be sloppy and simply drop or ignore effects of the modal mass. This could result in significant errors down the road if we assume that the formulae we developed for the unity modal mass case also apply to the non-unity modal mass case. It is therefore
+better to develop and check all the formulae with non-unity modal mass to ensure that we understand its effects and have accounted for it successfully. We will deliberately normalize to a value not equal to 1, because many eigenvector solvers will automatically normalize for us.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[9]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Normalize Eigenvectors using einsum to pull out the m_ii terms from the modal normalization matrix</span>
+<span class="c1"># We will multiply by 2 to ensure non-unity modal mass.</span>
+<span class="n">E_2ndOrder</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">E_2ndOrder</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_2ndOrder</span><span class="p">,</span><span class="n">M</span><span class="p">,</span><span class="n">E_2ndOrder</span><span class="p">))</span>
+<span class="n">E_undamped</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">E_undamped</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_undamped</span><span class="p">,</span><span class="n">A_undamped</span><span class="p">,</span><span class="n">E_undamped</span><span class="p">))</span>
+<span class="n">E_proportional</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">E_proportional</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_proportional</span><span class="p">,</span><span class="n">A_proportional</span><span class="p">,</span><span class="n">E_proportional</span><span class="p">))</span>
+<span class="n">E_general</span> <span class="o">=</span> <span class="mi">2</span><span class="o">*</span><span class="n">E_general</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_general</span><span class="p">,</span><span class="n">A_general</span><span class="p">,</span><span class="n">E_general</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<p>Now that we have the eigenvectors normalized to a non-unity modal mass, we will compute the Modal Mass and Modal A matrices to extract the modal mass.</p>
+<p>We will use <code class="docutils literal notranslate"><span class="pre">np.einsum</span></code> in the same way we used previously but here we will describe it a bit better. Numpy’s <code class="docutils literal notranslate"><span class="pre">einsum</span></code> function implements a generalized version of Einstein Summation Notation. While we can represent matrix operations like multiplication simply by using symbols for the matrices</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{C} = \mathbf{A}\mathbf{B}\]</div>
+<p>we can also represent those operations with the indices</p>
+<div class="math notranslate nohighlight">
+\[C_{ik} = A_{ij}B_{jk}\]</div>
+<p>In the previous equation, we have the index <span class="math notranslate nohighlight">\(i\)</span> representing the rows of <span class="math notranslate nohighlight">\(\mathbf{A}\)</span>, and the index <span class="math notranslate nohighlight">\(j\)</span> representing the columns. Similarly for <span class="math notranslate nohighlight">\(\mathbf{B}\)</span>, we have the indices <span class="math notranslate nohighlight">\(j\)</span> representing the rows and <span class="math notranslate nohighlight">\(k\)</span> representing the columns. When we do matrix multiplication, we sum over the inner dimension, e.g., the <span class="math notranslate nohighlight">\(j\)</span> indices, and that index drops out. We are left with rows <span class="math notranslate nohighlight">\(i\)</span> and columns <span class="math notranslate nohighlight">\(k\)</span> of our output matrix <span class="math notranslate nohighlight">\(\mathbf{C}\)</span>. This
+operation would be represented in Numpy’s <code class="docutils literal notranslate"><span class="pre">einsum</span></code> function as</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">C</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ij,jk-&gt;ik&#39;</span><span class="p">,</span><span class="n">A</span><span class="p">,</span><span class="n">B</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The first argument <code class="docutils literal notranslate"><span class="pre">'ij,jk-&gt;ik'</span></code> is a string that gives the index operations to be performed. We see that there is an arrow <code class="docutils literal notranslate"><span class="pre">-&gt;</span></code> that separates input indices and output indices, and the different input indices are separated by a comma, and there is only one set of output indices. The remaining arguments are the actual matrices corresponding to the input indices passed in the first argument. Therefore <code class="docutils literal notranslate"><span class="pre">ij</span></code> will be associated with <code class="docutils literal notranslate"><span class="pre">A</span></code>, and <code class="docutils literal notranslate"><span class="pre">jk</span></code> will be associated with <code class="docutils literal notranslate"><span class="pre">B</span></code>. The output
+indices on the right side of the arrow <code class="docutils literal notranslate"><span class="pre">ik</span></code> will be associated with the function’s output <code class="docutils literal notranslate"><span class="pre">C</span></code>. We note that any index not listed in the output indices will be summed over and drop out.</p>
+<p>This notation is very useful for writing complex matrix expressions in a compact way. For example, if instead of <span class="math notranslate nohighlight">\(\mathbf{C} = \mathbf{A}\mathbf{B}\)</span> we wanted to do <span class="math notranslate nohighlight">\(\mathbf{C} = \mathbf{A}^T\mathbf{B}\)</span>, we can simply swap the indices on <span class="math notranslate nohighlight">\(\mathbf{A}\)</span>, which now means we are summing across the rows of <span class="math notranslate nohighlight">\(\mathbf{A}\)</span>, which is equivalent to matrix multiplication by a transpose.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{C} = \mathbf{A}^T\mathbf{B}\]</div>
+<div class="math notranslate nohighlight">
+\[C_{ik} = A_{ji}B_{jk}\]</div>
+<p>We can therefore represent a more complex product like</p>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{M}}=\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\]</div>
+<p>as</p>
+<div class="math notranslate nohighlight">
+\[\tilde{{M}}_{il}={\phi}_{ji}{M}_{jk}{\phi}_{kl}\]</div>
+<p>In the previous equation, we can see that the <span class="math notranslate nohighlight">\(j\)</span> index corresponds to the rows of the mass matrix and the rows of the mode shape matrix. Because we sum over <span class="math notranslate nohighlight">\(j\)</span>, this is equivalent to multiplying the matrix <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> by <span class="math notranslate nohighlight">\(\mathbf{\phi}^T\)</span>. We can indeed improve upon this if, for example, we wanted a 1D array of modal mass values, rather than a 2D matrix that we will then need to extract the diagonals from. We can replace the <span class="math notranslate nohighlight">\(l\)</span> index with another <span class="math notranslate nohighlight">\(i\)</span> index
+and have the output specify just a single <span class="math notranslate nohighlight">\(i\)</span>, and we will get a 1D array of modal mass values. As an aside, this is a syntax that is not acceptable in true Einstein Summation Notation, where a repeated index is automatically summed over. In Numpy’s generalized version, it subverts some of these very strict rules to give more flexibility to the user.</p>
+<div class="math notranslate nohighlight">
+\[\tilde{{M}}_{i}={\phi}_{ji}{M}_{jk}{\phi}_{ki}\]</div>
+<p>We can then perform these operations in code to solve for the modal mass and modal A quantities.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mm_2ndOrder</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_2ndOrder</span><span class="p">,</span><span class="n">M</span><span class="p">,</span><span class="n">E_2ndOrder</span><span class="p">)</span>
+<span class="n">ma_undamped</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_undamped</span><span class="p">,</span><span class="n">A_undamped</span><span class="p">,</span><span class="n">E_undamped</span><span class="p">)</span>
+<span class="n">ma_proportional</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_proportional</span><span class="p">,</span><span class="n">A_proportional</span><span class="p">,</span><span class="n">E_proportional</span><span class="p">)</span>
+<span class="n">ma_general</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_general</span><span class="p">,</span><span class="n">A_general</span><span class="p">,</span><span class="n">E_general</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>The last thing we will do is to pull out the mode shapes from the eigenvectors. For the 2nd Order case, the mode shapes are the eigenvectors. But for the complex case, the mode shapes are the lower half of the eigenvector matrix.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">phi</span> <span class="o">=</span> <span class="n">E_2ndOrder</span>
+<span class="n">psi_undamped</span> <span class="o">=</span> <span class="n">E_undamped</span><span class="p">[</span><span class="n">E_undamped</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:,:]</span>
+<span class="n">psi_proportional</span> <span class="o">=</span> <span class="n">E_proportional</span><span class="p">[</span><span class="n">E_proportional</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:,:]</span>
+<span class="n">psi_general</span> <span class="o">=</span> <span class="n">E_general</span><span class="p">[</span><span class="n">E_general</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:,:]</span>
+</pre></div>
+</div>
+</div>
+<p>We should be able to check that the top half of the eigenvector matrix is the bottom half multiplied by the eigenvalue.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\lambda \mathbf{\psi}\\\mathbf{\psi}\end{bmatrix}\end{split}\]</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">E_general</span><span class="p">[:</span><span class="n">E_general</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">,:]</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,
+        -3.16635239-3.71568777j],
+       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,
+         2.00036437+2.07501753j],
+       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,
+        -3.52944456-3.37788878j]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lam_general</span><span class="o">*</span><span class="n">psi_general</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,
+        -3.16635239-3.71568777j],
+       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,
+         2.00036437+2.07501753j],
+       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,
+        -3.52944456-3.37788878j]])
+</pre></div></div>
+</div>
+<p>One final thing to note is that in general, the mode shapes extracted from a complex analysis will not diagonalize the orignal mass, stiffness, or damping matrices, as these were not the matrices the eigensolution was performed on. There will be off-diagonal terms, and they will be complex.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_general</span><span class="o">.</span><span class="n">T</span><span class="nd">@M@psi_general</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-6.13061679e-08-7.34176433e-02j, -1.43810774e-04+9.83528782e-06j,
+         1.15410477e-04+2.13846966e-06j],
+       [-1.43810774e-04+9.83528782e-06j, -1.24812005e-07-3.65342888e-02j,
+        -1.36140173e-04+1.88235958e-06j],
+       [ 1.15410477e-04+2.13846966e-06j, -1.36140173e-04+1.88235958e-06j,
+         1.86118173e-07-3.33825843e-02j]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_general</span><span class="o">.</span><span class="n">T</span><span class="nd">@C_general@psi_general</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-0.00014982-0.10829239j,  0.00048468+0.01181488j,
+         0.00045558-0.01005105j],
+       [ 0.00048468+0.01181488j, -0.00125664-0.1096167j ,
+        -0.00020565+0.01561439j],
+       [ 0.00045558-0.01005105j, -0.00020565+0.01561439j,
+         0.00140645-0.10654111j]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_general</span><span class="o">.</span><span class="n">T</span><span class="nd">@K@psi_general</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-6.49635076e-05-5.45227499e+01j,  2.15177460e-01-2.97517095e-03j,
+        -1.87975974e-01-1.36004490e-02j],
+       [ 2.15177460e-01-2.97517095e-03j, -1.51087546e-03-1.09568400e+02j,
+         4.46495302e-01+1.79584080e-02j],
+       [-1.87975974e-01-1.36004490e-02j,  4.46495302e-01+1.79584080e-02j,
+         1.57583896e-03-1.19907950e+02j]])
+</pre></div></div>
+</div>
+</section>
+<section id="Frequency-Response-Functions-and-Modal-Parameters">
+<h2>Frequency Response Functions and Modal Parameters<a class="headerlink" href="#Frequency-Response-Functions-and-Modal-Parameters" title="Permalink to this heading"></a></h2>
+<p>Now that we have familiarized ourselves with the modal parameters for real and complex modes, let’s discuss the relationship to the frequency response function. This is of utmost importance, because we eventually would like to fit the modal parameters from the frequency response functions, as well as reconstruct frequency response functions from the modal parameters.</p>
+<section id="Real-Modes">
+<h3>Real Modes<a class="headerlink" href="#Real-Modes" title="Permalink to this heading"></a></h3>
+<p>Starting with the modal system of equations, and assuming proportional damping, we have</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{\phi}^T\mathbf{M}\mathbf{\phi}\ddot{\mathbf{q}} + \mathbf{\phi}^T\mathbf{C}\mathbf{\phi}\dot{\mathbf{q}} + \mathbf{\phi}^T\mathbf{K}\mathbf{\phi}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<div class="math notranslate nohighlight">
+\[\tilde{\mathbf{M}}\ddot{\mathbf{q}} + \tilde{\mathbf{C}}\dot{\mathbf{q}} + \tilde{\mathbf{K}}{\mathbf{q}} = \mathbf{\phi}^T\mathbf{F}\]</div>
+<p>We can also substitute in the diagonal values for the modal parameters. <em>Note the inclusion of the modal mass</em> <span class="math notranslate nohighlight">\(m_r\)</span> <em>in all terms, because we have not assumed mass normalized modes in this case</em>. We will represent the generalized modal force <span class="math notranslate nohighlight">\(\mathbf{\phi}^T\mathbf{F}\)</span> as <span class="math notranslate nohighlight">\(\mathbf{Q}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\left[m_r\right]\ddot{\mathbf{q}} + \left[2\zeta_r\omega_r m_r\right]\dot{\mathbf{q}} + \left[\omega_r^2m_r\right]{\mathbf{q}} = \mathbf{Q}\]</div>
+<p>We can then use the <em>Laplace Transform</em> to make this differential equation into an algebraic one.</p>
+<div class="math notranslate nohighlight">
+\[\left[m_r\right]s^2{\mathbf{q}} + \left[2\zeta_r\omega_r m_r\right]s{\mathbf{q}} + \left[\omega_r^2m_r\right]{\mathbf{q}} = \mathbf{Q}\]</div>
+<p>We can collect terms of <span class="math notranslate nohighlight">\(\mathbf{q}\)</span> to create an equation of the form <span class="math notranslate nohighlight">\(\mathbf{A}\mathbf{x} = \mathbf{b}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\left[m_rs^2 + 2\zeta_r\omega_r m_rs + \omega_r^2m_r\right]{\mathbf{q}} = \mathbf{Q}\]</div>
+<p>Because the equation is diagonal, inverting the coefficient matrix is trival.</p>
+<div class="math notranslate nohighlight">
+\[{\mathbf{q}} = \left[\frac{1}{m_rs^2 + 2\zeta_r\omega_r m_rs + \omega_r^2m_r}\right]\mathbf{Q}\]</div>
+<p>This is now a representation of a <em>modal</em> transfer function. If we apply a set of <em>modal</em> forces <span class="math notranslate nohighlight">\(\mathbf{Q}\)</span>, we get out a set of <em>modal</em> responses <span class="math notranslate nohighlight">\(\mathbf{q}\)</span>. To transform to physical space, we need to transform our modal quantities to physical quantities. We know the substitution for modal forces <span class="math notranslate nohighlight">\(\mathbf{Q} = \mathbf{\phi}^T\mathbf{F}\)</span>, and we know our equation for physical response from modal responses <span class="math notranslate nohighlight">\(\mathbf{x} = \mathbf{\phi}\mathbf{q}\)</span>. Therefore we can make
+the subsitution for modal force, and pre-multiply the entire equation by the mode shape matrix.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{x} = \mathbf{\phi}{\mathbf{q}} = \mathbf{\phi}\left[\frac{1}{m_rs^2 + 2\zeta_r\omega_r m_rs + \omega_r^2m_r}\right]\mathbf{\phi}^T\mathbf{F}\]</div>
+<p>If instead of the entire Laplace domain we only consider sinusoidal responses, we can substitute <span class="math notranslate nohighlight">\(s=j\omega\)</span> to get the familiar form for the frequency response function.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H} = \mathbf{\phi}\left[\frac{1}{-m_r\omega^2 + 2j\zeta_r\omega_r m_r\omega + \omega_r^2m_r}\right]\mathbf{\phi}^T\]</div>
+<div class="math notranslate nohighlight">
+\[\mathbf{H} = \sum_{r=1}^N\frac{\mathbf{\phi}_r\mathbf{\phi}_r^T}{-m_r\omega^2 + 2j\zeta_r\omega_r m_r\omega + \omega_r^2m_r}\]</div>
+<p>To pull out a specific entry in the FRF matrix, we can index the mode shapes.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\frac{\phi_{ir}\phi_{jr}}{-m_r\omega^2 + 2j\zeta_r\omega_r m_r\omega + \omega_r^2m_r}\]</div>
+</section>
+<section id="Complex-Modes">
+<h3>Complex Modes<a class="headerlink" href="#Complex-Modes" title="Permalink to this heading"></a></h3>
+<p>The derivation for the frequency response function in terms of complex modes is similar. Recall that our physical vector for the complex state space formulation includes velocities and displacements. Similarly our eigenvectors consisted of pairs of shapes, the shape and its complex conjugate. Therefore, in the case of complex modes, the projection from modal space to physical space is</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{v}\\\mathbf{x}\end{bmatrix} = \begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix}\end{split}\]</div>
+<p>Applying the transformation to the complex state space equation</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\begin{bmatrix}\dot{\mathbf{v}}\\\dot{\mathbf{x}}\end{bmatrix} + \begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}{\mathbf{v}}\\{\mathbf{x}}\end{bmatrix} = \begin{bmatrix}\mathbf{0}\\\mathbf{F}\end{bmatrix}\end{split}\]</div>
+<p>Gives</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\dot{\mathbf{q}}\\\dot{\mathbf{q}}^*\end{bmatrix} + \begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix} = \begin{bmatrix}\mathbf{0}\\\mathbf{F}\end{bmatrix}\end{split}\]</div>
+<p>And then pre-multiplying by the eigenvector matrix transpose completes the transformation into modal space.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}^T\begin{bmatrix}\mathbf{0} &amp; \mathbf{M} \\ \mathbf{M} &amp; \mathbf{C}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\dot{\mathbf{q}}\\\dot{\mathbf{q}}^*\end{bmatrix} + \begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}^T\begin{bmatrix} -\mathbf{M} &amp; \mathbf{0} \\ \mathbf{0} &amp; \mathbf{K}\end{bmatrix}\begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix} = \begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}^T\begin{bmatrix}\mathbf{0}\\\mathbf{F}\end{bmatrix}\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}= \begin{bmatrix}\lambda \mathbf{\psi}^T &amp; \mathbf{\psi}^T \\ \lambda^* {\mathbf{\psi}^*}^T &amp; {\mathbf{\psi}^*}^T\end{bmatrix}\begin{bmatrix}\mathbf{0}\\\mathbf{F}\end{bmatrix} = \begin{bmatrix}\mathbf{\psi}^T \\ {\mathbf{\psi}^*}^T\end{bmatrix}\mathbf{F}\end{split}\]</div>
+<p>Transforming into the modal matrices</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\tilde{\mathbf{A}}\begin{bmatrix}\dot{\mathbf{q}}\\\dot{\mathbf{q}}^*\end{bmatrix} + \tilde{\mathbf{B}}\begin{bmatrix}{\mathbf{q}}\\{\mathbf{q}}^*\end{bmatrix} = \begin{bmatrix}\mathbf{Q} \\ \mathbf{Q}^*\end{bmatrix}\end{split}\]</div>
+<p>We recognize that the diagonal terms of <span class="math notranslate nohighlight">\(\tilde{\mathbf{A}}\)</span> are simply what we’ve defined as the modal mass, and the diagonal terms of <span class="math notranslate nohighlight">\(\tilde{\mathbf{B}}\)</span> are simply the negative modal mass times the eigenvector <span class="math notranslate nohighlight">\(\tilde{\mathbf{B}}_{rr} = -\lambda_r \tilde{\mathbf{A}}_{rr}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\left[m_r\right] &amp; \mathbf{0} \\ \mathbf{0} &amp; \left[m_r\right]\end{bmatrix}\begin{bmatrix}\dot{\mathbf{q}}\\\dot{\mathbf{q}}^*\end{bmatrix} - \begin{bmatrix}\lambda_r\left[m_r\right] &amp; \mathbf{0} \\ \mathbf{0} &amp; {\lambda_r}^*\left[m_r\right]\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix}  = \begin{bmatrix}\mathbf{Q} \\ \mathbf{Q}^*\end{bmatrix}\end{split}\]</div>
+<p>Once again, transforming to the Laplace Domain,</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}s\left[m_r\right] &amp; \mathbf{0} \\ \mathbf{0} &amp; s\left[m_r\right]\end{bmatrix}\begin{bmatrix}{\mathbf{q}}\\{\mathbf{q}}^*\end{bmatrix} - \begin{bmatrix}\lambda_r\left[m_r\right] &amp; \mathbf{0} \\ \mathbf{0} &amp; {\lambda_r}^*\left[m_r\right]\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix}  = \begin{bmatrix}\mathbf{Q} \\ \mathbf{Q}^*\end{bmatrix}\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}(s-\lambda)\left[m_r\right] &amp; \mathbf{0} \\ \mathbf{0} &amp; (s-\lambda^*)\left[m_r\right]\end{bmatrix}\begin{bmatrix}{\mathbf{q}}\\{\mathbf{q}}^*\end{bmatrix}  = \begin{bmatrix}\mathbf{Q} \\ \mathbf{Q}^*\end{bmatrix}\end{split}\]</div>
+<p>Once again, because this matrix is diagonal, it is easy to invert.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}{\mathbf{q}}\\{\mathbf{q}}^*\end{bmatrix} = \begin{bmatrix}\frac{1}{(s-\lambda)\left[m_r\right]} &amp; \mathbf{0} \\ \mathbf{0} &amp; \frac{1}{(s-\lambda^*)\left[m_r\right]}\end{bmatrix}\begin{bmatrix}\mathbf{Q} \\ \mathbf{Q}^*\end{bmatrix}\end{split}\]</div>
+<p>This is the modal transfer function.</p>
+<p>To compute the transfer functions between the force <span class="math notranslate nohighlight">\(\mathbf{F}\)</span> and the response <span class="math notranslate nohighlight">\(\mathbf{x}\)</span> (we ignore <span class="math notranslate nohighlight">\(\mathbf{v}\)</span> here), we can use the identities <span class="math notranslate nohighlight">\(\begin{bmatrix}\mathbf{Q} \\ \mathbf{Q}^*\end{bmatrix} = \begin{bmatrix}\mathbf{\psi}^T \\ {\mathbf{\psi}^*}^T\end{bmatrix}\mathbf{F}\)</span>, which we defined previously, and the last row of
+<span class="math notranslate nohighlight">\(\begin{bmatrix}\mathbf{v}\\\mathbf{x}\end{bmatrix} = \begin{bmatrix}\lambda \mathbf{\psi} &amp; \lambda^* \mathbf{\psi}^* \\ \mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix}\)</span>, which is <span class="math notranslate nohighlight">\(\mathbf{x} = \begin{bmatrix}\mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\mathbf{q}\\\mathbf{q}^*\end{bmatrix}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{x} = \begin{bmatrix}\mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}{\mathbf{q}}\\{\mathbf{q}}^*\end{bmatrix} = \begin{bmatrix}\mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\frac{1}{(s-\lambda)\left[m_r\right]} &amp; \mathbf{0} \\ \mathbf{0} &amp; \frac{1}{(s-\lambda^*)\left[m_r\right]}\end{bmatrix}\begin{bmatrix}\mathbf{\psi}^T \\ {\mathbf{\psi}^*}^T\end{bmatrix}\mathbf{F}\end{split}\]</div>
+<p>The transfer function in the <span class="math notranslate nohighlight">\(s\)</span>-domain is then</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{H} = \begin{bmatrix}\mathbf{\psi} &amp; \mathbf{\psi}^*\end{bmatrix}\begin{bmatrix}\frac{1}{(s-\lambda)\left[m_r\right]} &amp; \mathbf{0} \\ \mathbf{0} &amp; \frac{1}{(s-\lambda^*)\left[m_r\right]}\end{bmatrix}\begin{bmatrix}\mathbf{\psi}^T \\ {\mathbf{\psi}^*}^T\end{bmatrix}\end{split}\]</div>
+<p>Because it is diagonal, we can take it out of matrix form and put it into summation form, which reveals the usual partial fraction form of the transfer function.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H} = \sum_{r=1}^N\left( \frac{\mathbf{\psi}_r\mathbf{\psi}_r^T}{m_r(s-\lambda_r)} + \frac{\mathbf{\psi}_r^*{\mathbf{\psi}_r^*}^T}{m_r(s-\lambda_r^*)}\right)\]</div>
+<p>We can extract a specific transfer function <span class="math notranslate nohighlight">\(\mathbf{H}_{ij}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\left( \frac{\mathbf{\psi}_{ir}\mathbf{\psi}_{jr}}{m_r(s-\lambda_r)} + \frac{\mathbf{\psi}_{ir}^*\mathbf{\psi}_{jr}^*}{m_r(s-\lambda_r^*)}\right)\]</div>
+<p>To translate into more intuitive variables, we can make the following substitutions.</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\lambda_r = -\sigma_r + j\omega_{dr} = -\zeta_r\omega_r + j\omega_r\sqrt{1-\zeta_r^2}\)</span> - Substitute the poles for the natural frequency and critical damping ratio.</p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathbf{\psi}_{ir} = \mathbf{\psi}_{ir}^R + j\mathbf{\psi}_{ir}^I\)</span> - Split the mode shapes into real and imaginary parts.</p></li>
+</ul>
+<p>From these, it follows that the complex conjugate versions simply have the sign flipped on their imaginary components.</p>
+<ul class="simple">
+<li><p><span class="math notranslate nohighlight">\(\lambda_r^* = -\sigma_r - j\omega_{dr} = -\zeta_r\omega_r - j\omega_r\sqrt{1-\zeta_r^2}\)</span></p></li>
+<li><p><span class="math notranslate nohighlight">\(\mathbf{\psi}_{ir} = \mathbf{\psi}_{ir}^R - j\mathbf{\psi}_{ir}^I\)</span></p></li>
+</ul>
+<p>Substituting these into the previous equation, we get</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\left( \frac{\left(\mathbf{\psi}_{ir}^R + j\mathbf{\psi}_{ir}^I\right)\left(\mathbf{\psi}_{jr}^R + j\mathbf{\psi}_{jr}^I\right)}{m_r(s-\left(-\sigma_r + j\omega_{dr}\right))} + \frac{\left(\mathbf{\psi}_{ir}^R - j\mathbf{\psi}_{ir}^I\right)\left(\mathbf{\psi}_{jr}^R - j\mathbf{\psi}_{jr}^I\right)}{m_r(s-\left(-\sigma_r - j\omega_{dr}\right))}\right)\]</div>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\left( \frac{\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right) + j\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)}{m_r\left(s + \sigma_r - j\omega_{dr}\right)} + \frac{\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right) - j\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)}{m_r\left(s+ \sigma_r + j\omega_{dr}\right)}\right)\]</div>
+<p>If we put the two terms over a common denominator, we get</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\left(
+\frac{
+  \left(\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right) + j\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\right)
+  \left(s+ \sigma_r + j\omega_{dr}\right)
++ \left(\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right) - j\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\right)
+  \left(s + \sigma_r - j\omega_{dr}\right)}
+{m_r\left(s+ \sigma_r + j\omega_{dr}\right)\left(s + \sigma_r - j\omega_{dr}\right)}
+\right)\]</div>
+<p>We can expand and reduce this equation</p>
+<div class="math notranslate nohighlight">
+\[ \mathbf{H}_{ij} = 2\sum_{r=1}^N\left(
+\frac{
+\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right)\left(s+ \sigma_r\right) -
+\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr}
+}
+{m_r\left(\left(s+\sigma_r\right)^2 + \omega_{dr}^2\right)
+}
+\right)\]</div>
+</section>
+<section id="What-are-Real-Modes-in-the-Complex-Mode-Framework?">
+<h3>What are Real Modes in the Complex Mode Framework?<a class="headerlink" href="#What-are-Real-Modes-in-the-Complex-Mode-Framework?" title="Permalink to this heading"></a></h3>
+<p>One might naively believe that a real mode is simply a complex mode where the imaginary part is zero. However, it isn’t clear whether or not that should be true, because the eigenvalue problem was posed differently. What is clear, however, is that real modes should certainly be some subset of complex modes; in other words, we should be able to represent a real mode as a complex mode. In this section, we will investigate that relationship.</p>
+<p>Starting with the previous equation, we can further expand the denominator with natural frequency and damping ratio, where <span class="math notranslate nohighlight">\(\sigma_r = \zeta_r\omega_r\)</span> and <span class="math notranslate nohighlight">\(\omega_{dr} = \omega_r\sqrt{1-\zeta_r^2}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[ \mathbf{H}_{ij} = 2\sum_{r=1}^N\left(
+\frac{
+\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right)\left(s+ \sigma_r\right) -
+\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr}
+}
+{m_r\left(\left(s+-\zeta_r\omega_r\right)^2 + \left(\omega_r\sqrt{1-\zeta_r^2}\right)^2\right)
+}
+\right)\]</div>
+<div class="math notranslate nohighlight">
+\[ \mathbf{H}_{ij} = 2\sum_{r=1}^N\left(
+\frac{
+\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right)\left(s+ \sigma_r\right) -
+\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr}
+}
+{m_r\left(\left(s^2+2s\zeta_r\omega_r + \omega_r^2\zeta_r^2\right) + \left(\omega_r^2 - \omega_r^2\zeta_r^2\right)\right)
+}
+\right)\]</div>
+<div class="math notranslate nohighlight">
+\[ \mathbf{H}_{ij} = 2\sum_{r=1}^N\left(
+\frac{
+\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right)\left(s+ \sigma_r\right) -
+\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr}
+}
+{m_rs^2+2\zeta_r\omega_rm_rs + m_r\omega_r^2
+}
+\right)\]</div>
+<p>The denominator in the previous equation is identical to that from the real modes equation.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\frac{\phi_{ir}\phi_{jr}}{m_rs^2 + 2\zeta_r\omega_r m_rs + \omega_r^2m_r}\]</div>
+<p>The numerators should also be equivalent.</p>
+<div class="math notranslate nohighlight">
+\[2\left(\left( \psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I \right)\left(s+ \sigma_r\right) -
+\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr}\right) = \phi_{ir}\phi_{jr}\]</div>
+<p>We note that there is no terms with <span class="math notranslate nohighlight">\(s\)</span> on the real modes side of the equation, so any term containing <span class="math notranslate nohighlight">\(s\)</span> on the complex modes side of the equation must be zero in the case of real modes.</p>
+<div class="math notranslate nohighlight">
+\[\psi_{ir}^R\psi_{jr}^R - \psi_{ir}^I\psi_{jr}^I = 0\]</div>
+<p>This then leaves the remainder of the equation</p>
+<div class="math notranslate nohighlight">
+\[-2\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr} = \phi_{ir}\phi_{jr}\]</div>
+<p>We must find a solution for these two equations simultaneously. Since the modeshape at location <span class="math notranslate nohighlight">\(i\)</span> and the mode shape at location <span class="math notranslate nohighlight">\(j\)</span> are generally independent of one another, we can treat them as independent. In other words, <span class="math notranslate nohighlight">\(\psi_{ir}\)</span> cannot be a function <span class="math notranslate nohighlight">\(\psi_{jr}\)</span>. This means for the first equation, there are two possibilities; <span class="math notranslate nohighlight">\(\psi_{ir}^I = -\psi_{ir}^R\)</span> or <span class="math notranslate nohighlight">\(\psi_{ir}^I = \psi_{ir}^R\)</span>.</p>
+<p>To figure out which, we will look at the drive point transfer function where <span class="math notranslate nohighlight">\(i = j\)</span>, as the relationship should hold for that point as much as any other point.</p>
+<div class="math notranslate nohighlight">
+\[-2\left( \psi_{ir}^R\psi_{ir}^I + \psi_{ir}^I\psi_{ir}^R \right)\omega_{dr} = \phi_{ir}^2\]</div>
+<div class="math notranslate nohighlight">
+\[\psi_{ir}^R\psi_{ir}^I + \psi_{ir}^I\psi_{ir}^R = \frac{\phi_{ir}^2}{-2\omega_{dr}}\]</div>
+<p>The since term <span class="math notranslate nohighlight">\(\phi_{ir}^2\)</span> cannot be negative and <span class="math notranslate nohighlight">\(\omega_{dr}\)</span> is also non-negative, the term <span class="math notranslate nohighlight">\(\psi_{ir}^R\psi_{ir}^I + \psi_{ir}^I\psi_{ir}^R\)</span> must be negative. This will be the case if <span class="math notranslate nohighlight">\(\psi_{ir}^I = -\psi_{ir}^R\)</span>, but will not be the case if <span class="math notranslate nohighlight">\(\psi_{ir}^I = \psi_{ir}^R\)</span>.</p>
+<p>Substituting that expression,</p>
+<div class="math notranslate nohighlight">
+\[-{\psi_{ir}^R}^2 + -{\psi_{ir}^R}^2 = \frac{\phi_{ir}^2}{-2\omega_{dr}}\]</div>
+<div class="math notranslate nohighlight">
+\[-2{\psi_{ir}^R}^2 = \frac{\phi_{ir}^2}{-2\omega_{dr}}\]</div>
+<div class="math notranslate nohighlight">
+\[{\psi_{ir}^R}^2 = \frac{\phi_{ir}^2}{4\omega_{dr}}\]</div>
+<div class="math notranslate nohighlight">
+\[{\psi_{ir}^R} = \frac{\phi_{ir}}{2\sqrt{\omega_{dr}}} = -\psi_{ir}^I\]</div>
+<p>To verify that this is the general expression, let’s plug it into the original equation.</p>
+<div class="math notranslate nohighlight">
+\[-2\left( \psi_{ir}^R\psi_{jr}^I + \psi_{ir}^I\psi_{jr}^R \right)\omega_{dr} = \phi_{ir}\phi_{jr}\]</div>
+<div class="math notranslate nohighlight">
+\[-2\left(
+-\frac{\phi_{ir}}{2\sqrt{\omega_{dr}}}
+\frac{\phi_{jr}}{2\sqrt{\omega_{dr}}} +
+-\frac{\phi_{ir}}{2\sqrt{\omega_{dr}}}
+\frac{\phi_{jr}}{2\sqrt{\omega_{dr}}}
+\right)\omega_{dr} = \phi_{ir}\phi_{jr}\]</div>
+<div class="math notranslate nohighlight">
+\[-2\left(
+-\frac{\phi_{ir}\phi_{jr}}{4\omega_{dr}}
+-\frac{\phi_{ir}\phi_{jr}}{4\omega_{dr}}
+\right)\omega_{dr} = \phi_{ir}\phi_{jr}\]</div>
+<div class="math notranslate nohighlight">
+\[-2\left(
+-2\frac{\phi_{ir}\phi_{jr}}{4\omega_{dr}}
+\right)\omega_{dr} = \phi_{ir}\phi_{jr}\]</div>
+<div class="math notranslate nohighlight">
+\[\phi_{ir}\phi_{jr} = \phi_{ir}\phi_{jr}\]</div>
+<p>Therefore for a real mode represented in a complex mode, we should expect the imaginary part to be equal in magnitude to the real part, with phase angle of -45<span class="math notranslate nohighlight">\(^\circ\)</span> instead of zero degrees, which may have been expected.</p>
+</section>
+<section id="Back-to-our-Example-Problem">
+<h3>Back to our Example Problem<a class="headerlink" href="#Back-to-our-Example-Problem" title="Permalink to this heading"></a></h3>
+<p>Let’s return to our example problem to compute frequency response functions from the matrices as well as from modal parameters. We will start with computing the frequency response functions from dynamic stiffness matrices.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Get frequency lines</span>
+<span class="n">omegas</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">omega_2ndOrder</span><span class="p">)</span><span class="o">*</span><span class="mf">1.5</span><span class="p">,</span><span class="mi">1000</span><span class="p">)</span>
+<span class="c1"># Set up omega for broadcasting the FRF matrix, the first dimension will be frequency line.</span>
+<span class="c1"># The second two dimensions will be i,j degrees of freedom.</span>
+<span class="n">omegas_bc</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+<span class="c1"># Construct Dynamic Stiffness</span>
+<span class="n">Z_undamped</span> <span class="o">=</span> <span class="n">M</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">K</span>
+<span class="n">Z_proportional</span> <span class="o">=</span> <span class="n">M</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">C_proportional</span><span class="o">*</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span> <span class="o">+</span> <span class="n">K</span>
+<span class="n">Z_general</span> <span class="o">=</span> <span class="n">M</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">C_general</span><span class="o">*</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span> <span class="o">+</span> <span class="n">K</span>
+<span class="c1"># Invert to get frequency response functions</span>
+<span class="n">H_undamped</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">Z_undamped</span><span class="p">)</span>
+<span class="n">H_proportional</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">Z_proportional</span><span class="p">)</span>
+<span class="n">H_general</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">Z_general</span><span class="p">)</span>
+<span class="c1"># Plot the FRFs</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">H_undamped</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">H_undamped</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_u</span><span class="p">,</span><span class="n">f_p</span><span class="p">,</span><span class="n">f_g</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                          <span class="n">H_undamped</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                          <span class="n">H_proportional</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                          <span class="n">H_general</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_u</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_p</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_g</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_u</span><span class="p">))</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_p</span><span class="p">))</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_g</span><span class="p">))</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Undamped&#39;</span><span class="p">,</span><span class="s1">&#39;Proportional Damping&#39;</span><span class="p">,</span><span class="s1">&#39;General Damping&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_50_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_50_0.png" />
+</div>
+</div>
+<p>Now let’s reconstruct the frequency response functions from modal parameters and see if they match those constructed from the dynamic stiffness matrices.</p>
+<p>For the second-order case,</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H} = \sum_{r=1}^N\frac{\mathbf{\phi}_r\mathbf{\phi}_r^T}{-m_r\omega^2 + 2j\zeta_r\omega_r m_r\omega + \omega_r^2m_r}\]</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 2nd Order Case</span>
+<span class="c1"># First we need to find the modal damping ratios from the damping matrix</span>
+<span class="n">zeta_2ndOrder</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ji,jk,ki-&gt;i&#39;</span><span class="p">,</span><span class="n">E_2ndOrder</span><span class="p">,</span><span class="n">C_proportional</span><span class="p">,</span><span class="n">E_2ndOrder</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">mm_2ndOrder</span><span class="o">*</span><span class="n">omega_2ndOrder</span><span class="p">)</span>
+
+<span class="c1"># Now Construct the FRF matrix from modal parameters</span>
+<span class="n">num_modes</span> <span class="o">=</span> <span class="n">omega_2ndOrder</span><span class="o">.</span><span class="n">size</span>
+<span class="n">H_2ndOrder_fromModes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span>
+    <span class="n">phi</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="nd">@phi</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">/</span> <span class="c1"># The newaxis turns the array from a 1D array back into a 2D array that can be transposed</span>
+    <span class="p">(</span><span class="o">-</span><span class="n">mm_2ndOrder</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omegas_bc</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="n">j</span><span class="o">*</span><span class="n">zeta_2ndOrder</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_2ndOrder</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">mm_2ndOrder</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omegas_bc</span> <span class="o">+</span> <span class="n">omega_2ndOrder</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="o">*</span><span class="n">mm_2ndOrder</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">H_proportional</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">H_proportional</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_d</span><span class="p">,</span><span class="n">f_m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_proportional</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_2ndOrder_fromModes</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_d</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_m</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_d</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_m</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;From Dynamic Stiffness&#39;</span><span class="p">,</span><span class="s1">&#39;From Modal Parameters&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_52_0.png" />
+</div>
+</div>
+<p>We see we get identical FRFs.</p>
+<p>For the complex mode cases:</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H} = \sum_{r=1}^N\left( \frac{\mathbf{\psi}_r\mathbf{\psi}_r^T}{m_r(s-\lambda_r)} + \frac{\mathbf{\psi}_r^*{\mathbf{\psi}_r^*}^T}{m_r(s-\lambda_r^*)}\right)\]</div>
+<p>Note that for a frequency response function, which assumes a single frequency line excitation, the Laplace variable <span class="math notranslate nohighlight">\(s = j\omega\)</span>. The pole <span class="math notranslate nohighlight">\(\lambda_r = -\zeta_r\omega_r + j\omega_r\sqrt{1-\zeta_r^2}\)</span>.</p>
+<p>Here is the proportional damped case:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_modes</span> <span class="o">=</span> <span class="n">omega_proportional</span><span class="o">.</span><span class="n">size</span>
+<span class="c1"># Compute the laplace variables</span>
+<span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span>
+
+<span class="c1"># Compute FRFs</span>
+<span class="n">H_proportional_fromModes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span>
+    <span class="n">psi_proportional</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="nd">@psi_proportional</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">/</span>
+    <span class="p">(</span><span class="n">ma_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="o">+</span> <span class="n">psi_proportional</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="nd">@psi_proportional</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="o">/</span>
+    <span class="p">(</span><span class="n">ma_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_proportional</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">H_proportional</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">H_proportional</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_d</span><span class="p">,</span><span class="n">f_m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_proportional</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_proportional_fromModes</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_d</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_m</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_d</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_m</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;From Dynamic Stiffness&#39;</span><span class="p">,</span><span class="s1">&#39;From Modal Parameters&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_54_0.png" />
+</div>
+</div>
+<p>And here is the general damped case:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_modes</span> <span class="o">=</span> <span class="n">omega_general</span><span class="o">.</span><span class="n">size</span>
+<span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span>
+
+<span class="n">H_general_fromModes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span>
+    <span class="n">psi_general</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="nd">@psi_general</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">/</span>
+    <span class="p">(</span><span class="n">ma_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="o">+</span> <span class="n">psi_general</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="nd">@psi_general</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="o">/</span>
+    <span class="p">(</span><span class="n">ma_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">H_general</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">H_general</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_d</span><span class="p">,</span><span class="n">f_m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_general</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_general_fromModes</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_d</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_m</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_d</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_m</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;From Dynamic Stiffness&#39;</span><span class="p">,</span><span class="s1">&#39;From Modal Parameters&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_56_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_56_0.png" />
+</div>
+</div>
+<p>We can see that we understand the relationships between the modal parameters and the frequency response functions.</p>
+</section>
+</section>
+<section id="Solving-for-Modal-Parameters-from-FRFs">
+<h2>Solving for Modal Parameters from FRFs<a class="headerlink" href="#Solving-for-Modal-Parameters-from-FRFs" title="Permalink to this heading"></a></h2>
+<p>The process of Experimental Modal Analysis involves measuring FRFs and then fitting modal parameters to those FRFs. Many mode fitting software packages utilize a two-step process. First, the poles (frequency and damping) are identified, then the shape information is found. Some curve fitters identify modal participation factors in the first step, which are essentially the mode shapes at the reference degrees of freedom.</p>
+<p>This section of the document will follow the implementation of the paper <a class="reference external" href="https://content.iospress.com/articles/shock-and-vibration/sav00272">Peeters, Bart et al. ‘The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation?’ 1 Jan. 2004 : 395 – 409</a>, which implements the popular PolyMax modal parameter estimator. This is the algorithm implemented in SDynPy’s <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> fitter. Other modal parameter estimators may give similar results.</p>
+<section id="Setting-up-the-System-of-Equations">
+<h3>Setting up the System of Equations<a class="headerlink" href="#Setting-up-the-System-of-Equations" title="Permalink to this heading"></a></h3>
+<p>Let us consider the frequency response function that we may measure during a modal test. We understand that we will only measure a finite bandwidth during the test, therefore there may be contributions to the measured FRFs from modes outside the bandwidth. We will turn our typical FRF matrix equation</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H} = \sum_{r=1}^N\left( \frac{\mathbf{\psi}_r\mathbf{\psi}_r^T}{m_r(s-\lambda_r)} + \frac{\mathbf{\psi}_r^*{\mathbf{\psi}_r^*}^T}{m_r(s-\lambda_r^*)}\right)\]</div>
+<p>into a “measured” version. We will replace the value <span class="math notranslate nohighlight">\(s\)</span> with <span class="math notranslate nohighlight">\(j\omega\)</span>. We will also introduce lower and upper “residuals” (<span class="math notranslate nohighlight">\(\mathbf{R}_l\)</span> and <span class="math notranslate nohighlight">\(\mathbf{R}_u\)</span>, respectively), to try to fit portions of the FRFs from out-of-band modes. We will explicitly add the notation to show that the frequency response function matrix is a function of frequency <span class="math notranslate nohighlight">\(\omega\)</span>. Additionally, we will call the mode shapes of the references the <em>modal participation factor</em>
+<span class="math notranslate nohighlight">\(\mathbf{l}_r\)</span> and the mode shapes corresponding to the responses <span class="math notranslate nohighlight">\(\mathbf{v}\)</span>. Finally, We will remove the modal mass term, which will make the assumption that the modes we fit from the algorithm will be mass normalized. Note that the format of the residual terms implies that the frequency response functions we are investigating here are <em>admittance</em> frequency response functions, which relate the force at a location to the displacement at a location.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}(\omega) = \sum_{r=1}^N\left( \frac{\mathbf{v}_r\mathbf{l}_r^T}{j\omega-\lambda_r} + \frac{\mathbf{v}_r^*{\mathbf{l}_r^*}^T}{j\omega-\lambda_r^*}\right) + \frac{\mathbf{R}_l}{(j\omega)^2} + \mathbf{R}_u\]</div>
+<p>Recall the poles <span class="math notranslate nohighlight">\(\lambda_r = -\zeta_r\omega_r + j\omega_r\sqrt{1-\zeta_r^2}\)</span> and <span class="math notranslate nohighlight">\(\lambda_r^* = -\zeta_r\omega_r - j\omega_r\sqrt{1-\zeta_r^2}\)</span>.</p>
+<p>The PolyMax approach assumes that the frequency response function can be represented by a right matrix-fraction model.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}(\omega) = \mathbf{B}(\omega)\mathbf{A}(\omega)^{-1}\]</div>
+<p>Note that these are not the same <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> and <span class="math notranslate nohighlight">\(\mathbf{B}\)</span> matrices from the previous sections, which represent the state space formulation. However, this document attempts to match the notation from the referenced paper, so we will override those previous definitions and apologize for any confusion it may cause.</p>
+<p>We can see similarities between the two formulations. The FRF equation has a numerator and a denominator, as well as a summation over a number of modes. Similarly, the right matrix-fraction model has a matrix that is not inverted (representing something like a numerator), a matrix that is inverted (representing something like a denominator), and a summation operation inherent in the matrix multiplication that is performed. Terms in <span class="math notranslate nohighlight">\(\mathbf{B}\)</span> will relate to the shape information in the
+numerator of the FRF equation, and terms in <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> will relate to the pole information in the denominator of the FRF equation.</p>
+<p>The numerator matrix <span class="math notranslate nohighlight">\(\mathbf{B}\)</span> will have a number of rows equal to the number of responses or outputs <span class="math notranslate nohighlight">\(N_o\)</span> and number of columns equal to the number of references or inputs <span class="math notranslate nohighlight">\(N_i\)</span>. The denominator matrix <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> will have a number of rows and columns equal to the number of inputs <span class="math notranslate nohighlight">\(N_i\)</span>, and is therefore square.</p>
+<p>We can split up the above equation and look at an individual row of the FRF matrix, which we will denote <span class="math notranslate nohighlight">\(\mathbf{H}_o\)</span> to represent a single output. This will involve selecting a single row from <span class="math notranslate nohighlight">\(\mathbf{B}_o\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_o(\omega) = \mathbf{B}_o(\omega)\mathbf{A}(\omega)^{-1}\]</div>
+<p>Each entry in the <span class="math notranslate nohighlight">\(\mathbf{B}_o\)</span> and <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> matrices is represented as a set of coefficients multiplied by a set of basis functions, similar to how we represent displacements in modal analysis by a set of modal coefficients multiplied by a set of mode shapes. The basis functions <span class="math notranslate nohighlight">\(\Omega_r(\omega)\)</span> are a set of functions that depend on the frequency <span class="math notranslate nohighlight">\(\omega\)</span>. The coefficients are constant with respect to <span class="math notranslate nohighlight">\(\mathbf{\omega}\)</span>. We will call the coefficient of the
+<span class="math notranslate nohighlight">\(\mathbf{B}\)</span> matrix corresponding to the <span class="math notranslate nohighlight">\(o\)</span>th row and the <span class="math notranslate nohighlight">\(r\)</span>th basis function <span class="math notranslate nohighlight">\(\beta_{or}\)</span>. Similarly, we will call the coefficient of the <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> matrix for the <span class="math notranslate nohighlight">\(r\)</span>th basis function <span class="math notranslate nohighlight">\(\alpha_r\)</span>. The number of coefficients and basis functions will be equal to the polynomial order <span class="math notranslate nohighlight">\(p+1\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{B}_o = \sum_{r=0}^p \Omega_r(\omega)\beta_{or}\]</div>
+<div class="math notranslate nohighlight">
+\[\mathbf{A} = \sum_{r=0}^p \Omega_r(\omega)\alpha_{r}\]</div>
+<p>The secret of PolyMax is that instead of using a polynomial basis function for <span class="math notranslate nohighlight">\(\Omega_r(\omega)\)</span>, it instead transforms to the <span class="math notranslate nohighlight">\(z\)</span>-domain. For larger model orders, polynomials will have large exponents, which when applied to large frequency ranges will result in huge differences between the largest and smallest values, which is a sure-fire way to run into numerical issues. Instead, by using the <span class="math notranslate nohighlight">\(z\)</span>-domain, the PolyMax approach avoids these numerical issues and generally
+achieves better results. For a test where the minimum measured frequency is <span class="math notranslate nohighlight">\(\omega_0\)</span> and the maximum measured frequency is <span class="math notranslate nohighlight">\(\omega_{end}\)</span>, this frequency range is mapped onto a half-unit circle.</p>
+<div class="math notranslate nohighlight">
+\[\Omega_r(\omega) = e^{-j\omega_z \Delta t_z r}\]</div>
+<p>where <span class="math notranslate nohighlight">\(\omega_z = \omega - \omega_0\)</span> and <span class="math notranslate nohighlight">\(\Delta t_z = \frac{\pi}{\omega_{end}-\omega_0}\)</span>. If instead of angular frequencies, the frequency range is represented in Hz with minimum measured frequency <span class="math notranslate nohighlight">\(f_0\)</span> and maximum measured frequency <span class="math notranslate nohighlight">\(f_{end}\)</span>, then the values are instead <span class="math notranslate nohighlight">\(\omega_z = 2\pi(f - f_0)\)</span> and <span class="math notranslate nohighlight">\(\Delta t_z = \frac{1}{2(f_{end}-f_0)}\)</span> where <span class="math notranslate nohighlight">\(f=\frac{\omega}{2\pi}\)</span>.</p>
+<p>The polynomnial coefficients are stacked into matrices.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\beta_o = \begin{bmatrix}\beta_{o0}\\\beta_{o1}\\\beta_{o2}\\\vdots\\\beta_{op}\end{bmatrix}\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}\alpha = \begin{bmatrix}\alpha_{0}\\\alpha_{1}\\\alpha_{2}\\\vdots\\\alpha_{p}\end{bmatrix}\end{split}\]</div>
+<p>Here <span class="math notranslate nohighlight">\(\beta_o\)</span> has number of rows equal to the polynomial order <span class="math notranslate nohighlight">\(p+1\)</span> and number of columns equal to the number of references or inputs <span class="math notranslate nohighlight">\(N_i\)</span>. Each row of <span class="math notranslate nohighlight">\(\beta_o\)</span> corresponds to the coefficients of one polynomial basis function for all of the references.</p>
+<p><span class="math notranslate nohighlight">\(\alpha\)</span> also has a number of columns equal to the number of references or inputs <span class="math notranslate nohighlight">\(N_i\)</span>. However, each row partition <span class="math notranslate nohighlight">\(\alpha_r\)</span> of <span class="math notranslate nohighlight">\(\alpha\)</span> has a number of rows equal to the number of inputs <span class="math notranslate nohighlight">\(N_i\)</span> as well. This means after stacking <span class="math notranslate nohighlight">\(p+1\)</span> of these matrices on top of one another, the final number of rows of <span class="math notranslate nohighlight">\(\alpha\)</span> is equal to <span class="math notranslate nohighlight">\(N_i(p+1)\)</span>.</p>
+<p>We finally stack all of these coefficients into a single coefficient matrix <span class="math notranslate nohighlight">\(\theta\)</span>, recalling that there is one matrix <span class="math notranslate nohighlight">\(\beta_o\)</span> for each of the <span class="math notranslate nohighlight">\(N_o\)</span> outputs of the system.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\theta = \begin{bmatrix}\beta_{1}\\\beta_{2}\\\beta_{3}\\\vdots\\\beta_{N_o}\\\alpha\end{bmatrix}\end{split}\]</div>
+<p>Each row partition in <span class="math notranslate nohighlight">\(\theta\)</span> has columns equal to the number of inputs <span class="math notranslate nohighlight">\(N_i\)</span>. Each of the <span class="math notranslate nohighlight">\(\beta_o\)</span> row partions has number of rows equal to the polynomial order <span class="math notranslate nohighlight">\(p+1\)</span>, and there are <span class="math notranslate nohighlight">\(N_o\)</span> of those partitions, resulting in the <span class="math notranslate nohighlight">\(\beta\)</span> portion of the <span class="math notranslate nohighlight">\(\theta\)</span> matrix having <span class="math notranslate nohighlight">\(N_o(p+1)\)</span> rows. Added to the <span class="math notranslate nohighlight">\(N_i(p+1)\)</span> rows of the <span class="math notranslate nohighlight">\(\alpha\)</span> partition, the final <span class="math notranslate nohighlight">\(\theta\)</span> matrix has <span class="math notranslate nohighlight">\((N_i+N_o)(p+1)\)</span> rows.</p>
+<p>At this point, we can represent the FRF matrix as a function of frequency <span class="math notranslate nohighlight">\(\omega\)</span> as well as a function of the coefficients <span class="math notranslate nohighlight">\(\theta\)</span>, <span class="math notranslate nohighlight">\(\mathbf{H}(\omega,\theta)\)</span>.</p>
+</section>
+<section id="Selecting-Coefficients-that-Minimize-Errors">
+<h3>Selecting Coefficients that Minimize Errors<a class="headerlink" href="#Selecting-Coefficients-that-Minimize-Errors" title="Permalink to this heading"></a></h3>
+<p>It may be obvious, but the goal is to eventually find the parameters <span class="math notranslate nohighlight">\(\theta\)</span> that best fit some measured frequency response functions. In a real test, we will have measured frequency response functions <span class="math notranslate nohighlight">\(\hat{\mathbf{H}}\)</span> at <span class="math notranslate nohighlight">\(N_f\)</span> discrete frequency values <span class="math notranslate nohighlight">\(\omega_k\)</span>. We would like to find the coefficients <span class="math notranslate nohighlight">\(\theta\)</span> that minimize the error between the fit frequency response functions and the measured frequency response functions. These coefficients can be found by
+minimizing the weighted non-linear least-squares error for the <span class="math notranslate nohighlight">\(o\)</span>th output <span class="math notranslate nohighlight">\(\varepsilon_o^{NLS}\)</span></p>
+<div class="math notranslate nohighlight">
+\[\varepsilon_o^{NLS}(\omega_k,\theta) = w_o(\omega_k)\left(\mathbf{H}_o(\omega_k,\theta) - \hat{\mathbf{H}}_o(\omega_k)\right)\]</div>
+<div class="math notranslate nohighlight">
+\[\varepsilon_o^{NLS}(\omega_k,\theta) = w_o(\omega_k)\left(\mathbf{B}_o(\omega_k,\beta_o)\mathbf{A}^{-1}(\omega_k,\alpha) - \hat{\mathbf{H}}_o(\omega_k)\right)\]</div>
+<p>where <span class="math notranslate nohighlight">\(w_o(\omega_k)\)</span> is a weighting function as a function of the output degree of freedom and the frequency line that allows taking into account data quality in the least-squares solution. The previous equation is a matrix equation, and we would ideally like to transform it into a scalar equation that can be minimized which is only a function of the parameters <span class="math notranslate nohighlight">\(\theta\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[l^{NLS}(\theta) = \sum_{o=1}^{N_o}\sum_{k=1}^{N_f}\mathrm{tr}\left(\left(\varepsilon_o^{NLS}(\omega_k,\theta)\right)^H\varepsilon_o^{NLS}(\omega_k,\theta)\right)\]</div>
+<p>We multiply the value <span class="math notranslate nohighlight">\(\varepsilon_o^{NLS}(\omega_k,\theta)\)</span> complex-conjugate-transpose (<span class="math notranslate nohighlight">\(\bullet^H\)</span>) by itself, and compute the trace (<span class="math notranslate nohighlight">\(\mathrm{tr}(\bullet)\)</span>). We then sum over all frequency lines and all outputs.</p>
+<p>This equation is solved by setting the derivatives of the equation with respect to the unknown modal coefficients to zero. It is reasonably clear that this leads to nonlinear equations due to the matrix inverse in the error equation. We can construct a linear set of equations that approximates the original equations by simply post-multiplying the equation by the matrix <span class="math notranslate nohighlight">\(\mathbf{A}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\varepsilon_o^{LS}(\omega_k,\theta) = w_o(\omega_k)\left(\mathbf{B}_o(\omega_k,\beta_o) - \hat{\mathbf{H}}_o(\omega_k)\mathbf{A}(\omega_k,\alpha)\right)\]</div>
+<p>Substituting for the basis functions and coefficients:</p>
+<div class="math notranslate nohighlight">
+\[\varepsilon_o^{LS}(\omega_k,\theta) = w_o(\omega_k)\sum_{r=0}^p\left(
+\Omega_r(\omega_k)\beta_{or}
+- \Omega_r(\omega_k)\hat{\mathbf{H}}_o(\omega_k)\alpha_r\right)\]</div>
+<p>We can stack this equation for all frequency lines (<span class="math notranslate nohighlight">\(\omega_1\)</span>, <span class="math notranslate nohighlight">\(\omega_2\)</span>, … <span class="math notranslate nohighlight">\(\omega_{N_f}\)</span>)</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}E_o^{LS}(\theta) = \begin{bmatrix}
+\varepsilon_o^{LS}(\omega_1,\theta) \\
+\varepsilon_o^{LS}(\omega_2,\theta) \\
+\vdots \\
+\varepsilon_o^{LS}(\omega_{N_f},\theta) \\
+\end{bmatrix} = \begin{bmatrix}\mathbf{X}_o &amp; \mathbf{Y}_o \end{bmatrix}\begin{bmatrix}\beta_o\\\alpha\end{bmatrix}\end{split}\]</div>
+<p>This will result in <span class="math notranslate nohighlight">\(E_o^{LS}(\theta)\)</span> having a number of rows equal to the number of frequency lines in the test, and the number of columns equal to the number of references or inputs <span class="math notranslate nohighlight">\(N_i\)</span>.</p>
+<p>The <span class="math notranslate nohighlight">\(\mathbf{X}_o\)</span> matrix can be constructed from pulling off the coefficients of the <span class="math notranslate nohighlight">\(\beta\)</span> terms, which are generally the basis functions <span class="math notranslate nohighlight">\(\Omega_r\)</span> evaluated at each frequency line <span class="math notranslate nohighlight">\(\omega_k\)</span>. Each row will correspond to a separate frequency line, and each column will correspond to a basis function order.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{X}_o = \begin{bmatrix}
+w_o(\omega_1)\Omega_0(\omega_1) &amp; w_o(\omega_1)\Omega_1(\omega_1) &amp; \cdots &amp; w_o(\omega_1)\Omega_p(\omega_1)\\
+w_o(\omega_2)\Omega_0(\omega_2) &amp; w_o(\omega_2)\Omega_1(\omega_2) &amp; \cdots &amp; w_o(\omega_2)\Omega_p(\omega_2)\\
+\vdots &amp; \vdots &amp; \ddots &amp; \vdots\\
+w_o(\omega_{N_f})\Omega_0(\omega_{N_f}) &amp; w_o(\omega_{N_f})\Omega_1(\omega_{N_f}) &amp; \cdots &amp; w_o(\omega_{N_f})\Omega_p(\omega_{N_f})\\
+\end{bmatrix}\end{split}\]</div>
+<p>The <span class="math notranslate nohighlight">\(\mathbf{Y}_o\)</span> matrix can be constructed by pulling off the coefficients of the <span class="math notranslate nohighlight">\(\alpha\)</span> terms, which are generally the basis functions somehow multiplied by the FRFs. We will explore this expression a bit more here. The relevant portion of <span class="math notranslate nohighlight">\(\varepsilon_o^{LS}(\omega_k,\theta)\)</span> containing the <span class="math notranslate nohighlight">\(\alpha\)</span> terms is</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{Y}_o \alpha = -\begin{bmatrix}
+    \sum_{r=0}^pw_o(\omega_1)\Omega_r(\omega_1)\hat{\mathbf{H}}_o(\omega_1)\alpha_r \\
+    \sum_{r=0}^pw_o(\omega_2)\Omega_r(\omega_2)\hat{\mathbf{H}}_o(\omega_2)\alpha_r \\
+    \vdots \\
+    \sum_{r=0}^pw_o(\omega_{N_f})\Omega_r(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f})\alpha_r\end{bmatrix}\end{split}\]</div>
+<p>Expanding the equation out in terms of the summation can help visualize which terms go where in the <span class="math notranslate nohighlight">\(\mathbf{Y}_o\)</span> matrix.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{Y}_o \begin{bmatrix}
+        \alpha_0 \\
+        \alpha_1 \\
+        \vdots \\
+        \alpha_p
+        \end{bmatrix} = -\begin{bmatrix}
+    w_o(\omega_1) \left( \Omega_0(\omega_1)\hat{\mathbf{H}}_o(\omega_1)\alpha_0 + \Omega_1(\omega_1)\hat{\mathbf{H}}_o(\omega_1)\alpha_1 + \dots + \Omega_p(\omega_1)\hat{\mathbf{H}}_o(\omega_1)\alpha_p \right) \\
+    w_o(\omega_2) \left( \Omega_0(\omega_2)\hat{\mathbf{H}}_o(\omega_2)\alpha_0 + \Omega_1(\omega_2)\hat{\mathbf{H}}_o(\omega_2)\alpha_1 + \dots + \Omega_p(\omega_2)\hat{\mathbf{H}}_o(\omega_2)\alpha_p \right) \\
+    \vdots \\
+    w_o(\omega_{N_f}) \left( \Omega_0(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f})\alpha_0 + \Omega_1(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f})\alpha_1 + \dots + \Omega_p(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f})\alpha_p \right) \\
+    \end{bmatrix}\end{split}\]</div>
+<p>or in matrix form</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{Y}_o \begin{bmatrix}
+        \alpha_0 \\
+        \alpha_1 \\
+        \vdots \\
+        \alpha_p
+        \end{bmatrix} = -\begin{bmatrix}
+    w_o(\omega_1)\Omega_0(\omega_1)\hat{\mathbf{H}}_o(\omega_1) &amp; w_o(\omega_1)\Omega_1(\omega_1)\hat{\mathbf{H}}_o(\omega_1) &amp; \dots &amp; w_o(\omega_1)\Omega_p(\omega_1)\hat{\mathbf{H}}_o(\omega_1) \\
+    w_o(\omega_2)\Omega_0(\omega_2)\hat{\mathbf{H}}_o(\omega_2) &amp; w_o(\omega_2)\Omega_1(\omega_2)\hat{\mathbf{H}}_o(\omega_2) &amp; \dots &amp; w_o(\omega_2)\Omega_p(\omega_2)\hat{\mathbf{H}}_o(\omega_2) \\
+    \vdots &amp; \vdots &amp; \ddots &amp; \vdots \\
+    w_o(\omega_{N_f})\Omega_0(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f}) &amp; w_o(\omega_{N_f})\Omega_1(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f}) &amp; \dots &amp; w_o(\omega_{N_f})\Omega_p(\omega_{N_f})\hat{\mathbf{H}}_o(\omega_{N_f}) \\
+    \end{bmatrix}\begin{bmatrix}
+        \alpha_0 \\
+        \alpha_1 \\
+        \vdots \\
+        \alpha_p
+        \end{bmatrix}\end{split}\]</div>
+<p>We can recognize the pattern <span class="math notranslate nohighlight">\(\begin{bmatrix}\Omega_0\hat{\mathbf{H}}_o &amp; \Omega_1\hat{\mathbf{H}}_o &amp; \dots &amp; \Omega_p\hat{\mathbf{H}}_o\end{bmatrix}\)</span> as the <a class="reference external" href="https://en.wikipedia.org/wiki/Kronecker_product">Kronecker Product</a> <span class="math notranslate nohighlight">\(\mathbf{\Omega} \otimes \hat{\mathbf{H}}_o\)</span> where the matrix <span class="math notranslate nohighlight">\(\mathbf{\Omega}\)</span> has only one row. Therefore,</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{X}_o = \begin{bmatrix}
+w_o(\omega_1)\mathbf{\Omega}(\omega_1)\\
+w_o(\omega_2)\mathbf{\Omega}(\omega_2)\\
+\vdots \\
+w_o(\omega_{N_f})\mathbf{\Omega}(\omega_{N_f})\\
+\end{bmatrix}\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{Y}_o = -\begin{bmatrix}
+    w_o(\omega_1)\mathbf{\Omega}(\omega_1)\otimes\hat{\mathbf{H}}_o(\omega_1)\\
+    w_o(\omega_2)\mathbf{\Omega}(\omega_2)\otimes\hat{\mathbf{H}}_o(\omega_2)\\
+    \vdots\\
+    w_o(\omega_{N_f})\mathbf{\Omega}(\omega_{N_f})\otimes\hat{\mathbf{H}}_o(\omega_{N_f})\\
+    \end{bmatrix}\end{split}\]</div>
+<p>where <span class="math notranslate nohighlight">\(\mathbf{\Omega}(\omega) = \begin{bmatrix}\Omega_0(\omega)&amp;\Omega_1(\omega)&amp;\dots&amp;\Omega_p(\omega)\end{bmatrix}\)</span></p>
+<p>Similarly to the nonlinear least squares solution, we can construct an error cost function to minimize based on the linearized equation.</p>
+<div class="math notranslate nohighlight">
+\[l^{LS}(\theta) = \sum_{o=1}^{N_o}\sum_{k=1}^{N_f}\mathrm{tr}\left(\left(\varepsilon_o^{LS}(\omega_k,\theta)\right)^H\varepsilon_o^{LS}(\omega_k,\theta)\right)\]</div>
+<p>or</p>
+<div class="math notranslate nohighlight">
+\[l^{LS}(\theta) = \sum_{o=1}^{N_o}\mathrm{tr}\left(\left(E_o^{LS}(\omega_k,\theta)\right)^HE_o^{LS}(\omega_k,\theta)\right)\]</div>
+<p>If we substitute the matrix equations, we get</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}l^{LS}(\theta) = \sum_{o=1}^{N_o}\mathrm{tr}\left(
+\begin{bmatrix}\beta_o^T &amp; \alpha^T\end{bmatrix}\begin{bmatrix}\mathbf{X}_o^H \\ \mathbf{Y}_o^H\end{bmatrix}\begin{bmatrix}\mathbf{X}_o &amp; \mathbf{Y}_o\end{bmatrix}\begin{bmatrix}\beta_o \\ \alpha\end{bmatrix}\right)\end{split}\]</div>
+<p>Ideally, we would like to remove the summation in the previous equation and end up with an equation of the form <span class="math notranslate nohighlight">\(l^{LS}(\theta) = \mathrm{tr}\left(\mathbf{\theta}^T\mathbf{J}^H\mathbf{J}\mathbf{\theta}\right)\)</span>. We recognize that we can stack the equations for <span class="math notranslate nohighlight">\(E_o^{LS}\)</span> for all outputs and arrive at an equation</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}E_1^{LS} \\ E_2^{LS} \\ \vdots \\ E_{N_o}^{LS} \end{bmatrix} =
+\begin{bmatrix}
+    \begin{bmatrix}\mathbf{X}_1 &amp; \mathbf{Y}_1\end{bmatrix}\begin{bmatrix}\beta_1 \\ \alpha\end{bmatrix} \\
+    \begin{bmatrix}\mathbf{X}_2 &amp; \mathbf{Y}_2\end{bmatrix}\begin{bmatrix}\beta_2 \\ \alpha\end{bmatrix} \\
+    \vdots \\
+    \begin{bmatrix}\mathbf{X}_{N_o} &amp; \mathbf{Y}_{N_o}\end{bmatrix}\begin{bmatrix}\beta_{N_o} \\ \alpha\end{bmatrix}\end{bmatrix} =
+\begin{bmatrix}\mathbf{X}_1 &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0} &amp; \mathbf{Y}_1 \\
+               \mathbf{0} &amp; \mathbf{X}_2 &amp; \dots &amp; \mathbf{0} &amp; \mathbf{Y}_2 \\
+               \vdots &amp; \vdots &amp; \ddots &amp; \vdots &amp; \vdots \\
+               \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{X}_{N_o} &amp; \mathbf{Y}_{N_o} \end{bmatrix}\begin{bmatrix}\beta_{1}\\\beta_{2}\\\vdots\\\beta_{N_o}\\\alpha\end{bmatrix}\end{split}\]</div>
+<p>where</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{J} = \begin{bmatrix}\mathbf{X}_1 &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0} &amp; \mathbf{Y}_1 \\
+               \mathbf{0} &amp; \mathbf{X}_2 &amp; \dots &amp; \mathbf{0} &amp; \mathbf{Y}_2 \\
+               \vdots &amp; \vdots &amp; \ddots &amp; \vdots &amp; \vdots \\
+               \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{X}_{N_o} &amp; \mathbf{Y}_{N_o} \end{bmatrix}\end{split}\]</div>
+<p>and as we’ve seen previously,</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{\theta} = \begin{bmatrix}\beta_{1}\\\beta_{2}\\\vdots\\\beta_{N_o}\\\alpha\end{bmatrix}\end{split}\]</div>
+<p>Therefore our cost function can be simplified to</p>
+<div class="math notranslate nohighlight">
+\[l^{LS}(\theta) = \mathrm{tr}\left(\mathbf{\theta}^T\mathbf{J}^H\mathbf{J}\mathbf{\theta}\right)\]</div>
+<p>In general, the quantity <span class="math notranslate nohighlight">\(\mathbf{J}^H\mathbf{J}\)</span> will be conjugate-symmetric or <a class="reference external" href="https://en.wikipedia.org/wiki/Hermitian_matrix">Hermitian</a>. This means that its upper triangle will be equal to the complex conjugate of the lower triangle transposed, and the diagonal will be real valued. While the matrix <span class="math notranslate nohighlight">\(\mathbf{J}^H\mathbf{J}\)</span> is therefore complex, in the previous equation, because it multiplies by real valued coefficients <span class="math notranslate nohighlight">\(\mathbf{\theta}\)</span> and we take the trace, which
+should only select the real-valued diagonal, we can replace the quantity <span class="math notranslate nohighlight">\(\mathbf{J}^H\mathbf{J}\)</span> with its real part <span class="math notranslate nohighlight">\(\Re\left(\mathbf{J}^H\mathbf{J}\right)\)</span></p>
+<p>Computing <span class="math notranslate nohighlight">\(\Re\left(\mathbf{J}^H\mathbf{J}\right)\)</span> gives</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\Re\left(\mathbf{J}^H\mathbf{J}\right) = \begin{bmatrix}\mathbf{R}_1 &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0} &amp; \mathbf{S}_1 \\
+                                                          \mathbf{0} &amp; \mathbf{R}_2 &amp; \dots &amp; \mathbf{0} &amp; \mathbf{S}_2 \\
+                                                          \vdots &amp; \vdots &amp; \ddots &amp; \vdots &amp; \vdots \\
+                                                          \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{R}_{N_o} &amp; \mathbf{S}_{N_o} \\
+                                                          \mathbf{S}_1^T &amp; \mathbf{S}_2^T &amp; \dots &amp; \mathbf{S}_{N_o} &amp; \sum_{o=1}^{N_o}T_o
+                                                          \end{bmatrix}\end{split}\]</div>
+<p>where</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{R}_o = \Re\left(\mathbf{X}_o^H\mathbf{X}_o\right)\]</div>
+<div class="math notranslate nohighlight">
+\[\mathbf{S}_o = \Re\left(\mathbf{X}_o^H\mathbf{Y}_o\right)\]</div>
+<div class="math notranslate nohighlight">
+\[\mathbf{T}_o = \Re\left(\mathbf{Y}_o^H\mathbf{Y}_o\right)\]</div>
+<p>We minimize the cost function by setting the derivatives of the cost function with respect to the coefficients <span class="math notranslate nohighlight">\(\mathbf{\theta}\)</span> to zero.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}l^{LS}(\theta) = \mathrm{tr}\left(\begin{bmatrix}\beta_{1}\\\beta_{2}\\\vdots\\\beta_{N_o}\\\alpha\end{bmatrix}^T\begin{bmatrix}\mathbf{R}_1 &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0} &amp; \mathbf{S}_1 \\
+                                                          \mathbf{0} &amp; \mathbf{R}_2 &amp; \dots &amp; \mathbf{0} &amp; \mathbf{S}_2 \\
+                                                          \vdots &amp; \vdots &amp; \ddots &amp; \vdots &amp; \vdots \\
+                                                          \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{R}_{N_o} &amp; \mathbf{S}_{N_o} \\
+                                                          \mathbf{S}_1^T &amp; \mathbf{S}_2^T &amp; \dots &amp; \mathbf{S}_{N_o} &amp; \sum_{o=1}^{N_o}T_o
+                                                          \end{bmatrix}\begin{bmatrix}\beta_{1}\\\beta_{2}\\\vdots\\\beta_{N_o}\\\alpha\end{bmatrix}\right)\end{split}\]</div>
+<p>Expanding the multiplication gives</p>
+<div class="math notranslate nohighlight">
+\[l^{LS}(\theta) = \mathrm{tr}\left(
+    \sum_{o=1}^{N_o}\left(\beta_o^T\mathbf{R}_o\beta_o\right) +
+    \sum_{o=1}^{N_o}\left(\alpha^T\mathbf{S}_o^T\beta_o\right) +
+    \sum_{o=1}^{N_o}\left(\beta_o^T\mathbf{S}_o\alpha\right) +
+    \sum_{o=1}^{N_o}\left(\alpha^T\mathbf{T}_o\alpha\right)\right)\]</div>
+<div class="math notranslate nohighlight">
+\[l^{LS}(\theta) = \mathrm{tr}\sum_{o=1}^{N_o}\left(
+    \beta_o^T\mathbf{R}_o\beta_o +
+    \alpha^T\mathbf{S}_o^T\beta_o +
+    \beta_o^T\mathbf{S}_o\alpha +
+    \alpha^T\mathbf{T}_o\alpha\right)\]</div>
+<p>We can therefore compute the partial derivatives with respect to the unknown coefficients and set them to zero.</p>
+<div class="math notranslate nohighlight">
+\[\frac{\partial l^{LS}(\theta)}{\partial \beta_o} =
+    2\mathbf{R}_o\beta_o +
+    2\mathbf{S}_o\alpha = \mathbf{0}\]</div>
+<div class="math notranslate nohighlight">
+\[\frac{\partial l^{LS}(\theta)}{\partial \alpha} =
+    \sum_{o=1}^{N_o}\left(
+    2\mathbf{S}_o^T\beta_o +
+    2\mathbf{T}_o\alpha\right) =\mathbf{0}\]</div>
+<p>As a first pass, we only care about the poles, which come from the denominator terms <span class="math notranslate nohighlight">\(\alpha\)</span>. We can eliminate the <span class="math notranslate nohighlight">\(\beta_o\)</span> terms using the first of these constraints</p>
+<div class="math notranslate nohighlight">
+\[\beta_o = -\mathbf{R}_o^{-1}\mathbf{S}_o\alpha\]</div>
+<p>We can then plug this into the second constraint to get an equation for <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\left[2\sum_{o=1}^{N_o}\left(
+    \mathbf{T}_o - \mathbf{S}_o^T\mathbf{R}_o^{-1}\mathbf{S}_o\right)\right]\alpha= \mathbf{M}\alpha= \mathbf{0}\]</div>
+<p>This matrix <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> is a square matrix with number of rows and columns equal to the number of inputs times the polynomial order <span class="math notranslate nohighlight">\(N_i(p+1) \times N_i(p+1)\)</span>, and depends only on the measured FRFs from the test. Note, however, that this equation is not unique. For example, the trival solution <span class="math notranslate nohighlight">\(\alpha = \mathbf{0}\)</span> satisfies this equation. Additionally, one may see that in the right matrix-fraction formulation $ <span class="math">\mathbf{H}`(:nbsphinx-math:</span>omega`) =
+<span class="math">\mathbf{B}`(:nbsphinx-math:</span>omega`):nbsphinx-math:<cite>mathbf{A}`(:nbsphinx-math:</cite>omega`)^{-1}$, one could multiply both <span class="math notranslate nohighlight">\(\mathbf{A}\)</span> and <span class="math notranslate nohighlight">\(\mathbf{B}\)</span> by the same matrix, and the equation would still be satisfied. Therefore, there is not a unique value for <span class="math notranslate nohighlight">\(\alpha\)</span>.</p>
+</section>
+<section id="Solving-the-Constrained-Problem">
+<h3>Solving the Constrained Problem<a class="headerlink" href="#Solving-the-Constrained-Problem" title="Permalink to this heading"></a></h3>
+<p>To solve a problem of the form <span class="math notranslate nohighlight">\(\mathbf{A}\mathbf{x} = \mathbf{0}\)</span> for the non-trivial <span class="math notranslate nohighlight">\(\mathbf{x}\)</span>, we must apply some constraint to the problem.</p>
+<p>To solve the problem, we will partition the matrix <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> as follows</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{M} = \begin{bmatrix}\mathbf{M}_{aa} &amp; \mathbf{M}_{ab} \\
+                              \mathbf{M}_{ba} &amp; \mathbf{M}_{bb}\end{bmatrix}\end{split}\]</div>
+<p>where the <span class="math notranslate nohighlight">\(a\)</span> indices are the first <span class="math notranslate nohighlight">\(N_ip\)</span> rows or columns and the <span class="math notranslate nohighlight">\(b\)</span> indices are the last <span class="math notranslate nohighlight">\(p\)</span> columns, recalling the size of <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> is <span class="math notranslate nohighlight">\(N_i(p+1)\)</span>. Therefore the matrix <span class="math notranslate nohighlight">\(\mathbf{M}_{aa}\)</span> matrix will have size <span class="math notranslate nohighlight">\(N_ip\times N_ip\)</span>, the <span class="math notranslate nohighlight">\(\mathbf{M}_{ab}\)</span> matrix will have size <span class="math notranslate nohighlight">\(N_ip\times p\)</span>, the <span class="math notranslate nohighlight">\(\mathbf{M}_{ba}\)</span> matrix will have size <span class="math notranslate nohighlight">\(p\times N_ip\)</span>, and the <span class="math notranslate nohighlight">\(\mathbf{M}_{bb}\)</span> matrix will have size <span class="math notranslate nohighlight">\(p\times p\)</span>.</p>
+<p>Because we must apply constraints to our problem, we will make the assumption that our solution matrix <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span> will have the form</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{\alpha} = \begin{bmatrix}\hat{\mathbf{\alpha}}  \\
+                                   \mathbf{I}\end{bmatrix}\end{split}\]</div>
+<p>We can see that if we plug this version of <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span> into the equation, we get an expression for the portion of <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span> that was not constrained.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{M}_{aa} &amp; \mathbf{M}_{ab} \\
+                 \mathbf{M}_{ba} &amp; \mathbf{M}_{bb}\end{bmatrix}
+  \begin{bmatrix}\hat{\mathbf{\alpha}}  \\
+                 \mathbf{I}\end{bmatrix} =
+  \begin{bmatrix}\mathbf{M}_{aa}\hat{\alpha} + \mathbf{M}_{ab}\\
+                 \mathbf{M}_{ba}\hat{\alpha} + \mathbf{M}_{bb}\end{bmatrix} =
+   \begin{bmatrix}\mathbf{0} \\
+                  \mathbf{0}\end{bmatrix}\end{split}\]</div>
+<p>We can then simply take the top partition to solve for <span class="math notranslate nohighlight">\(\hat{\alpha}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\hat{\alpha} = -\mathbf{M}_{aa}^{-1}\mathbf{M}_{ab}\]</div>
+</section>
+<section id="Extracting-Poles-and-Participation-Factors">
+<h3>Extracting Poles and Participation Factors<a class="headerlink" href="#Extracting-Poles-and-Participation-Factors" title="Permalink to this heading"></a></h3>
+<p>Now that we have the polynomial coefficients <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span> for the right matrix-fraction model, we would like to extract the roots from it. The roots of this polynomial are the poles of the frequency response function. We will do this by setting up a <a class="reference external" href="https://en.wikipedia.org/wiki/Companion_matrix">Companion Matrix</a>. A Companion matrix of a polynomial is a matrix whose characteristic equation is equal to that polynomial. Then the eigenvalues of the matrix (which are the roots of the
+characteristic equation) are also the roots of the polynomial. In our case, the eigenvectors are also useful, as they are related to the participation factors.</p>
+<p>For a polynomial <span class="math notranslate nohighlight">\(p(x) = \alpha_p x^p + \alpha_{p-1} x^{p-1} + \dots + \alpha_2 x^2 + \alpha_1 x + \alpha_0\)</span>, the companion matrix will be</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{C} = \begin{bmatrix} 0 &amp; 1 &amp; 0 &amp; \dots &amp; 0\\
+                               0 &amp; 0 &amp; 1 &amp; \dots &amp; 0\\
+                               \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots\\
+                               0 &amp; 0 &amp; 0 &amp; \dots &amp; 1\\
+                               -\frac{\alpha_0}{\alpha_p} &amp; -\frac{\alpha_1}{\alpha_p} &amp; -\frac{\alpha_2}{\alpha_p} &amp; \dots &amp; -\frac{\alpha_{p-1}}{\alpha_p}
+                               \end{bmatrix}\end{split}\]</div>
+<p>Readers can verify that <span class="math notranslate nohighlight">\(\mathrm{det}(\mathbf{I}x - \mathbf{C}) = p(x)\)</span>.</p>
+<p>Our present case is slightly different, because our coefficients are matrices rather than scalars.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{C} = \begin{bmatrix} \mathbf{0} &amp; \mathbf{I} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0}\\
+                               \mathbf{0} &amp; \mathbf{0} &amp; \mathbf{I} &amp; \dots &amp; \mathbf{0}\\
+                               \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots\\
+                               \mathbf{0} &amp; \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{I}\\
+                               -\alpha_0^T{\alpha_p^T}^{-1} &amp; -\alpha_1^T{\alpha_p^T}^{-1} &amp; -\alpha_2^T{\alpha_p^T}^{-1} &amp; \dots &amp; -\alpha_{p-1}^T{\alpha_p^T}^{-1}
+                               \end{bmatrix}\end{split}\]</div>
+<p>However, because we applied the constraint such that <span class="math notranslate nohighlight">\(\alpha_p = \mathbf{I}\)</span> when solving for <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span>, the quantity <span class="math notranslate nohighlight">\(\alpha_p^{-1} = \mathbf{I}\)</span>, and the remaining terms are those in <span class="math notranslate nohighlight">\(\hat{\mathbf{\alpha}}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{C} = \begin{bmatrix} \mathbf{0} &amp; \mathbf{I} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0}\\
+                               \mathbf{0} &amp; \mathbf{0} &amp; \mathbf{I} &amp; \dots &amp; \mathbf{0}\\
+                               \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots\\
+                               \mathbf{0} &amp; \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{I}\\
+                               -\hat{\alpha_0}^T &amp; -\hat{\alpha_1}^T &amp; -\hat{\alpha_2}^T &amp; \dots &amp; -\hat{\alpha}_{p-1}^T
+                               \end{bmatrix}\end{split}\]</div>
+<p>The <span class="math notranslate nohighlight">\(\mathbf{C}\)</span> matrix will be a square matrix with size <span class="math notranslate nohighlight">\(N_ip \times N_ip\)</span> where <span class="math notranslate nohighlight">\(N_i\)</span> is the number of inputs or references, and <span class="math notranslate nohighlight">\(p\)</span> is the order of the polynomial.</p>
+<p>We can therefore notate the companion matrix compactly as</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{C} = \begin{bmatrix} \begin{matrix}\mathbf{0} &amp; \mathbf{I}\end{matrix} \\ \begin{matrix}-\hat{\alpha}^T\end{matrix}\end{bmatrix}\end{split}\]</div>
+<p>We can then solve for the eigenvalues and eigenvectors of this matrix. The companion matrix is set up such that its characteristic polynomial is identical to the polynomial it represents. Therefore, its eigenvalues, which are roots of the characteristic polynomial, will be roots of the polynomial it represents.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{C}\mathbf{V} = \begin{bmatrix} \mathbf{0} &amp; \mathbf{I} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{0}\\
+                                \mathbf{0} &amp; \mathbf{0} &amp; \mathbf{I} &amp; \dots &amp; \mathbf{0}\\
+                                \vdots &amp; \vdots &amp; \vdots &amp; \ddots &amp; \vdots\\
+                                \mathbf{0} &amp; \mathbf{0} &amp; \mathbf{0} &amp; \dots &amp; \mathbf{I}\\
+                                -\hat{\alpha_0}^T &amp; -\hat{\alpha_1}^T &amp; -\hat{\alpha_2}^T &amp; \dots &amp; -\hat{\alpha}_{p-1}^T
+                                \end{bmatrix}\mathbf{V}= \mathbf{\Lambda}\mathbf{V}\end{split}\]</div>
+<p>The diagonals of the eigenvector matrix <span class="math notranslate nohighlight">\(\bar{\lambda}_r = \mathbf{\Lambda}_{rr}\)</span> are the eigenvalues of the companion matrix. These are the poles of the system; however, we must remember they are represented in the <span class="math notranslate nohighlight">\(z\)</span>-domain, so we must return to the <span class="math notranslate nohighlight">\(s\)</span>-domain <span class="math notranslate nohighlight">\(\lambda_r\)</span> in order to solve for the natural frequency and damping ratio.</p>
+<div class="math notranslate nohighlight">
+\[\bar{\lambda}_r = e^{-\lambda_r \Delta t_z}\]</div>
+<div class="math notranslate nohighlight">
+\[\lambda_r = -\frac{\log{\lambda_r}}{\Delta t_z}\]</div>
+<p>where <span class="math notranslate nohighlight">\(\Delta t_z = \frac{\pi}{\omega_{end}-\omega_0}\)</span>.</p>
+<p>We can then solve for the natural frequency and damping ratio from the poles. Recall that the frequency was shifted by <span class="math notranslate nohighlight">\(\omega_0\)</span> during the mapping, so we will need to take that into account.</p>
+<div class="math notranslate nohighlight">
+\[{\omega_z}_r = \left|\lambda_r\right|\]</div>
+<div class="math notranslate nohighlight">
+\[{\omega_r} = {\omega_z}_r + \omega_0\]</div>
+<div class="math notranslate nohighlight">
+\[\zeta_r = -\frac{\Re\left({\lambda_r}\right)}{\omega_r}\]</div>
+<p>Note that there will be in general <span class="math notranslate nohighlight">\(N_ip\)</span> eigenvalues. Half of them will be complex conjugates of the other half, so we can ignore those. Similarly, we can ignore non-physical eigenvalues. A criteria that is successfully used is to only keep poles where the real part is less than zero and the imaginary part is greater than zero.</p>
+<p>The modal participation factors <span class="math notranslate nohighlight">\(\mathbf{l}_r\)</span> can be determined from the eigenvectors of the companion matrix. They will be the last <span class="math notranslate nohighlight">\(N_i\)</span> rows of the eigenvector matrix. One must also be sure that for each eigenvalue discarded for being non-physical or complex conjugate, the corresponding eigenvector should also be discarded.</p>
+</section>
+<section id="Stabilization-Diagrams">
+<h3>Stabilization Diagrams<a class="headerlink" href="#Stabilization-Diagrams" title="Permalink to this heading"></a></h3>
+<p>Solving for a polynomial of a particular order will in general result in a large number of poles. For a typical problem, only a subset of these poles will correspond to true modes of the structure. For a typical modal test, it may not be obvious how many modes are in a given bandwidth anyways. Therefore, we need some kind of metric to determine if a pole is valid or not, and for this we utilize the stabilization diagram.</p>
+<p>A stabilization diagram is constructed by computing the poles over a large number of polynomial orders. As we increase the polynomial orders, more poles may be found in the system. We declare a <em>stable pole</em> to be one which does not change significantly as the polynomial order changes. Computational poles may bounce around the measurement bandwidth. Real poles of the system, on the other hand, will generally result in a similar frequency, damping ratio, and set of participation factors
+regardless of the polynomial order. We then construct a final set of poles by selecting from the stable poles in the stabilization diagram.</p>
+</section>
+<section id="Back-to-the-Example-Problem">
+<h3>Back to the Example Problem<a class="headerlink" href="#Back-to-the-Example-Problem" title="Permalink to this heading"></a></h3>
+<p>To demonstrate the equations shown here, we will implement the previous equations in code. We will demonstrate solution of one polynomial order to demonstrate the process, and then rely on the implementation in SDynPy to perform the rest of the analysis. We will initially focus only on the general damping case rather than repeating the analysis for all the damping matrices.</p>
+<p>To start, we will pull in the measured frequency response functions and corresponding frequency values. We will shape the frequency response function such that it is a 3D array with shape <span class="math notranslate nohighlight">\(N_o \times N_i \times N_f\)</span>. We will also assume that we only peformed measurements with inputs at the first and second degrees of freedom.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">H</span> <span class="o">=</span> <span class="n">H_general</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)[:,:</span><span class="mi">2</span><span class="p">,:]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">H</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(3, 2, 1000)
+</pre></div></div>
+</div>
+<p>We will also assume that we only performed measurements over a limited bandwidth from 10 rad/s to 80 rad/s.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">frequency_lines_to_keep</span> <span class="o">=</span> <span class="p">(</span><span class="n">omegas</span> <span class="o">&gt;</span> <span class="mi">10</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">omegas</span> <span class="o">&lt;</span> <span class="mi">80</span><span class="p">)</span>
+<span class="n">H</span> <span class="o">=</span> <span class="n">H</span><span class="p">[:,:,</span><span class="n">frequency_lines_to_keep</span><span class="p">]</span>
+<span class="n">omegas</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[</span><span class="n">frequency_lines_to_keep</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Now that we have truncated the frequency domain, we will define some helper variables to make the code a bit more intuitive.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_output</span><span class="p">,</span> <span class="n">num_input</span><span class="p">,</span> <span class="n">num_freq</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">shape</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Num Outputs: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">Num Inputs: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">Num Frequencies: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_output</span><span class="p">,</span> <span class="n">num_input</span><span class="p">,</span> <span class="n">num_freq</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Num Outputs: 3
+Num Inputs: 2
+Num Frequencies: 778
+</pre></div></div>
+</div>
+<p>We will also set our polynomial order. In this case, we know that there are 3 modes of the structure in the bandwidth. However, let’s select 5 as the polynomial order, as we generally would like to overfit the model. Keep in mind that this will result in 6 polynomial coefficients: 0, 1, 2, 3, 4, and 5. Therefore, you shouldn’t be confused when dimensions of arrays associated with the polynomial order have length 6 instead of length 5.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">order</span> <span class="o">=</span> <span class="mi">5</span>
+</pre></div>
+</div>
+</div>
+<p>We will assume no weighting, or rather the weighting is identically 1 across all outputs and across all frequencies.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">weighting</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">num_freq</span><span class="p">,</span><span class="n">num_output</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<p>The first thing we will do is to set up our <span class="math notranslate nohighlight">\(\mathbf{\Omega}_r(\omega) = e^{-j\omega_z\Delta t_z r}\)</span> vectors. We will set this up as an array with number of rows equal to number of frequency lines and number of columns equal to the number of polynomial coefficients.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">omega_z</span> <span class="o">=</span> <span class="n">omegas</span> <span class="o">-</span> <span class="n">omegas</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="c1"># Perform the shift</span>
+<span class="n">deltat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">omegas</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="c1"># Compute the scale factor</span>
+<span class="n">Omega</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">omega_z</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">deltat</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span> <span class="c1"># Compute Omega</span>
+<span class="n">Omega</span><span class="o">.</span><span class="n">shape</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+(778, 6)
+</pre></div></div>
+</div>
+<p>In the previous code, we use broadcasting to set up our <span class="math notranslate nohighlight">\(\mathbf{\Omega}_r\)</span> array. We add a <code class="docutils literal notranslate"><span class="pre">newaxis</span></code> to our shifted frequency vector <span class="math notranslate nohighlight">\(\omega_z\)</span> to make it a <code class="docutils literal notranslate"><span class="pre">num_freq</span></code> <span class="math notranslate nohighlight">\(\times\)</span> 1 array. This is then multiplied by an array <code class="docutils literal notranslate"><span class="pre">[0,1,2,3,4,5]</span></code> constructed from the <code class="docutils literal notranslate"><span class="pre">arange</span></code> function. Per <a class="reference external" href="https://numpy.org/doc/stable/user/basics.broadcasting.html">Numpy’s Broadcasting Rules</a>, a <code class="docutils literal notranslate"><span class="pre">num_freq</span></code> <span class="math notranslate nohighlight">\(\times\)</span> 1 array and a <code class="docutils literal notranslate"><span class="pre">order+1</span></code> array will be broadcast to a <code class="docutils literal notranslate"><span class="pre">num_freq</span></code>
+<span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">order+1</span></code> array. Multiplying by scalars <code class="docutils literal notranslate"><span class="pre">deltat</span></code> and <code class="docutils literal notranslate"><span class="pre">-1j</span></code> get performed elementwise, as does the exponential function <code class="docutils literal notranslate"><span class="pre">exp</span></code>.</p>
+<p>The next step will be to assemble the <span class="math notranslate nohighlight">\(\mathbf{R}_o\)</span>, <span class="math notranslate nohighlight">\(\mathbf{S}_o\)</span>, and <span class="math notranslate nohighlight">\(\mathbf{T}_o\)</span> matrices. These are built from the <span class="math notranslate nohighlight">\(\mathbf{X}_o\)</span> and <span class="math notranslate nohighlight">\(\mathbf{Y}_o\)</span> matrices.</p>
+<p>Before we do this let’s think about the dimensions of the arrays. The matrix <span class="math notranslate nohighlight">\(\mathbf{R}_o\)</span> for each output will have dimension <code class="docutils literal notranslate"><span class="pre">order+1</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">order+1</span></code>. The matrix <span class="math notranslate nohighlight">\(\mathbf{S}_o\)</span> for each output will have dimension <code class="docutils literal notranslate"><span class="pre">order</span> <span class="pre">+</span> <span class="pre">1</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">num_input*(order+1)</span></code>. The <span class="math notranslate nohighlight">\(\mathbf{T}_o\)</span> matrix for each output will have dimension <code class="docutils literal notranslate"><span class="pre">num_input*(order+1)</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">num_input*(order+1)</span></code>. Because we will have <code class="docutils literal notranslate"><span class="pre">num_output</span></code> of each of these matrices, we will
+consider assembling them into a 3D array with dimension <code class="docutils literal notranslate"><span class="pre">num_output</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">...</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">...</span></code> .</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[28]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">num_output</span><span class="p">,</span> <span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">])</span>
+<span class="n">S</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">num_output</span><span class="p">,</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num_input</span> <span class="o">*</span> <span class="p">(</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
+<span class="n">T</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">num_output</span><span class="p">,</span> <span class="n">num_input</span> <span class="o">*</span> <span class="p">(</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">num_input</span> <span class="o">*</span> <span class="p">(</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  R: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  S: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  T: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">R</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">S</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">T</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  R: (3, 6, 6)
+  S: (3, 6, 12)
+  T: (3, 12, 12)
+</pre></div></div>
+</div>
+<p>We will then loop through each of the outputs and assemble the corresponding portions of the matrices.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_output</span><span class="p">):</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accumulating Data: Progress = </span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span> <span class="o">/</span> <span class="n">num_output</span> <span class="o">*</span> <span class="mf">100.0</span><span class="p">))</span>
+    <span class="c1"># The Xo matrix depends only on the weighting and Omega.  We use</span>
+    <span class="c1"># broadcasting to extend the weighting across all polynomial coefficients</span>
+    <span class="n">Xo</span> <span class="o">=</span> <span class="n">weighting</span><span class="p">[:,</span> <span class="n">o</span><span class="p">][</span><span class="o">...</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span> <span class="o">*</span> <span class="n">Omega</span>
+    <span class="c1"># The Yo matrix will need the measured frequency response functions</span>
+    <span class="c1"># at the output o.</span>
+    <span class="n">Ho</span> <span class="o">=</span> <span class="n">H</span><span class="p">[</span><span class="n">o</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
+    <span class="c1"># We note that rather than re-multiplying the weighting by Omega in the</span>
+    <span class="c1"># Yo matrix, we can simply use Xo.  Here we must loop over all frequency</span>
+    <span class="c1"># lines to perform the kronecker product.  The frequency lines are the</span>
+    <span class="c1"># rows of X, and the columns of Ho, so we transpose Ho.</span>
+    <span class="c1"># Xk is then the polynomial coefficients at the k frequency line and</span>
+    <span class="c1"># Hk is the FRF for each input at that frequency line.</span>
+    <span class="n">Yo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">Xk</span><span class="p">,</span> <span class="n">Hk</span><span class="p">)</span> <span class="k">for</span> <span class="n">Xk</span><span class="p">,</span> <span class="n">Hk</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">Xo</span><span class="p">,</span> <span class="n">Ho</span><span class="o">.</span><span class="n">transpose</span><span class="p">())])</span>
+    <span class="c1"># We then construct the R, S, and T matrices for that output from their</span>
+    <span class="c1"># definitions</span>
+    <span class="n">Ro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">Xo</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">@</span> <span class="n">Xo</span><span class="p">)</span>
+    <span class="n">So</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">Xo</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">@</span> <span class="n">Yo</span><span class="p">)</span>
+    <span class="n">To</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">Yo</span><span class="o">.</span><span class="n">conjugate</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span> <span class="o">@</span> <span class="n">Yo</span><span class="p">)</span>
+    <span class="c1"># We stick the matrices into the 3D arrays for future usage.</span>
+    <span class="n">R</span><span class="p">[</span><span class="n">o</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">Ro</span>
+    <span class="n">S</span><span class="p">[</span><span class="n">o</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">So</span>
+    <span class="n">T</span><span class="p">[</span><span class="n">o</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">To</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Accumulating Data: Progress = </span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">o</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">num_output</span> <span class="o">*</span> <span class="mf">100.0</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  Xo: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  Yo: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  Ro: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  So: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  To: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">Xo</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">Yo</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">Ro</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">So</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">To</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Accumulating Data: Progress = 0.00%
+Accumulating Data: Progress = 33.33%
+Accumulating Data: Progress = 66.67%
+Accumulating Data: Progress = 100.00%
+Shapes:
+  Xo: (778, 6)
+  Yo: (778, 12)
+  Ro: (6, 6)
+  So: (6, 12)
+  To: (12, 12)
+</pre></div></div>
+</div>
+<p>We will now compute the <span class="math notranslate nohighlight">\(\mathbf{M}\)</span> matrix, which is equal to <span class="math notranslate nohighlight">\(\sum_{o=1}^{N_o}\left(\mathbf{T}_o - \mathbf{S}_o^T\mathbf{R}_o^{-1}\mathbf{S}_o\right)\)</span>. Note that we dropped the factor of 2 in front of the expression because the final answer <span class="math notranslate nohighlight">\(\alpha\)</span> is only defined up to a matrix product. Multiplying the answer by 2 will not result in any meaningful change.</p>
+<p>We will compute the contribution <span class="math notranslate nohighlight">\(\mathbf{M}_o = \mathbf{T}_o - \mathbf{S}_o^T\mathbf{R}_o^{-1}\mathbf{S}_o\)</span> for each output and then sum them together to construct <span class="math notranslate nohighlight">\(\mathbf{M}\)</span>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Because S is a 3D array, to transpose it, we will only want to</span>
+<span class="c1"># swap the last two dimensions and leave the first output dimension</span>
+<span class="c1"># out front.</span>
+<span class="c1"># We compute R^-1 using np.linalg.solve, which solves a problem.</span>
+<span class="c1"># np.linalg.solve(R,S) is equivalent to R^-1@S, but is faster and</span>
+<span class="c1"># better conditioned.</span>
+<span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">T</span> <span class="o">-</span> <span class="n">S</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span> <span class="o">@</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">R</span><span class="p">,</span> <span class="n">S</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  M: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">M</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  M: (12, 12)
+</pre></div></div>
+</div>
+<p>Now we will solve for the parameters <span class="math notranslate nohighlight">\(\mathbf{\alpha}\)</span> and construct the companion matrix. Recall that in our solution we will assume <span class="math notranslate nohighlight">\(\mathbf{\alpha}_p\)</span> is equal to the identity matrix and drops out of the companion matrix computation, so we really only need to compute <span class="math notranslate nohighlight">\(\hat{\alpha} = -\mathbf{M}_{aa}^{-1}\mathbf{M}_{ab}\)</span>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[31]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">Maa</span> <span class="o">=</span> <span class="n">M</span><span class="p">[:</span><span class="n">order</span> <span class="o">*</span> <span class="n">num_input</span><span class="p">,</span> <span class="p">:</span><span class="n">order</span> <span class="o">*</span> <span class="n">num_input</span><span class="p">]</span>
+<span class="n">Mbb</span> <span class="o">=</span> <span class="o">-</span><span class="n">M</span><span class="p">[:</span><span class="n">order</span> <span class="o">*</span> <span class="n">num_input</span><span class="p">,</span> <span class="n">order</span> <span class="o">*</span> <span class="n">num_input</span><span class="p">:]</span>
+<span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span><span class="n">Maa</span><span class="p">,</span> <span class="n">Mbb</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  Maa: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  Mbb: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  alpha: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">Maa</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">Mbb</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">alpha</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  Maa: (10, 10)
+  Mbb: (10, 2)
+  alpha: (10, 2)
+</pre></div></div>
+</div>
+<p>We will then construct the companion matrix <span class="math notranslate nohighlight">\(\mathbf{C} = \begin{bmatrix} \begin{matrix}\mathbf{0} &amp; \mathbf{1}\end{matrix} \\ \begin{matrix}-\hat{\alpha}^T\end{matrix}\end{bmatrix}\)</span>. The partition <span class="math notranslate nohighlight">\(\mathbf{0}\)</span> will have shape <code class="docutils literal notranslate"><span class="pre">(order-1)*num_input</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">num_input</span></code>, and the partition <span class="math notranslate nohighlight">\(\mathbf{I}\)</span> will have shape <code class="docutils literal notranslate"><span class="pre">(order-1)*num_input</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">(order-1)*num_input</span></code>.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">C_top_left</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([(</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">num_input</span><span class="p">,</span> <span class="n">num_input</span><span class="p">])</span>
+<span class="n">C_top_right</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">((</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">num_input</span><span class="p">)</span>
+<span class="n">C_bottom</span> <span class="o">=</span> <span class="o">-</span><span class="n">alpha</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span>
+<span class="n">C</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+    <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">C_top_left</span><span class="p">,</span> <span class="n">C_top_right</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+     <span class="n">C_bottom</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  C: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">C</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  C: (10, 10)
+</pre></div></div>
+</div>
+<p>Finally, we can compute the eigenvalues and eigenvectors from this matrix, which should be the solutions to the polynomial. Remember that the eigenvalues will be in the <span class="math notranslate nohighlight">\(z\)</span>-domain, and must be transformed back to the <span class="math notranslate nohighlight">\(s\)</span> domain for us to extract natural frequency and damping ratio: <span class="math notranslate nohighlight">\(\lambda_r = -\frac{\log{\lambda_r}}{\Delta t_z}\)</span>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">zpoles</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">C</span><span class="p">)</span>
+<span class="n">spoles</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">zpoles</span><span class="p">)</span><span class="o">/</span><span class="n">deltat</span>
+</pre></div>
+</div>
+</div>
+<p>We will immediately discard any poles where the imaginary part is less than zero or the real part is greater than zero, ensuring that we also discard the corresponding eigenvectors.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[34]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">keep_poles</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">imag</span><span class="p">(</span><span class="n">spoles</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">spoles</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span>
+<span class="n">spoles</span> <span class="o">=</span> <span class="n">spoles</span><span class="p">[</span><span class="n">keep_poles</span><span class="p">]</span>
+<span class="n">V</span> <span class="o">=</span> <span class="n">V</span><span class="p">[:,</span><span class="n">keep_poles</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>We can then extract the (shifted) natural frequency and damping ratios from the poles. We unshift the natural frequency from the pole to recover the natural frequency of the structure.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[35]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">omega_general_fit</span> <span class="o">=</span> <span class="nb">abs</span><span class="p">(</span><span class="n">spoles</span><span class="p">)</span> <span class="o">+</span> <span class="n">omegas</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">zeta_general_fit</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">spoles</span><span class="p">)</span><span class="o">/</span><span class="n">omega_general_fit</span>
+</pre></div>
+</div>
+</div>
+<p>The modal participation factors <span class="math notranslate nohighlight">\(\mathbf{l}_r\)</span> are then the last <code class="docutils literal notranslate"><span class="pre">num_input</span></code> rows of the eigenvectors.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[36]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lr</span> <span class="o">=</span> <span class="n">V</span><span class="p">[</span><span class="o">-</span><span class="n">num_input</span><span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<p>Let’s compare the fit natural frequencies to those from the test problem. We will sort them ascending so they match those computed directly from the eigenvalues of the state space formulation. Make sure that we also sort the damping and participation factors identically.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[37]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">sort_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">omega_general_fit</span><span class="p">)</span>
+<span class="n">omega_general_fit</span> <span class="o">=</span> <span class="n">omega_general_fit</span><span class="p">[</span><span class="n">sort_indices</span><span class="p">]</span>
+<span class="n">zeta_general_fit</span> <span class="o">=</span> <span class="n">zeta_general_fit</span><span class="p">[</span><span class="n">sort_indices</span><span class="p">]</span>
+<span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span><span class="p">[:,</span><span class="n">sort_indices</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Let’s now set up a table to show the differences. We will compare the participation factors against the same degrees of freedom from the corresponding mode shapes. Note that the participation factors may be scaled differently and rotated differently than the mode shapes, so we must rescale and rotate them to judge how well they fit.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[38]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Frequency From System Matrices&#39;</span><span class="p">,</span><span class="s1">&#39;Frequency From FRF Fits&#39;</span><span class="p">,</span><span class="s1">&#39;Frequency Error&#39;</span><span class="p">,</span>
+                             <span class="s1">&#39;Damping From System Matrices&#39;</span><span class="p">,</span><span class="s1">&#39;Damping From FRF Fits&#39;</span><span class="p">,</span><span class="s1">&#39;Damping Error&#39;</span><span class="p">,</span>
+                             <span class="s1">&#39;Participation From System Matrices&#39;</span><span class="p">,</span><span class="s1">&#39;Participation From FRF Fits&#39;</span><span class="p">],</span>
+                  <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">wm</span><span class="p">,</span><span class="n">wf</span><span class="p">,</span><span class="n">zm</span><span class="p">,</span><span class="n">zf</span><span class="p">,</span><span class="n">lm</span><span class="p">,</span><span class="n">lf</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
+    <span class="n">omega_general</span><span class="p">,</span><span class="n">omega_general_fit</span><span class="p">,</span>
+    <span class="n">zeta_general</span><span class="p">,</span> <span class="n">zeta_general_fit</span><span class="p">,</span>
+    <span class="n">psi_general</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">lr</span><span class="o">.</span><span class="n">T</span><span class="p">)):</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Frequency From System Matrices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">wm</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Frequency From FRF Fits&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">wf</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Frequency Error&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">wf</span><span class="o">-</span><span class="n">wm</span><span class="p">)</span><span class="o">/</span><span class="n">wm</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Damping From System Matrices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">zm</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Damping From FRF Fits&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">zf</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Damping Error&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">zf</span><span class="o">-</span><span class="n">zm</span><span class="p">)</span><span class="o">/</span><span class="n">zm</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="c1"># Find the maximum participation degree of freedom from the</span>
+    <span class="c1"># system matrices</span>
+    <span class="n">check_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lm</span><span class="p">))</span>
+    <span class="c1"># Compute the angle of that index</span>
+    <span class="n">check_angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">lm</span><span class="p">[</span><span class="n">check_index</span><span class="p">])</span>
+    <span class="c1"># Compute the same angle from the fit</span>
+    <span class="n">this_angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">lf</span><span class="p">[</span><span class="n">check_index</span><span class="p">])</span>
+    <span class="n">rotation</span> <span class="o">=</span> <span class="n">check_angle</span> <span class="o">-</span> <span class="n">this_angle</span>
+    <span class="c1"># Compute the magnitudes of the vectors</span>
+    <span class="n">check_magnitude</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lm</span><span class="p">))</span>
+    <span class="n">this_magnitude</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lf</span><span class="p">))</span>
+    <span class="c1"># Correct rotation and scaling</span>
+    <span class="n">lf</span> <span class="o">=</span> <span class="n">lf</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">rotation</span><span class="p">)</span><span class="o">*</span><span class="n">check_magnitude</span><span class="o">/</span><span class="n">this_magnitude</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Participation From System Matrices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lm</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Participation From FRF Fits&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lf</span><span class="p">)</span>
+
+<span class="n">pretty_print_table</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area rendered_html docutils container">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Frequency From System Matrices</th>
+      <th>Frequency From FRF Fits</th>
+      <th>Frequency Error</th>
+      <th>Damping From System Matrices</th>
+      <th>Damping From FRF Fits</th>
+      <th>Damping Error</th>
+      <th>Participation From System Matrices</th>
+      <th>Participation From FRF Fits</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>27.25</td>
+      <td>27.30</td>
+      <td>0.19%</td>
+      <td>2.71%</td>
+      <td>2.60%</td>
+      <td>-3.90%</td>
+      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>
+      <td>[-0.04318693+0.04408044j -0.09857685+0.09838141j]</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>54.78</td>
+      <td>54.86</td>
+      <td>0.15%</td>
+      <td>2.74%</td>
+      <td>2.76%</td>
+      <td>0.67%</td>
+      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>
+      <td>[0.06534612-0.06998435j 0.00118367+0.00286075j]</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>59.91</td>
+      <td>59.88</td>
+      <td>-0.06%</td>
+      <td>2.66%</td>
+      <td>2.69%</td>
+      <td>1.02%</td>
+      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>
+      <td>[-0.06070207+0.05458388j  0.03320861-0.0344524j ]</td>
+    </tr>
+  </tbody>
+</table></div>
+</div>
+<p>We can verify that we get very good agreement between the quantities derived from the system matrices and those fit by the FRFs. However, we may have simply been lucky: our system of equations actually solved for <code class="docutils literal notranslate"><span class="pre">order*num_inputs</span></code> or 10 poles, and it just so happened that 3 of them ended up being physically realizable with positive damping and frequency. It is possible that more of them would have been physically realizable, in which case we would have had to go through and try to figure out
+which poles are “real” and which are “computational”, and without <em>a priori</em> knowledge of the true pole information, it could be very difficult to do this.</p>
+<p>Instead, we use a stabilization diagram. We solve for a range of different polynomial orders and we plot the resulting frequencies on a figure. We then identify poles that appear in many different model orders to be “stable” and we accept these as real poles.</p>
+<p>Rather than code all this from scratch, we will simply rely on SDynPy’s <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> implementation to help us out. <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> relies on the FRF being structured as a <code class="docutils literal notranslate"><span class="pre">TransferFunctionArray</span></code>, so we must first create one from our data. Note that <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> works with frequencies in Hz rather than in radians per second, so we must convert our <code class="docutils literal notranslate"><span class="pre">omegas</span></code> variable by dividing by <span class="math notranslate nohighlight">\(2\pi\)</span>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[39]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sdynpy</span> <span class="k">as</span> <span class="nn">sdpy</span>
+<span class="n">tfarray</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">data_array</span><span class="p">(</span><span class="n">sdpy</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">FunctionTypes</span><span class="o">.</span><span class="n">FREQUENCY_RESPONSE_FUNCTION</span><span class="p">,</span>
+                          <span class="n">abscissa</span> <span class="o">=</span> <span class="n">omegas</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">),</span> <span class="n">ordinate</span> <span class="o">=</span> <span class="n">H</span><span class="p">,</span>
+                          <span class="n">coordinate</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">coordinate</span><span class="o">.</span><span class="n">outer_product</span><span class="p">(</span>
+                              <span class="n">sdpy</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span><span class="s1">&#39;X+&#39;</span><span class="p">),</span>
+                              <span class="n">sdpy</span><span class="o">.</span><span class="n">coordinate_array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">],</span><span class="s1">&#39;X+&#39;</span><span class="p">)))</span>
+<span class="n">pp</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">PolyPy</span><span class="p">(</span><span class="n">tfarray</span><span class="p">,</span> <span class="n">displacement_derivative</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="c1"># We tell PolyPy these are displacement FRFs.</span>
+</pre></div>
+</div>
+</div>
+<p>We can then tell <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> which polynomial orders to solve for.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[40]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pp</span><span class="o">.</span><span class="n">compute_poles</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">30</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Accumulating Data: Progress = 0.00%
+Accumulating Data: Progress = 33.33%
+Accumulating Data: Progress = 66.67%
+Solving for 29 roots (1 of 13)
+Solving for 27 roots (2 of 13)
+Solving for 25 roots (3 of 13)
+Solving for 23 roots (4 of 13)
+Solving for 21 roots (5 of 13)
+Solving for 19 roots (6 of 13)
+Solving for 17 roots (7 of 13)
+Solving for 15 roots (8 of 13)
+Solving for 13 roots (9 of 13)
+Solving for 11 roots (10 of 13)
+Solving for 9 roots (11 of 13)
+Solving for 7 roots (12 of 13)
+Solving for 5 roots (13 of 13)
+</pre></div></div>
+</div>
+<p>We can then plot the stabilization diagram. The stabilization diagram will have different markers to denote the stability of a given pole. We generally overlay these poles with a mode indicator function to help us figure out which poles are “real”. In this case, a red X denotes an unstable pole, a blue triangle denotes that the frequency has stablized, a blue square means that the frequency and damping have stabilized, and a green circle means that the frequency, damping, and participation
+factor have stabilized.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cmif_axis</span><span class="p">,</span> <span class="n">order_axis</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">plot_stability</span><span class="p">()</span>
+<span class="n">cmif_axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;CMIF&#39;</span><span class="p">)</span>
+<span class="n">cmif_axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+<span class="n">order_axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Polynomial Order&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[41]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;Polynomial Order&#39;)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_109_1.png" />
+</div>
+</div>
+<p>We can see that as we move up to a higher model order, we start to see more computational poles appear, as shown by the red Xs throughout the frequency band. However we see the three main columns of green circles denoting three stable poles in our fits. We would then select these three stable poles.</p>
+<p>In <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> we can select them by index, and we can determine the indices by plotting the stabilization diagram with labels.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cmif_axis</span><span class="p">,</span> <span class="n">order_axis</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">plot_stability</span><span class="p">(</span><span class="n">label_poles</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">cmif_axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;CMIF&#39;</span><span class="p">)</span>
+<span class="n">cmif_axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Frequency (Hz)&#39;</span><span class="p">)</span>
+<span class="n">order_axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Polynomial Order&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[42]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Text(0, 0.5, &#39;Polynomial Order&#39;)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_111_1.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_111_1.png" />
+</div>
+</div>
+<p>We can then extract a final pole list from those indices. For example we may wish to select poles <code class="docutils literal notranslate"><span class="pre">(4,0)</span></code>, <code class="docutils literal notranslate"><span class="pre">(4,1)</span></code>, and <code class="docutils literal notranslate"><span class="pre">(4,2)</span></code>.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[43]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pole_indices</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">4</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">2</span><span class="p">)]</span>
+<span class="n">pole_list</span> <span class="o">=</span> <span class="n">pp</span><span class="o">.</span><span class="n">pole_list_from_indices</span><span class="p">(</span><span class="n">pole_indices</span><span class="p">)</span>
+<span class="n">omega_general_stab</span> <span class="o">=</span> <span class="n">pole_list</span><span class="p">[</span><span class="s1">&#39;omega&#39;</span><span class="p">]</span>
+<span class="n">zeta_general_stab</span> <span class="o">=</span> <span class="n">pole_list</span><span class="p">[</span><span class="s1">&#39;zeta&#39;</span><span class="p">]</span>
+<span class="n">lr_stab</span> <span class="o">=</span> <span class="n">pole_list</span><span class="p">[</span><span class="s1">&#39;Lr_complex&#39;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>The stabilization diagram actually reveals that for our test case of <code class="docutils literal notranslate"><span class="pre">order=5</span></code>, our poles may not have yet stabilized, and therefore our results may be inaccurate! Let’s compare the same quantities extracted from the stabilization diagram with those computed from the system matrices.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[44]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Frequency From System Matrices&#39;</span><span class="p">,</span><span class="s1">&#39;Frequency From FRF Fits&#39;</span><span class="p">,</span><span class="s1">&#39;Frequency Error&#39;</span><span class="p">,</span>
+                             <span class="s1">&#39;Damping From System Matrices&#39;</span><span class="p">,</span><span class="s1">&#39;Damping From FRF Fits&#39;</span><span class="p">,</span><span class="s1">&#39;Damping Error&#39;</span><span class="p">,</span>
+                             <span class="s1">&#39;Participation From System Matrices&#39;</span><span class="p">,</span><span class="s1">&#39;Participation From FRF Fits&#39;</span><span class="p">],</span>
+                  <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,(</span><span class="n">wm</span><span class="p">,</span><span class="n">wf</span><span class="p">,</span><span class="n">zm</span><span class="p">,</span><span class="n">zf</span><span class="p">,</span><span class="n">lm</span><span class="p">,</span><span class="n">lf</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span>
+    <span class="n">omega_general</span><span class="p">,</span><span class="n">omega_general_stab</span><span class="p">,</span>
+    <span class="n">zeta_general</span><span class="p">,</span> <span class="n">zeta_general_stab</span><span class="p">,</span>
+    <span class="n">psi_general</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:]</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">lr_stab</span><span class="p">)):</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Frequency From System Matrices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">wm</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Frequency From FRF Fits&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">wf</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Frequency Error&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">wf</span><span class="o">-</span><span class="n">wm</span><span class="p">)</span><span class="o">/</span><span class="n">wm</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Damping From System Matrices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">zm</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Damping From FRF Fits&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">zf</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Damping Error&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{:0.2f}</span><span class="s1">%&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">((</span><span class="n">zf</span><span class="o">-</span><span class="n">zm</span><span class="p">)</span><span class="o">/</span><span class="n">zm</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+    <span class="c1"># Find the maximum participation degree of freedom from the</span>
+    <span class="c1"># system matrices</span>
+    <span class="n">check_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lm</span><span class="p">))</span>
+    <span class="c1"># Compute the angle of that index</span>
+    <span class="n">check_angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">lm</span><span class="p">[</span><span class="n">check_index</span><span class="p">])</span>
+    <span class="c1"># Compute the same angle from the fit</span>
+    <span class="n">this_angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">lf</span><span class="p">[</span><span class="n">check_index</span><span class="p">])</span>
+    <span class="n">rotation</span> <span class="o">=</span> <span class="n">check_angle</span> <span class="o">-</span> <span class="n">this_angle</span>
+    <span class="c1"># Compute the magnitudes of the vectors</span>
+    <span class="n">check_magnitude</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lm</span><span class="p">))</span>
+    <span class="n">this_magnitude</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">lf</span><span class="p">))</span>
+    <span class="c1"># Correct rotation and scaling</span>
+    <span class="n">lf</span> <span class="o">=</span> <span class="n">lf</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">rotation</span><span class="p">)</span><span class="o">*</span><span class="n">check_magnitude</span><span class="o">/</span><span class="n">this_magnitude</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Participation From System Matrices&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lm</span><span class="p">)</span>
+    <span class="n">df</span><span class="o">.</span><span class="n">at</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="s1">&#39;Participation From FRF Fits&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">lf</span><span class="p">)</span>
+
+<span class="n">pretty_print_table</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area rendered_html docutils container">
+<table border="1" class="dataframe">
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Frequency From System Matrices</th>
+      <th>Frequency From FRF Fits</th>
+      <th>Frequency Error</th>
+      <th>Damping From System Matrices</th>
+      <th>Damping From FRF Fits</th>
+      <th>Damping Error</th>
+      <th>Participation From System Matrices</th>
+      <th>Participation From FRF Fits</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>27.25</td>
+      <td>27.26</td>
+      <td>0.02%</td>
+      <td>2.71%</td>
+      <td>2.71%</td>
+      <td>-0.02%</td>
+      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>
+      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>54.78</td>
+      <td>54.79</td>
+      <td>0.01%</td>
+      <td>2.74%</td>
+      <td>2.74%</td>
+      <td>-0.01%</td>
+      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>
+      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>59.91</td>
+      <td>59.92</td>
+      <td>0.01%</td>
+      <td>2.66%</td>
+      <td>2.66%</td>
+      <td>-0.01%</td>
+      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>
+      <td>[-0.06059006+0.05448316j  0.03373322-0.03429902j]</td>
+    </tr>
+  </tbody>
+</table></div>
+</div>
+<p>We now see that we have extracted nearly identical modes to those that were computed directly from the system matrices, meaning the fit has significantly improved by using a higher-order polynomial.</p>
+<p>Note that this section has used the code-based <code class="docutils literal notranslate"><span class="pre">PolyPy</span></code> implementation. However, in SDynPy there is also a <code class="docutils literal notranslate"><span class="pre">PolyPy_GUI</span></code> which can be used to interactively select poles.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">ppgui</span> <span class="o">=</span> <span class="n">sdpy</span><span class="o">.</span><span class="n">PolyPy_GUI</span><span class="p">(</span><span class="n">tfarray</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><img alt="image.png" 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" 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" /></p>
+</section>
+</section>
+<section id="Fitting-Mode-Shapes-to-FRF-Data">
+<h2>Fitting Mode Shapes to FRF Data<a class="headerlink" href="#Fitting-Mode-Shapes-to-FRF-Data" title="Permalink to this heading"></a></h2>
+<p>Once the poles and participation factors are known, we can go ahead and fit the mode shapes of the system. Looking again at the frequency response function equation</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\left( \frac{v_{ir}l_{jr}}{j\omega-\lambda_r} + \frac{v_{ir}^*l_{jr}^*}{j\omega-\lambda_r^*}\right) + \frac{R_{Lij}}{(j\omega)^2} + R_{Uij}\]</div>
+<p>containing the frequency response function <span class="math notranslate nohighlight">\(\mathbf{H}\)</span>, the participation factors <span class="math notranslate nohighlight">\(\mathbf{l}_r\)</span>, the frequency lines <span class="math notranslate nohighlight">\(\omega\)</span>, and the poles <span class="math notranslate nohighlight">\(\lambda_r\)</span>. We wish to solve for the mode shapes <span class="math notranslate nohighlight">\(\mathbf{v}_r\)</span> and the residuals <span class="math notranslate nohighlight">\(\mathbf{R}_u\)</span> and <span class="math notranslate nohighlight">\(\mathbf{R}_l\)</span>.</p>
+<p>We want to pose this problem in an entirely real set of equations <span class="math notranslate nohighlight">\(\mathbf{A}\mathbf{x}=\mathbf{b}\)</span>.</p>
+<p>The mode shapes will generally be complex <span class="math notranslate nohighlight">\(\mathbf{v}_r = \mathbf{v}_r^R + j \mathbf{v}_r^I\)</span>, so we will want to solve simultaneously for the real and imaginary parts. Similarly, the frequency response function is complex, so we will need to split that up into its real and imaginary parts. Ignoring the residuals for now:</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\mathbf{H}^R\\\mathbf{H}^I\end{bmatrix} = \mathbf{P}\begin{bmatrix}\mathbf{v}^R\\\mathbf{v}^I\end{bmatrix}\end{split}\]</div>
+<p>The goal is to then find the coefficient matrix <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> which allows us to solve for the mode shape coefficients.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\mathbf{P} = \begin{bmatrix}\mathbf{P}_{RR} &amp; \mathbf{P}_{RI}\\\mathbf{P}_{IR} &amp; \mathbf{P}_{II}\end{bmatrix}\end{split}\]</div>
+<p>We will start with the term</p>
+<div class="math notranslate nohighlight">
+\[\frac{\mathbf{v}_r\mathbf{l}_r^T}{j\omega-\lambda_r} + \frac{\mathbf{v}_r^*{\mathbf{l}_r^*}^T}{j\omega-\lambda_r^*}\]</div>
+<p>We will substitute each term with its real and imaginary parts to ensure that all of the variables in the expression are real.</p>
+<div class="math notranslate nohighlight">
+\[\frac{\left(- j l^{I}_{jr} + l^{R}_{jr}\right) \left(- j v^{I}_{ir} + v^{R}_{ir}\right)}{j \lambda^{I}_{r} - \lambda^{R}_{r} + j \omega} + \frac{\left(j l^{I}_{jr} + l^{R}_{jr}\right) \left(j v^{I}_{ir} + v^{R}_{ir}\right)}{- j \lambda^{I}_{r} - \lambda^{R}_{r} + j \omega}\]</div>
+<p>As we wish to collect real and imaginary parts, we will want to multiply each term’s numerator and denominator by the denominator’s complex conjugate.</p>
+<div class="math notranslate nohighlight">
+\[\frac{\left(- j l^{I}_{jr} + l^{R}_{jr}\right) \left(- j v^{I}_{ir} + v^{R}_{ir}\right) \left(- j \lambda^{I}_{r} - \lambda^{R}_{r} - j \omega\right)}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{\left(j l^{I}_{jr} + l^{R}_{jr}\right) \left(j v^{I}_{ir} + v^{R}_{ir}\right) \left(j \lambda^{I}_{r} - \lambda^{R}_{r} - j \omega\right)}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\]</div>
+<p>We would then like to collect real and imaginary terms, as well as collecting terms that have the real and imaginary part of the mode shapes. <span class="math notranslate nohighlight">\(\mathbf{P}_{RR}\)</span> will contain real coefficients of the real part of the mode shapes. <span class="math notranslate nohighlight">\(\mathbf{P}_{RI}\)</span> will contain real coefficients of the imaginary part of the mode shapes. <span class="math notranslate nohighlight">\(\mathbf{P}_{IR}\)</span> will contain imaginary coefficients of the real part of the mode shapes. Finally, <span class="math notranslate nohighlight">\(\mathbf{P}_{II}\)</span> will contain imaginary coefficients of
+the imaginary part of the mode shapes.</p>
+<div class="math notranslate nohighlight">
+\[j v^{I}_{ir} \left(\frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega + l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\right) + v^{I}_{ir} \left(\frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} + l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\right)\]</div>
+<div class="math notranslate nohighlight">
+\[+ v^{R}_{ir} \left(\frac{- l^{I}_{jr} \lambda^{I}_{r} - l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\right) + j v^{R}_{ir} \left(\frac{- l^{I}_{jr} \lambda^{R}_{r} + l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\right)\]</div>
+<p>We can substitute this into a matrix form where the real equations are on the top row and the imaginary equations are on the bottom row.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\left[\begin{matrix}H_r^{R}\\j H_r^{I}\end{matrix}\right] = \left[\begin{matrix}\frac{- l^{I}_{jr} \lambda^{I}_{r} - l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} + l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\\j \left(\frac{- l^{I}_{jr} \lambda^{R}_{r} + l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\right) &amp; j \left(\frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega + l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\right)\end{matrix}\right] \left[\begin{matrix}v^{R}_{ir}\\v^{I}_{ir}\end{matrix}\right]\end{split}\]</div>
+<p>Cancelling out the <span class="math notranslate nohighlight">\(j\)</span> terms in the bottom row reveals the final matrix equation for the contribution to the frequency response function from one mode shape <span class="math notranslate nohighlight">\(H_r\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\left[\begin{matrix}H_r^{R}\\H_r^{I}\end{matrix}\right] = \left[\begin{matrix}\frac{- l^{I}_{jr} \lambda^{I}_{r} - l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} + l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\\\frac{- l^{I}_{jr} \lambda^{R}_{r} + l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega + l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\end{matrix}\right] \left[\begin{matrix}v^{R}_{ir}\\v^{I}_{ir}\end{matrix}\right]\end{split}\]</div>
+<p>We can then solve this equation in a least squares sense by using the pseudoinverse.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\left[\begin{matrix}v^{R}_{ir}\\v^{I}_{ir}\end{matrix}\right] = \left[\begin{matrix}\frac{- l^{I}_{jr} \lambda^{I}_{r} - l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} + l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\\\frac{- l^{I}_{jr} \lambda^{R}_{r} + l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega + l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}}\end{matrix}\right]^+ \left[\begin{matrix}H_r^{R}\\H_r^{I}\end{matrix}\right]\end{split}\]</div>
+<p>Note that this equation must be generalized to solve for all modal coefficients simultaneously in terms of all frequency response functions for all degrees of freedom at all frequency lines. For this we will give some thought as to how to structure the matrices.</p>
+<p>At the end of the computation, the matrix <span class="math notranslate nohighlight">\(\left[\begin{matrix}v^{R}_{ir}\\v^{I}_{ir}\end{matrix}\right]\)</span> should have real and imaginary parts for each degree of freedom for each mode being solved for. We will solve for these terms over all frequency lines simultaneously. However, because there is no frequency line dimension in the mode shape matrix, this reveals that the frequency line must be on the inner dimension of the matrix product. The frequency lines must correspond to the rows of
+<span class="math notranslate nohighlight">\(\left[\begin{matrix}H_r^{R}\\H_r^{I}\end{matrix}\right]\)</span> and <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> (which will be the columns of <span class="math notranslate nohighlight">\(\mathbf{P}^+\)</span>). We also see that the columns of the FRF matrix corresponding to the references at each frequency line do not appear in the final mode shape matrix (they are related to the participation factors, which are known). Therefore this dimension must also be encoded in the inner dimensions of the matrix product.</p>
+<p>We can therefore start to envision what the structure of each term in <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> should look like. The columns of <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> (the rows of <span class="math notranslate nohighlight">\(\mathbf{P}^+\)</span>) must correspond to the mode shape matrix, and they should correspond to the mode index rather than the response degree of freedom index because we need matrix multiplication of <span class="math notranslate nohighlight">\(\mathbf{P}\mathbf{v}\)</span> to sum over modes, and summation occurs on the inner dimensions of the matrices in matrix multiplication. The rows of
+<span class="math notranslate nohighlight">\(\mathbf{P}\)</span> (the columns of <span class="math notranslate nohighlight">\(\mathbf{P}^+\)</span>) must correspond to both the mode participation factor (reference degrees of freedom) and frequency lines, separated into real and imaginary parts. If we compute each partition of <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> as a <code class="docutils literal notranslate"><span class="pre">num_input</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">num_freq</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">num_modes</span></code> array, and then reshape it into a <code class="docutils literal notranslate"><span class="pre">num_input*num_freq</span></code> <span class="math notranslate nohighlight">\(\times\)</span> <code class="docutils literal notranslate"><span class="pre">num_modes</span></code> array, this should produce the correct size matrix.</p>
+<p>Each partition of the mode shape matrix <span class="math notranslate nohighlight">\(v^{R}_{ir}\)</span> and <span class="math notranslate nohighlight">\(v^{I}_{ir}\)</span> will then be revealed as a matrix with number of rows equal to the number of modes and columns equal to the number of response degrees of freedom.</p>
+<p>We can similarly construct the dimensions of the partitions <span class="math notranslate nohighlight">\(H_r^{R}\)</span> and <span class="math notranslate nohighlight">\(H_r^{I}\)</span>, as they must now be consistent with the rest of the terms in the equation. The number of rows of these partitions must match the number of rows of partitions in <span class="math notranslate nohighlight">\(\mathbf{P}\)</span>, meaning that the FRF must be similarly structured with mode participation factor (reference degrees of freedom) and frequency lines along its rows and response degrees of freedom along its columns.</p>
+<p>This generalization into matrix form can be somewhat abstract to visualize, but should become more obvious when applied to real data arrays.</p>
+<section id="Fitting-Residuals-for-Out-of-Band-Modes">
+<h3>Fitting Residuals for Out-of-Band Modes<a class="headerlink" href="#Fitting-Residuals-for-Out-of-Band-Modes" title="Permalink to this heading"></a></h3>
+<p>We note that the terms <span class="math notranslate nohighlight">\(v_{ir}\)</span> are not the only unknowns in the frequency response function formulation. In general, we can never fit all of the modes of a structure, so there will always be some contributions from out-of-band modes in our frequency response functions. As we try to solve for the mode shapes of the structure, these out-of-band modes can contaminate the response. Therefore we also include terms to try to match the contributions from these out-of-band modes, and we call
+these terms residuals.</p>
+<p>We will in general have a lower residual <span class="math notranslate nohighlight">\(R_{Lij}\)</span> and an upper residual <span class="math notranslate nohighlight">\(R_{Uij}\)</span>, and as the expression suggests, there can be one term in the residual for each pair of input and output degrees of freedom. We must therefore solve for these terms simultaneously with the mode shapes. We can think of these terms as “extra modes” of the structure, which therefore should imply that the <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> matrix should have columns added corresponding to the coefficients of the residual
+variables, and the <span class="math notranslate nohighlight">\(\left[\begin{matrix}v^{R}_{ir}\\v^{I}_{ir}\end{matrix}\right]\)</span> matrix should have rows appended corresponding to the real and imaginary parts of these terms.</p>
+<p>Indeed, the coefficients are easily picked directly from the frequency response function.</p>
+<div class="math notranslate nohighlight">
+\[\mathbf{H}_{ij} = \sum_{r=1}^N\left( \frac{v_{ir}l_{jr}}{j\omega-\lambda_r} + \frac{v_{ir}^*l_{jr}^*}{j\omega-\lambda_r^*}\right) + \frac{R_{Lij}}{(j\omega)^2} + R_{Uij}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}\left[\begin{matrix}H_r^{R}\\H_r^{I}\end{matrix}\right]
+= \left[\begin{matrix}\frac{- l^{I}_{jr} \lambda^{I}_{r} - l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} + l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; -\frac{1}{\omega^2} &amp; 0 &amp; 1 &amp; 0 \\
+\frac{- l^{I}_{jr} \lambda^{R}_{r} + l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{R}_{r} - l^{R}_{jr} \lambda^{I}_{r} - l^{R}_{jr} \omega}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; \frac{- l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega - l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} - 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} + \frac{l^{I}_{jr} \lambda^{I}_{r} + l^{I}_{jr} \omega + l^{R}_{jr} \lambda^{R}_{r}}{\left(\lambda^{I}_{r}\right)^{2} + 2 \lambda^{I}_{r} \omega + \left(\lambda^{R}_{r}\right)^{2} + \omega^{2}} &amp; 0 &amp; -\frac{1}{\omega^2} &amp; 0  &amp; 1\end{matrix}\right] \left[\begin{matrix}v^{R}_{ir}\\v^{I}_{ir}\\R^R_{Lij}\\R^I_{Lij}\\R^R_{Uij}\\R^I_{Uij}\end{matrix}\right]\end{split}\]</div>
+<p>When generalizing to multiple references, responses, and frequency lines, each residual term <span class="math notranslate nohighlight">\(R^R_{Lij}\)</span>, <span class="math notranslate nohighlight">\(R^I_{Lij}\)</span>, <span class="math notranslate nohighlight">\(R^R_{Uij}\)</span> and <span class="math notranslate nohighlight">\(R^I_{Uij}\)</span> will have a number of rows equal to the number of references and a number of columns equal to the number of responses. Similarly the coefficient terms (<span class="math notranslate nohighlight">\(0\)</span>, <span class="math notranslate nohighlight">\(1\)</span>, <span class="math notranslate nohighlight">\(-\frac{1}{\omega}\)</span>) will have a number of rows equal to the number of <code class="docutils literal notranslate"><span class="pre">num_freq*num_input</span></code> and number of columns equal to <code class="docutils literal notranslate"><span class="pre">num_input</span></code>. Because
+of the structure of the matrices, they are easily constructed using Kronecker products of identity matrices multiplied by either <span class="math notranslate nohighlight">\(1\)</span> or <span class="math notranslate nohighlight">\(-\frac{1}{\omega^2}\)</span>.</p>
+</section>
+<section id="A-Note-on-Types-of-FRFs">
+<h3>A Note on Types of FRFs<a class="headerlink" href="#A-Note-on-Types-of-FRFs" title="Permalink to this heading"></a></h3>
+<p>Note that the mode shape derivations described in the previous sections are valid for <em>receptance</em> FRFs, meaning FRFs between displacement and force. When transforming to from <em>receptance</em> to <em>mobility</em> (velocity over force) to <em>accelerance</em> (acceleration over force) FRFs, we multiply the FRF expression by <span class="math notranslate nohighlight">\(j\omega\)</span> at each step. This will clearly change which terms are real and which terms are imaginary, as well as the coefficients on each term. As this document is already quite long,
+these will not be derived, but the same approach as described above can be used to find the coefficient matrices for those cases; simply substitute for real and imaginary parts of each variable, collect imaginary and real coefficients for imaginary and real parts of each term of interest, and place them into a matrix form.</p>
+</section>
+<section id="Normalizing-Mode-Shapes">
+<h3>Normalizing Mode Shapes<a class="headerlink" href="#Normalizing-Mode-Shapes" title="Permalink to this heading"></a></h3>
+<p>Once we solve for the mode shapes, our job is not quite done. Due to how the problem is formulated, the particpation factors have some arbitrary scaling and rotation applied to them. This means when the shapes are found, they will also have an arbitrary scaling and rotation applied to them. There is no constraint that the participation factors are identical to the mode shapes at the input locations.</p>
+<p>To perform the normalization, we compute the residue matrices for each mode</p>
+<div class="math notranslate nohighlight">
+\[R_{rij} = \psi_{ir}l_{jr}\]</div>
+<p>We recognize the at the drive point, the residue is equal to the mode shape squared or the participation factor squared if the mode shapes are scaled such that the participation factors are the mode shapes.</p>
+<div class="math notranslate nohighlight">
+\[R_{rii} = \psi_{ir}l_{ir} = \psi_{ir}^2 = l_{ir}^2\]</div>
+<p>However, we currently do not have such a scenario. In our case, the fit shapes <span class="math notranslate nohighlight">\(\bar{\psi}_{ir}\)</span> are scaled by some complex coefficient <span class="math notranslate nohighlight">\(c\)</span>, and the participation factors <span class="math notranslate nohighlight">\(\bar{l}_{jr}\)</span> are scaled by some factor <span class="math notranslate nohighlight">\({\frac{1}{c}}\)</span>.</p>
+<div class="math notranslate nohighlight">
+\[\psi_{ir} = c\bar{\psi}_{ir}\]</div>
+<div class="math notranslate nohighlight">
+\[l_{jr} = \frac{1}{c}\bar{l}_{jr}\]</div>
+<p>We note that due to the reciprocal scale factors, we can still compute the residuals. We can therefore set up a set of equations to solve for the scale factor that gets applied to the mode shapes.</p>
+<div class="math notranslate nohighlight">
+\[R_{rii} = \psi_{ir}l_{ir} = c\bar{\psi}_{ir}\frac{1}{c}\bar{l}_{ir} = \bar{\psi}_{ir}\bar{l}_{ir}\]</div>
+<div class="math notranslate nohighlight">
+\[R_{rii} = \psi_{ir}^2 = c^2\bar{\psi}_{ir}^2\]</div>
+<div class="math notranslate nohighlight">
+\[\sqrt{R_{rii}} = c \bar{\psi}_{ir}\]</div>
+<p>Since we have participation factors and shapes at multiple degrees of freedom for a multiple-input solution, we can solve for the scale factor <span class="math notranslate nohighlight">\(c\)</span> in a least-squares sense for each mode.</p>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\sqrt{R_{r11}} \\ \sqrt{R_{r22}} \\ \vdots \\ \sqrt{R_{r{N_i}{N_i}}}\end{bmatrix} = \begin{bmatrix}\bar{\psi}_{1r}\\\bar{\psi}_{2r}\\\vdots\\\bar{\psi}_{{N_i}r}\end{bmatrix}c\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}\begin{bmatrix}\bar{\psi}_{1r}\\\bar{\psi}_{2r}\\\vdots\\\bar{\psi}_{{N_i}r}\end{bmatrix}^+\begin{bmatrix}\sqrt{R_{r11}} \\ \sqrt{R_{r22}} \\ \vdots \\ \sqrt{R_{r{N_i}{N_i}}}\end{bmatrix} = c\end{split}\]</div>
+<p><strong>BE AWARE:</strong> when taking the square root of the residue matrix terms, there are two values that can be obtained (e.g. <span class="math notranslate nohighlight">\(\sqrt{4} = \pm2\)</span>). In the case of real numbers, most square root algorithms will only return the positive root. However for complex numbers, it is not so obvious which sign the root will have (positive real part? positive imaginary part?). Flipping a sign on one term in the matrix
+<span class="math notranslate nohighlight">\(\begin{bmatrix}\sqrt{R_{r11}} \\ \sqrt{R_{r22}} \\ \vdots \\ \sqrt{R_{r{N_i}{N_i}}}\end{bmatrix}\)</span> will make the least squares solution incorrect, therefore we must make sure the signs are consistent between the mode shapes <span class="math notranslate nohighlight">\(\bar{\psi}_{ir}\)</span> and the residue terms <span class="math notranslate nohighlight">\(\sqrt{R_{rii}}\)</span>.</p>
+<p>We can do this by dividing the termes <span class="math notranslate nohighlight">\(\sqrt{R_{rii}}\)</span> by <span class="math notranslate nohighlight">\(\bar{\psi}_{ir}\)</span> and looking at the direction of the complex vectors. If the vectors are all pointing nominally the same direction, then the least squares problem will solve for that direction successfully. If some are pointing opposite due to the sign of the output of the square root, then the scale factor found by the least squares solution will be incorrect.</p>
+</section>
+<section id="id1">
+<h3>Back to the Example Problem<a class="headerlink" href="#id1" title="Permalink to this heading"></a></h3>
+<p>While the symbolic mathematics in the previous sections was straightforward (though perhaps a bit tedious), the discussion of the construction of a system of equations to solve for each coefficient of the mode shape matrix was perhaps a bit abstract. We will therefore make it more concrete by applying it to our specific example problem. Note that we will take advantage heavily of broadcasting and dimensional manipulation to help us put these equations together. Readers are encouraged to run
+these problems themselves to understand exactly what operations are being performed at each step, and the resulting shapes of all of the arrays.</p>
+<p>We will first generate the <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> matrix described above. We will start with only the mode shape coefficients, then show how it can be expanded to include residuals. This will involve translating our symbolic code into numeric Numpy code. To help us repeat this process for different sets of modes and frequency ranges when we introduce the residuals later, we will define the process to generate the <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> matrix as a function.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[45]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">P_modal</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span> <span class="n">participation_factors</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Construct the mode shape coefficient matrix</span>
+
+<span class="sd">    Constructs the coefficients from the system poles,</span>
+<span class="sd">    participation factors, and frequency lines.</span>
+
+<span class="sd">    Arguments should be passed as arrays and will be broadcast</span>
+<span class="sd">    together.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="c1"># We want the output array to be n_input x n_freq*2 x n_modes*2</span>
+    <span class="c1"># So let&#39;s adjust the terms so they have the right shapes</span>
+    <span class="c1"># We want frequency lines to be the middle dimension</span>
+    <span class="n">omegas</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="c1"># We want inputs to be the first dimension and modes the</span>
+    <span class="c1"># last dimension</span>
+    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">T</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+    <span class="c1"># Split up terms into real and imaginary parts</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">pi</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">imag</span>
+    <span class="n">lr</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">li</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">imag</span>
+    <span class="n">P_blocks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+        <span class="p">[(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+         <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">+</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)],</span>
+        <span class="p">[(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+         <span class="p">(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">+</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)]])</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]],</span>
+                     <span class="p">[</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]]])</span>
+</pre></div>
+</div>
+</div>
+<p>We can now create our pole vector from the extracted frequencies and damping ratios.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[46]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute poles from the extracted data</span>
+<span class="n">poles</span> <span class="o">=</span> <span class="o">-</span><span class="n">omega_general_stab</span><span class="o">*</span><span class="n">zeta_general_stab</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+<span class="c1"># Compute P with our function</span>
+<span class="n">P</span> <span class="o">=</span> <span class="n">P_modal</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">poles</span><span class="p">,</span> <span class="n">lr_stab</span><span class="p">)</span>
+
+<span class="c1"># Check shapes</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  omegas: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  poles: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  participation_factors: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  P: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">omegas</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">poles</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">lr_stab</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  omegas: (778,)
+  poles: (3,)
+  participation_factors: (3, 2)
+  P: (2, 1556, 6)
+</pre></div></div>
+</div>
+<p>We must now set up the frequency response functions to perform the least squares computation.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[47]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">H_LS</span> <span class="o">=</span> <span class="n">H</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H_LS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">H_LS</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">H_LS</span><span class="o">.</span><span class="n">imag</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  H_LS: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  H_LS: (2, 1556, 3)
+</pre></div></div>
+</div>
+<p>To construct the least-squares set of equations, we simply squash the first two dimensions into a single dimension, in which case the least-squares solution will solve over all frequency lines and all inputs simultaneously.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[48]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shapes_LS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">H_LS</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>We can reconstruct the FRF by muliplying the output shapes by the coefficient matrix and extracting real and imaginary parts.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[49]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">reconstructed_frfs</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="nd">@shapes_LS</span>
+<span class="n">H_recon</span> <span class="o">=</span> <span class="n">reconstructed_frfs</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H_recon</span> <span class="o">=</span> <span class="n">H_recon</span><span class="p">[:</span><span class="n">H_recon</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">H_recon</span><span class="p">[</span><span class="n">H_recon</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[50]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_modes</span> <span class="o">=</span> <span class="n">omega_general_stab</span><span class="o">.</span><span class="n">size</span>
+<span class="n">omegas_bc</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+<span class="c1"># Compute the laplace variables</span>
+<span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span>
+
+<span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">num_output</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">num_input</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_d</span><span class="p">,</span><span class="n">f_m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_general</span><span class="p">[</span><span class="n">frequency_lines_to_keep</span><span class="p">,:,:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_recon</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_d</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_m</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_d</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_m</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Truth&#39;</span><span class="p">,</span><span class="s1">&#39;Reconstructed from</span><span class="se">\n</span><span class="s1">Least-Squares Solution&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_135_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_135_0.png" />
+</div>
+</div>
+<p>We have now solved in a least-squares sense for our modal coefficients. We should extract the shape arrays from the matrix. The top partition will be the real part of the shape, and the bottom part will be the imaginary part of the shape. Remember also that the terms are transposed from the typical mode shape matrix.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[51]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_stab</span> <span class="o">=</span> <span class="p">(</span><span class="n">shapes_LS</span><span class="p">[:</span><span class="n">shapes_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">shapes_LS</span><span class="p">[</span><span class="n">shapes_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:])</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+<p>Note that because the participation factors were arbitrarily rotated, these shapes will also be arbitrarily rotated. Therefore we need to normalize the mode shapes. We will do this by computing the residue matrices.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[52]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">residues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">&#39;ik,jk-&gt;kij&#39;</span><span class="p">,</span><span class="n">psi_stab</span><span class="p">,</span><span class="n">lr_stab</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>We can get the drive point response indices corresponding to the participation factors and pull out the drive point residues. In general, the drive point residues should have a negative imaginary part, so we can use this to discard ‘bad’ drive point data where the drive point does not excite the mode well.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[53]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">drive_indices_response</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">drive_indices_input</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">drive_residues</span> <span class="o">=</span> <span class="n">residues</span><span class="p">[:,</span><span class="n">drive_indices_response</span><span class="p">,</span><span class="n">drive_indices_input</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>We will now go through each mode and compute the correct shape scaling using only the good drive points where the imaginary part is negative and not close to zero. First, let’s go through and make sure that our participation factors and mode shapes that we solved for are actually scaled versions of the true mode shapes. I’m going to mass normalize my mode shapes for better comparisions to the fit data without having to carry around the modal mass term.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[54]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Mass normalize the mode shapes</span>
+<span class="n">psi_general</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">ma_general</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Let’s find the scale factor between the participation factors and the mode shapes at the participation factors. We will average over all of the degrees of freedom for each mode.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[55]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lr_scaling</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">psi_general</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:]</span><span class="o">/</span><span class="n">lr_stab</span><span class="o">.</span><span class="n">T</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[56]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lr_scaling</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[56]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([ 0.18452662-0.18416078j,  0.08928666-0.0956242j ,
+       -0.093857  +0.08439714j])
+</pre></div></div>
+</div>
+<p>We can then check to see if we reconstruct the mode shape by applying the scaling to the participation factors.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[57]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lr_stab</span><span class="o">.</span><span class="n">T</span><span class="o">*</span><span class="n">lr_scaling</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[57]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-0.02160023+0.02202919j,  0.03267784-0.03499731j,
+        -0.03029502+0.02724158j],
+       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,
+         0.01686661-0.01714951j]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[58]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_general</span><span class="p">[:</span><span class="mi">2</span><span class="p">,:]</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[58]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,
+        -0.03029503+0.02724158j],
+       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,
+         0.0168666 -0.01714951j]])
+</pre></div></div>
+</div>
+<p>Now we will do the same thing for the mode shapes.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[59]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_scaling</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">psi_general</span><span class="o">/</span><span class="n">psi_stab</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[60]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_scaling</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[60]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,
+       -5.88158134-5.30751768j])
+</pre></div></div>
+</div>
+<p>Let’s reconstruct the mode shapes by scaling the fit mode shapes.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[61]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_stab</span><span class="o">*</span><span class="n">psi_scaling</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[61]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-0.02159971+0.02202864j,  0.03316166-0.03473379j,
+        -0.03033178+0.02722321j],
+       [-0.04928856+0.04919178j,  0.00094139+0.00090236j,
+         0.0168663 -0.01716533j],
+       [-0.02186329+0.02176496j, -0.03539605+0.03236945j,
+        -0.02735936+0.03018341j]])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[62]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_general</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[62]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,
+        -0.03029503+0.02724158j],
+       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,
+         0.0168666 -0.01714951j],
+       [-0.02186239+0.02176446j, -0.03496025+0.03263839j,
+        -0.02739619+0.03019566j]])
+</pre></div></div>
+</div>
+<p>Since the particpation factors times the mode shapes should be equal to the scaled mode shapes squared, we should be able to see that the scale factors are reciprocals of one another.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[63]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_scaling</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[63]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,
+       -5.88158134-5.30751768j])
+</pre></div></div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[64]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span><span class="o">/</span><span class="n">lr_scaling</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[64]:
+</pre></div>
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+array([ 2.71501354+2.70963081j,  5.2165319 +5.58679996j,
+       -5.89109483-5.29733029j])
+</pre></div></div>
+</div>
+<p>This was a sanity check to make sure the mode shapes and participation factors were identified consistently. Unfortunately in a real situation we don’t have the true mode shapes, so we need to compute them from the residue matrices.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[65]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">psi_stab_normalized</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">drive_residue</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">drive_residues</span><span class="p">):</span>
+    <span class="c1"># Throw away non-negative imaginary parts</span>
+    <span class="n">bad_indices_positive</span> <span class="o">=</span> <span class="n">drive_residue</span><span class="o">.</span><span class="n">imag</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    <span class="c1"># Throw away small values compared to the average value (only considering negative imaginary parts)</span>
+    <span class="n">bad_indices_small</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">drive_residue</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mf">0.01</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">drive_residue</span><span class="p">[</span><span class="o">~</span><span class="n">bad_indices_positive</span><span class="p">]))</span>
+    <span class="c1"># Combine into a single criteria</span>
+    <span class="n">bad_indices</span> <span class="o">=</span> <span class="n">bad_indices_positive</span> <span class="o">|</span> <span class="n">bad_indices_small</span>
+    <span class="c1"># Get the good indices that are remaining</span>
+    <span class="n">remaining_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="o">~</span><span class="n">bad_indices</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="c1"># We will then construct the least squares solution</span>
+    <span class="n">shape_coefficients</span> <span class="o">=</span> <span class="n">psi_stab</span><span class="p">[</span><span class="n">drive_indices_response</span><span class="p">[</span><span class="n">remaining_indices</span><span class="p">],</span><span class="n">k</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="n">residue_coefficients</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">drive_residue</span><span class="p">[</span><span class="n">remaining_indices</span><span class="p">])[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="c1"># Before we compute the scale, we need to make sure that we have all of the signs the same way.</span>
+    <span class="c1"># This is because the square root can give you +/- root where root**2 = complex number</span>
+    <span class="c1"># This will mess up the least squares at it will try to find something between the</span>
+    <span class="c1"># two vectors.</span>
+    <span class="n">scale_vector</span> <span class="o">=</span> <span class="p">(</span><span class="n">residue_coefficients</span><span class="o">/</span><span class="n">shape_coefficients</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+    <span class="n">sign_vector</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">((</span><span class="n">scale_vector</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">scale_vector</span><span class="o">.</span><span class="n">imag</span><span class="p">))</span>
+    <span class="c1"># Get the signs</span>
+    <span class="n">signs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sign</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">sign_vector</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span><span class="n">sign_vector</span><span class="p">))</span>
+    <span class="n">residue_coefficients</span> <span class="o">=</span> <span class="n">residue_coefficients</span><span class="o">*</span><span class="n">signs</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="c1"># Now compute the least-squares solution</span>
+    <span class="n">scale</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">shape_coefficients</span><span class="p">,</span><span class="n">residue_coefficients</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Scale for mode </span><span class="si">{:}</span><span class="s1">: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">k</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span><span class="n">scale</span><span class="p">))</span>
+    <span class="n">psi_stab_normalized</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">psi_stab</span><span class="p">[:,</span><span class="n">k</span><span class="p">]</span><span class="o">*</span><span class="n">scale</span><span class="p">)</span>
+<span class="n">psi_stab_normalized</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">psi_stab_normalized</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Scale for mode 1: (-2.709418738512416-2.7149722671668157j)
+Scale for mode 2: (5.211812790651124+5.590252990269536j)
+Scale for mode 3: (5.886705065831037+5.299694143958719j)
+</pre></div></div>
+</div>
+<p>Now that we have our mode shapes, we should resynthesize frequency response functions from them to make sure they fit our original data.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[66]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_modes</span> <span class="o">=</span> <span class="n">omega_general_stab</span><span class="o">.</span><span class="n">size</span>
+<span class="n">omegas_bc</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+<span class="c1"># Compute the laplace variables</span>
+<span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omegas_bc</span>
+
+<span class="n">H_general_fromUnNormModes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span>
+    <span class="n">psi_stab</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="nd">@lr_stab</span><span class="o">.</span><span class="n">T</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">/</span>
+    <span class="p">((</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="o">+</span> <span class="n">psi_stab</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="nd">@lr_stab</span><span class="o">.</span><span class="n">T</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="o">/</span>
+    <span class="p">((</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">H_general_fromFitModes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span>
+    <span class="n">psi_stab_normalized</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="nd">@psi_stab_normalized</span><span class="p">[</span><span class="n">drive_indices_input</span><span class="p">,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">/</span>
+    <span class="p">((</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="o">+</span> <span class="n">psi_stab_normalized</span><span class="p">[:,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="nd">@psi_stab_normalized</span><span class="p">[</span><span class="n">drive_indices_input</span><span class="p">,</span><span class="n">r</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="o">/</span>
+    <span class="p">((</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="n">H_general_fromFitResidues</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span>
+    <span class="n">residues</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">/</span>
+    <span class="p">((</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="o">+</span> <span class="n">residues</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">.</span><span class="n">conj</span><span class="p">()</span><span class="o">/</span>
+    <span class="p">((</span><span class="n">s</span><span class="o">-</span>
+        <span class="p">(</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">omega_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">zeta_general_stab</span><span class="p">[</span><span class="n">r</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">))))</span>
+    <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_modes</span><span class="p">)],</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+<span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_t</span><span class="p">,</span><span class="n">f_m</span><span class="p">,</span><span class="n">f_r</span><span class="p">,</span><span class="n">f_u</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_general</span><span class="p">[</span><span class="n">frequency_lines_to_keep</span><span class="p">,:</span><span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],:</span><span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_general_fromFitModes</span><span class="p">[:,:</span><span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],:</span><span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_general_fromFitResidues</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_general_fromUnNormModes</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+                      <span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_t</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_u</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_m</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_r</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_t</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_u</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_m</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_r</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Truth&#39;</span><span class="p">,</span><span class="s1">&#39;Unnormalized&#39;</span><span class="p">,</span><span class="s1">&#39;Normalized&#39;</span><span class="p">,</span><span class="s1">&#39;Residues&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_162_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_162_0.png" />
+</div>
+</div>
+<p>The last demonstration will be to understand how residuals can be implemented. To do this, we will truncate the frequency domain to remove the first mode. We will then use residuals to try to fit the effect of this mode while only truly fitting the second two modes. We will assume we have correctly identified the poles and participation factors from the first analysis, and only consider solving the shape problem here.</p>
+<p>We will update our function to solve for the <span class="math notranslate nohighlight">\(\mathbf{P}\)</span> matrix to also solve for residual terms.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[67]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">P_modal_w_residuals</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span> <span class="n">poles</span><span class="p">,</span> <span class="n">participation_factors</span><span class="p">,</span> <span class="n">lower_residuals</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">upper_residuals</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;Construct the mode shape coefficient matrix</span>
+
+<span class="sd">    Constructs the coefficients from the system poles,</span>
+<span class="sd">    participation factors, and frequency lines.</span>
+
+<span class="sd">    Arguments should be passed as arrays and will be broadcast</span>
+<span class="sd">    together.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="c1"># Get the number of modes and number of inputs from the shape</span>
+    <span class="c1"># of the participation factors.</span>
+    <span class="n">n_modes</span><span class="p">,</span><span class="n">n_inputs</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">shape</span>
+    <span class="n">n_freq</span><span class="p">,</span> <span class="o">=</span> <span class="n">omegas</span><span class="o">.</span><span class="n">shape</span>
+    <span class="c1"># We want the output array to be n_input x n_freq*2 x n_modes*2</span>
+    <span class="c1"># So let&#39;s adjust the terms so they have the right shapes</span>
+    <span class="c1"># We want frequency lines to be the middle dimension</span>
+    <span class="n">omegas</span> <span class="o">=</span> <span class="n">omegas</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+    <span class="c1"># We want inputs to be the first dimension and modes the</span>
+    <span class="c1"># last dimension</span>
+    <span class="n">participation_factors</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">T</span><span class="p">[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,:]</span>
+    <span class="c1"># Split up terms into real and imaginary parts</span>
+    <span class="n">pr</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">pi</span> <span class="o">=</span> <span class="n">poles</span><span class="o">.</span><span class="n">imag</span>
+    <span class="n">lr</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">real</span>
+    <span class="n">li</span> <span class="o">=</span> <span class="n">participation_factors</span><span class="o">.</span><span class="n">imag</span>
+    <span class="n">P_blocks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span>
+        <span class="p">[(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+         <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">+</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)],</span>
+        <span class="p">[(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">li</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">lr</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">),</span>
+         <span class="p">(</span><span class="o">-</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">-</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+          <span class="o">+</span> <span class="p">(</span><span class="n">pr</span><span class="o">*</span><span class="n">lr</span> <span class="o">+</span> <span class="n">pi</span><span class="o">*</span><span class="n">li</span> <span class="o">+</span> <span class="n">li</span><span class="o">*</span><span class="n">omegas</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">pr</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="n">pi</span><span class="o">**</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">pi</span><span class="o">*</span><span class="n">omegas</span> <span class="o">+</span> <span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">)]])</span>
+    <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+        <span class="n">RL_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="o">/</span><span class="n">omegas</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+        <span class="n">RU_block</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">])),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="mi">1</span><span class="p">)),</span><span class="n">np</span><span class="o">.</span><span class="n">kron</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_inputs</span><span class="p">)[:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]))],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">([[</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]],</span>
+                  <span class="p">[</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">],</span><span class="n">P_blocks</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">]]])</span>
+    <span class="k">if</span> <span class="n">lower_residuals</span><span class="p">:</span>
+        <span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">P</span><span class="p">,</span><span class="n">RL_block</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">upper_residuals</span><span class="p">:</span>
+        <span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">P</span><span class="p">,</span><span class="n">RU_block</span><span class="p">),</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">P</span>
+</pre></div>
+</div>
+</div>
+<p>We can then call the function with a subset of the modes that we aim to solve for.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[68]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">min_freq</span> <span class="o">=</span> <span class="mi">45</span>
+<span class="n">mode_indices</span> <span class="o">=</span> <span class="nb">slice</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+<span class="n">P</span> <span class="o">=</span> <span class="n">P_modal_w_residuals</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span> <span class="o">&gt;</span> <span class="n">min_freq</span><span class="p">],</span>
+                        <span class="n">poles</span><span class="p">[</span><span class="n">mode_indices</span><span class="p">],</span>
+                        <span class="n">lr_stab</span><span class="p">[</span><span class="n">mode_indices</span><span class="p">],</span>
+                        <span class="n">lower_residuals</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">upper_residuals</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
+
+<span class="n">H_LS</span> <span class="o">=</span> <span class="n">H</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">]</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H_LS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">H_LS</span><span class="o">.</span><span class="n">real</span><span class="p">,</span><span class="n">H_LS</span><span class="o">.</span><span class="n">imag</span><span class="p">),</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Shapes:</span><span class="se">\n</span><span class="s1">  P: </span><span class="si">{:}</span><span class="se">\n</span><span class="s1">  H_LS: </span><span class="si">{:}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Shapes:
+  P: (2, 778, 12)
+  H_LS: (2, 778, 3)
+</pre></div></div>
+</div>
+<p>We can then solve the least squares problem over all frequency lines and inputs.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[69]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">shapes_LS</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">P</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">H_LS</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Now at this point, we can reconstruct the portions of the FRFs from the modes and those from the residuals.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[70]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">P_full</span> <span class="o">=</span> <span class="n">P</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">P</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+<span class="n">P_modes</span> <span class="o">=</span> <span class="n">P_full</span><span class="p">[</span><span class="o">...</span><span class="p">,:</span><span class="mi">2</span><span class="o">*</span><span class="n">poles</span><span class="p">[</span><span class="n">mode_indices</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">]</span>
+<span class="n">P_residuals</span> <span class="o">=</span> <span class="n">P_full</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">2</span><span class="o">*</span><span class="n">poles</span><span class="p">[</span><span class="n">mode_indices</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">:]</span>
+
+<span class="n">shapes_modes</span> <span class="o">=</span> <span class="n">shapes_LS</span><span class="p">[:</span><span class="mi">2</span><span class="o">*</span><span class="n">poles</span><span class="p">[</span><span class="n">mode_indices</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">]</span>
+<span class="n">shapes_residuals</span> <span class="o">=</span> <span class="n">shapes_LS</span><span class="p">[</span><span class="mi">2</span><span class="o">*</span><span class="n">poles</span><span class="p">[</span><span class="n">mode_indices</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">:]</span>
+
+<span class="n">H_modes</span> <span class="o">=</span> <span class="p">(</span><span class="n">P_modes</span><span class="nd">@shapes_modes</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H_modes</span> <span class="o">=</span> <span class="n">H_modes</span><span class="p">[:</span><span class="n">H_modes</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">H_modes</span><span class="p">[</span><span class="n">H_modes</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span>
+
+<span class="n">H_residuals</span> <span class="o">=</span> <span class="p">(</span><span class="n">P_residuals</span><span class="nd">@shapes_residuals</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H_residuals</span> <span class="o">=</span> <span class="n">H_residuals</span><span class="p">[:</span><span class="n">H_residuals</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">H_residuals</span><span class="p">[</span><span class="n">H_residuals</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span>
+
+<span class="n">H_full</span> <span class="o">=</span> <span class="p">(</span><span class="n">P_full</span><span class="nd">@shapes_LS</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">*</span><span class="n">H_LS</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">H_full</span> <span class="o">=</span> <span class="n">H_full</span><span class="p">[:</span><span class="n">H_full</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="n">j</span><span class="o">*</span><span class="n">H_full</span><span class="p">[</span><span class="n">H_full</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">//</span><span class="mi">2</span><span class="p">:]</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[71]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Now plot them</span>
+<span class="n">fig</span><span class="p">,</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">H_full</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">2</span><span class="p">,</span><span class="n">H_full</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">sharey</span><span class="o">=</span><span class="s1">&#39;row&#39;</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_t</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_general</span><span class="p">[</span><span class="n">frequency_lines_to_keep</span><span class="p">,:,:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_t</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_t</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ax</span><span class="p">,</span><span class="n">f_r</span><span class="p">,</span><span class="n">f_m</span><span class="p">,</span><span class="n">f_f</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">axes</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="c1"># Reshape so we pull of a mag and phase plot for each entry</span>
+                      <span class="n">H_residuals</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_modes</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">,</span>
+                      <span class="n">H_full</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
+                      <span class="p">):</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_f</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_r</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">angle</span><span class="p">(</span><span class="n">f_m</span><span class="p">)</span><span class="o">*</span><span class="mi">180</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_f</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_r</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">omegas</span><span class="p">[</span><span class="n">omegas</span><span class="o">&gt;</span><span class="n">min_freq</span><span class="p">],</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">f_m</span><span class="p">),</span><span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;Truth&#39;</span><span class="p">,</span><span class="s1">&#39;Fit&#39;</span><span class="p">,</span><span class="s1">&#39;Residual Contribution&#39;</span><span class="p">,</span><span class="s1">&#39;Mode Contribution&#39;</span><span class="p">])</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="o">-</span><span class="mi">180</span><span class="p">,</span><span class="o">-</span><span class="mi">90</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">90</span><span class="p">,</span><span class="mi">180</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Phase (deg)&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="mf">1e-5</span><span class="p">,</span><span class="mf">1e-2</span><span class="p">])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Magnitude (deg)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_172_0.png" src="../../_images/modal_tutorials_Modal_06_Complex_Modes_Modal_06_Complex_Modes_172_0.png" />
+</div>
+</div>
+<p>We see that the fit matches the measured data very due to the inclusion of the residual terms. If we were to just include the modal contributions, it would not fit as well, but by adding in residual contributions we can get a much better match.</p>
+</section>
+</section>
+<section id="Summary">
+<h2>Summary<a class="headerlink" href="#Summary" title="Permalink to this heading"></a></h2>
+<p>In this document, we have extensively studied complex modes. We first explored the real-modes case to understand the limitations of that approach. We then derived the state space system of equations from the typical 2nd order differential equation to construct the eigenvalue equation to solve for the complex modes.</p>
+<p>We then related the modal parameters to the frequency response function, which allowed us to compute a frequency response function given the modal parameters.</p>
+<p>We finally spent a good deal of time discussing how to fit modal parameters to frequency response functions using the PolyMax approach, which is implemented in SDynPy as PolyPy.</p>
+</section>
+</section>
+
+
+           </div>
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diff --git a/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb b/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb
new file mode 100644
index 0000000..5af974c
--- /dev/null
+++ b/modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.ipynb
@@ -0,0 +1,3842 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cc4417ad-c73f-4426-8e76-c7a69ae33dd5",
+   "metadata": {},
+   "source": [
+    "# Modal Tutorial 06: Complex Modes\n",
+    "Complex modes can be unintuitive and difficult to understand compared to real normal modes.  George Fox Lang wrote an article in Sound and Vibration magazine called \"Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints.\"  He states:\n",
+    "\n",
+    "> Complex Modes is one of those topics that every vibration practitioner understands -- just not fully.\n",
+    "\n",
+    "The author of this document agrees with Lang; in fact, his own lack of knowledge of complex modes is what caused him to write this document, to teach himself what is going on!\n",
+    "\n",
+    "To better understand complex modes, we will start from the differential equations of motion and develop the theory of complex modes, discussing:\n",
+    "\n",
+    "  - Real Modes and their Limitations\n",
+    "  - The state space transformation to derive the complex mode eigenvalue problem\n",
+    "  - Computing natural frequency, damping, and complex mode shapes\n",
+    "  - Computing FRFs from the modal parameters\n",
+    "\n",
+    "This document will largely follow the equations from the following sources:\n",
+    "\n",
+    "  - [An engineering interpretation of the complex modal mass in\n",
+    "structural dynamics by Lopez and Brinckner](https://doi.org/10.1016/j.ymssp.2023.110621)\n",
+    "  - [Matrix Madness and Complex Confusion... A Review of Complex Modes from Multiple Viewpoints by Lang](http://www.sandv.com/downloads/1211lang.pdf)\n",
+    "  - [The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation? by Peeters, et al.](https://content.iospress.com/articles/shock-and-vibration/sav00272)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fee8375-b72c-474f-bdac-a2ddd842070c",
+   "metadata": {},
+   "source": [
+    "## The Second-Order, Linear Differential System of Equations\n",
+    "Most vibration engineers are familiar with the standard system of second-order, linear differential equations.\n",
+    "\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "where $\\mathbf{M}$ is the system mass matrix, $\\mathbf{C}$ is the system damping matrix, $\\mathbf{K}$ is the system stiffness matrix, $\\mathbf{F}$ is the forcing applied to the system, and $\\mathbf{x}$ is the system displacement response.  $\\dot{\\mathbf{x}}$ and $\\ddot{\\mathbf{x}}$ represent the first and second derivatives of the response, velocity and acceleration, respectively.\n",
+    "\n",
+    "For a standard linear, time-invariant (LTI) system, the mass, stiffness matrices are not functions of time (i.e. the mass of the system doesn't change).  However, the forces and responses are generally a function of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24f927e0-5131-4f23-91a1-7c42a9ec8a37",
+   "metadata": {},
+   "source": [
+    "## Real Modes -- Damping is Hard\n",
+    "\n",
+    "Many times, when we are just starting out, we will simply ignore the damping in our part because it is \"hard\" or it doesn't fit with our elegant mathematical tools.\n",
+    "\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "To try to understand the free vibration response of the this system, we can make the following assumptions:\n",
+    "\n",
+    "  - $\\mathbf{F} = \\mathbf{0}$ -- There is no force on the system.\n",
+    "  - $\\mathbf{x} = \\mathbf{\\phi}\\cos(\\omega t)$ -- The response of the system looks like some shape $\\mathbf{\\phi}$ that will vibrate over time (represented mathematically by multiplying by a cosine function), and the vibration will have a frequency of $\\omega$ radians per second.\n",
+    "\n",
+    "Using the chain rule for differentiation, it follows that $\\ddot{\\mathbf{x}} = -\\omega^2\\mathbf{\\phi}\\cos(\\omega t)$\n",
+    "\n",
+    "We can substitute these equations into the previous equation to arrive at an equation in the form of the well-known [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem).\n",
+    "\n",
+    "First we make the substitutions.\n",
+    "\n",
+    "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}\\cos(\\omega t)) + \\mathbf{K}\\mathbf{\\phi}\\cos(\\omega t) = \\mathbf{0}$$\n",
+    "\n",
+    "We can then recognize that the term $\\cos(\\omega t))$ is in general nonzero so can be cancelled.\n",
+    "\n",
+    "$$\\mathbf{M}(-\\omega^2\\mathbf{\\phi}) + \\mathbf{K}\\mathbf{\\phi} = \\mathbf{0}$$\n",
+    "\n",
+    "We can then add $\\omega^2\\mathbf{M}\\mathbf{\\phi}$ to both sides of the equation.\n",
+    "\n",
+    "$$\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "\n",
+    "This is now the same form of the [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem), which is $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae951b07-3074-4634-8c80-ab60435a9630",
+   "metadata": {},
+   "source": [
+    "Here it can help to further rearrange the equation by premultiplying by the inverse of the mass matrix $\\mathbf{M}^{-1}$.  Note that this is generally acceptable to do, because the mass matrix should generally be positive definite and therefore invertable (said another way, every degree of freedom in the system has mass associated with it).\n",
+    "\n",
+    "$$\\mathbf{M}^{-1}\\mathbf{K}\\mathbf{\\phi} = \\omega^2\\mathbf{\\phi}$$\n",
+    "\n",
+    "### Geometric Interpretation of the Eigenvalues and Eigenvectors\n",
+    "\n",
+    "We can gain intuition here by thinking about what the eigenvalue problem means *geometrically*.  We have a vector $\\mathbf{\\phi}$ that represents some shape.  Looking at the right side of the equation, if we multiply that vector by a scalar $\\omega^2$, it will simply scale the length of the vector, but not change its direction in which it's pointing.  In the context of a vibration shape, this means that it will scale the shape larger or smaller, but it won't change the shape of vibration.  You get a bigger or smaller vibration with the same shape.\n",
+    "\n",
+    "Now because of the equality, the left-hand side of the equation must be equivalent to the right-hand side of the equation, which means that the left-hand side must also be equivalent to simply scaling the original vector $\\mathbf{\\phi}$. However, we know from linear algebra that multiplying a vector by a matrix will in general change the length of the vector *and* where the vector is pointing.  In the context of our vibration shape $\\mathbf{\\phi}$, this means that multiplying a vibration shape by a matrix will in general change not only the size of the vibration shape, but the shape itself.\n",
+    "\n",
+    "Let's look at a brief example to illustrate this point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e58c8729-2539-49d2-a7c7-5913080653e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scalar:\n",
+      "0.5507979025745755\n",
+      "Matrix:\n",
+      "[[0.70814782 0.29090474]\n",
+      " [0.51082761 0.89294695]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Set random seed so we can get repeatable results for the documentation.\n",
+    "# Remove this line to allow the matrices and vectors to be random.\n",
+    "np.random.seed(3)\n",
+    "\n",
+    "# Create a vector to test\n",
+    "vector = np.array([[1],\n",
+    "                   [2]])\n",
+    "\n",
+    "# Now create a random scalar\n",
+    "scalar = np.random.random()\n",
+    "print('Scalar:')\n",
+    "print(scalar)\n",
+    "\n",
+    "# Now create a random matrix\n",
+    "matrix = np.random.random((2,2))\n",
+    "print('Matrix:')\n",
+    "print(matrix)\n",
+    "\n",
+    "# Now multiply the vector by the scalar, and multiply the vector by the matrix\n",
+    "vector_scalar = scalar*vector\n",
+    "vector_matrix = matrix@vector # Matrix multiplication is @ in numpy.\n",
+    "\n",
+    "# Now plot them\n",
+    "fig, ax = plt.subplots(1,2,figsize=(10,5), sharex=True,sharey=True)\n",
+    "ax[0].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[0].quiver(*vector_scalar, color='red', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[0].set_title('Multiply Vector by Scalar')\n",
+    "ax[1].quiver(*vector, color='blue', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[1].quiver(*vector_matrix, color='red', angles='xy', scale_units='xy', scale=1)\n",
+    "ax[1].legend(['Original Vector','Modified Vector'])\n",
+    "ax[1].set_title('Multiply Vector by Matrix')\n",
+    "ax[0].set_ylim([-3,3])\n",
+    "ax[0].set_xlim([-3,3]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3c7ea4b-d78c-45ca-b5f9-29ecf27aa4ed",
+   "metadata": {},
+   "source": [
+    "Clearly, multiplying by a scalar has resulted in a vector with the same direction and a different length, whereas multiplying by a matrix has changed not only the length, but also the direction of the vector.\n",
+    "\n",
+    "The resulting direction of the vector will not only be dependent on the matrix that it is multiplied by, but also the original vector itself.  Multiplying different vectors by the same matrix will generally result in a different final direction of the resulting vectors.  We can investigate this by multiplying many different vectors by the matrix from the previous step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "45bc6338-9cbb-46cd-a241-093ef02801bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Here we make a 20x20 grid of 2x1 vectors that we can multiply by our matrix\n",
+    "vectors = np.mgrid[-3:3:20j,-3:3:20j][np.newaxis].transpose()\n",
+    "\n",
+    "scaled_vectors = matrix @ vectors\n",
+    "\n",
+    "# Reshape for easier plotting\n",
+    "vectors_for_plotting = vectors.reshape(-1,2).transpose()\n",
+    "scaled_vectors_for_plotting = scaled_vectors.reshape(-1,2).transpose()\n",
+    "fig, ax = plt.subplots(figsize=(10,10))\n",
+    "ax.quiver(*vectors_for_plotting, *vectors_for_plotting, color='blue', angles='xy', scale_units='xy', scale=10)\n",
+    "ax.quiver(*vectors_for_plotting, *scaled_vectors_for_plotting, color='red', angles='xy', scale_units='xy', scale=10)\n",
+    "ax.legend(['Original Vector','Multiplied Vector']);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d4844ae-ad85-46cd-b741-2321b59604c9",
+   "metadata": {},
+   "source": [
+    "Clearly, we can see that in general, vectors in different directions will be modified by the matrix differently.  However, looking closely at the above plot, we can see that there are vectors in particular directions that maintain their original direction after multiplication by the matrix (a change in length is acceptable).  And if you recall, we are looking for exactly such a vector that when multiplied by a matrix retains its direction.  Therefore, in order to  solve the eigenvalue equation listed previously, we need to find these special directions.  We can't just use any shape $\\mathbf{\\phi}$.  We have to find particular shapes $\\mathbf{\\phi}$ that when multiplied by the system matrices $\\mathbf{M}^{-1}\\mathbf{K}$ do not change shape but perhaps get bigger or smaller.  Mathematically, we call these vectors the *eigenvectors* where the prefix *eigen* is German for *particular*.  In structural dynamics, we call these particular shapes the *mode shapes*, and they have many special properties.\n",
+    "\n",
+    "Similarly, we call the scalar quantity $\\omega^2$ that gets multiplied by the eigenvector the *eigenvalue*.  It is the particular value that goes along with the particular vector.  We will generally have as many eigenvector/eigenvalue pairs as we have degrees of freedom in the system.\n",
+    "\n",
+    "We note that there are linear algebra tools available to solve for the eigenvalues and eigenvectors of a given matrix.  For example, in Numpy, we can use `np.linalg.eig`.  We can then plot the eigenvector directions on the above figure to show that they indeed align with the directions where the vectors are scaled but the direction does not change.  Note also that there are two degrees of freedom in this problem, so we find two eigenvectors, or two directions where the direction of the vectors do not change."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "92c994f7-31b6-4014-ade5-8ce28df278a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eigenvalues, eigenvectors = np.linalg.eig(matrix)\n",
+    "# Plot the positive and negative eigenvectors so it looks like a line through the origin\n",
+    "# Also reshape them so they are easier to plot\n",
+    "# Multiply by 4 so they take up the whole plot\n",
+    "eigenvectors_to_plot = 4*np.array((eigenvectors,-eigenvectors)).transpose(1,0,2)\n",
+    "ax.plot(*eigenvectors_to_plot,'k--')\n",
+    "ax.set_ylim([-3,3])\n",
+    "ax.set_xlim([-3,3])\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e4b233b-ab96-49cf-afe7-21d8ac40ac72",
+   "metadata": {},
+   "source": [
+    "We see that vectors that are along the directions represented by the eigenvectors do not change direction, only change scale."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83279b35-f120-4a31-88f8-3d6087c90379",
+   "metadata": {},
+   "source": [
+    "Without approximation or loss of generality, we can take *all* of the eigenvectors or mode shapes of a system as column vectors and *stack* them side-by-side to form a matrix.  This matrix forms a coordinate transformation: in the same way we can transform between a local and global coordinate system, we can also transform between *physical* and *modal* degrees of freedom.  Physical degrees of freedom are generally represented by some physical quantity (e.g., Point 1 moves 2.5 centimeters in the vertical direction).  Modal degrees of freedom are generally represented by some *modal degree of freedom* or *modal quantity*.  Motions are then represented by how much of each mode shape is present in the motion (e.g., the system moves with a shape that looks like mode shape 1 scaled up by a factor of 2, plus mode shape 2 scaled down by a factor of 1/2).  We can represent this mathematically by saying physical motions $\\mathbf{x}$ are represented by a linear combination of shapes $\\mathbf{\\phi}$.  We call the coefficient matrix $\\mathbf{q}$ the *modal coefficients* or *modal degrees of freedom*, because they describe how much of each mode shape in $\\mathbf{\\phi}$ is present in $\\mathbf{x}$.\n",
+    "$$\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9a4baa0-4a8d-44fc-84a8-14e0885710e3",
+   "metadata": {},
+   "source": [
+    "We can substitute this transformation into our original undamped equations of motion to get equations of motion in terms of the modal degrees of freedom $\\mathbf{q}$.\n",
+    "$$\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{F}$$\n",
+    "\n",
+    "We note that the mode shapes $\\mathbf{\\phi}$ are invariant in time, (they are a function of the matrices $\\mathbf{M}$ and $\\mathbf{K}$, which are also invariant in time).  Therefore taking the derivative of the physical response $\\mathbf{x}$ is simply\n",
+    "$$\\ddot{\\mathbf{x}} = \\mathbf{\\phi}\\ddot{\\mathbf{q}}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c218db3-a489-4015-861e-492ef63345eb",
+   "metadata": {},
+   "source": [
+    "While the transformed equation above is technically valid, we generally do not like this form, because it breaks the symmetry of the mass and stiffness matrices.  For all real structures, the mass, stiffness, and damping matrices should be symmetric.  This is a function of Netwton's third law of equal and opposite reactions: if there is a force developing at degree of freedom 1 due to motion of degree of freedom 2, an equal and opposite force will develop on degree of freedom 2 due to degree of freedom 1 pushing back on it.  Currently, however, in our transformed equations of motion, the new effective mass matrix $\\mathbf{M}\\mathbf{\\phi}$ is not symmetric, nor is the equivalent effective stiffness matrix.  We therefore premultiply the equations of motion by $\\mathbf{\\phi}^T$.\n",
+    "\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "In this final format, the effective mass and stiffness matrices $\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$ and $\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$ are symmetric.  We will often make a substitution\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "$$\\tilde{\\mathbf{K}}=\\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}$$\n",
+    "where $\\tilde{\\mathbf{M}}$ and $\\tilde{\\mathbf{K}}$ are referred to as the *modal mass* and *modal stiffness* matrices.  The modal system of equations is then\n",
+    "\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "The symmetry of the system matrices is not the only interesting property when we transform into modal coordinates.  It turns out that the mode shapes $\\mathbf{\\phi}$ *diagonalize* the mass and stiffness matrices.  The modal mass and stiffness matrices, formed by pre- and post-multiplying the mass and stiffness matrices by the mode shapes, are diagonal.  This physically means that there is no coupling between modal degrees of freedom; the response of mode 1 does not depend at all on the response of mode 2.  They can be treated as entirely separate equations.  Compare this to a physical mass and stiffness matrix, where there would be coupling between the degrees of freedom.\n",
+    "\n",
+    "We can think about this concept physically.  In the physical domain, if I pluck the end of a beam, all of the degrees of freedom on the beam will eventually vibrate.  This is because the physical degrees of freedom are all connected to one another through the mass and stiffness of a beam.  In the modal domain, however, if I pluck \"mode 1\" (an admittedly hard thing to accomplish in practice, as you would need to deform the beam into its first mode shape and then let it go instantaneously), none of the other modes will be excited at all, because there is no coupling between them.  Note that this still means that the entire beam will vibrate, because the mode shape of the beam contains all the beam degrees of freedom, so if the mode shape vibrates, the whole beam vibrates.  In fact because the modal representation described above is exact, plucking the end of a beam will result in exactly the same beam motion regardless of whether it is represented by modal or physical degrees of freedom.  The modal transformation is simply a different way to represent a dynamic system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0db175db-4400-49c1-915c-f81bbede2855",
+   "metadata": {},
+   "source": [
+    "### An Infinite Number of Mode Shapes\n",
+    "One last concept to describe before we move on is the concept of mode shape scaling.  From the previous figure, we saw that each eigenvector of a matrix mostly represented a particular direction, rather than a specific vector.  Any vector in that direction would be only scaled by the matrix instead of having its direction modified.  Therefore, eigenvectors are only really unique up to a scale factor.  An eigenvector times a scalar is still a valid eigenvector of a matrix, so you could argue there are an infinite number of eigenvectors of any matrix.\n",
+    "\n",
+    "Rather than allowing for any arbitrary scaling of eigenvectors, we will often scale them to some preferred normalization.  One might, for example, scale an eigenvector such that it has a length of 1, i.e. make it a unit vector.  Another might, for example, scale a specific degree of freedom to 1.  The most common scaling on an eigenvector, particularly in the context of structural dynamics where an eigenvector represents a mode shape, is to scale the eigenvectors such that the modal mass matrix $\\tilde{\\mathbf{M}}$ is equal to the identity matrix.  This is known as *mass normalization*.  We have already described how the modal mass matrix is diagonal.  To mass-normalize a mode shape, we only need to scale the mode shape such that the diagonal entries obtained when computing the modal mass matrix end up being 1.  To do this for the $r$th mode, we can simply compute the modal mass matrix with a single unscaled mode shape $\\tilde{\\mathbf{\\phi}}_r$, then divide the mode shapes by the square-root of the diagonal entry of the modal mass matrix corresponding to that mode shape.\n",
+    "\n",
+    "$$\\mathbf{\\phi}_r = \\frac{\\tilde{\\mathbf{\\phi}}_r}{\\sqrt{{\\tilde{\\mathbf{\\phi}}_r}^T\\mathbf{M}\\tilde{\\mathbf{\\phi}}_r}}$$\n",
+    "\n",
+    "When we compute the modal stiffness using mass normalized modes, the entries on the diagonal of the modal stiffness matrix will be equal to the natural frequency squared.  For example, for the $r$th mode shape:\n",
+    "\n",
+    "$${\\mathbf{\\phi}_r}^T\\mathbf{K}\\mathbf{\\phi}_r = {\\omega_r}^2$$\n",
+    "\n",
+    "### Units with Scaled Mode Shapes\n",
+    "Note that when we mass-normalize the mode shapes, the mode shapes will have a unit of $\\frac{1}{\\sqrt{m}}$.  The mass matrix has dimensions of $m$ where $m$ is the unit of mass, and to equal the dimensionless identity matrix when the modal mass matrix is assembled, the mode shape units from pre- and post-multiplying must cancel out the units of the mass matrix.  The modal coefficient $\\mathbf{q}$ must then have units of ${l}{\\sqrt{m}}$ where $l$ is a length dimension, and one must recall that taking a derivative over time adds a $\\frac{1}{t}$ where $t$ is the unit representing time.\n",
+    "\n",
+    "As an example, we will assume we have a meter/kilogram/newton/second unit system.  The modal transformation equation must have consistent units.\n",
+    "$$ \\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$$\n",
+    "$$ \\left[m\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right]$$\n",
+    "\n",
+    "Similarly, the equations of motion must have consistent units.\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{N}{m}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[N\\right]$$\n",
+    "\n",
+    "We can substitute $\\left[N\\right] = \\left[\\frac{kg{\\,}m}{s^2}\\right]$\n",
+    "$$\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[kg\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{m{\\,}s^2}\\right]\\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[m\\sqrt{kg}\\right] = \\left[\\frac{1}{\\sqrt{kg}}\\right]\\left[\\frac{kg{\\,}m}{s^2}\\right]$$\n",
+    "\n",
+    "Then with a good deal of cancelling units\n",
+    "$$\\left[\\frac{m\\sqrt{kg}}{s^2}\\right] + \\left[\\frac{m\\sqrt{kg}}{s^2}\\right] = \\left[\\frac{m\\sqrt{kg}}{s^2}\\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd03536b-95a6-405b-8249-153b7887f208",
+   "metadata": {},
+   "source": [
+    "### Adding Damping\n",
+    "Eventually, we will need to consider the damping values that we simply disregarded previously.  If we bring back our original damped system of equations, and transform to modal space, we end up with the following equations of motion:\n",
+    "$$\\mathbf{M}\\ddot{\\mathbf{x}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "Here the matrix $\\tilde{\\mathbf{C}} = \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$ is referred to as the modal damping matrix.\n",
+    "\n",
+    "The important consideration that we must make to determine if the mode shapes that we have computed are still valid mode shapes is to understand if the modal damping matrix is also diagonalized by the mode shape matrix.  If it is, then our mode shapes are still valid: they represent independent, uncoupled degrees of freedom in our system.  However if the modal damping matrix is not diagonal, that means there is coupling between the degrees of freedom, and the degrees of freedom represented by the equation are not actually modal degrees of freedom.\n",
+    "\n",
+    "The mode shapes can diagonalize the damping matrix, but only in a very limited case.  Since the mode shapes only decouple the mass and stiffness matrices from which they were computed, the damping matrix will only be decoupled completely if the physical damping matrix is a linear combination of the physical mass and stiffness matrices.\n",
+    "\n",
+    "$$\\mathbf{C} = \\alpha\\mathbf{M} + \\beta\\mathbf{K}$$\n",
+    "\n",
+    "This is known as *proportional damping*, where the damping is proportional to the mass and stiffness of the system.  Transforming to modal coordinates will diagonalize this matrix.  If we do have proportional damping, and we use mass normalized modes for the modal transformation, the modal damping matrix will be diagonal, and the entry corresponding to the $r$th mode will be equal to\n",
+    "\n",
+    "$$\\tilde{\\mathbf{C}}_{rr} = {\\mathbf{\\phi}_r}^T\\mathbf{C}\\mathbf{\\phi}_r = 2\\zeta_r\\omega_r = \\alpha+\\beta\\omega_r^2$$\n",
+    "\n",
+    "where $\\omega_r$ is the natural frequency and $\\zeta_r$ is the fraction of critical damping for mode $r$.\n",
+    "\n",
+    "We only get a diagonal modal damping matrix in this case.  For any other case, we will still have off-diagonal terms.  Luckily for us, these diagonal terms are often small, so using the real mode shapes derived from the mass and stiffness of the system is not a terrible approximation.  However, there are cases where the approximation is not good, and in those cases, we need to turn to a different toolset to help us analyze the system."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3590f2c-04ea-443e-903d-cb05fdeabd13",
+   "metadata": {},
+   "source": [
+    "## Complex Modes -- The General Solution\n",
+    "A more general solution to decoupling damped vibration problems requires us to rearange our equations of motion.  Rather than treating the system of equations as $N$ equations with a second order derivative, we will instead treat the system of equations as $2N$ first-order equations using a simple introducton of a new variable $\\mathbf{v} = \\dot{\\mathbf{x}}$ to represent velocity.  In this way, we can eliminate the double-derivative in the acceleration term ($\\ddot{\\mathbf{x}} = \\dot{\\mathbf{v}}$)\n",
+    "\n",
+    "$$\\mathbf{M}\\dot{\\mathbf{v}} + \\mathbf{C}\\dot{\\mathbf{x}} + \\mathbf{K}{\\mathbf{x}} = \\mathbf{F}$$\n",
+    "\n",
+    "However, we now need to add some relationship between $v$ and $x$.  We could simply write $\\mathbf{v} = \\dot{\\mathbf{x}}$ as we showed above.  However, it will be useful to keep our matrices symmetric, so we will construct the equations in a slightly more complex way.\n",
+    "\n",
+    "$$\\mathbf{M}\\dot{\\mathbf{x}} - \\mathbf{M}\\mathbf{v} = \\mathbf{0}$$\n",
+    "\n",
+    "We can combine these two sets of equations into one set of equations of the form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "Here we note that our coefficient matrices are both symmetric, the first term contains all of the derivatives, the second term contains no derivatives, and the right-hand side of the equation contains our input forces."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e95a290-9795-4fa3-8b31-d71776e2db66",
+   "metadata": {},
+   "source": [
+    "We can perform a similar operation as previous to try to understand the free response of the system.  We will make the following assumptions.\n",
+    "\n",
+    "- $\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$ -- The force into the system is zero.\n",
+    "- $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$ -- The response is a complex sinusoid.\n",
+    "\n",
+    "In this case, the first assumption is essentially identical to the first assumption in the real mode shape case.  The second assumption is essentially a complex generalization of the second assumption made in the real mode shape case.  In this generalization, instead of a \"real\" shape where all responses are either in-phase or 180$^\\circ$ out-of-phase, the shape can now be complex, meaning there can be any phasing between different values in the shape.  The exponential $e$ now represents an oscillatory portion and decaying or growing portion of the response ($\\lambda$ is in general a complex number.  The imaginary portion of it will result in sinusoidal motion of the form $e^{j\\theta} = \\cos{\\theta} + j\\sin{\\theta}$ where $j = \\sqrt{-1}$.  The real part will result in exponential decay or exponential growth of the form $e^{x}$).  The term $\\lambda$ appears on the first row of the second assumption due to the chain rule when differentiating $\\mathbf{v} = \\dot{x}$.\n",
+    "\n",
+    "Using the chain rule for differentiation, we can also say $\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} = \\lambda\\begin{bmatrix}\\lambda\\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e824ad9-e3a4-4638-8b43-ace3c75bc707",
+   "metadata": {},
+   "source": [
+    "We can then apply these assumptions to our equations of motion:\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}e^{\\lambda t} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can again recognize that the term $e^{\\lambda t}$ is in general nonzero and therefore can be cancelled.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\left(\\lambda \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}\\right) + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can then subtract $\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$ from both sides of the equation and we end up with\n",
+    "\n",
+    "$$ \\lambda \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = - \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39b05928-6886-4d5a-b714-081a5d10abfd",
+   "metadata": {},
+   "source": [
+    "While a little more complex, this equation is also a [Generalized Eigenvalue Problem](https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Generalized_eigenvalue_problem)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9aa2bf5c-7063-4df0-97eb-4d0ad1cfb50e",
+   "metadata": {},
+   "source": [
+    "Transforming the eigenvalues and eigenvectors into natural frequencies and mode shapes is not as straightforward in the present case with complex modes.  In general, we will solve for $2N$ complex eigenvalues; however, due to the fact that the matrices are real, the eigenvalues will be complex conjugates of one another.  We will often hear these values called *poles* because they are zeros of the denominator polynomials in the frequency response function formulation that we will discuss later.\n",
+    "\n",
+    "The eigenvalues $\\lambda$ are often expressed as a function of the critical damping ratio $\\zeta$ and the natural frequency $\\omega$.  For the $r$th mode,\n",
+    "\n",
+    "$$\\lambda_r = -\\zeta_r \\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$$\n",
+    "\n",
+    "The real part $-\\zeta_r \\omega_r$ drives the exponential decay of the sinusoid.  The imaginary part drives the oscillatory behavior.  Because of this, the term $\\omega_r\\sqrt{1-\\zeta_r}$ is often referred to as the *damped natural frequency* $\\omega_d$\n",
+    "\n",
+    "$$\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$$\n",
+    "\n",
+    "To recover the natural frequency $\\omega_r$, we can simply take the magnitude of the eigenvalue.\n",
+    "\n",
+    "$$\\left|\\lambda_r\\right| = \\sqrt{\\Re{(\\lambda_r)}^2 + \\Im{(\\lambda_r)}^2} = \\sqrt{(\\zeta_r^2 \\omega_r^2) + (\\omega_r^2 (1-\\zeta_r^2))} = \\sqrt{\\omega_r^2 + \\omega_r^2\\zeta_r^2 - \\omega_r^2\\zeta_r^2} = \\omega_r$$\n",
+    "\n",
+    "To recover the damping ratio, we can divide the negative real part of the eigenvalue by the magnitude of the eigenvalue.\n",
+    "\n",
+    "$$- \\frac{\\Re{(\\lambda_r)}}{\\left|\\lambda_r\\right|} = \\frac{\\zeta_r\\omega_r}{\\omega_r} = \\zeta_r$$ \n",
+    "\n",
+    "Note that for complex modes, the damping is part of the solution to the free vibration problem and appears in the solution, whereas in the real mode case where damping was ignored, it was not.\n",
+    "\n",
+    "We will also solve for $2N$ eigenvectors.  These will also appear in complex conjugate pairs.  They will be of the form\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$\n",
+    "\n",
+    "We can extract the mode shape from the eigenvector simply by selecting the bottom partition of the vector.\n",
+    "\n",
+    "The equations to compute the modal mass and modal damping matrices are the same as those used in the case of real modes.  However, in general, the modal mass and modal damping matrices are not diagonal and have complex elements.\n",
+    "\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "$$\\tilde{\\mathbf{C}}=\\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}$$\n",
+    "\n",
+    "Recall that the eigenvalue problem we solved for was with respect to the generalized form $\\mathbf{A}\\dot{\\mathbf{z}}+\\mathbf{B}\\mathbf{z} = \\mathbf{u}$.  Therefore the eigenvectors will diagonalize these matrices.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{A}}$$\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}^T\\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\tilde{\\mathbf{B}}$$\n",
+    "\n",
+    "We often refer to these matrices $\\tilde{\\mathbf{A}}$ and $\\tilde{\\mathbf{B}}$ as the *Modal A* and *Modal B* matrices.\n",
+    "\n",
+    "Modal mass is defined in different ways for complex modes.  Some references will treat the diagonal entries in Modal A $\\tilde{\\mathbf{A}}_{rr}$ directly as the modal mass values.  Other references define the modal mass for the $r$th mode $m_r$ as:\n",
+    "\n",
+    "$$\\tilde{\\mathbf{A}}_{rr} = 2\\lambda_r m_r + 2\\zeta_r\\omega_r m_r = 2j\\omega_{dr} m_r$$\n",
+    "$$m_r = \\frac{\\tilde{\\mathbf{A}}_{rr}}{2j\\omega_{dr}}$$\n",
+    "\n",
+    "It doesn't really matter which definition is used as long as the definition is consistently used when reconstructing responses from modal parameters.  This document will proceed with the modal mass defined as the diagonal terms of $\\tilde{\\mathbf{A}}$.\n",
+    "\n",
+    "We can also see that if we premultiply the eigenvalue equation by our eigenvectors, we get a relationship between the Modal A and Modal B matrices.\n",
+    "\n",
+    "$$\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f2a6ce4-2692-4839-8826-5ef45900ff85",
+   "metadata": {},
+   "source": [
+    "## An Example Problem\n",
+    "Before we get too far, let's illustrate some of these concepts with a small example problem.  We will generate a 3-degree-of-freedom problem that we will simulate.  We will explore undamped, proportionally damped, and generally damped cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c6eb06f7-2c41-44fc-bf7b-d160b7bd4303",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import scipy.linalg as la\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "def pretty_print_table(df):\n",
+    "    return display( HTML( df.to_html().replace(\"\\\\n\",\"<br>\") ) )\n",
+    "\n",
+    "# Set up physical matrices\n",
+    "K = np.array([[ 6000, -2000,     0],\n",
+    "              [-2000,  4000, -2000],\n",
+    "              [    0, -2000,  6000]])\n",
+    "M = np.array([[2, 0, 0],\n",
+    "              [0, 3, 0],\n",
+    "              [0, 0, 2]])\n",
+    "alpha = 1.36/2\n",
+    "beta = 0.00154865/2\n",
+    "C_proportional = alpha*M+beta*K\n",
+    "\n",
+    "C_general = np.array([[14, -2, 0],\n",
+    "                      [-2, 10, -2],\n",
+    "                      [0, -2, 10]])/2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "457c045b-1adb-4923-a5b9-a64cd6294ba4",
+   "metadata": {},
+   "source": [
+    "For the complex mode cases, let's construct the $\\mathbf{A}$ and $\\mathbf{B}$ matrices.  The $\\mathbf{A}$ matrix will depend on damping, the $\\mathbf{B}$ matrix will not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0065867a-cd5c-4548-8cf2-dd6580fbf2ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For convenience, construct a matrix of zeros\n",
+    "Z = np.zeros(M.shape)\n",
+    "# Assemble State Space Matrices\n",
+    "A_undamped = np.block([[Z, M],\n",
+    "                       [M, Z]])\n",
+    "A_proportional = np.block([[Z, M],\n",
+    "                           [M, C_proportional]])\n",
+    "A_general = np.block([[Z, M],\n",
+    "                      [M, C_general]])\n",
+    "B = np.block([[-M, Z],\n",
+    "              [ Z, K]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6031364-f521-4438-a584-335ff3e83900",
+   "metadata": {},
+   "source": [
+    "Now let's compute the eigenvalues and eigenvectors.  For the 2nd-order eigenvalue using the mass and stiffness matrices directly, we can use the `eigh` function which assumes Hermetian, positive definite matrices.  For the complex mode cases, we will have to use the more general `eig` function.\n",
+    "\n",
+    "When using an eigenvalue solver, be wary of the order that matrices occur in the function call.  For example, in the [scipy.linalg.eig documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.linalg.eig.html), it states the following:\n",
+    "\n",
+    "> **scipy.linalg.eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False)**\n",
+    ">\n",
+    "> Solve an ordinary or generalized eigenvalue problem of a square matrix.\n",
+    "> \n",
+    "> Find eigenvalues w and right or left eigenvectors of a general matrix:\n",
+    ">\n",
+    "> ```python\n",
+    ">  a   vr[:,i] = w[i]        b   vr[:,i]\n",
+    ">  a.H vl[:,i] = w[i].conj() b.H vl[:,i]\n",
+    ">   ```\n",
+    ">\n",
+    "> where `.H` is the Hermitian conjugation.\n",
+    "\n",
+    "SciPy uses the definition $\\mathbf{A}\\mathbf{v} = \\lambda\\mathbf{B}\\mathbf{v}$ for its eigenvalue problem, and our equation we have defined as $- \\mathbf{B}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix} = \\lambda \\mathbf{A}\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$. Note that what SciPy calls `a` in its function call is what we have defined as $-\\mathbf{B}$, and what SciPy calls `b` is what we have defined as $\\mathbf{A}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "954836de-ba9e-4019-8e02-5e730de1fdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute Eigenvalues and Eigenvectors\n",
+    "lam_2ndOrder,E_2ndOrder = la.eigh(K,M)\n",
+    "lam_undamped, E_undamped = la.eig(-B,A_undamped)\n",
+    "lam_proportional, E_proportional = la.eig(-B,A_proportional)\n",
+    "lam_general, E_general = la.eig(-B,A_general)\n",
+    "\n",
+    "# Since the half the complex eigenvalues and eigenvectors are complex conjugates of one another,\n",
+    "# we will only keep those corresponding to the eigenvalues with imaginary part > 0\n",
+    "keep_undamped = lam_undamped.imag > 0\n",
+    "keep_proportional = lam_proportional.imag > 0\n",
+    "keep_general = lam_general.imag > 0\n",
+    "\n",
+    "# Now reduce to just those we want to keep\n",
+    "lam_undamped = lam_undamped[keep_undamped]\n",
+    "E_undamped = E_undamped[:,keep_undamped]\n",
+    "lam_proportional = lam_proportional[keep_proportional]\n",
+    "E_proportional = E_proportional[:,keep_proportional]\n",
+    "lam_general = lam_general[keep_general]\n",
+    "E_general = E_general[:,keep_general]\n",
+    "\n",
+    "# Let's also sort the eigenvalues so they are all ascending\n",
+    "for evals,evects in zip([lam_2ndOrder, lam_undamped, lam_proportional, lam_general],\n",
+    "                        [E_2ndOrder, E_undamped, E_proportional, E_general]):\n",
+    "    isort = np.argsort(np.abs(evals))\n",
+    "    evals[...] = evals[isort]\n",
+    "    evects[...] = evects[:,isort]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c785b0f3-4103-44b9-a3f6-99c149557c88",
+   "metadata": {},
+   "source": [
+    "Now we can extract the natural frequencies and damping ratios from the eigenvalues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "99f8881b-af4f-4ac1-9131-a90cd1fb0566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute natural frequencies\n",
+    "omega_2ndOrder = np.sqrt(lam_2ndOrder)\n",
+    "omega_undamped = np.abs(lam_undamped)\n",
+    "omega_proportional = np.abs(lam_proportional)\n",
+    "omega_general = np.abs(lam_general)\n",
+    "\n",
+    "# Compute damping ratios\n",
+    "zeta_undamped = -np.real(lam_undamped)/np.abs(lam_undamped)\n",
+    "zeta_proportional = -np.real(lam_proportional)/np.abs(lam_proportional)\n",
+    "zeta_general = -np.real(lam_general)/np.abs(lam_general)\n",
+    "\n",
+    "# Compute damped natural frequencies\n",
+    "omega_d_undamped = omega_undamped*np.sqrt(1-zeta_undamped**2)\n",
+    "omega_d_proportional = omega_proportional*np.sqrt(1-zeta_proportional**2)\n",
+    "omega_d_general = omega_general*np.sqrt(1-zeta_general**2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5ea9635-f9d9-49fd-b4a2-88f8540f2f24",
+   "metadata": {},
+   "source": [
+    "Let's plot these datasets in a table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8643d599-fbb1-42d8-991f-50e46bc40f3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Undamped</th>\n",
+       "      <th>Proportional Damping</th>\n",
+       "      <th>General Damping</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Mass Matrix</th>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "      <td>[[2 0 0]<br> [0 3 0]<br> [0 0 2]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Stiffness Matrix</th>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "      <td>[[ 6000 -2000     0]<br> [-2000  4000 -2000]<br> [    0 -2000  6000]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Damping Matrix</th>\n",
+       "      <td>[[0. 0. 0.]<br> [0. 0. 0.]<br> [0. 0. 0.]]</td>\n",
+       "      <td>[[ 6.00595 -1.54865  0.     ]<br> [-1.54865  5.1373  -1.54865]<br> [ 0.      -1.54865  6.00595]]</td>\n",
+       "      <td>[[ 7. -1.  0.]<br> [-1.  5. -1.]<br> [ 0. -1.  5.]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poles</th>\n",
+       "      <td>[[0.+27.2518998j ]<br> [0.+54.77225575j]<br> [0.+59.92217695j]]</td>\n",
+       "      <td>[[-0.62753244+27.24467371j]<br> [-1.5014875 +54.75167153j]<br> [-1.73017173+59.89719356j]]</td>\n",
+       "      <td>[[-0.73753202+27.24242803j]<br> [-1.50037636+54.76028673j]<br> [-1.59542495+59.89042233j]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Natural Frequencies</th>\n",
+       "      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>\n",
+       "      <td>[[27.2518998 ]<br> [54.77225575]<br> [59.92217695]]</td>\n",
+       "      <td>[[27.25240977]<br> [54.78083727]<br> [59.91166888]]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Damping Ratios</th>\n",
+       "      <td>[[-0.]<br> [-0.]<br> [-0.]]</td>\n",
+       "      <td>[[0.02302711]<br> [0.02741329]<br> [0.02887365]]</td>\n",
+       "      <td>[[0.027063  ]<br> [0.02738871]<br> [0.02662962]]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Undamped','Proportional Damping','General Damping'],\n",
+    "                  index=['Mass Matrix','Stiffness Matrix','Damping Matrix','Poles','Natural Frequencies','Damping Ratios'],\n",
+    "                  dtype=object)\n",
+    "df.at['Mass Matrix', 'Undamped'] = str(M)\n",
+    "df.at['Mass Matrix', 'Proportional Damping'] = str(M)\n",
+    "df.at['Mass Matrix', 'General Damping'] = str(M)\n",
+    "df.at['Stiffness Matrix', 'Undamped'] = str(K)\n",
+    "df.at['Stiffness Matrix', 'Proportional Damping'] = str(K)\n",
+    "df.at['Stiffness Matrix', 'General Damping'] = str(K)\n",
+    "df.at['Damping Matrix', 'Undamped'] = str(np.zeros(C_proportional.shape))\n",
+    "df.at['Damping Matrix', 'Proportional Damping'] = str(C_proportional)\n",
+    "df.at['Damping Matrix', 'General Damping'] = str(C_general)\n",
+    "df.at['Poles', 'Undamped'] = str(lam_undamped[:,np.newaxis])\n",
+    "df.at['Poles', 'Proportional Damping'] = str(lam_proportional[:,np.newaxis])\n",
+    "df.at['Poles', 'General Damping'] = str(lam_general[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'Undamped'] = str(omega_undamped[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'Proportional Damping'] = str(omega_proportional[:,np.newaxis])\n",
+    "df.at['Natural Frequencies', 'General Damping'] = str(omega_general[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'Undamped'] = str(zeta_undamped[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'Proportional Damping'] = str(zeta_proportional[:,np.newaxis])\n",
+    "df.at['Damping Ratios', 'General Damping'] = str(zeta_general[:,np.newaxis])\n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c1b1019-07d2-47a9-b59d-834e6fb3e08a",
+   "metadata": {},
+   "source": [
+    "We will now look at the eigenvectors.  To start out, we will normalize the eigenvectors **to a value that is NOT equal to unity modal mass or unity Modal A**.  This is useful while we are learning, because if we have unity modal mass, it allows us to be sloppy and simply drop or ignore effects of the modal mass.  This could result in significant errors down the road if we assume that the formulae we developed for the unity modal mass case also apply to the non-unity modal mass case.  It is therefore better to develop and check all the formulae with non-unity modal mass to ensure that we understand its effects and have accounted for it successfully.  We will deliberately normalize to a value not equal to 1, because many eigenvector solvers will automatically normalize for us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aa9e0cb3-b626-4b88-9a55-b67e71cfab75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize Eigenvectors using einsum to pull out the m_ii terms from the modal normalization matrix\n",
+    "# We will multiply by 2 to ensure non-unity modal mass.\n",
+    "E_2ndOrder = 2*E_2ndOrder/np.sqrt(np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder))\n",
+    "E_undamped = 2*E_undamped/np.sqrt(np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped))\n",
+    "E_proportional = 2*E_proportional/np.sqrt(np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional))\n",
+    "E_general = 2*E_general/np.sqrt(np.einsum('ji,jk,ki->i',E_general,A_general,E_general))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8a0830-55f4-48b3-bfee-8d3fff1c6ec1",
+   "metadata": {},
+   "source": [
+    "Now that we have the eigenvectors normalized to a non-unity modal mass, we will compute the Modal Mass and Modal A matrices to extract the modal mass.\n",
+    "\n",
+    "We will use `np.einsum` in the same way we used previously but here we will describe it a bit better.  Numpy's `einsum` function implements a generalized version of Einstein Summation Notation.  While we can represent matrix operations like multiplication simply by using symbols for the matrices\n",
+    "$$\\mathbf{C} = \\mathbf{A}\\mathbf{B}$$\n",
+    "we can also represent those operations with the indices\n",
+    "$$C_{ik} = A_{ij}B_{jk}$$\n",
+    "In the previous equation, we have the index $i$ representing the rows of $\\mathbf{A}$, and the index $j$ representing the columns.  Similarly for $\\mathbf{B}$, we have the indices $j$ representing the rows and $k$ representing the columns.  When we do matrix multiplication, we sum over the inner dimension, e.g., the $j$ indices, and that index drops out.  We are left with rows $i$ and columns $k$ of our output matrix $\\mathbf{C}$.  This operation would be represented in Numpy's `einsum` function as\n",
+    "```python\n",
+    "C = np.einsum('ij,jk->ik',A,B)\n",
+    "```\n",
+    "The first argument `'ij,jk->ik'` is a string that gives the index operations to be performed.  We see that there is an arrow `->` that separates input indices and output indices, and the different input indices are separated by a comma, and there is only one set of output indices.  The remaining arguments are the actual matrices corresponding to the input indices passed in the first argument.  Therefore `ij` will be associated with `A`, and `jk` will be associated with `B`.  The output indices on the right side of the arrow `ik` will be associated with the function's output `C`.  We note that any index not listed in the output indices will be summed over and drop out.\n",
+    "\n",
+    "This notation is very useful for writing complex matrix expressions in a compact way.  For example, if instead of $\\mathbf{C} = \\mathbf{A}\\mathbf{B}$ we wanted to do $\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$, we can simply swap the indices on $\\mathbf{A}$, which now means we are summing across the rows of $\\mathbf{A}$, which is equivalent to matrix multiplication by a transpose.\n",
+    "$$\\mathbf{C} = \\mathbf{A}^T\\mathbf{B}$$\n",
+    "$$C_{ik} = A_{ji}B_{jk}$$\n",
+    "\n",
+    "We can therefore represent a more complex product like\n",
+    "$$\\tilde{\\mathbf{M}}=\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}$$\n",
+    "as\n",
+    "$$\\tilde{{M}}_{il}={\\phi}_{ji}{M}_{jk}{\\phi}_{kl}$$\n",
+    "In the previous equation, we can see that the $j$ index corresponds to the rows of the mass matrix and the rows of the mode shape matrix.  Because we sum over $j$, this is equivalent to multiplying the matrix $\\mathbf{M}$ by $\\mathbf{\\phi}^T$.  We can indeed improve upon this if, for example, we wanted a 1D array of modal mass values, rather than a 2D matrix that we will then need to extract the diagonals from.  We can replace the $l$ index with another $i$ index and have the output specify just a single $i$, and we will get a 1D array of modal mass values.  As an aside, this is a syntax that is not acceptable in true Einstein Summation Notation, where a repeated index is automatically summed over.  In Numpy's generalized version, it subverts some of these very strict rules to give more flexibility to the user.\n",
+    "$$\\tilde{{M}}_{i}={\\phi}_{ji}{M}_{jk}{\\phi}_{ki}$$\n",
+    "\n",
+    "We can then perform these operations in code to solve for the modal mass and modal A quantities."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0e7c1966-278c-4e88-b4cd-bcb8246b282b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mm_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,M,E_2ndOrder)\n",
+    "ma_undamped = np.einsum('ji,jk,ki->i',E_undamped,A_undamped,E_undamped)\n",
+    "ma_proportional = np.einsum('ji,jk,ki->i',E_proportional,A_proportional,E_proportional)\n",
+    "ma_general = np.einsum('ji,jk,ki->i',E_general,A_general,E_general)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7184e6a9-807d-473a-a1d2-5a12854ea046",
+   "metadata": {},
+   "source": [
+    "The last thing we will do is to pull out the mode shapes from the eigenvectors.  For the 2nd Order case, the mode shapes are the eigenvectors.  But for the complex case, the mode shapes are the lower half of the eigenvector matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "806b85b5-ef45-441e-bd98-d0cb087350b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = E_2ndOrder\n",
+    "psi_undamped = E_undamped[E_undamped.shape[0]//2:,:]\n",
+    "psi_proportional = E_proportional[E_proportional.shape[0]//2:,:]\n",
+    "psi_general = E_general[E_general.shape[0]//2:,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f52d5276-14d0-4ae8-a0f0-924b20ecddb3",
+   "metadata": {},
+   "source": [
+    "We should be able to check that the top half of the eigenvector matrix is the bottom half multiplied by the eigenvalue.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\lambda \\mathbf{\\psi}\\\\\\mathbf{\\psi}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2533c3db-1a36-4fd3-aca6-770889ed201e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,\n",
+       "        -3.16635239-3.71568777j],\n",
+       "       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n",
+       "         2.00036437+2.07501753j],\n",
+       "       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n",
+       "        -3.52944456-3.37788878j]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "E_general[:E_general.shape[0]//2,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "87a69d80-b352-43e3-9c7f-55f1fe6c272b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.16839552-1.20937963j,  3.7348689 +3.68391729j,\n",
+       "        -3.16635239-3.71568777j],\n",
+       "       [-2.60749708-2.75808733j, -0.10439594+0.09881697j,\n",
+       "         2.00036437+2.07501753j],\n",
+       "       [-1.15358519-1.22327319j, -3.46966805-3.92680586j,\n",
+       "        -3.52944456-3.37788878j]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lam_general*psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6db7704-a5c2-4394-8d5d-422e23ae82d6",
+   "metadata": {},
+   "source": [
+    "One final thing to note is that in general, the mode shapes extracted from a complex analysis will not diagonalize the orignal mass, stiffness, or damping matrices, as these were not the matrices the eigensolution was performed on.  There will be off-diagonal terms, and they will be complex."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3e036cbb-b641-4aac-ac0b-2769f764020d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-6.13061679e-08-7.34176433e-02j, -1.43810774e-04+9.83528782e-06j,\n",
+       "         1.15410477e-04+2.13846966e-06j],\n",
+       "       [-1.43810774e-04+9.83528782e-06j, -1.24812005e-07-3.65342888e-02j,\n",
+       "        -1.36140173e-04+1.88235958e-06j],\n",
+       "       [ 1.15410477e-04+2.13846966e-06j, -1.36140173e-04+1.88235958e-06j,\n",
+       "         1.86118173e-07-3.33825843e-02j]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@M@psi_general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "6ce545ce-0d31-4d53-a589-4bd891eea2b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.00014982-0.10829239j,  0.00048468+0.01181488j,\n",
+       "         0.00045558-0.01005105j],\n",
+       "       [ 0.00048468+0.01181488j, -0.00125664-0.1096167j ,\n",
+       "        -0.00020565+0.01561439j],\n",
+       "       [ 0.00045558-0.01005105j, -0.00020565+0.01561439j,\n",
+       "         0.00140645-0.10654111j]])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@C_general@psi_general"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "bed4c0f0-266e-4434-a7e4-65002b1bfeed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-6.49635076e-05-5.45227499e+01j,  2.15177460e-01-2.97517095e-03j,\n",
+       "        -1.87975974e-01-1.36004490e-02j],\n",
+       "       [ 2.15177460e-01-2.97517095e-03j, -1.51087546e-03-1.09568400e+02j,\n",
+       "         4.46495302e-01+1.79584080e-02j],\n",
+       "       [-1.87975974e-01-1.36004490e-02j,  4.46495302e-01+1.79584080e-02j,\n",
+       "         1.57583896e-03-1.19907950e+02j]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general.T@K@psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb5b9b23-80f7-4940-bd63-759731824312",
+   "metadata": {},
+   "source": [
+    "## Frequency Response Functions and Modal Parameters\n",
+    "Now that we have familiarized ourselves with the modal parameters for real and complex modes, let's discuss the relationship to the frequency response function.  This is of utmost importance, because we eventually would like to fit the modal parameters from the frequency response functions, as well as reconstruct frequency response functions from the modal parameters.\n",
+    "\n",
+    "### Real Modes\n",
+    "\n",
+    "Starting with the modal system of equations, and assuming proportional damping, we have\n",
+    "\n",
+    "$$\\mathbf{\\phi}^T\\mathbf{M}\\mathbf{\\phi}\\ddot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{C}\\mathbf{\\phi}\\dot{\\mathbf{q}} + \\mathbf{\\phi}^T\\mathbf{K}\\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "$$\\tilde{\\mathbf{M}}\\ddot{\\mathbf{q}} + \\tilde{\\mathbf{C}}\\dot{\\mathbf{q}} + \\tilde{\\mathbf{K}}{\\mathbf{q}} = \\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "We can also substitute in the diagonal values for the modal parameters.  *Note the inclusion of the modal mass* $m_r$ *in all terms, because we have not assumed mass normalized modes in this case*.  We will represent the generalized modal force $\\mathbf{\\phi}^T\\mathbf{F}$ as $\\mathbf{Q}$.\n",
+    "$$\\left[m_r\\right]\\ddot{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]\\dot{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "\n",
+    "We can then use the *Laplace Transform* to make this differential equation into an algebraic one.\n",
+    "$$\\left[m_r\\right]s^2{\\mathbf{q}} + \\left[2\\zeta_r\\omega_r m_r\\right]s{\\mathbf{q}} + \\left[\\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "We can collect terms of $\\mathbf{q}$ to create an equation of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{b}$.\n",
+    "$$\\left[m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r\\right]{\\mathbf{q}} = \\mathbf{Q}$$\n",
+    "\n",
+    "Because the equation is diagonal, inverting the coefficient matrix is trival.\n",
+    "$${\\mathbf{q}} = \\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{Q}$$\n",
+    "\n",
+    "This is now a representation of a *modal* transfer function.  If we apply a set of *modal* forces $\\mathbf{Q}$, we get out a set of *modal* responses $\\mathbf{q}$.  To transform to physical space, we need to transform our modal quantities to physical quantities.  We know the substitution for modal forces $\\mathbf{Q} = \\mathbf{\\phi}^T\\mathbf{F}$, and we know our equation for physical response from modal responses $\\mathbf{x} = \\mathbf{\\phi}\\mathbf{q}$.  Therefore we can make the subsitution for modal force, and pre-multiply the entire equation by the mode shape matrix.\n",
+    "\n",
+    "$$\\mathbf{x} = \\mathbf{\\phi}{\\mathbf{q}} = \\mathbf{\\phi}\\left[\\frac{1}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T\\mathbf{F}$$\n",
+    "\n",
+    "If instead of the entire Laplace domain we only consider sinusoidal responses, we can substitute $s=j\\omega$ to get the familiar form for the frequency response function.\n",
+    "\n",
+    "$$\\mathbf{H} = \\mathbf{\\phi}\\left[\\frac{1}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}\\right]\\mathbf{\\phi}^T$$\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$\n",
+    "\n",
+    "To pull out a specific entry in the FRF matrix, we can index the mode shapes.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96ab4e96-9288-4414-bf16-82a4534ad509",
+   "metadata": {},
+   "source": [
+    "### Complex Modes\n",
+    "The derivation for the frequency response function in terms of complex modes is similar.  Recall that our physical vector for the complex state space formulation includes velocities and displacements.  Similarly our eigenvectors consisted of pairs of shapes, the shape and its complex conjugate.  Therefore, in the case of complex modes, the projection from modal space to physical space is\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "Applying the transformation to the complex state space equation\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{v}}\\\\\\dot{\\mathbf{x}}\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}{\\mathbf{v}}\\\\{\\mathbf{x}}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "\n",
+    "Gives\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "\n",
+    "And then pre-multiplying by the eigenvector matrix transpose completes the transformation into modal space.\n",
+    "\n",
+    "$$ \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0} & \\mathbf{M} \\\\ \\mathbf{M} & \\mathbf{C}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix} -\\mathbf{M} & \\mathbf{0} \\\\ \\mathbf{0} & \\mathbf{K}\\end{bmatrix}\\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix}$$\n",
+    "$$= \\begin{bmatrix}\\lambda \\mathbf{\\psi}^T & \\mathbf{\\psi}^T \\\\ \\lambda^* {\\mathbf{\\psi}^*}^T & {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{0}\\\\\\mathbf{F}\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$\n",
+    "\n",
+    "Transforming into the modal matrices\n",
+    "$$\\tilde{\\mathbf{A}}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} + \\tilde{\\mathbf{B}}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "We recognize that the diagonal terms of $\\tilde{\\mathbf{A}}$ are simply what we've defined as the modal mass, and the diagonal terms of $\\tilde{\\mathbf{B}}$ are simply the negative modal mass times the eigenvector $\\tilde{\\mathbf{B}}_{rr} = -\\lambda_r \\tilde{\\mathbf{A}}_{rr}$.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & \\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\dot{\\mathbf{q}}\\\\\\dot{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e520d365-73f4-45dc-a5f0-24158348da43",
+   "metadata": {},
+   "source": [
+    "Once again, transforming to the Laplace Domain,\n",
+    "\n",
+    "$$\\begin{bmatrix}s\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & s\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} - \\begin{bmatrix}\\lambda_r\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & {\\lambda_r}^*\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "$$\\begin{bmatrix}(s-\\lambda)\\left[m_r\\right] & \\mathbf{0} \\\\ \\mathbf{0} & (s-\\lambda^*)\\left[m_r\\right]\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix}  = \\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "Once again, because this matrix is diagonal, it is easy to invert.\n",
+    "\n",
+    "$$\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix}$$\n",
+    "\n",
+    "This is the modal transfer function.\n",
+    "\n",
+    "To compute the transfer functions between the force $\\mathbf{F}$ and the response $\\mathbf{x}$ (we ignore $\\mathbf{v}$ here), we can use the identities $\\begin{bmatrix}\\mathbf{Q} \\\\ \\mathbf{Q}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$, which we defined previously, and the last row of $\\begin{bmatrix}\\mathbf{v}\\\\\\mathbf{x}\\end{bmatrix} = \\begin{bmatrix}\\lambda \\mathbf{\\psi} & \\lambda^* \\mathbf{\\psi}^* \\\\ \\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$, which is $\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\mathbf{q}\\\\\\mathbf{q}^*\\end{bmatrix}$. \n",
+    "\n",
+    "$$\\mathbf{x} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}{\\mathbf{q}}\\\\{\\mathbf{q}}^*\\end{bmatrix} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}\\mathbf{F}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4afe2217-3136-4cef-a027-a4be65bc7982",
+   "metadata": {},
+   "source": [
+    "The transfer function in the $s$-domain is then\n",
+    "\n",
+    "$$\\mathbf{H} = \\begin{bmatrix}\\mathbf{\\psi} & \\mathbf{\\psi}^*\\end{bmatrix}\\begin{bmatrix}\\frac{1}{(s-\\lambda)\\left[m_r\\right]} & \\mathbf{0} \\\\ \\mathbf{0} & \\frac{1}{(s-\\lambda^*)\\left[m_r\\right]}\\end{bmatrix}\\begin{bmatrix}\\mathbf{\\psi}^T \\\\ {\\mathbf{\\psi}^*}^T\\end{bmatrix}$$\n",
+    "\n",
+    "Because it is diagonal, we can take it out of matrix form and put it into summation form, which reveals the usual partial fraction form of the transfer function.\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "We can extract a specific transfer function $\\mathbf{H}_{ij}$\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_{ir}\\mathbf{\\psi}_{jr}}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_{ir}^*\\mathbf{\\psi}_{jr}^*}{m_r(s-\\lambda_r^*)}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "17543413-bd30-4a68-80b1-d1b7f117cf34",
+   "metadata": {},
+   "source": [
+    "To translate into more intuitive variables, we can make the following substitutions.\n",
+    "\n",
+    "- $\\lambda_r = -\\sigma_r + j\\omega_{dr} = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$  - Substitute the poles for the natural frequency and critical damping ratio.\n",
+    "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I$ - Split the mode shapes into real and imaginary parts.\n",
+    "\n",
+    "From these, it follows that the complex conjugate versions simply have the sign flipped on their imaginary components.\n",
+    "\n",
+    "- $\\lambda_r^* = -\\sigma_r - j\\omega_{dr} = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$\n",
+    "- $\\mathbf{\\psi}_{ir} = \\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I$\n",
+    "\n",
+    "Substituting these into the previous equation, we get\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left(\\mathbf{\\psi}_{ir}^R + j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R + j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r + j\\omega_{dr}\\right))} + \\frac{\\left(\\mathbf{\\psi}_{ir}^R - j\\mathbf{\\psi}_{ir}^I\\right)\\left(\\mathbf{\\psi}_{jr}^R - j\\mathbf{\\psi}_{jr}^I\\right)}{m_r(s-\\left(-\\sigma_r - j\\omega_{dr}\\right))}\\right)$$\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s + \\sigma_r - j\\omega_{dr}\\right)} + \\frac{\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)}{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)}\\right)$$\n",
+    "\n",
+    "If we put the two terms over a common denominator, we get\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "  \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) + j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n",
+    "  \\left(s+ \\sigma_r + j\\omega_{dr}\\right)\n",
+    "+ \\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right) - j\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\right)\n",
+    "  \\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n",
+    "{m_r\\left(s+ \\sigma_r + j\\omega_{dr}\\right)\\left(s + \\sigma_r - j\\omega_{dr}\\right)}\n",
+    "\\right)$$\n",
+    "\n",
+    "We can expand and reduce this equation\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s+\\sigma_r\\right)^2 + \\omega_{dr}^2\\right)\n",
+    "}\n",
+    "\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97b4be2f-c392-4c79-950e-00d0e32a038f",
+   "metadata": {},
+   "source": [
+    "### What are Real Modes in the Complex Mode Framework?\n",
+    "\n",
+    "One might naively believe that a real mode is simply a complex mode where the imaginary part is zero.  However, it isn't clear whether or not that should be true, because the eigenvalue problem was posed differently.  What is clear, however, is that real modes should certainly be some subset of complex modes; in other words, we should be able to represent a real mode as a complex mode.  In this section, we will investigate that relationship.\n",
+    "\n",
+    "Starting with the previous equation, we can further expand the denominator with natural frequency and damping ratio, where $\\sigma_r = \\zeta_r\\omega_r$ and $\\omega_{dr} = \\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s+-\\zeta_r\\omega_r\\right)^2 + \\left(\\omega_r\\sqrt{1-\\zeta_r^2}\\right)^2\\right)\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_r\\left(\\left(s^2+2s\\zeta_r\\omega_r + \\omega_r^2\\zeta_r^2\\right) + \\left(\\omega_r^2 - \\omega_r^2\\zeta_r^2\\right)\\right)\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "$$ \\mathbf{H}_{ij} = 2\\sum_{r=1}^N\\left(\n",
+    "\\frac{\n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\n",
+    "}\n",
+    "{m_rs^2+2\\zeta_r\\omega_rm_rs + m_r\\omega_r^2\n",
+    "}\n",
+    "\\right)$$\n",
+    "\n",
+    "The denominator in the previous equation is identical to that from the real modes equation.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\frac{\\phi_{ir}\\phi_{jr}}{m_rs^2 + 2\\zeta_r\\omega_r m_rs + \\omega_r^2m_r}$$\n",
+    "\n",
+    "The numerators should also be equivalent.\n",
+    "\n",
+    "$$2\\left(\\left( \\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I \\right)\\left(s+ \\sigma_r\\right) - \n",
+    "\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr}\\right) = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "We note that there is no terms with $s$ on the real modes side of the equation, so any term containing $s$ on the complex modes side of the equation must be zero in the case of real modes.\n",
+    "\n",
+    "$$\\psi_{ir}^R\\psi_{jr}^R - \\psi_{ir}^I\\psi_{jr}^I = 0$$\n",
+    "\n",
+    "This then leaves the remainder of the equation\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "We must find a solution for these two equations simultaneously.  Since the modeshape at location $i$ and the mode shape at location $j$ are generally independent of one another, we can treat them as independent.  In other words, $\\psi_{ir}$ cannot be a function $\\psi_{jr}$.  This means for the first equation, there are two possibilities; $\\psi_{ir}^I = -\\psi_{ir}^R$ or $\\psi_{ir}^I = \\psi_{ir}^R$.\n",
+    "\n",
+    "To figure out which, we will look at the drive point transfer function where $i = j$, as the relationship should hold for that point as much as any other point.\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R \\right)\\omega_{dr} = \\phi_{ir}^2$$\n",
+    "$$\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "\n",
+    "The since term $\\phi_{ir}^2$ cannot be negative and $\\omega_{dr}$ is also non-negative, the term $\\psi_{ir}^R\\psi_{ir}^I + \\psi_{ir}^I\\psi_{ir}^R$ must be negative.  This will be the case if $\\psi_{ir}^I = -\\psi_{ir}^R$, but will not be the case if $\\psi_{ir}^I = \\psi_{ir}^R$.\n",
+    "\n",
+    "Substituting that expression,\n",
+    "\n",
+    "$$-{\\psi_{ir}^R}^2 + -{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "$$-2{\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{-2\\omega_{dr}}$$\n",
+    "$${\\psi_{ir}^R}^2 = \\frac{\\phi_{ir}^2}{4\\omega_{dr}}$$\n",
+    "$${\\psi_{ir}^R} = \\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}} = -\\psi_{ir}^I$$\n",
+    "\n",
+    "To verify that this is the general expression, let's plug it into the original equation.\n",
+    "\n",
+    "$$-2\\left( \\psi_{ir}^R\\psi_{jr}^I + \\psi_{ir}^I\\psi_{jr}^R \\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}} + \n",
+    "-\\frac{\\phi_{ir}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\frac{\\phi_{jr}}{2\\sqrt{\\omega_{dr}}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "-\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$-2\\left( \n",
+    "-2\\frac{\\phi_{ir}\\phi_{jr}}{4\\omega_{dr}}\n",
+    "\\right)\\omega_{dr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "$$\\phi_{ir}\\phi_{jr} = \\phi_{ir}\\phi_{jr}$$\n",
+    "\n",
+    "Therefore for a real mode represented in a complex mode, we should expect the imaginary part to be equal in magnitude to the real part, with phase angle of -45$^\\circ$ instead of zero degrees, which may have been expected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b59fa20c-70e7-44b3-ab9b-b584f3b15400",
+   "metadata": {},
+   "source": [
+    "### Back to our Example Problem\n",
+    "\n",
+    "Let's return to our example problem to compute frequency response functions from the matrices as well as from modal parameters.  We will start with computing the frequency response functions from dynamic stiffness matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ae0359c0-8d92-45f2-ab35-1815657a7f89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get frequency lines\n",
+    "omegas = np.linspace(0,np.max(omega_2ndOrder)*1.5,1000)\n",
+    "# Set up omega for broadcasting the FRF matrix, the first dimension will be frequency line.\n",
+    "# The second two dimensions will be i,j degrees of freedom.\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Construct Dynamic Stiffness\n",
+    "Z_undamped = M*(1j*omegas_bc)**2 + K\n",
+    "Z_proportional = M*(1j*omegas_bc)**2 + C_proportional*1j*omegas_bc + K\n",
+    "Z_general = M*(1j*omegas_bc)**2 + C_general*1j*omegas_bc + K\n",
+    "# Invert to get frequency response functions\n",
+    "H_undamped = np.linalg.inv(Z_undamped)\n",
+    "H_proportional = np.linalg.inv(Z_proportional)\n",
+    "H_general = np.linalg.inv(Z_general)\n",
+    "# Plot the FRFs\n",
+    "fig,axes = plt.subplots(H_undamped.shape[1]*2,H_undamped.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_u,f_p,f_g in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                          H_undamped.reshape(omegas.size,-1).T,\n",
+    "                          H_proportional.reshape(omegas.size,-1).T,\n",
+    "                          H_general.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_u)*180/np.pi)\n",
+    "    ax[0].plot(omegas,np.angle(f_p)*180/np.pi)\n",
+    "    ax[0].plot(omegas,np.angle(f_g)*180/np.pi)\n",
+    "    ax[1].plot(omegas,np.abs(f_u))\n",
+    "    ax[1].plot(omegas,np.abs(f_p))\n",
+    "    ax[1].plot(omegas,np.abs(f_g))\n",
+    "    ax[1].set_yscale('log')\n",
+    "ax[1].legend(['Undamped','Proportional Damping','General Damping'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8819a537-c842-495b-b0c3-484a69f959e8",
+   "metadata": {},
+   "source": [
+    "Now let's reconstruct the frequency response functions from modal parameters and see if they match those constructed from the dynamic stiffness matrices.\n",
+    "\n",
+    "For the second-order case,\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\frac{\\mathbf{\\phi}_r\\mathbf{\\phi}_r^T}{-m_r\\omega^2 + 2j\\zeta_r\\omega_r m_r\\omega + \\omega_r^2m_r}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "25f2ed27-2e2d-4d85-9092-8da2c49e1a95",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 2nd Order Case\n",
+    "# First we need to find the modal damping ratios from the damping matrix\n",
+    "zeta_2ndOrder = np.einsum('ji,jk,ki->i',E_2ndOrder,C_proportional,E_2ndOrder)/(2*mm_2ndOrder*omega_2ndOrder)\n",
+    "\n",
+    "# Now Construct the FRF matrix from modal parameters\n",
+    "num_modes = omega_2ndOrder.size\n",
+    "H_2ndOrder_fromModes = np.sum([\n",
+    "    phi[:,r,np.newaxis]@phi[:,r,np.newaxis].T/ # The newaxis turns the array from a 1D array back into a 2D array that can be transposed\n",
+    "    (-mm_2ndOrder[r]*omegas_bc**2 + 2j*zeta_2ndOrder[r]*omega_2ndOrder[r]*mm_2ndOrder[r]*omegas_bc + omega_2ndOrder[r]**2*mm_2ndOrder)\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_proportional.reshape(omegas.size,-1).T,\n",
+    "                      H_2ndOrder_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "031edfd9-54c1-44e0-9ddb-851744aca873",
+   "metadata": {},
+   "source": [
+    "We see we get identical FRFs.\n",
+    "\n",
+    "For the complex mode cases:\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "Note that for a frequency response function, which assumes a single frequency line excitation, the Laplace variable $s = j\\omega$.  The pole $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "Here is the proportional damped case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "43e01c80-adc6-41e4-b882-09c24e203d50",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_proportional.size\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "# Compute FRFs\n",
+    "H_proportional_fromModes = np.sum([\n",
+    "    psi_proportional[:,r,np.newaxis]@psi_proportional[:,r,np.newaxis].T/\n",
+    "    (ma_proportional[r]*(s-\n",
+    "        (-zeta_proportional[r]*omega_proportional[r] + 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n",
+    "    + psi_proportional[:,r,np.newaxis].conj()@psi_proportional[:,r,np.newaxis].T.conj()/\n",
+    "    (ma_proportional[r]*(s-\n",
+    "        (-zeta_proportional[r]*omega_proportional[r] - 1j*omega_proportional[r]*np.sqrt(1-zeta_proportional[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_proportional.shape[1]*2,H_proportional.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_proportional.reshape(omegas.size,-1).T,\n",
+    "                      H_proportional_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "811671ce-53fa-48a5-8851-bfad6a333687",
+   "metadata": {},
+   "source": [
+    "And here is the general damped case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "4c6bbc32-5c2a-4f85-af36-18c8856b976a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 18 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general.size\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "H_general_fromModes = np.sum([\n",
+    "    psi_general[:,r,np.newaxis]@psi_general[:,r,np.newaxis].T/\n",
+    "    (ma_general[r]*(s-\n",
+    "        (-zeta_general[r]*omega_general[r] + 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n",
+    "    + psi_general[:,r,np.newaxis].conj()@psi_general[:,r,np.newaxis].T.conj()/\n",
+    "    (ma_general[r]*(s-\n",
+    "        (-zeta_general[r]*omega_general[r] - 1j*omega_general[r]*np.sqrt(1-zeta_general[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_general.shape[1]*2,H_general.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general.reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromModes.reshape(omegas.size,-1).T):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['From Dynamic Stiffness','From Modal Parameters'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "335edf60-cc3b-4bcf-8671-5f36dec4965b",
+   "metadata": {},
+   "source": [
+    "We can see that we understand the relationships between the modal parameters and the frequency response functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d0912a4-8590-4b7e-83b4-860e436cd0ac",
+   "metadata": {},
+   "source": [
+    "## Solving for Modal Parameters from FRFs\n",
+    "The process of Experimental Modal Analysis involves measuring FRFs and then fitting modal parameters to those FRFs.  Many mode fitting software packages utilize a two-step process.  First, the poles (frequency and damping) are identified, then the shape information is found.  Some curve fitters identify modal participation factors in the first step, which are essentially the mode shapes at the reference degrees of freedom.\n",
+    "\n",
+    "This section of the document will follow the implementation of the paper [Peeters, Bart et al. ‘The PolyMAX Frequency-domain Method: a New Standard for Modal Parameter Estimation?’ 1 Jan. 2004 : 395 – 409](https://content.iospress.com/articles/shock-and-vibration/sav00272), which implements the popular PolyMax modal parameter estimator.  This is the algorithm implemented in SDynPy's `PolyPy` fitter.  Other modal parameter estimators may give similar results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99330d1c-fe11-4cb4-91c8-339a638b8c7e",
+   "metadata": {},
+   "source": [
+    "### Setting up the System of Equations\n",
+    "\n",
+    "Let us consider the frequency response function that we may measure during a modal test.  We understand that we will only measure a finite bandwidth during the test, therefore there may be contributions to the measured FRFs from modes outside the bandwidth.  We will turn our typical FRF matrix equation\n",
+    "\n",
+    "$$\\mathbf{H} = \\sum_{r=1}^N\\left( \\frac{\\mathbf{\\psi}_r\\mathbf{\\psi}_r^T}{m_r(s-\\lambda_r)} + \\frac{\\mathbf{\\psi}_r^*{\\mathbf{\\psi}_r^*}^T}{m_r(s-\\lambda_r^*)}\\right)$$\n",
+    "\n",
+    "into a \"measured\" version.  We will replace the value $s$ with $j\\omega$.  We will also introduce lower and upper \"residuals\" ($\\mathbf{R}_l$ and $\\mathbf{R}_u$, respectively), to try to fit portions of the FRFs from out-of-band modes.  We will explicitly add the notation to show that the frequency response function matrix is a function of frequency $\\omega$.  Additionally, we will call the mode shapes of the references the *modal participation factor* $\\mathbf{l}_r$ and the mode shapes corresponding to the responses $\\mathbf{v}$.  Finally, We will remove the modal mass term, which will make the assumption that the modes we fit from the algorithm will be mass normalized.  Note that the format of the residual terms implies that the frequency response functions we are investigating here are *admittance* frequency response functions, which relate the force at a location to the displacement at a location.\n",
+    "\n",
+    "$$\\mathbf{H}(\\omega) = \\sum_{r=1}^N\\left( \\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}\\right) + \\frac{\\mathbf{R}_l}{(j\\omega)^2} + \\mathbf{R}_u$$\n",
+    "\n",
+    "Recall the poles $\\lambda_r = -\\zeta_r\\omega_r + j\\omega_r\\sqrt{1-\\zeta_r^2}$ and $\\lambda_r^* = -\\zeta_r\\omega_r - j\\omega_r\\sqrt{1-\\zeta_r^2}$.\n",
+    "\n",
+    "The PolyMax approach assumes that the frequency response function can be represented by a right matrix-fraction model.\n",
+    "\n",
+    "$$ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$$\n",
+    "\n",
+    "Note that these are not the same $\\mathbf{A}$ and $\\mathbf{B}$ matrices from the previous sections, which represent the state space formulation.  However, this document attempts to match the notation from the referenced paper, so we will override those previous definitions and apologize for any confusion it may cause.\n",
+    "\n",
+    "We can see similarities between the two formulations.  The FRF equation has a numerator and a denominator, as well as a summation over a number of modes.  Similarly, the right matrix-fraction model has a matrix that is not inverted (representing something like a numerator), a matrix that is inverted (representing something like a denominator), and a summation operation inherent in the matrix multiplication that is performed.  Terms in $\\mathbf{B}$ will relate to the shape information in the numerator of the FRF equation, and terms in $\\mathbf{A}$ will relate to the pole information in the denominator of the FRF equation.\n",
+    "\n",
+    "The numerator matrix $\\mathbf{B}$ will have a number of rows equal to the number of responses or outputs $N_o$ and number of columns equal to the number of references or inputs $N_i$.  The denominator matrix $\\mathbf{A}$ will have a number of rows and columns equal to the number of inputs $N_i$, and is therefore square.\n",
+    "\n",
+    "We can split up the above equation and look at an individual row of the FRF matrix, which we will denote $\\mathbf{H}_o$ to represent a single output.  This will involve selecting a single row from $\\mathbf{B}_o$.\n",
+    "\n",
+    "$$ \\mathbf{H}_o(\\omega) = \\mathbf{B}_o(\\omega)\\mathbf{A}(\\omega)^{-1}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23ba4d5b-112a-42e4-8034-3e2616448aad",
+   "metadata": {},
+   "source": [
+    "Each entry in the $\\mathbf{B}_o$ and $\\mathbf{A}$ matrices is represented as a set of coefficients multiplied by a set of basis functions, similar to how we represent displacements in modal analysis by a set of modal coefficients multiplied by a set of mode shapes.  The basis functions $\\Omega_r(\\omega)$ are a set of functions that depend on the frequency $\\omega$.  The coefficients are constant with respect to $\\mathbf{\\omega}$.  We will call the coefficient of the $\\mathbf{B}$ matrix corresponding to the $o$th row and the $r$th basis function $\\beta_{or}$.  Similarly, we will call the coefficient of the $\\mathbf{A}$ matrix for the $r$th basis function $\\alpha_r$.  The number of coefficients and basis functions will be equal to the polynomial order $p+1$.\n",
+    "\n",
+    "$$\\mathbf{B}_o = \\sum_{r=0}^p \\Omega_r(\\omega)\\beta_{or}$$\n",
+    "$$\\mathbf{A} = \\sum_{r=0}^p \\Omega_r(\\omega)\\alpha_{r}$$\n",
+    "\n",
+    "The secret of PolyMax is that instead of using a polynomial basis function for $\\Omega_r(\\omega)$, it instead transforms to the $z$-domain.  For larger model orders, polynomials will have large exponents, which when applied to large frequency ranges will result in huge differences between the largest and smallest values, which is a sure-fire way to run into numerical issues.  Instead, by using the $z$-domain, the PolyMax approach avoids these numerical issues and generally achieves better results.  For a test where the minimum measured frequency is $\\omega_0$ and the maximum measured frequency is $\\omega_{end}$, this frequency range is mapped onto a half-unit circle.\n",
+    "\n",
+    "$$\\Omega_r(\\omega) = e^{-j\\omega_z \\Delta t_z r}$$\n",
+    "where $\\omega_z = \\omega - \\omega_0$ and $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.  If instead of angular frequencies, the frequency range is represented in Hz with minimum measured frequency $f_0$ and maximum measured frequency $f_{end}$, then the values are instead $\\omega_z = 2\\pi(f - f_0)$ and $\\Delta t_z = \\frac{1}{2(f_{end}-f_0)}$ where $f=\\frac{\\omega}{2\\pi}$.\n",
+    "\n",
+    "The polynomnial coefficients are stacked into matrices.\n",
+    "\n",
+    "$$\\beta_o = \\begin{bmatrix}\\beta_{o0}\\\\\\beta_{o1}\\\\\\beta_{o2}\\\\\\vdots\\\\\\beta_{op}\\end{bmatrix}$$\n",
+    "\n",
+    "$$\\alpha = \\begin{bmatrix}\\alpha_{0}\\\\\\alpha_{1}\\\\\\alpha_{2}\\\\\\vdots\\\\\\alpha_{p}\\end{bmatrix}$$\n",
+    "\n",
+    "Here $\\beta_o$ has number of rows equal to the polynomial order $p+1$ and number of columns equal to the number of references or inputs $N_i$.  Each row of $\\beta_o$ corresponds to the coefficients of one polynomial basis function for all of the references.\n",
+    "\n",
+    "$\\alpha$ also has a number of columns equal to the number of references or inputs $N_i$.  However, each row partition $\\alpha_r$ of $\\alpha$ has a number of rows equal to the number of inputs $N_i$ as well.  This means after stacking $p+1$ of these matrices on top of one another, the final number of rows of $\\alpha$ is equal to $N_i(p+1)$.\n",
+    "\n",
+    "We finally stack all of these coefficients into a single coefficient matrix $\\theta$, recalling that there is one matrix $\\beta_o$ for each of the $N_o$ outputs of the system.\n",
+    "\n",
+    "$$\\theta = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\beta_{3}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "Each row partition in $\\theta$ has columns equal to the number of inputs $N_i$.  Each of the $\\beta_o$ row partions has number of rows equal to the polynomial order $p+1$, and there are $N_o$ of those partitions, resulting in the $\\beta$ portion of the $\\theta$ matrix having $N_o(p+1)$ rows.  Added to the $N_i(p+1)$ rows of the $\\alpha$ partition, the final $\\theta$ matrix has $(N_i+N_o)(p+1)$ rows.\n",
+    "\n",
+    "At this point, we can represent the FRF matrix as a function of frequency $\\omega$ as well as a function of the coefficients $\\theta$, $\\mathbf{H}(\\omega,\\theta)$.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81e2b187-5adc-488c-a62d-9ce63f2ddc6f",
+   "metadata": {},
+   "source": [
+    "### Selecting Coefficients that Minimize Errors\n",
+    "It may be obvious, but the goal is to eventually find the parameters $\\theta$ that best fit some measured frequency response functions.  In a real test, we will have measured frequency response functions $\\hat{\\mathbf{H}}$ at $N_f$ discrete frequency values $\\omega_k$.  We would like to find the coefficients $\\theta$ that minimize the error between the fit frequency response functions and the measured frequency response functions.  These coefficients can be found by minimizing the weighted non-linear least-squares error for the $o$th output $\\varepsilon_o^{NLS}$\n",
+    "\n",
+    "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{H}_o(\\omega_k,\\theta) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n",
+    "$$\\varepsilon_o^{NLS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o)\\mathbf{A}^{-1}(\\omega_k,\\alpha) - \\hat{\\mathbf{H}}_o(\\omega_k)\\right)$$\n",
+    "\n",
+    "where $w_o(\\omega_k)$ is a weighting function as a function of the output degree of freedom and the frequency line that allows taking into account data quality in the least-squares solution.  The previous equation is a matrix equation, and we would ideally like to transform it into a scalar equation that can be minimized which is only a function of the parameters $\\theta$.\n",
+    "\n",
+    "$$l^{NLS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{NLS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "We multiply the value $\\varepsilon_o^{NLS}(\\omega_k,\\theta)$ complex-conjugate-transpose ($\\bullet^H$) by itself, and compute the trace ($\\mathrm{tr}(\\bullet)$).  We then sum over all frequency lines and all outputs.\n",
+    "\n",
+    "This equation is solved by setting the derivatives of the equation with respect to the unknown modal coefficients to zero.  It is reasonably clear that this leads to nonlinear equations due to the matrix inverse in the error equation.  We can construct a linear set of equations that approximates the original equations by simply post-multiplying the equation by the matrix $\\mathbf{A}$.\n",
+    "\n",
+    "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\left(\\mathbf{B}_o(\\omega_k,\\beta_o) - \\hat{\\mathbf{H}}_o(\\omega_k)\\mathbf{A}(\\omega_k,\\alpha)\\right)$$\n",
+    "Substituting for the basis functions and coefficients:\n",
+    "$$\\varepsilon_o^{LS}(\\omega_k,\\theta) = w_o(\\omega_k)\\sum_{r=0}^p\\left(\n",
+    "\\Omega_r(\\omega_k)\\beta_{or}\n",
+    "- \\Omega_r(\\omega_k)\\hat{\\mathbf{H}}_o(\\omega_k)\\alpha_r\\right)$$\n",
+    "\n",
+    "We can stack this equation for all frequency lines ($\\omega_1$, $\\omega_2$, ... $\\omega_{N_f}$)\n",
+    "\n",
+    "$$E_o^{LS}(\\theta) = \\begin{bmatrix}\n",
+    "\\varepsilon_o^{LS}(\\omega_1,\\theta) \\\\\n",
+    "\\varepsilon_o^{LS}(\\omega_2,\\theta) \\\\\n",
+    "\\vdots \\\\\n",
+    "\\varepsilon_o^{LS}(\\omega_{N_f},\\theta) \\\\\n",
+    "\\end{bmatrix} = \\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o \\end{bmatrix}\\begin{bmatrix}\\beta_o\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "This will result in $E_o^{LS}(\\theta)$ having a number of rows equal to the number of frequency lines in the test, and the number of columns equal to the number of references or inputs $N_i$.\n",
+    "\n",
+    "The $\\mathbf{X}_o$ matrix can be constructed from pulling off the coefficients of the $\\beta$ terms, which are generally the basis functions $\\Omega_r$ evaluated at each frequency line $\\omega_k$.  Each row will correspond to a separate frequency line, and each column will correspond to a basis function order.\n",
+    "\n",
+    "$$\\mathbf{X}_o = \\begin{bmatrix}\n",
+    "w_o(\\omega_1)\\Omega_0(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1) & \\cdots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\\\\n",
+    "w_o(\\omega_2)\\Omega_0(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2) & \\cdots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\\\\n",
+    "\\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f}) & \\cdots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\\\\n",
+    "\\end{bmatrix} $$\n",
+    "\n",
+    "The $\\mathbf{Y}_o$ matrix can be constructed by pulling off the coefficients of the $\\alpha$ terms, which are generally the basis functions somehow multiplied by the FRFs.  We will explore this expression a bit more here.  The relevant portion of $\\varepsilon_o^{LS}(\\omega_k,\\theta)$ containing the $\\alpha$ terms is \n",
+    "\n",
+    "$$\\mathbf{Y}_o \\alpha = -\\begin{bmatrix}\n",
+    "    \\sum_{r=0}^pw_o(\\omega_1)\\Omega_r(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_r \\\\\n",
+    "    \\sum_{r=0}^pw_o(\\omega_2)\\Omega_r(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_r \\\\\n",
+    "    \\vdots \\\\\n",
+    "    \\sum_{r=0}^pw_o(\\omega_{N_f})\\Omega_r(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_r\\end{bmatrix}$$\n",
+    "\n",
+    "Expanding the equation out in terms of the summation can help visualize which terms go where in the $\\mathbf{Y}_o$ matrix.\n",
+    "\n",
+    "$$\\mathbf{Y}_o \\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix} = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1) \\left( \\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_0 + \\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_1 + \\dots + \\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1)\\alpha_p \\right) \\\\\n",
+    "    w_o(\\omega_2) \\left( \\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_0 + \\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_1 + \\dots + \\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2)\\alpha_p \\right) \\\\\n",
+    "    \\vdots \\\\\n",
+    "    w_o(\\omega_{N_f}) \\left( \\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_0 + \\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_1 + \\dots + \\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\alpha_p \\right) \\\\\n",
+    "    \\end{bmatrix}$$\n",
+    "or in matrix form\n",
+    "$$\\mathbf{Y}_o \\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix} = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1)\\Omega_0(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & w_o(\\omega_1)\\Omega_1(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) & \\dots & w_o(\\omega_1)\\Omega_p(\\omega_1)\\hat{\\mathbf{H}}_o(\\omega_1) \\\\\n",
+    "    w_o(\\omega_2)\\Omega_0(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & w_o(\\omega_2)\\Omega_1(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) & \\dots & w_o(\\omega_2)\\Omega_p(\\omega_2)\\hat{\\mathbf{H}}_o(\\omega_2) \\\\\n",
+    "    \\vdots & \\vdots & \\ddots & \\vdots \\\\ \n",
+    "    w_o(\\omega_{N_f})\\Omega_0(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & w_o(\\omega_{N_f})\\Omega_1(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) & \\dots & w_o(\\omega_{N_f})\\Omega_p(\\omega_{N_f})\\hat{\\mathbf{H}}_o(\\omega_{N_f}) \\\\\n",
+    "    \\end{bmatrix}\\begin{bmatrix}\n",
+    "        \\alpha_0 \\\\\n",
+    "        \\alpha_1 \\\\\n",
+    "        \\vdots \\\\\n",
+    "        \\alpha_p\n",
+    "        \\end{bmatrix}$$\n",
+    "\n",
+    "We can recognize the pattern $\\begin{bmatrix}\\Omega_0\\hat{\\mathbf{H}}_o & \\Omega_1\\hat{\\mathbf{H}}_o & \\dots & \\Omega_p\\hat{\\mathbf{H}}_o\\end{bmatrix}$ as the [Kronecker Product](https://en.wikipedia.org/wiki/Kronecker_product) $\\mathbf{\\Omega} \\otimes \\hat{\\mathbf{H}}_o$ where the matrix $\\mathbf{\\Omega}$ has only one row.  Therefore, \n",
+    "\n",
+    "$$\\mathbf{X}_o = \\begin{bmatrix}\n",
+    "w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\\\\n",
+    "w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\\\\n",
+    "\\vdots \\\\\n",
+    "w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\\\\n",
+    "\\end{bmatrix} $$\n",
+    "$$\\mathbf{Y}_o = -\\begin{bmatrix}\n",
+    "    w_o(\\omega_1)\\mathbf{\\Omega}(\\omega_1)\\otimes\\hat{\\mathbf{H}}_o(\\omega_1)\\\\\n",
+    "    w_o(\\omega_2)\\mathbf{\\Omega}(\\omega_2)\\otimes\\hat{\\mathbf{H}}_o(\\omega_2)\\\\\n",
+    "    \\vdots\\\\ \n",
+    "    w_o(\\omega_{N_f})\\mathbf{\\Omega}(\\omega_{N_f})\\otimes\\hat{\\mathbf{H}}_o(\\omega_{N_f})\\\\\n",
+    "    \\end{bmatrix}$$\n",
+    "\n",
+    "where $\\mathbf{\\Omega}(\\omega) = \\begin{bmatrix}\\Omega_0(\\omega)&\\Omega_1(\\omega)&\\dots&\\Omega_p(\\omega)\\end{bmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "873cdaf8-293e-46d6-837b-29eedf756c3b",
+   "metadata": {},
+   "source": [
+    "Similarly to the nonlinear least squares solution, we can construct an error cost function to minimize based on the linearized equation.\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\sum_{k=1}^{N_f}\\mathrm{tr}\\left(\\left(\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)^H\\varepsilon_o^{LS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "or \n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\\left(E_o^{LS}(\\omega_k,\\theta)\\right)^HE_o^{LS}(\\omega_k,\\theta)\\right)$$\n",
+    "\n",
+    "If we substitute the matrix equations, we get\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\sum_{o=1}^{N_o}\\mathrm{tr}\\left(\n",
+    "\\begin{bmatrix}\\beta_o^T & \\alpha^T\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o^H \\\\ \\mathbf{Y}_o^H\\end{bmatrix}\\begin{bmatrix}\\mathbf{X}_o & \\mathbf{Y}_o\\end{bmatrix}\\begin{bmatrix}\\beta_o \\\\ \\alpha\\end{bmatrix}\\right)$$\n",
+    "\n",
+    "Ideally, we would like to remove the summation in the previous equation and end up with an equation of the form $l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$.  We recognize that we can stack the equations for $E_o^{LS}$ for all outputs and arrive at an equation\n",
+    "\n",
+    "$$\\begin{bmatrix}E_1^{LS} \\\\ E_2^{LS} \\\\ \\vdots \\\\ E_{N_o}^{LS} \\end{bmatrix} = \n",
+    "\\begin{bmatrix}\n",
+    "    \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{Y}_1\\end{bmatrix}\\begin{bmatrix}\\beta_1 \\\\ \\alpha\\end{bmatrix} \\\\ \n",
+    "    \\begin{bmatrix}\\mathbf{X}_2 & \\mathbf{Y}_2\\end{bmatrix}\\begin{bmatrix}\\beta_2 \\\\ \\alpha\\end{bmatrix} \\\\\n",
+    "    \\vdots \\\\\n",
+    "    \\begin{bmatrix}\\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o}\\end{bmatrix}\\begin{bmatrix}\\beta_{N_o} \\\\ \\alpha\\end{bmatrix}\\end{bmatrix} = \n",
+    "\\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n",
+    "               \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n",
+    "               \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "               \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "$$\\mathbf{J} = \\begin{bmatrix}\\mathbf{X}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{Y}_1 \\\\\n",
+    "               \\mathbf{0} & \\mathbf{X}_2 & \\dots & \\mathbf{0} & \\mathbf{Y}_2 \\\\\n",
+    "               \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "               \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{X}_{N_o} & \\mathbf{Y}_{N_o} \\end{bmatrix}$$\n",
+    "and as we've seen previously,\n",
+    "$$\\mathbf{\\theta} = \\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}$$\n",
+    "\n",
+    "Therefore our cost function can be simplified to\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\mathbf{\\theta}^T\\mathbf{J}^H\\mathbf{J}\\mathbf{\\theta}\\right)$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "347b489b-ba1c-4564-a8ef-ad96fa26c986",
+   "metadata": {},
+   "source": [
+    "In general, the quantity $\\mathbf{J}^H\\mathbf{J}$ will be conjugate-symmetric or [Hermitian](https://en.wikipedia.org/wiki/Hermitian_matrix).  This means that its upper triangle will be equal to the complex conjugate of the lower triangle transposed, and the diagonal will be real valued.  While the matrix $\\mathbf{J}^H\\mathbf{J}$ is therefore complex, in the previous equation, because it multiplies by real valued coefficients $\\mathbf{\\theta}$ and we take the trace, which should only select the real-valued diagonal, we can replace the quantity $\\mathbf{J}^H\\mathbf{J}$ with its real part $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$\n",
+    "\n",
+    "Computing $\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right)$ gives\n",
+    "\n",
+    "$$\\Re\\left(\\mathbf{J}^H\\mathbf{J}\\right) = \\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n",
+    "                                                          \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n",
+    "                                                          \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n",
+    "                                                          \\end{bmatrix} $$\n",
+    "where\n",
+    "$$\\mathbf{R}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{X}_o\\right)$$\n",
+    "$$\\mathbf{S}_o = \\Re\\left(\\mathbf{X}_o^H\\mathbf{Y}_o\\right)$$\n",
+    "$$\\mathbf{T}_o = \\Re\\left(\\mathbf{Y}_o^H\\mathbf{Y}_o\\right)$$\n",
+    "\n",
+    "We minimize the cost function by setting the derivatives of the cost function with respect to the coefficients $\\mathbf{\\theta}$ to zero.\n",
+    "\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}^T\\begin{bmatrix}\\mathbf{R}_1 & \\mathbf{0} & \\dots & \\mathbf{0} & \\mathbf{S}_1 \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{R}_2 & \\dots & \\mathbf{0} & \\mathbf{S}_2 \\\\\n",
+    "                                                          \\vdots & \\vdots & \\ddots & \\vdots & \\vdots \\\\\n",
+    "                                                          \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{R}_{N_o} & \\mathbf{S}_{N_o} \\\\\n",
+    "                                                          \\mathbf{S}_1^T & \\mathbf{S}_2^T & \\dots & \\mathbf{S}_{N_o} & \\sum_{o=1}^{N_o}T_o\n",
+    "                                                          \\end{bmatrix}\\begin{bmatrix}\\beta_{1}\\\\\\beta_{2}\\\\\\vdots\\\\\\beta_{N_o}\\\\\\alpha\\end{bmatrix}\\right)$$\n",
+    "Expanding the multiplication gives\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\left(\n",
+    "    \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{R}_o\\beta_o\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{S}_o^T\\beta_o\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\beta_o^T\\mathbf{S}_o\\alpha\\right) + \n",
+    "    \\sum_{o=1}^{N_o}\\left(\\alpha^T\\mathbf{T}_o\\alpha\\right)\\right)$$\n",
+    "$$l^{LS}(\\theta) = \\mathrm{tr}\\sum_{o=1}^{N_o}\\left(\n",
+    "    \\beta_o^T\\mathbf{R}_o\\beta_o + \n",
+    "    \\alpha^T\\mathbf{S}_o^T\\beta_o + \n",
+    "    \\beta_o^T\\mathbf{S}_o\\alpha + \n",
+    "    \\alpha^T\\mathbf{T}_o\\alpha\\right)$$\n",
+    "\n",
+    "We can therefore compute the partial derivatives with respect to the unknown coefficients and set them to zero.\n",
+    "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\beta_o} = \n",
+    "    2\\mathbf{R}_o\\beta_o + \n",
+    "    2\\mathbf{S}_o\\alpha = \\mathbf{0}$$\n",
+    "$$\\frac{\\partial l^{LS}(\\theta)}{\\partial \\alpha} = \n",
+    "    \\sum_{o=1}^{N_o}\\left( \n",
+    "    2\\mathbf{S}_o^T\\beta_o + \n",
+    "    2\\mathbf{T}_o\\alpha\\right) =\\mathbf{0}$$\n",
+    "\n",
+    "As a first pass, we only care about the poles, which come from the denominator terms $\\alpha$.  We can eliminate the $\\beta_o$ terms using the first of these constraints\n",
+    "$$\\beta_o = -\\mathbf{R}_o^{-1}\\mathbf{S}_o\\alpha$$\n",
+    "\n",
+    "We can then plug this into the second constraint to get an equation for $\\mathbf{\\alpha}$.\n",
+    "\n",
+    "$$\\left[2\\sum_{o=1}^{N_o}\\left( \n",
+    "    \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)\\right]\\alpha= \\mathbf{M}\\alpha= \\mathbf{0}$$\n",
+    "\n",
+    "This matrix $\\mathbf{M}$ is a square matrix with number of rows and columns equal to the number of inputs times the polynomial order $N_i(p+1) \\times N_i(p+1)$, and depends only on the measured FRFs from the test.  Note, however, that this equation is not unique.  For example, the trival solution $\\alpha = \\mathbf{0}$ satisfies this equation.  Additionally, one may see that in the right matrix-fraction formulation $ \\mathbf{H}(\\omega) = \\mathbf{B}(\\omega)\\mathbf{A}(\\omega)^{-1}$, one could multiply both $\\mathbf{A}$ and $\\mathbf{B}$ by the same matrix, and the equation would still be satisfied.  Therefore, there is not a unique value for $\\alpha$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc9b2e09-e498-4663-9717-d1ffa3085ee7",
+   "metadata": {},
+   "source": [
+    "### Solving the Constrained Problem\n",
+    "To solve a problem of the form $\\mathbf{A}\\mathbf{x} = \\mathbf{0}$ for the non-trivial $\\mathbf{x}$, we must apply some constraint to the problem.\n",
+    "\n",
+    "To solve the problem, we will partition the matrix $\\mathbf{M}$ as follows\n",
+    "\n",
+    "$$\\mathbf{M} = \\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n",
+    "                              \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}$$\n",
+    "\n",
+    "where the $a$ indices are the first $N_ip$ rows or columns and the $b$ indices are the last $p$ columns, recalling the size of $\\mathbf{M}$ is $N_i(p+1)$.  Therefore the matrix $\\mathbf{M}_{aa}$ matrix will have size $N_ip\\times N_ip$, the $\\mathbf{M}_{ab}$ matrix will have size $N_ip\\times p$, the $\\mathbf{M}_{ba}$ matrix will have size $p\\times N_ip$, and the $\\mathbf{M}_{bb}$ matrix will have size $p\\times p$.\n",
+    "\n",
+    "Because we must apply constraints to our problem, we will make the assumption that our solution matrix $\\mathbf{\\alpha}$ will have the form\n",
+    "\n",
+    "$$\\mathbf{\\alpha} = \\begin{bmatrix}\\hat{\\mathbf{\\alpha}}  \\\\ \n",
+    "                                   \\mathbf{I}\\end{bmatrix}$$\n",
+    "\n",
+    "We can see that if we plug this version of $\\mathbf{\\alpha}$ into the equation, we get an expression for the portion of $\\mathbf{\\alpha}$ that was not constrained.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{M}_{aa} & \\mathbf{M}_{ab} \\\\ \n",
+    "                 \\mathbf{M}_{ba} & \\mathbf{M}_{bb}\\end{bmatrix}\n",
+    "  \\begin{bmatrix}\\hat{\\mathbf{\\alpha}}  \\\\ \n",
+    "                 \\mathbf{I}\\end{bmatrix} =\n",
+    "  \\begin{bmatrix}\\mathbf{M}_{aa}\\hat{\\alpha} + \\mathbf{M}_{ab}\\\\\n",
+    "                 \\mathbf{M}_{ba}\\hat{\\alpha} + \\mathbf{M}_{bb}\\end{bmatrix} = \n",
+    "   \\begin{bmatrix}\\mathbf{0} \\\\\n",
+    "                  \\mathbf{0}\\end{bmatrix}$$\n",
+    "\n",
+    "We can then simply take the top partition to solve for $\\hat{\\alpha}$.\n",
+    "\n",
+    "$$\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38d7b7fb-bb03-4710-b443-d5f1989876a8",
+   "metadata": {},
+   "source": [
+    "### Extracting Poles and Participation Factors\n",
+    "Now that we have the polynomial coefficients $\\mathbf{\\alpha}$ for the right matrix-fraction model, we would like to extract the roots from it.  The roots of this polynomial are the poles of the frequency response function.  We will do this by setting up a [Companion Matrix](https://en.wikipedia.org/wiki/Companion_matrix).  A Companion matrix of a polynomial is a matrix whose characteristic equation is equal to that polynomial.  Then the eigenvalues of the matrix (which are the roots of the characteristic equation) are also the roots of the polynomial.  In our case, the eigenvectors are also useful, as they are related to the participation factors.\n",
+    "\n",
+    "For a polynomial $p(x) = \\alpha_p x^p + \\alpha_{p-1} x^{p-1} + \\dots + \\alpha_2 x^2 + \\alpha_1 x + \\alpha_0$, the companion matrix will be \n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} 0 & 1 & 0 & \\dots & 0\\\\\n",
+    "                                0 & 0 & 1 & \\dots & 0\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                0 & 0 & 0 & \\dots & 1\\\\\n",
+    "                                -\\frac{\\alpha_0}{\\alpha_p} & -\\frac{\\alpha_1}{\\alpha_p} & -\\frac{\\alpha_2}{\\alpha_p} & \\dots & -\\frac{\\alpha_{p-1}}{\\alpha_p} \n",
+    "                                \\end{bmatrix}$$\n",
+    "\n",
+    "Readers can verify that $\\mathrm{det}(\\mathbf{I}x - \\mathbf{C}) = p(x)$.\n",
+    "\n",
+    "Our present case is slightly different, because our coefficients are matrices rather than scalars.\n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\alpha_0^T{\\alpha_p^T}^{-1} & -\\alpha_1^T{\\alpha_p^T}^{-1} & -\\alpha_2^T{\\alpha_p^T}^{-1} & \\dots & -\\alpha_{p-1}^T{\\alpha_p^T}^{-1}\n",
+    "                                \\end{bmatrix}$$\n",
+    "However, because we applied the constraint such that $\\alpha_p = \\mathbf{I}$ when solving for $\\mathbf{\\alpha}$, the quantity $\\alpha_p^{-1} = \\mathbf{I}$, and the remaining terms are those in $\\hat{\\mathbf{\\alpha}}$.\n",
+    "\n",
+    "$$ \\mathbf{C} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n",
+    "                                \\end{bmatrix}$$\n",
+    "\n",
+    "The $\\mathbf{C}$ matrix will be a square matrix with size $N_ip \\times N_ip$ where $N_i$ is the number of inputs or references, and $p$ is the order of the polynomial.\n",
+    "\n",
+    "We can therefore notate the companion matrix compactly as\n",
+    "$$\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{I}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b34449b0-790f-426e-9f61-7590aa486efc",
+   "metadata": {},
+   "source": [
+    "We can then solve for the eigenvalues and eigenvectors of this matrix.  The companion matrix is set up such that its characteristic polynomial is identical to the polynomial it represents.  Therefore, its eigenvalues, which are roots of the characteristic polynomial, will be roots of the polynomial it represents.\n",
+    "\n",
+    "$$\\mathbf{C}\\mathbf{V} = \\begin{bmatrix} \\mathbf{0} & \\mathbf{I} & \\mathbf{0} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{I} & \\dots & \\mathbf{0}\\\\\n",
+    "                                \\vdots & \\vdots & \\vdots & \\ddots & \\vdots\\\\\n",
+    "                                \\mathbf{0} & \\mathbf{0} & \\mathbf{0} & \\dots & \\mathbf{I}\\\\\n",
+    "                                -\\hat{\\alpha_0}^T & -\\hat{\\alpha_1}^T & -\\hat{\\alpha_2}^T & \\dots & -\\hat{\\alpha}_{p-1}^T\n",
+    "                                \\end{bmatrix}\\mathbf{V}= \\mathbf{\\Lambda}\\mathbf{V}$$\n",
+    "The diagonals of the eigenvector matrix $\\bar{\\lambda}_r = \\mathbf{\\Lambda}_{rr}$ are the eigenvalues of the companion matrix.  These are the poles of the system; however, we must remember they are represented in the $z$-domain, so we must return to the $s$-domain $\\lambda_r$ in order to solve for the natural frequency and damping ratio.\n",
+    "\n",
+    "$$\\bar{\\lambda}_r = e^{-\\lambda_r \\Delta t_z}$$\n",
+    "$$\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$$\n",
+    "\n",
+    "where $\\Delta t_z = \\frac{\\pi}{\\omega_{end}-\\omega_0}$.\n",
+    "\n",
+    "We can then solve for the natural frequency and damping ratio from the poles.  Recall that the frequency was shifted by $\\omega_0$ during the mapping, so we will need to take that into account.\n",
+    "\n",
+    "$${\\omega_z}_r = \\left|\\lambda_r\\right|$$\n",
+    "$${\\omega_r} = {\\omega_z}_r + \\omega_0$$\n",
+    "$$\\zeta_r = -\\frac{\\Re\\left({\\lambda_r}\\right)}{\\omega_r}$$\n",
+    "\n",
+    "Note that there will be in general $N_ip$ eigenvalues.  Half of them will be complex conjugates of the other half, so we can ignore those.  Similarly, we can ignore non-physical eigenvalues.  A criteria that is successfully used is to only keep poles where the real part is less than zero and the imaginary part is greater than zero.\n",
+    "\n",
+    "The modal participation factors $\\mathbf{l}_r$ can be determined from the eigenvectors of the companion matrix.  They will be the last $N_i$ rows of the eigenvector matrix.  One must also be sure that for each eigenvalue discarded for being non-physical or complex conjugate, the corresponding eigenvector should also be discarded."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724700d5-1b64-4f54-8c17-505f115c560f",
+   "metadata": {},
+   "source": [
+    "### Stabilization Diagrams\n",
+    "Solving for a polynomial of a particular order will in general result in a large number of poles.  For a typical problem, only a subset of these poles will correspond to true modes of the structure.  For a typical modal test, it may not be obvious how many modes are in a given bandwidth anyways.  Therefore, we need some kind of metric to determine if a pole is valid or not, and for this we utilize the stabilization diagram.\n",
+    "\n",
+    "A stabilization diagram is constructed by computing the poles over a large number of polynomial orders.  As we increase the polynomial orders, more poles may be found in the system.  We declare a *stable pole* to be one which does not change significantly as the polynomial order changes.  Computational poles may bounce around the measurement bandwidth.  Real poles of the system, on the other hand, will generally result in a similar frequency, damping ratio, and set of participation factors regardless of the polynomial order.  We then construct a final set of poles by selecting from the stable poles in the stabilization diagram."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e84ee11-8387-4196-8c61-dc3e1c944e31",
+   "metadata": {},
+   "source": [
+    "### Back to the Example Problem\n",
+    "To demonstrate the equations shown here, we will implement the previous equations in code.  We will demonstrate solution of one polynomial order to demonstrate the process, and then rely on the implementation in SDynPy to perform the rest of the analysis.  We will initially focus only on the general damping case rather than repeating the analysis for all the damping matrices.\n",
+    "\n",
+    "To start, we will pull in the measured frequency response functions and corresponding frequency values.  We will shape the frequency response function such that it is a 3D array with shape $N_o \\times N_i \\times N_f$.  We will also assume that we only peformed measurements with inputs at the first and second degrees of freedom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0e5df1a5-cdc8-4e07-a7f9-e041e22c4a96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "H = H_general.transpose(1,2,0)[:,:2,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c834190f-3f0d-41fe-866f-e616e36e9cfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 2, 1000)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "H.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20e3730a-73f0-4316-9c23-6545c19572aa",
+   "metadata": {},
+   "source": [
+    "We will also assume that we only performed measurements over a limited bandwidth from 10 rad/s to 80 rad/s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a274ff4c-0012-48b5-9e62-48351fac2513",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frequency_lines_to_keep = (omegas > 10) & (omegas < 80)\n",
+    "H = H[:,:,frequency_lines_to_keep]\n",
+    "omegas = omegas[frequency_lines_to_keep]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b873134-0289-4846-8bcc-824d57174fc3",
+   "metadata": {},
+   "source": [
+    "Now that we have truncated the frequency domain, we will define some helper variables to make the code a bit more intuitive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f7dcec4f-fbf1-4afd-bccb-e9f1b84364b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Num Outputs: 3\n",
+      "Num Inputs: 2\n",
+      "Num Frequencies: 778\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_output, num_input, num_freq = H.shape\n",
+    "print('Num Outputs: {:}\\nNum Inputs: {:}\\nNum Frequencies: {:}'.format(num_output, num_input, num_freq))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f5f1720-01be-4a77-bfd2-074f1d23d420",
+   "metadata": {},
+   "source": [
+    "We will also set our polynomial order.  In this case, we know that there are 3 modes of the structure in the bandwidth.  However, let's select 5 as the polynomial order, as we generally would like to overfit the model.  Keep in mind that this will result in 6 polynomial coefficients: 0, 1, 2, 3, 4, and 5.  Therefore, you shouldn't be confused when dimensions of arrays associated with the polynomial order have length 6 instead of length 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3c9da069-ffcd-4640-a8c7-fa281bd33fa1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order = 5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae930b28-a34b-4a65-a03a-05977c5809d7",
+   "metadata": {},
+   "source": [
+    "We will assume no weighting, or rather the weighting is identically 1 across all outputs and across all frequencies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "4df3d9a0-b12a-4ba5-b2e3-11d818d4cca2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weighting = np.ones((num_freq,num_output))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e27b2ab-82df-4618-bae9-7dfb65ec472a",
+   "metadata": {},
+   "source": [
+    "The first thing we will do is to set up our $\\mathbf{\\Omega}_r(\\omega) = e^{-j\\omega_z\\Delta t_z r}$ vectors.  We will set this up as an array with number of rows equal to number of frequency lines and number of columns equal to the number of polynomial coefficients."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "4942b941-1e13-4e88-97b1-f03a17335542",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(778, 6)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "omega_z = omegas - omegas[0] # Perform the shift\n",
+    "deltat = np.pi/(omegas[-1] - omegas[0]) # Compute the scale factor\n",
+    "Omega = np.exp(-1j * omega_z[:,np.newaxis] * deltat * np.arange(order+1)) # Compute Omega\n",
+    "Omega.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ea75991-908b-41c4-b425-29b5e46c286a",
+   "metadata": {},
+   "source": [
+    "In the previous code, we use broadcasting to set up our $\\mathbf{\\Omega}_r$ array.  We add a `newaxis` to our shifted frequency vector $\\omega_z$ to make it a `num_freq` $\\times$ 1 array.  This is then multiplied by an array `[0,1,2,3,4,5]` constructed from the `arange` function.  Per [Numpy's Broadcasting Rules](https://numpy.org/doc/stable/user/basics.broadcasting.html), a `num_freq` $\\times$ 1 array and a `order+1` array will be broadcast to a `num_freq` $\\times$ `order+1` array.  Multiplying by scalars `deltat` and `-1j` get performed elementwise, as does the exponential function `exp`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b46fbf38-0e64-49fa-b8c2-4fcb128ef1a3",
+   "metadata": {},
+   "source": [
+    "The next step will be to assemble the $\\mathbf{R}_o$, $\\mathbf{S}_o$, and $\\mathbf{T}_o$ matrices.  These are built from the $\\mathbf{X}_o$ and $\\mathbf{Y}_o$ matrices.\n",
+    "\n",
+    "Before we do this let's think about the dimensions of the arrays.  The matrix $\\mathbf{R}_o$ for each output will have dimension `order+1` $\\times$ `order+1`.  The matrix $\\mathbf{S}_o$ for each output will have dimension `order + 1` $\\times$ `num_input*(order+1)`.  The $\\mathbf{T}_o$ matrix for each output will have dimension `num_input*(order+1)` $\\times$ `num_input*(order+1)`.  Because we will have `num_output` of each of these matrices, we will consider assembling them into a 3D array with dimension `num_output` $\\times$ `...` $\\times$ `...` ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "97c9d6a9-be8a-4e44-a440-7ff0ac71a911",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  R: (3, 6, 6)\n",
+      "  S: (3, 6, 12)\n",
+      "  T: (3, 12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "R = np.zeros([num_output, order + 1, order + 1])\n",
+    "S = np.zeros([num_output,order + 1, num_input * (order + 1)])\n",
+    "T = np.zeros([num_output, num_input * (order + 1), num_input * (order + 1)])\n",
+    "print('Shapes:\\n  R: {:}\\n  S: {:}\\n  T: {:}'.format(R.shape,S.shape,T.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f86ddef-0627-4f7d-8554-66900ad82bc1",
+   "metadata": {},
+   "source": [
+    "We will then loop through each of the outputs and assemble the corresponding portions of the matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c050e1b7-3872-4bfe-9344-9758c8403463",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accumulating Data: Progress = 0.00%\n",
+      "Accumulating Data: Progress = 33.33%\n",
+      "Accumulating Data: Progress = 66.67%\n",
+      "Accumulating Data: Progress = 100.00%\n",
+      "Shapes:\n",
+      "  Xo: (778, 6)\n",
+      "  Yo: (778, 12)\n",
+      "  Ro: (6, 6)\n",
+      "  So: (6, 12)\n",
+      "  To: (12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for o in range(num_output):\n",
+    "    print('Accumulating Data: Progress = {:0.2f}%'.format(o / num_output * 100.0))\n",
+    "    # The Xo matrix depends only on the weighting and Omega.  We use\n",
+    "    # broadcasting to extend the weighting across all polynomial coefficients\n",
+    "    Xo = weighting[:, o][..., np.newaxis] * Omega\n",
+    "    # The Yo matrix will need the measured frequency response functions\n",
+    "    # at the output o.\n",
+    "    Ho = H[o, ...]\n",
+    "    # We note that rather than re-multiplying the weighting by Omega in the\n",
+    "    # Yo matrix, we can simply use Xo.  Here we must loop over all frequency\n",
+    "    # lines to perform the kronecker product.  The frequency lines are the\n",
+    "    # rows of X, and the columns of Ho, so we transpose Ho.\n",
+    "    # Xk is then the polynomial coefficients at the k frequency line and\n",
+    "    # Hk is the FRF for each input at that frequency line.\n",
+    "    Yo = np.array([-np.kron(Xk, Hk) for Xk, Hk in zip(Xo, Ho.transpose())])\n",
+    "    # We then construct the R, S, and T matrices for that output from their\n",
+    "    # definitions\n",
+    "    Ro = np.real(Xo.conjugate().transpose() @ Xo)\n",
+    "    So = np.real(Xo.conjugate().transpose() @ Yo)\n",
+    "    To = np.real(Yo.conjugate().transpose() @ Yo)\n",
+    "    # We stick the matrices into the 3D arrays for future usage.\n",
+    "    R[o, :, :] = Ro\n",
+    "    S[o, :, :] = So\n",
+    "    T[o, :, :] = To\n",
+    "print('Accumulating Data: Progress = {:0.2f}%'.format((o+1) / num_output * 100.0))\n",
+    "print('Shapes:\\n  Xo: {:}\\n  Yo: {:}\\n  Ro: {:}\\n  So: {:}\\n  To: {:}'.format(\n",
+    "    Xo.shape,Yo.shape,Ro.shape,So.shape,To.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7004a057-06f0-42bc-882c-39cdb34aa075",
+   "metadata": {},
+   "source": [
+    "We will now compute the $\\mathbf{M}$ matrix, which is equal to $\\sum_{o=1}^{N_o}\\left(\\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o\\right)$.  Note that we dropped the factor of 2 in front of the expression because the final answer $\\alpha$ is only defined up to a matrix product.  Multiplying the answer by 2 will not result in any meaningful change.\n",
+    "\n",
+    "We will compute the contribution $\\mathbf{M}_o = \\mathbf{T}_o - \\mathbf{S}_o^T\\mathbf{R}_o^{-1}\\mathbf{S}_o$ for each output and then sum them together to construct $\\mathbf{M}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "cbee950b-972c-45aa-8ead-6d65fdd1afc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  M: (12, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Because S is a 3D array, to transpose it, we will only want to\n",
+    "# swap the last two dimensions and leave the first output dimension\n",
+    "# out front.\n",
+    "# We compute R^-1 using np.linalg.solve, which solves a problem.\n",
+    "# np.linalg.solve(R,S) is equivalent to R^-1@S, but is faster and\n",
+    "# better conditioned.\n",
+    "M = np.sum(T - S.transpose(0,2,1) @ np.linalg.solve(R, S),axis=0)\n",
+    "print('Shapes:\\n  M: {:}'.format(M.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26545d69-262d-459a-919c-72a6fcfc2dc6",
+   "metadata": {},
+   "source": [
+    "Now we will solve for the parameters $\\mathbf{\\alpha}$ and construct the companion matrix.  Recall that in our solution we will assume $\\mathbf{\\alpha}_p$ is equal to the identity matrix and drops out of the companion matrix computation, so we really only need to compute $\\hat{\\alpha} = -\\mathbf{M}_{aa}^{-1}\\mathbf{M}_{ab}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "40d56445-b74b-4f9b-af2e-1e98251a22ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  Maa: (10, 10)\n",
+      "  Mbb: (10, 2)\n",
+      "  alpha: (10, 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "Maa = M[:order * num_input, :order * num_input]\n",
+    "Mbb = -M[:order * num_input, order * num_input:]\n",
+    "alpha = np.linalg.solve(Maa, Mbb)\n",
+    "print('Shapes:\\n  Maa: {:}\\n  Mbb: {:}\\n  alpha: {:}'.format(\n",
+    "    Maa.shape,Mbb.shape,alpha.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f24809a1-e67f-4446-b842-19f139beadd7",
+   "metadata": {},
+   "source": [
+    "We will then construct the companion matrix $\\mathbf{C} = \\begin{bmatrix} \\begin{matrix}\\mathbf{0} & \\mathbf{1}\\end{matrix} \\\\ \\begin{matrix}-\\hat{\\alpha}^T\\end{matrix}\\end{bmatrix}$.  The partition $\\mathbf{0}$ will have shape `(order-1)*num_input` $\\times$ `num_input`, and the partition $\\mathbf{I}$ will have shape `(order-1)*num_input` $\\times$ `(order-1)*num_input`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "788bc574-8350-4be2-b8db-acfab146d5b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  C: (10, 10)\n"
+     ]
+    }
+   ],
+   "source": [
+    "C_top_left = np.zeros([(order - 1) * num_input, num_input])\n",
+    "C_top_right = np.eye((order - 1) * num_input)\n",
+    "C_bottom = -alpha.transpose()\n",
+    "C = np.concatenate(\n",
+    "    (np.concatenate((C_top_left, C_top_right), axis=1),\n",
+    "     C_bottom), axis=0)\n",
+    "print('Shapes:\\n  C: {:}'.format(\n",
+    "    C.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab985b8c-3070-4041-a128-e24fa02ba8ec",
+   "metadata": {},
+   "source": [
+    "Finally, we can compute the eigenvalues and eigenvectors from this matrix, which should be the solutions to the polynomial.  Remember that the eigenvalues will be in the $z$-domain, and must be transformed back to the $s$ domain for us to extract natural frequency and damping ratio: $\\lambda_r = -\\frac{\\log{\\lambda_r}}{\\Delta t_z}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "5f2a5fec-6a50-4f5e-9286-ab7ab4b95ade",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zpoles, V = np.linalg.eig(C)\n",
+    "spoles = -np.log(zpoles)/deltat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be16c045-b456-42dd-acf1-c4704cc460fb",
+   "metadata": {},
+   "source": [
+    "We will immediately discard any poles where the imaginary part is less than zero or the real part is greater than zero, ensuring that we also discard the corresponding eigenvectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "9070751d-05f8-4510-8fbf-41d66fb9942f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "keep_poles = (np.imag(spoles) > 0) & (np.real(spoles) < 0)\n",
+    "spoles = spoles[keep_poles]\n",
+    "V = V[:,keep_poles]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbc83e04-6f70-44ce-8720-e4cd0bf0a96b",
+   "metadata": {},
+   "source": [
+    "We can then extract the (shifted) natural frequency and damping ratios from the poles.  We unshift the natural frequency from the pole to recover the natural frequency of the structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "b06e5f64-ca4f-4ffe-8e8e-56304150103d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "omega_general_fit = abs(spoles) + omegas[0]\n",
+    "zeta_general_fit = -np.real(spoles)/omega_general_fit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f11f8a6a-7f07-4e72-bd4f-bbf181f354cf",
+   "metadata": {},
+   "source": [
+    "The modal participation factors $\\mathbf{l}_r$ are then the last `num_input` rows of the eigenvectors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "cd6279de-eb9f-401c-9baf-12ba4ebdc193",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = V[-num_input:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681c6a98-1297-4e84-b1a2-c787d2853b53",
+   "metadata": {},
+   "source": [
+    "Let's compare the fit natural frequencies to those from the test problem.  We will sort them ascending so they match those computed directly from the eigenvalues of the state space formulation.  Make sure that we also sort the damping and participation factors identically."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "74d9e3ed-63cc-4595-860f-2d2cad63f99c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sort_indices = np.argsort(omega_general_fit)\n",
+    "omega_general_fit = omega_general_fit[sort_indices]\n",
+    "zeta_general_fit = zeta_general_fit[sort_indices]\n",
+    "lr = lr[:,sort_indices]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c5091ad-1e98-4063-a1c7-7cd06a1053b5",
+   "metadata": {},
+   "source": [
+    "Let's now set up a table to show the differences.  We will compare the participation factors against the same degrees of freedom from the corresponding mode shapes.  Note that the participation factors may be scaled differently and rotated differently than the mode shapes, so we must rescale and rotate them to judge how well they fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "71fa8ee2-5eab-48e8-a0cb-6933f8b9d67e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frequency From System Matrices</th>\n",
+       "      <th>Frequency From FRF Fits</th>\n",
+       "      <th>Frequency Error</th>\n",
+       "      <th>Damping From System Matrices</th>\n",
+       "      <th>Damping From FRF Fits</th>\n",
+       "      <th>Damping Error</th>\n",
+       "      <th>Participation From System Matrices</th>\n",
+       "      <th>Participation From FRF Fits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.25</td>\n",
+       "      <td>27.30</td>\n",
+       "      <td>0.19%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>2.60%</td>\n",
+       "      <td>-3.90%</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "      <td>[-0.04318693+0.04408044j -0.09857685+0.09838141j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.78</td>\n",
+       "      <td>54.86</td>\n",
+       "      <td>0.15%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>2.76%</td>\n",
+       "      <td>0.67%</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "      <td>[0.06534612-0.06998435j 0.00118367+0.00286075j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>59.91</td>\n",
+       "      <td>59.88</td>\n",
+       "      <td>-0.06%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>2.69%</td>\n",
+       "      <td>1.02%</td>\n",
+       "      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>\n",
+       "      <td>[-0.06070207+0.05458388j  0.03320861-0.0344524j ]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n",
+    "                             'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n",
+    "                             'Participation From System Matrices','Participation From FRF Fits'],\n",
+    "                  dtype=object)\n",
+    "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n",
+    "    omega_general,omega_general_fit,\n",
+    "    zeta_general, zeta_general_fit,\n",
+    "    psi_general[:2,:].T, lr.T)):\n",
+    "    df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n",
+    "    df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n",
+    "    df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n",
+    "    df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n",
+    "    df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n",
+    "    df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n",
+    "    # Find the maximum participation degree of freedom from the\n",
+    "    # system matrices\n",
+    "    check_index = np.argmax(np.abs(lm))\n",
+    "    # Compute the angle of that index\n",
+    "    check_angle = np.angle(lm[check_index])\n",
+    "    # Compute the same angle from the fit\n",
+    "    this_angle = np.angle(lf[check_index])\n",
+    "    rotation = check_angle - this_angle\n",
+    "    # Compute the magnitudes of the vectors\n",
+    "    check_magnitude = np.linalg.norm(np.abs(lm))\n",
+    "    this_magnitude = np.linalg.norm(np.abs(lf))\n",
+    "    # Correct rotation and scaling\n",
+    "    lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n",
+    "    df.at[i,'Participation From System Matrices'] = str(lm)\n",
+    "    df.at[i,'Participation From FRF Fits'] = str(lf)\n",
+    "    \n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d4b3d62-f721-4cd0-93ad-60ca93746ee3",
+   "metadata": {},
+   "source": [
+    "We can verify that we get very good agreement between the quantities derived from the system matrices and those fit by the FRFs.  However, we may have simply been lucky: our system of equations actually solved for `order*num_inputs` or 10 poles, and it just so happened that 3 of them ended up being physically realizable with positive damping and frequency.  It is possible that more of them would have been physically realizable, in which case we would have had to go through and try to figure out which poles are \"real\" and which are \"computational\", and without *a priori* knowledge of the true pole information, it could be very difficult to do this.\n",
+    "\n",
+    "Instead, we use a stabilization diagram.  We solve for a range of different polynomial orders and we plot the resulting frequencies on a figure.  We then identify poles that appear in many different model orders to be \"stable\" and we accept these as real poles.\n",
+    "\n",
+    "Rather than code all this from scratch, we will simply rely on SDynPy's `PolyPy` implementation to help us out.  `PolyPy` relies on the FRF being structured as a `TransferFunctionArray`, so we must first create one from our data.  Note that `PolyPy` works with frequencies in Hz rather than in radians per second, so we must convert our `omegas` variable by dividing by $2\\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "547b6ff7-d71a-4f9c-9950-26f4d3cf9066",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sdynpy as sdpy\n",
+    "tfarray = sdpy.data_array(sdpy.data.FunctionTypes.FREQUENCY_RESPONSE_FUNCTION,\n",
+    "                          abscissa = omegas/(2*np.pi), ordinate = H, \n",
+    "                          coordinate = sdpy.coordinate.outer_product(\n",
+    "                              sdpy.coordinate_array([1,2,3],'X+'),\n",
+    "                              sdpy.coordinate_array([1,2],'X+')))\n",
+    "pp = sdpy.PolyPy(tfarray, displacement_derivative = 0) # We tell PolyPy these are displacement FRFs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c771ea7-f4a6-46ab-a8e9-1e71f32fd5e1",
+   "metadata": {},
+   "source": [
+    "We can then tell `PolyPy` which polynomial orders to solve for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "eefd5ef6-b2a6-4ba8-912b-d8428a3f7925",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accumulating Data: Progress = 0.00%\n",
+      "Accumulating Data: Progress = 33.33%\n",
+      "Accumulating Data: Progress = 66.67%\n",
+      "Solving for 29 roots (1 of 13)\n",
+      "Solving for 27 roots (2 of 13)\n",
+      "Solving for 25 roots (3 of 13)\n",
+      "Solving for 23 roots (4 of 13)\n",
+      "Solving for 21 roots (5 of 13)\n",
+      "Solving for 19 roots (6 of 13)\n",
+      "Solving for 17 roots (7 of 13)\n",
+      "Solving for 15 roots (8 of 13)\n",
+      "Solving for 13 roots (9 of 13)\n",
+      "Solving for 11 roots (10 of 13)\n",
+      "Solving for 9 roots (11 of 13)\n",
+      "Solving for 7 roots (12 of 13)\n",
+      "Solving for 5 roots (13 of 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pp.compute_poles(np.arange(5,30,2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d394d513-7cd6-4323-8ae9-fa540a477736",
+   "metadata": {},
+   "source": [
+    "We can then plot the stabilization diagram.  The stabilization diagram will have different markers to denote the stability of a given pole.  We generally overlay these poles with a mode indicator function to help us figure out which poles are \"real\".  In this case, a red X denotes an unstable pole, a blue triangle denotes that the frequency has stablized, a blue square means that the frequency and damping have stabilized, and a green circle means that the frequency, damping, and participation factor have stabilized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "a5ff5306-cdbf-46db-95cc-b539e9adc423",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Polynomial Order')"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmif_axis, order_axis = pp.plot_stability()\n",
+    "cmif_axis.set_ylabel('CMIF')\n",
+    "cmif_axis.set_xlabel('Frequency (Hz)')\n",
+    "order_axis.set_ylabel('Polynomial Order')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7e544c5-0ad6-4d0d-853e-5f91a09066ae",
+   "metadata": {},
+   "source": [
+    "We can see that as we move up to a higher model order, we start to see more computational poles appear, as shown by the red Xs throughout the frequency band.  However we see the three main columns of green circles denoting three stable poles in our fits.  We would then select these three stable poles.\n",
+    "\n",
+    "In `PolyPy` we can select them by index, and we can determine the indices by plotting the stabilization diagram with labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "56fd5c29-a2d3-4dd3-84f4-5d21ad3e38ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Polynomial Order')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cmif_axis, order_axis = pp.plot_stability(label_poles=True)\n",
+    "cmif_axis.set_ylabel('CMIF')\n",
+    "cmif_axis.set_xlabel('Frequency (Hz)')\n",
+    "order_axis.set_ylabel('Polynomial Order')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "96f1fb64-126c-4b63-9cd3-c2ea7bb2bf84",
+   "metadata": {},
+   "source": [
+    "We can then extract a final pole list from those indices.  For example we may wish to select poles `(4,0)`, `(4,1)`, and `(4,2)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "550a58ed-8f09-4a2c-a4d9-bb7d532ffe4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pole_indices = [(4,0), (4,1), (4,2)]\n",
+    "pole_list = pp.pole_list_from_indices(pole_indices)\n",
+    "omega_general_stab = pole_list['omega']\n",
+    "zeta_general_stab = pole_list['zeta']\n",
+    "lr_stab = pole_list['Lr_complex']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e01bd62-660d-4575-9f35-3d5295933814",
+   "metadata": {},
+   "source": [
+    "The stabilization diagram actually reveals that for our test case of `order=5`, our poles may not have yet stabilized, and therefore our results may be inaccurate!  Let's compare the same quantities extracted from the stabilization diagram with those computed from the system matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "43de5b7e-a171-48b8-a89a-94fcddfe309f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Frequency From System Matrices</th>\n",
+       "      <th>Frequency From FRF Fits</th>\n",
+       "      <th>Frequency Error</th>\n",
+       "      <th>Damping From System Matrices</th>\n",
+       "      <th>Damping From FRF Fits</th>\n",
+       "      <th>Damping Error</th>\n",
+       "      <th>Participation From System Matrices</th>\n",
+       "      <th>Participation From FRF Fits</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>27.25</td>\n",
+       "      <td>27.26</td>\n",
+       "      <td>0.02%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>2.71%</td>\n",
+       "      <td>-0.02%</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "      <td>[-0.04320045+0.04405838j -0.09857882+0.09838338j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54.78</td>\n",
+       "      <td>54.79</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>2.74%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "      <td>[0.06535574-0.06999465j 0.00185538+0.00185558j]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>59.91</td>\n",
+       "      <td>59.92</td>\n",
+       "      <td>0.01%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>2.66%</td>\n",
+       "      <td>-0.01%</td>\n",
+       "      <td>[-0.06059005+0.05448316j  0.03373321-0.03429903j]</td>\n",
+       "      <td>[-0.06059006+0.05448316j  0.03373322-0.03429902j]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame(columns = ['Frequency From System Matrices','Frequency From FRF Fits','Frequency Error',\n",
+    "                             'Damping From System Matrices','Damping From FRF Fits','Damping Error',\n",
+    "                             'Participation From System Matrices','Participation From FRF Fits'],\n",
+    "                  dtype=object)\n",
+    "for i,(wm,wf,zm,zf,lm,lf) in enumerate(zip(\n",
+    "    omega_general,omega_general_stab,\n",
+    "    zeta_general, zeta_general_stab,\n",
+    "    psi_general[:2,:].T, lr_stab)):\n",
+    "    df.at[i,'Frequency From System Matrices'] = '{:0.2f}'.format(wm)\n",
+    "    df.at[i,'Frequency From FRF Fits'] = '{:0.2f}'.format(wf)\n",
+    "    df.at[i,'Frequency Error'] = '{:0.2f}%'.format((wf-wm)/wm*100)\n",
+    "    df.at[i,'Damping From System Matrices'] = '{:0.2f}%'.format(zm*100)\n",
+    "    df.at[i,'Damping From FRF Fits'] = '{:0.2f}%'.format(zf*100)\n",
+    "    df.at[i,'Damping Error'] = '{:0.2f}%'.format((zf-zm)/zm*100)\n",
+    "    # Find the maximum participation degree of freedom from the\n",
+    "    # system matrices\n",
+    "    check_index = np.argmax(np.abs(lm))\n",
+    "    # Compute the angle of that index\n",
+    "    check_angle = np.angle(lm[check_index])\n",
+    "    # Compute the same angle from the fit\n",
+    "    this_angle = np.angle(lf[check_index])\n",
+    "    rotation = check_angle - this_angle\n",
+    "    # Compute the magnitudes of the vectors\n",
+    "    check_magnitude = np.linalg.norm(np.abs(lm))\n",
+    "    this_magnitude = np.linalg.norm(np.abs(lf))\n",
+    "    # Correct rotation and scaling\n",
+    "    lf = lf*np.exp(1j*rotation)*check_magnitude/this_magnitude\n",
+    "    df.at[i,'Participation From System Matrices'] = str(lm)\n",
+    "    df.at[i,'Participation From FRF Fits'] = str(lf)\n",
+    "    \n",
+    "pretty_print_table(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef16ad2f-08bb-458e-bf22-5143e52820b4",
+   "metadata": {},
+   "source": [
+    "We now see that we have extracted nearly identical modes to those that were computed directly from the system matrices, meaning the fit has significantly improved by using a higher-order polynomial.\n",
+    "\n",
+    "Note that this section has used the code-based `PolyPy` implementation.  However, in SDynPy there is also a `PolyPy_GUI` which can be used to interactively select poles."
+   ]
+  },
+  {
+   "attachments": {
+    "188f4ca3-1e6a-465f-8085-438e9f6aac2b.png": {
+     "image/png": 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"
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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "31e870ab-8990-4de7-8137-d3c2324bb9ed",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "ppgui = sdpy.PolyPy_GUI(tfarray)\n",
+    "```\n",
+    "![image.png](attachment:188f4ca3-1e6a-465f-8085-438e9f6aac2b.png)\n",
+    "![image.png](attachment:628e4999-feea-4f9c-888c-9e86b6bd0f32.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "278d5451-ab7a-4f7e-8a54-c4f12ace5031",
+   "metadata": {},
+   "source": [
+    "## Fitting Mode Shapes to FRF Data\n",
+    "Once the poles and participation factors are known, we can go ahead and fit the mode shapes of the system.  Looking again at the frequency response function equation\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n",
+    "containing the frequency response function $\\mathbf{H}$, the participation factors $\\mathbf{l}_r$, the frequency lines $\\omega$, and the poles $\\lambda_r$.  We wish to solve for the mode shapes $\\mathbf{v}_r$ and the residuals $\\mathbf{R}_u$ and $\\mathbf{R}_l$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "913fad3e-aa05-4d5e-a13c-8c08d3f5cc48",
+   "metadata": {},
+   "source": [
+    "We want to pose this problem in an entirely real set of equations $\\mathbf{A}\\mathbf{x}=\\mathbf{b}$.\n",
+    "\n",
+    "The mode shapes will generally be complex $\\mathbf{v}_r = \\mathbf{v}_r^R + j \\mathbf{v}_r^I$, so we will want to solve simultaneously for the real and imaginary parts.  Similarly, the frequency response function is complex, so we will need to split that up into its real and imaginary parts.  Ignoring the residuals for now:\n",
+    "\n",
+    "$$\\begin{bmatrix}\\mathbf{H}^R\\\\\\mathbf{H}^I\\end{bmatrix} = \\mathbf{P}\\begin{bmatrix}\\mathbf{v}^R\\\\\\mathbf{v}^I\\end{bmatrix}$$\n",
+    "\n",
+    "The goal is to then find the coefficient matrix $\\mathbf{P}$ which allows us to solve for the mode shape coefficients.\n",
+    "\n",
+    "$$\\mathbf{P} = \\begin{bmatrix}\\mathbf{P}_{RR} & \\mathbf{P}_{RI}\\\\\\mathbf{P}_{IR} & \\mathbf{P}_{II}\\end{bmatrix}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25037b11-5fbc-4520-83d5-b2ae1d418a15",
+   "metadata": {},
+   "source": [
+    "We will start with the term\n",
+    "\n",
+    "$$\\frac{\\mathbf{v}_r\\mathbf{l}_r^T}{j\\omega-\\lambda_r} + \\frac{\\mathbf{v}_r^*{\\mathbf{l}_r^*}^T}{j\\omega-\\lambda_r^*}$$\n",
+    "\n",
+    "We will substitute each term with its real and imaginary parts to ensure that all of the variables in the expression are real.\n",
+    "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right)}{j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right)}{- j \\lambda^{I}_{r} - \\lambda^{R}_{r} + j \\omega}$$\n",
+    "\n",
+    "As we wish to collect real and imaginary parts, we will want to multiply each term's numerator and denominator by the denominator's complex conjugate.\n",
+    "\n",
+    "$$\\frac{\\left(- j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(- j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(- j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{\\left(j l^{I}_{jr} + l^{R}_{jr}\\right) \\left(j v^{I}_{ir} + v^{R}_{ir}\\right) \\left(j \\lambda^{I}_{r} - \\lambda^{R}_{r} - j \\omega\\right)}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}$$\n",
+    "\n",
+    "We would then like to collect real and imaginary terms, as well as collecting terms that have the real and imaginary part of the mode shapes.  $\\mathbf{P}_{RR}$ will contain real coefficients of the real part of the mode shapes.  $\\mathbf{P}_{RI}$ will contain real coefficients of the imaginary part of the mode shapes.  $\\mathbf{P}_{IR}$ will contain imaginary coefficients of the real part of the mode shapes.  Finally, $\\mathbf{P}_{II}$ will contain imaginary coefficients of the imaginary part of the mode shapes.\n",
+    "\n",
+    "$$j v^{I}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + v^{I}_{ir} \\left(\\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n",
+    "$$+ v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) + j v^{R}_{ir} \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)$$\n",
+    "\n",
+    "We can substitute this into a matrix form where the real equations are on the top row and the imaginary equations are on the bottom row.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\j H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\j \\left(\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right) & j \\left(\\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\right)\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n",
+    "\n",
+    "Cancelling out the $j$ terms in the bottom row reveals the final matrix equation for the contribution to the frequency response function from one mode shape $H_r$.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$$\n",
+    "\n",
+    "We can then solve this equation in a least squares sense by using the pseudoinverse.\n",
+    "\n",
+    "$$\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right] = \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\\\\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}}\\end{matrix}\\right]^+ \\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "600deb13-308c-4883-b3d3-66147f5f863e",
+   "metadata": {},
+   "source": [
+    "Note that this equation must be generalized to solve for all modal coefficients simultaneously in terms of all frequency response functions for all degrees of freedom at all frequency lines.  For this we will give some thought as to how to structure the matrices.\n",
+    "\n",
+    "At the end of the computation, the matrix $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ should have real and imaginary parts for each degree of freedom for each mode being solved for.  We will solve for these terms over all frequency lines simultaneously.  However, because there is no frequency line dimension in the mode shape matrix, this reveals that the frequency line must be on the inner dimension of the matrix product.  The frequency lines must correspond to the rows of $\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]$ and $\\mathbf{P}$ (which will be the columns of $\\mathbf{P}^+$).  We also see that the columns of the FRF matrix corresponding to the references at each frequency line do not appear in the final mode shape matrix (they are related to the participation factors, which are known).  Therefore this dimension must also be encoded in the inner dimensions of the matrix product.\n",
+    "\n",
+    "We can therefore start to envision what the structure of each term in $\\mathbf{P}$ should look like.  The columns of $\\mathbf{P}$ (the rows of $\\mathbf{P}^+$) must correspond to the mode shape matrix, and they should correspond to the mode index rather than the response degree of freedom index because we need matrix multiplication of $\\mathbf{P}\\mathbf{v}$ to sum over modes, and summation occurs on the inner dimensions of the matrices in matrix multiplication.  The rows of $\\mathbf{P}$ (the columns of $\\mathbf{P}^+$) must correspond to both the mode participation factor (reference degrees of freedom) and frequency lines, separated into real and imaginary parts.  If we compute each partition of $\\mathbf{P}$ as a `num_input` $\\times$ `num_freq` $\\times$ `num_modes` array, and then reshape it into a `num_input*num_freq` $\\times$ `num_modes` array, this should produce the correct size matrix.\n",
+    "\n",
+    "Each partition of the mode shape matrix $v^{R}_{ir}$ and $v^{I}_{ir}$ will then be revealed as a matrix with number of rows equal to the number of modes and columns equal to the number of response degrees of freedom.\n",
+    "\n",
+    "We can similarly construct the dimensions of the partitions $H_r^{R}$ and $H_r^{I}$, as they must now be consistent with the rest of the terms in the equation.  The number of rows of these partitions must match the number of rows of partitions in $\\mathbf{P}$, meaning that the FRF must be similarly structured with mode participation factor (reference degrees of freedom) and frequency lines along its rows and response degrees of freedom along its columns.\n",
+    "\n",
+    "This generalization into matrix form can be somewhat abstract to visualize, but should become more obvious when applied to real data arrays."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c129812-1273-49d1-b18f-5d2851a401f0",
+   "metadata": {},
+   "source": [
+    "### Fitting Residuals for Out-of-Band Modes\n",
+    "We note that the terms $v_{ir}$ are not the only unknowns in the frequency response function formulation.  In general, we can never fit all of the modes of a structure, so there will always be some contributions from out-of-band modes in our frequency response functions.  As we try to solve for the mode shapes of the structure, these out-of-band modes can contaminate the response.  Therefore we also include terms to try to match the contributions from these out-of-band modes, and we call these terms residuals.\n",
+    "\n",
+    "We will in general have a lower residual $R_{Lij}$ and an upper residual $R_{Uij}$, and as the expression suggests, there can be one term in the residual for each pair of input and output degrees of freedom.  We must therefore solve for these terms simultaneously with the mode shapes.  We can think of these terms as \"extra modes\" of the structure, which therefore should imply that the $\\mathbf{P}$ matrix should have columns added corresponding to the coefficients of the residual variables, and the $\\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\end{matrix}\\right]$ matrix should have rows appended corresponding to the real and imaginary parts of these terms.\n",
+    "\n",
+    "Indeed, the coefficients are easily picked directly from the frequency response function.\n",
+    "\n",
+    "$$\\mathbf{H}_{ij} = \\sum_{r=1}^N\\left( \\frac{v_{ir}l_{jr}}{j\\omega-\\lambda_r} + \\frac{v_{ir}^*l_{jr}^*}{j\\omega-\\lambda_r^*}\\right) + \\frac{R_{Lij}}{(j\\omega)^2} + R_{Uij}$$\n",
+    "\n",
+    "$$\\left[\\begin{matrix}H_r^{R}\\\\H_r^{I}\\end{matrix}\\right]\n",
+    "= \\left[\\begin{matrix}\\frac{- l^{I}_{jr} \\lambda^{I}_{r} - l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} + l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & -\\frac{1}{\\omega^2} & 0 & 1 & 0 \\\\\n",
+    "\\frac{- l^{I}_{jr} \\lambda^{R}_{r} + l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{R}_{r} - l^{R}_{jr} \\lambda^{I}_{r} - l^{R}_{jr} \\omega}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & \\frac{- l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega - l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} - 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} + \\frac{l^{I}_{jr} \\lambda^{I}_{r} + l^{I}_{jr} \\omega + l^{R}_{jr} \\lambda^{R}_{r}}{\\left(\\lambda^{I}_{r}\\right)^{2} + 2 \\lambda^{I}_{r} \\omega + \\left(\\lambda^{R}_{r}\\right)^{2} + \\omega^{2}} & 0 & -\\frac{1}{\\omega^2} & 0  & 1\\end{matrix}\\right] \\left[\\begin{matrix}v^{R}_{ir}\\\\v^{I}_{ir}\\\\R^R_{Lij}\\\\R^I_{Lij}\\\\R^R_{Uij}\\\\R^I_{Uij}\\end{matrix}\\right]$$\n",
+    "\n",
+    "When generalizing to multiple references, responses, and frequency lines, each residual term $R^R_{Lij}$, $R^I_{Lij}$, $R^R_{Uij}$ and $R^I_{Uij}$ will have a number of rows equal to the number of references and a number of columns equal to the number of responses.  Similarly the coefficient terms ($0$, $1$, $-\\frac{1}{\\omega}$) will have a number of rows equal to the number of `num_freq*num_input` and number of columns equal to `num_input`.  Because of the structure of the matrices, they are easily constructed using Kronecker products of identity matrices multiplied by either $1$ or $-\\frac{1}{\\omega^2}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "533195b0-3bd2-4e9a-8314-dcf73de1d52d",
+   "metadata": {},
+   "source": [
+    "### A Note on Types of FRFs\n",
+    "Note that the mode shape derivations described in the previous sections are valid for *receptance* FRFs, meaning FRFs between displacement and force.  When transforming to from *receptance* to *mobility* (velocity over force) to *accelerance* (acceleration over force) FRFs, we multiply the FRF expression by $j\\omega$ at each step.  This will clearly change which terms are real and which terms are imaginary, as well as the coefficients on each term.  As this document is already quite long, these will not be derived, but the same approach as described above can be used to find the coefficient matrices for those cases; simply substitute for real and imaginary parts of each variable, collect imaginary and real coefficients for imaginary and real parts of each term of interest, and place them into a matrix form."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22c07fae-f815-4be9-8a10-04fec78dd6c2",
+   "metadata": {},
+   "source": [
+    "### Normalizing Mode Shapes\n",
+    "Once we solve for the mode shapes, our job is not quite done.  Due to how the problem is formulated, the particpation factors have some arbitrary scaling and rotation applied to them.  This means when the shapes are found, they will also have an arbitrary scaling and rotation applied to them.  There is no constraint that the participation factors are identical to the mode shapes at the input locations.\n",
+    "\n",
+    "To perform the normalization, we compute the residue matrices for each mode\n",
+    "\n",
+    "$$R_{rij} = \\psi_{ir}l_{jr}$$\n",
+    "\n",
+    "We recognize the at the drive point, the residue is equal to the mode shape squared or the participation factor squared if the mode shapes are scaled such that the participation factors are the mode shapes.\n",
+    "$$R_{rii} = \\psi_{ir}l_{ir} = \\psi_{ir}^2 = l_{ir}^2$$\n",
+    "\n",
+    "However, we currently do not have such a scenario.  In our case, the fit shapes $\\bar{\\psi}_{ir}$ are scaled by some complex coefficient $c$, and the participation factors $\\bar{l}_{jr}$ are scaled by some factor ${\\frac{1}{c}}$.\n",
+    "\n",
+    "$$\\psi_{ir} = c\\bar{\\psi}_{ir}$$\n",
+    "$$l_{jr} = \\frac{1}{c}\\bar{l}_{jr}$$\n",
+    "\n",
+    "We note that due to the reciprocal scale factors, we can still compute the residuals.  We can therefore set up a set of equations to solve for the scale factor that gets applied to the mode shapes.\n",
+    "\n",
+    "$$R_{rii} = \\psi_{ir}l_{ir} = c\\bar{\\psi}_{ir}\\frac{1}{c}\\bar{l}_{ir} = \\bar{\\psi}_{ir}\\bar{l}_{ir}$$\n",
+    "$$R_{rii} = \\psi_{ir}^2 = c^2\\bar{\\psi}_{ir}^2$$\n",
+    "$$\\sqrt{R_{rii}} = c \\bar{\\psi}_{ir}$$\n",
+    "\n",
+    "Since we have participation factors and shapes at multiple degrees of freedom for a multiple-input solution, we can solve for the scale factor $c$ in a least-squares sense for each mode.\n",
+    "\n",
+    "$$\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = \\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}c$$\n",
+    "\n",
+    "$$\\begin{bmatrix}\\bar{\\psi}_{1r}\\\\\\bar{\\psi}_{2r}\\\\\\vdots\\\\\\bar{\\psi}_{{N_i}r}\\end{bmatrix}^+\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix} = c$$\n",
+    "\n",
+    "**BE AWARE:** when taking the square root of the residue matrix terms, there are two values that can be obtained (e.g. $\\sqrt{4} = \\pm2$).  In the case of real numbers, most square root algorithms will only return the positive root.  However for complex numbers, it is not so obvious which sign the root will have (positive real part?  positive imaginary part?).  Flipping a sign on one term in the matrix $\\begin{bmatrix}\\sqrt{R_{r11}} \\\\ \\sqrt{R_{r22}} \\\\ \\vdots \\\\ \\sqrt{R_{r{N_i}{N_i}}}\\end{bmatrix}$ will make the least squares solution incorrect, therefore we must make sure the signs are consistent between the mode shapes $\\bar{\\psi}_{ir}$ and the residue terms $\\sqrt{R_{rii}}$.\n",
+    "\n",
+    "We can do this by dividing the termes $\\sqrt{R_{rii}}$ by $\\bar{\\psi}_{ir}$ and looking at the direction of the complex vectors.  If the vectors are all pointing nominally the same direction, then the least squares problem will solve for that direction successfully.  If some are pointing opposite due to the sign of the output of the square root, then the scale factor found by the least squares solution will be incorrect."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13f2483d-e733-4d8d-94e5-b4cfbfcfde11",
+   "metadata": {},
+   "source": [
+    "### Back to the Example Problem\n",
+    "While the symbolic mathematics in the previous sections was straightforward (though perhaps a bit tedious), the discussion of the construction of a system of equations to solve for each coefficient of the mode shape matrix was perhaps a bit abstract.  We will therefore make it more concrete by applying it to our specific example problem.  Note that we will take advantage heavily of broadcasting and dimensional manipulation to help us put these equations together.  Readers are encouraged to run these problems themselves to understand exactly what operations are being performed at each step, and the resulting shapes of all of the arrays.\n",
+    "\n",
+    "We will first generate the $\\mathbf{P}$ matrix described above.  We will start with only the mode shape coefficients, then show how it can be expanded to include residuals.  This will involve translating our symbolic code into numeric Numpy code.  To help us repeat this process for different sets of modes and frequency ranges when we introduce the residuals later, we will define the process to generate the $\\mathbf{P}$ matrix as a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9cc21035-44bd-4128-ac92-0709ab740be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def P_modal(omegas, poles, participation_factors):\n",
+    "    '''Construct the mode shape coefficient matrix\n",
+    "\n",
+    "    Constructs the coefficients from the system poles,\n",
+    "    participation factors, and frequency lines.\n",
+    "\n",
+    "    Arguments should be passed as arrays and will be broadcast\n",
+    "    together.\n",
+    "    '''\n",
+    "    # We want the output array to be n_input x n_freq*2 x n_modes*2\n",
+    "    # So let's adjust the terms so they have the right shapes\n",
+    "    # We want frequency lines to be the middle dimension\n",
+    "    omegas = omegas[np.newaxis,:,np.newaxis]\n",
+    "    # We want inputs to be the first dimension and modes the\n",
+    "    # last dimension\n",
+    "    participation_factors = participation_factors.T[:,np.newaxis,:]\n",
+    "    # Split up terms into real and imaginary parts\n",
+    "    pr = poles.real\n",
+    "    pi = poles.imag\n",
+    "    lr = participation_factors.real\n",
+    "    li = participation_factors.imag\n",
+    "    P_blocks = np.array([\n",
+    "        [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n",
+    "         (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n",
+    "        [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n",
+    "         (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n",
+    "    return np.block([[P_blocks[0,0],P_blocks[0,1]],\n",
+    "                     [P_blocks[1,0],P_blocks[1,1]]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cef05146-3d81-4003-86fe-1500592485de",
+   "metadata": {},
+   "source": [
+    "We can now create our pole vector from the extracted frequencies and damping ratios."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "0693d32a-2136-4c72-8f6e-fb67515ce6f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  omegas: (778,)\n",
+      "  poles: (3,)\n",
+      "  participation_factors: (3, 2)\n",
+      "  P: (2, 1556, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compute poles from the extracted data\n",
+    "poles = -omega_general_stab*zeta_general_stab + 1j*omega_general_stab*np.sqrt(1-zeta_general_stab**2)\n",
+    "# Compute P with our function\n",
+    "P = P_modal(omegas,poles, lr_stab)\n",
+    "\n",
+    "# Check shapes\n",
+    "print('Shapes:\\n  omegas: {:}\\n  poles: {:}\\n  participation_factors: {:}\\n  P: {:}'.format(\n",
+    "    omegas.shape, poles.shape, lr_stab.shape, P.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68336074-9c72-4186-9c32-76c4597712f8",
+   "metadata": {},
+   "source": [
+    "We must now set up the frequency response functions to perform the least squares computation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "b9a0bf64-820f-4786-b4e6-f78f7f209dad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  H_LS: (2, 1556, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "H_LS = H.transpose(1,2,0)\n",
+    "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n",
+    "\n",
+    "print('Shapes:\\n  H_LS: {:}'.format(\n",
+    "    H_LS.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0af6fc0-5e43-4fe6-838d-d30266bfb2f5",
+   "metadata": {},
+   "source": [
+    "To construct the least-squares set of equations, we simply squash the first two dimensions into a single dimension, in which case the least-squares solution will solve over all frequency lines and all inputs simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0ef24c44-cb79-4317-9aae-5a3b99ec51ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n",
+    "                            H_LS.reshape(-1,H_LS.shape[-1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "818bdad7-6a69-422e-9d1e-f5d2480acd83",
+   "metadata": {},
+   "source": [
+    "We can reconstruct the FRF by muliplying the output shapes by the coefficient matrix and extracting real and imaginary parts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "570bd80c-83d0-4f5e-a6ca-289637763d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reconstructed_frfs = P.reshape(-1,P.shape[-1])@shapes_LS\n",
+    "H_recon = reconstructed_frfs.reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_recon = H_recon[:H_recon.shape[0]//2] + 1j*H_recon[H_recon.shape[0]//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "a6e4c568-2976-4d5b-a4cb-76547020ee30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general_stab.size\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(num_output*2,num_input, sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_d,f_m in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n",
+    "                      H_recon.reshape(omegas.size,-1).T,\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_d)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_d),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "ax[1].legend(['Truth','Reconstructed from\\nLeast-Squares Solution'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "669682c7-558a-4ab6-995c-c24c8dccdea1",
+   "metadata": {},
+   "source": [
+    "We have now solved in a least-squares sense for our modal coefficients.  We should extract the shape arrays from the matrix.  The top partition will be the real part of the shape, and the bottom part will be the imaginary part of the shape.  Remember also that the terms are transposed from the typical mode shape matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "aa810667-ba40-42ca-8b2b-fab6ecfcbf3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_stab = (shapes_LS[:shapes_LS.shape[0]//2]+1j*shapes_LS[shapes_LS.shape[0]//2:]).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39a5a890-5f7e-478a-b0b2-985454864c4a",
+   "metadata": {},
+   "source": [
+    "Note that because the participation factors were arbitrarily rotated, these shapes will also be arbitrarily rotated.  Therefore we need to normalize the mode shapes.  We will do this by computing the residue matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "09077e1b-1ba5-4527-8898-cf6539d8c7b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "residues = np.einsum('ik,jk->kij',psi_stab,lr_stab.T)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a8143f5-66d3-41b2-a024-931218b1a1ba",
+   "metadata": {},
+   "source": [
+    "We can get the drive point response indices corresponding to the participation factors and pull out the drive point residues.  In general, the drive point residues should have a negative imaginary part, so we can use this to discard 'bad' drive point data where the drive point does not excite the mode well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "d6a3f830-7704-46ef-a7a0-a6388757dfd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drive_indices_response = np.array([0,1])\n",
+    "drive_indices_input = np.array([0,1])\n",
+    "drive_residues = residues[:,drive_indices_response,drive_indices_input]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56cd126e-1a06-4234-99ae-8929d52116a7",
+   "metadata": {},
+   "source": [
+    "We will now go through each mode and compute the correct shape scaling using only the good drive points where the imaginary part is negative and not close to zero.  First, let's go through and make sure that our participation factors and mode shapes that we solved for are actually scaled versions of the true mode shapes.  I'm going to mass normalize my mode shapes for better comparisions to the fit data without having to carry around the modal mass term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "883ccaa0-1f86-41ae-947e-2d0796702b0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Mass normalize the mode shapes\n",
+    "psi_general /= np.sqrt(ma_general)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aed6d5e9-2bb7-43a1-957b-b76d1bda87d6",
+   "metadata": {},
+   "source": [
+    "Let's find the scale factor between the participation factors and the mode shapes at the participation factors.  We will average over all of the degrees of freedom for each mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "bcf8a804-ecc4-45d6-8f55-e1e0874bfde2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_scaling = np.mean(psi_general[:2,:]/lr_stab.T,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a6b122a4-87ab-476a-8d7a-510fb951cd0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.18452662-0.18416078j,  0.08928666-0.0956242j ,\n",
+       "       -0.093857  +0.08439714j])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83c5bce4-a5cb-4e7d-9214-83b2800f6bf2",
+   "metadata": {},
+   "source": [
+    "We can then check to see if we reconstruct the mode shape by applying the scaling to the participation factors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "aa40a7de-d7e4-47e3-985a-f4f24fe5e3ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267784-0.03499731j,\n",
+       "        -0.03029502+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.01686661-0.01714951j]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_stab.T*lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "e887ee89-7ad0-4fae-bee5-be261d25caa6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,\n",
+       "        -0.03029503+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.0168666 -0.01714951j]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general[:2,:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2245282f-b4cc-4448-8e05-72d35a076f38",
+   "metadata": {},
+   "source": [
+    "Now we will do the same thing for the mode shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5458c528-b3dd-449f-87b7-01181b91c076",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psi_scaling = np.mean(psi_general/psi_stab,axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "349fc0a1-850a-41f4-aab0-8c1198a09e69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,\n",
+       "       -5.88158134-5.30751768j])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "805218db-fbff-419b-a61f-248fad8c7c63",
+   "metadata": {},
+   "source": [
+    "Let's reconstruct the mode shapes by scaling the fit mode shapes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "ec397820-33e5-4bd5-b968-8d6ea7d63cc0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02159971+0.02202864j,  0.03316166-0.03473379j,\n",
+       "        -0.03033178+0.02722321j],\n",
+       "       [-0.04928856+0.04919178j,  0.00094139+0.00090236j,\n",
+       "         0.0168663 -0.01716533j],\n",
+       "       [-0.02186329+0.02176496j, -0.03539605+0.03236945j,\n",
+       "        -0.02735936+0.03018341j]])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_stab*psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3e49f237-ee6c-48e2-8854-e52abd728337",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.02160023+0.02202919j,  0.03267787-0.03499732j,\n",
+       "        -0.03029503+0.02724158j],\n",
+       "       [-0.04928941+0.04919169j,  0.00092769+0.00092779j,\n",
+       "         0.0168666 -0.01714951j],\n",
+       "       [-0.02186239+0.02176446j, -0.03496025+0.03263839j,\n",
+       "        -0.02739619+0.03019566j]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_general"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0854c79-1cc4-492b-bf1c-c43526067a1a",
+   "metadata": {},
+   "source": [
+    "Since the particpation factors times the mode shapes should be equal to the scaled mode shapes squared, we should be able to see that the scale factors are reciprocals of one another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "7b80d0c2-c92a-479c-97a9-823c368e0cad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.70380647+2.72025259j,  5.15973139+5.66778918j,\n",
+       "       -5.88158134-5.30751768j])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "psi_scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "87376ad2-6f5c-4c78-8b9b-f92a5191f2a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.71501354+2.70963081j,  5.2165319 +5.58679996j,\n",
+       "       -5.89109483-5.29733029j])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/lr_scaling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050511f9-7ba5-4c09-a5e9-1f92d713f291",
+   "metadata": {},
+   "source": [
+    "This was a sanity check to make sure the mode shapes and participation factors were identified consistently.  Unfortunately in a real situation we don't have the true mode shapes, so we need to compute them from the residue matrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "5676966d-8021-4f4b-8da1-9fdff6a48330",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scale for mode 1: (-2.709418738512416-2.7149722671668157j)\n",
+      "Scale for mode 2: (5.211812790651124+5.590252990269536j)\n",
+      "Scale for mode 3: (5.886705065831037+5.299694143958719j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "psi_stab_normalized = []\n",
+    "for k,drive_residue in enumerate(drive_residues):\n",
+    "    # Throw away non-negative imaginary parts\n",
+    "    bad_indices_positive = drive_residue.imag > 0\n",
+    "    # Throw away small values compared to the average value (only considering negative imaginary parts)\n",
+    "    bad_indices_small = np.abs(drive_residue) < 0.01*np.mean(np.abs(drive_residue[~bad_indices_positive]))\n",
+    "    # Combine into a single criteria\n",
+    "    bad_indices = bad_indices_positive | bad_indices_small\n",
+    "    # Get the good indices that are remaining\n",
+    "    remaining_indices = np.where(~bad_indices)[0]\n",
+    "    # We will then construct the least squares solution\n",
+    "    shape_coefficients = psi_stab[drive_indices_response[remaining_indices],k][:,np.newaxis]\n",
+    "    residue_coefficients = np.sqrt(drive_residue[remaining_indices])[:,np.newaxis]\n",
+    "    # Before we compute the scale, we need to make sure that we have all of the signs the same way.\n",
+    "    # This is because the square root can give you +/- root where root**2 = complex number\n",
+    "    # This will mess up the least squares at it will try to find something between the\n",
+    "    # two vectors.\n",
+    "    scale_vector = (residue_coefficients/shape_coefficients).flatten()\n",
+    "    sign_vector = np.array((scale_vector.real,scale_vector.imag))\n",
+    "    # Get the signs\n",
+    "    signs = np.sign(np.dot(sign_vector[:,0],sign_vector))\n",
+    "    residue_coefficients = residue_coefficients*signs[:,np.newaxis]\n",
+    "    # Now compute the least-squares solution\n",
+    "    scale = np.linalg.lstsq(shape_coefficients,residue_coefficients)[0].squeeze()\n",
+    "    print('Scale for mode {:}: {:}'.format(k+1,scale))\n",
+    "    psi_stab_normalized.append(psi_stab[:,k]*scale)\n",
+    "psi_stab_normalized = np.array(psi_stab_normalized).T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab437cbd-420d-4421-8cff-040a1f1bf4cb",
+   "metadata": {},
+   "source": [
+    "Now that we have our mode shapes, we should resynthesize frequency response functions from them to make sure they fit our original data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "576a0ff3-1f51-4d50-85a6-a9b9985a4a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "num_modes = omega_general_stab.size\n",
+    "omegas_bc = omegas[:,np.newaxis,np.newaxis]\n",
+    "# Compute the laplace variables\n",
+    "s=1j*omegas_bc\n",
+    "\n",
+    "H_general_fromUnNormModes = np.sum([\n",
+    "    psi_stab[:,r,np.newaxis]@lr_stab.T[:,r,np.newaxis].T/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + psi_stab[:,r,np.newaxis].conj()@lr_stab.T[:,r,np.newaxis].T.conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "H_general_fromFitModes = np.sum([\n",
+    "    psi_stab_normalized[:,r,np.newaxis]@psi_stab_normalized[drive_indices_input,r,np.newaxis].T/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + psi_stab_normalized[:,r,np.newaxis].conj()@psi_stab_normalized[drive_indices_input,r,np.newaxis].T.conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "H_general_fromFitResidues = np.sum([\n",
+    "    residues[r]/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] + 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    + residues[r].conj()/\n",
+    "    ((s-\n",
+    "        (-zeta_general_stab[r]*omega_general_stab[r] - 1j*omega_general_stab[r]*np.sqrt(1-zeta_general_stab[r]**2))))\n",
+    "    for r in range(num_modes)],axis=0)\n",
+    "\n",
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_general_fromFitResidues.shape[1]*2,H_general_fromFitResidues.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_t,f_m,f_r,f_u in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromFitModes[:,:H_general_fromFitResidues.shape[1],:H_general_fromFitResidues.shape[2]].reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromFitResidues.reshape(omegas.size,-1).T,\n",
+    "                      H_general_fromUnNormModes.reshape(omegas.size,-1).T\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n",
+    "    ax[0].plot(omegas,np.angle(f_u)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas,np.angle(f_m)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas,np.angle(f_r)*180/np.pi,linewidth=1)\n",
+    "    ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_u),linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_m),linewidth=2)\n",
+    "    ax[1].plot(omegas,np.abs(f_r),linewidth=1)\n",
+    "ax[1].legend(['Truth','Unnormalized','Normalized','Residues'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c801ba95-827a-4b4d-9bdd-d1205b84c327",
+   "metadata": {},
+   "source": [
+    "The last demonstration will be to understand how residuals can be implemented.  To do this, we will truncate the frequency domain to remove the first mode.  We will then use residuals to try to fit the effect of this mode while only truly fitting the second two modes.  We will assume we have correctly identified the poles and participation factors from the first analysis, and only consider solving the shape problem here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "036eaad3-1f6a-4cb8-a107-5ddc8a079dea",
+   "metadata": {},
+   "source": [
+    "We will update our function to solve for the $\\mathbf{P}$ matrix to also solve for residual terms."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "fd95b767-a897-4fa4-b46f-23fb79831fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def P_modal_w_residuals(omegas, poles, participation_factors, lower_residuals = False, upper_residuals = False):\n",
+    "    '''Construct the mode shape coefficient matrix\n",
+    "\n",
+    "    Constructs the coefficients from the system poles,\n",
+    "    participation factors, and frequency lines.\n",
+    "\n",
+    "    Arguments should be passed as arrays and will be broadcast\n",
+    "    together.\n",
+    "    '''\n",
+    "    # Get the number of modes and number of inputs from the shape\n",
+    "    # of the participation factors.\n",
+    "    n_modes,n_inputs = participation_factors.shape\n",
+    "    n_freq, = omegas.shape\n",
+    "    # We want the output array to be n_input x n_freq*2 x n_modes*2\n",
+    "    # So let's adjust the terms so they have the right shapes\n",
+    "    # We want frequency lines to be the middle dimension\n",
+    "    omegas = omegas[np.newaxis,:,np.newaxis]\n",
+    "    # We want inputs to be the first dimension and modes the\n",
+    "    # last dimension\n",
+    "    participation_factors = participation_factors.T[:,np.newaxis,:]\n",
+    "    # Split up terms into real and imaginary parts\n",
+    "    pr = poles.real\n",
+    "    pi = poles.imag\n",
+    "    lr = participation_factors.real\n",
+    "    li = participation_factors.imag\n",
+    "    P_blocks = np.array([\n",
+    "        [(-pr*lr - pi*li - li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2),\n",
+    "         (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr + lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)],\n",
+    "        [(-pr*li + pi*lr - lr*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*li - pi*lr - lr*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2),\n",
+    "         (-pr*lr - pi*li + li*omegas)/(pr**2 + pi**2 - 2*pi*omegas + omegas**2)\n",
+    "          + (pr*lr + pi*li + li*omegas)/(pr**2 + pi**2 + 2*pi*omegas + omegas**2)]])\n",
+    "    if lower_residuals:\n",
+    "        RL_block = np.concatenate([\n",
+    "            np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n",
+    "            np.kron(-1/omegas**2,np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n",
+    "    if upper_residuals: \n",
+    "        RU_block = np.concatenate([\n",
+    "            np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[1,0])),\n",
+    "            np.kron(np.ones((omegas.size,1)),np.kron(np.eye(n_inputs)[:,np.newaxis],[0,1]))],axis=1)\n",
+    "    P = np.block([[P_blocks[0,0],P_blocks[0,1]],\n",
+    "                  [P_blocks[1,0],P_blocks[1,1]]])\n",
+    "    if lower_residuals:\n",
+    "        P = np.concatenate((P,RL_block),axis=-1)\n",
+    "    if upper_residuals:\n",
+    "        P = np.concatenate((P,RU_block),axis=-1)\n",
+    "    return P"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43af6e4a-d977-4586-af15-07d6fe168447",
+   "metadata": {},
+   "source": [
+    "We can then call the function with a subset of the modes that we aim to solve for."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "917fedde-e85a-4b05-8c3e-f253ab9dc214",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shapes:\n",
+      "  P: (2, 778, 12)\n",
+      "  H_LS: (2, 778, 3)\n"
+     ]
+    }
+   ],
+   "source": [
+    "min_freq = 45\n",
+    "mode_indices = slice(1,None)\n",
+    "P = P_modal_w_residuals(omegas[omegas > min_freq],\n",
+    "                        poles[mode_indices],\n",
+    "                        lr_stab[mode_indices],\n",
+    "                        lower_residuals = True, upper_residuals = True)\n",
+    "\n",
+    "H_LS = H[...,omegas>min_freq].transpose(1,2,0)\n",
+    "H_LS = np.concatenate((H_LS.real,H_LS.imag),axis=1)\n",
+    "\n",
+    "print('Shapes:\\n  P: {:}\\n  H_LS: {:}'.format(P.shape,H_LS.shape))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6151d42f-0920-4794-adb0-e66fd8d34f8d",
+   "metadata": {},
+   "source": [
+    "We can then solve the least squares problem over all frequency lines and inputs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "c6ee7e46-ed84-434a-aa91-5b438cc0e8d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shapes_LS = np.linalg.lstsq(P.reshape(-1,P.shape[-1]),\n",
+    "                            H_LS.reshape(-1,H_LS.shape[-1]))[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08ee6d5a-4f87-464b-a35b-53c94ab7282f",
+   "metadata": {},
+   "source": [
+    "Now at this point, we can reconstruct the portions of the FRFs from the modes and those from the residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "6aaeb549-5e36-4667-87e2-8f32afbfe4ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P_full = P.reshape(-1,P.shape[-1])\n",
+    "P_modes = P_full[...,:2*poles[mode_indices].size]\n",
+    "P_residuals = P_full[...,2*poles[mode_indices].size:]\n",
+    "\n",
+    "shapes_modes = shapes_LS[:2*poles[mode_indices].size]\n",
+    "shapes_residuals = shapes_LS[2*poles[mode_indices].size:]\n",
+    "\n",
+    "H_modes = (P_modes@shapes_modes).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_modes = H_modes[:H_modes.shape[0]//2] + 1j*H_modes[H_modes.shape[0]//2:]\n",
+    "\n",
+    "H_residuals = (P_residuals@shapes_residuals).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_residuals = H_residuals[:H_residuals.shape[0]//2] + 1j*H_residuals[H_residuals.shape[0]//2:]\n",
+    "\n",
+    "H_full = (P_full@shapes_LS).reshape(*H_LS.shape).transpose(1,2,0)\n",
+    "H_full = H_full[:H_full.shape[0]//2] + 1j*H_full[H_full.shape[0]//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bba9570d-2dcf-43bf-9c9f-b0ddb08213b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Now plot them\n",
+    "fig,axes = plt.subplots(H_full.shape[1]*2,H_full.shape[2], sharex=True,sharey='row',figsize=(10,10))\n",
+    "for ax,f_t in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_general[frequency_lines_to_keep,:,:2].reshape(omegas.size,-1).T,\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas,np.angle(f_t)*180/np.pi,linewidth=3)\n",
+    "    ax[1].plot(omegas,np.abs(f_t),linewidth=3)\n",
+    "for ax,f_r,f_m,f_f in zip(axes.T.reshape(-1, 2), # Reshape so we pull of a mag and phase plot for each entry\n",
+    "                      H_residuals.reshape(omegas[omegas>min_freq].size,-1).T,\n",
+    "                      H_modes.reshape(omegas[omegas>min_freq].size,-1).T,\n",
+    "                      H_full.reshape(omegas[omegas>min_freq].size,-1).T\n",
+    "                      ):\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_f)*180/np.pi,linewidth=2)\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_r)*180/np.pi,linewidth=1)\n",
+    "    ax[0].plot(omegas[omegas>min_freq],np.angle(f_m)*180/np.pi,linewidth=1)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_f),linewidth=2)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_r),linewidth=1)\n",
+    "    ax[1].plot(omegas[omegas>min_freq],np.abs(f_m),linewidth=1)\n",
+    "ax[1].legend(['Truth','Fit','Residual Contribution','Mode Contribution'])\n",
+    "for i,ax in enumerate(axes[:,0]):\n",
+    "    if i % 2 == 0:\n",
+    "        ax.set_ylim([-180,180])\n",
+    "        ax.set_yticks([-180,-90,0,90,180])\n",
+    "        ax.set_ylabel('Phase (deg)')\n",
+    "    else:\n",
+    "        ax.set_ylim([1e-5,1e-2])\n",
+    "        ax.set_ylabel('Magnitude (deg)')\n",
+    "        ax.set_yscale('log')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6f0d0b-7662-4165-b20b-9e2d1a111635",
+   "metadata": {},
+   "source": [
+    "We see that the fit matches the measured data very due to the inclusion of the residual terms.  If we were to just include the modal contributions, it would not fit as well, but by adding in residual contributions we can get a much better match."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "739c7f46-ba2e-4b8a-be4a-01b7fa034e55",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "In this document, we have extensively studied complex modes.  We first explored the real-modes case to understand the limitations of that approach.  We then derived the state space system of equations from the typical 2nd order differential equation to construct the eigenvalue equation to solve for the complex modes.\n",
+    "\n",
+    "We then related the modal parameters to the frequency response function, which allowed us to compute a frequency response function given the modal parameters.\n",
+    "\n",
+    "We finally spent a good deal of time discussing how to fit modal parameters to frequency response functions using the PolyMax approach, which is implemented in SDynPy as PolyPy."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/modules.html b/modules.html
index cdfb6ba..66db910 100644
--- a/modules.html
+++ b/modules.html
@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>SDynPy Programming Interface &mdash; SDynPy 0.11.0 documentation</title>
+  <title>SDynPy Programming Interface &mdash; SDynPy 0.14.0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
       <link rel="stylesheet" type="text/css" href="_static/plot_directive.css" />
@@ -29,7 +29,7 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="sdynpy" href="_autosummary/sdynpy.html" />
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+    <link rel="prev" title="Modal Tutorial 06: Complex Modes" href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -108,14 +108,14 @@ <h1>SDynPy Programming Interface<a class="headerlink" href="#sdynpy-programming-
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="modal_tutorials/Modal_05_Experimental_Modal_Analysis/Modal_05f_Comparing_Test_and_Analysis.html" class="btn btn-neutral float-left" title="Modal Tutorial 05 Part F: Comparing Test and Analysis" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="modal_tutorials/Modal_06_Complex_Modes/Modal_06_Complex_Modes.html" class="btn btn-neutral float-left" title="Modal Tutorial 06: Complex Modes" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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-<jinja2.runtime.BlockReference object at 0x7f99eca2feb0>
+<jinja2.runtime.BlockReference object at 0x7f5a26948370>
 <img src="_images/snl.jpg" alt="Sandia National Laboratories" style="height:40px">
 
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diff --git a/objects.inv b/objects.inv
index b6be086284876505382f2f391a97f81fbeb8388d..b7d5c060016ea69af817b17c8208f203fb188402 100644
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47, 48, 49, 50, 52, 53, 54, 55, 56, 62, 63, 66, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 97, 101, 104, 106, 114, 115, 117, 168, 170, 189, 190, 191, 193, 195, 201, 220, 229, 250, 251, 253, 254, 261, 263, 266, 302, 303, 304, 305, 313, 314, 315, 316, 317, 318, 320, 322, 323, 327, 328, 329, 335], "dtype": [3, 71, 74, 168, 312, 315, 316, 322, 325, 327, 329, 334], "float": [3, 28, 29, 31, 33, 34, 35, 40, 48, 50, 51, 52, 53, 55, 74, 81, 83, 88, 89, 111, 112, 117, 126, 129, 184, 185, 186, 193, 195, 220, 233, 238, 240, 245, 249, 254, 256, 257, 258, 259, 263, 289, 298, 302, 303, 304, 305, 307, 309, 310, 312, 323, 325, 334], "buffer": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 47, 49, 52, 54, 71, 72, 74], "none": [3, 6, 7, 16, 17, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 40, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 63, 64, 67, 69, 71, 72, 74, 76, 79, 80, 83, 84, 85, 86, 88, 89, 97, 103, 104, 106, 107, 112, 117, 119, 123, 126, 129, 130, 131, 132, 134, 136, 137, 138, 139, 142, 144, 145, 149, 159, 165, 181, 189, 193, 194, 195, 197, 201, 206, 217, 219, 224, 226, 227, 228, 229, 230, 231, 233, 242, 243, 244, 246, 251, 252, 254, 257, 258, 261, 263, 265, 266, 277, 285, 292, 300, 301, 304, 305, 308, 312, 313, 314, 315, 316, 317, 318, 322, 323, 327, 334], "offset": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 47, 49, 50, 52, 54, 71, 72, 74, 88, 312, 313, 315, 318, 334], "0": [3, 6, 7, 17, 18, 19, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 40, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 67, 69, 71, 72, 74, 81, 85, 86, 88, 94, 97, 101, 102, 103, 104, 105, 106, 112, 117, 126, 149, 162, 168, 182, 185, 192, 193, 195, 197, 204, 206, 213, 217, 219, 221, 228, 233, 246, 251, 254, 261, 263, 266, 267, 289, 292, 299, 302, 303, 304, 305, 308, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334], "stride": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 47, 49, 52, 54, 71, 72, 74], "order": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 47, 49, 50, 52, 54, 71, 72, 74, 110, 111, 126, 181, 183, 185, 284, 312, 314, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334, 335], "sourc": [3, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 62, 63, 64, 66, 67, 68, 69, 71, 72, 74, 79, 81, 82, 83, 85, 86, 88, 92, 94, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 110, 111, 112, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 126, 129, 130, 131, 132, 133, 134, 136, 137, 138, 139, 141, 142, 143, 144, 146, 149, 150, 152, 153, 155, 156, 157, 159, 160, 162, 163, 165, 166, 168, 169, 171, 172, 174, 175, 176, 178, 179, 181, 182, 183, 184, 185, 186, 189, 190, 192, 193, 194, 195, 197, 198, 199, 201, 203, 204, 205, 206, 207, 208, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 224, 226, 227, 228, 229, 230, 231, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 250, 251, 252, 253, 254, 256, 257, 258, 259, 261, 262, 263, 264, 265, 266, 267, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 280, 282, 283, 284, 285, 286, 287, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 316, 327, 328, 335], "superclass": [3, 28], "implement": [3, 29, 34, 35, 74, 196, 202, 312, 314, 316, 317, 328, 334], "thei": [3, 31, 34, 50, 74, 84, 181, 183, 251, 253, 305, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 334], "were": [3, 28, 31, 33, 35, 50, 52, 74, 82, 304, 312, 313, 314, 315, 316, 317, 318, 322, 323, 325, 327, 328, 329, 334], "__init__": [3, 6, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 47, 48, 49, 50, 51, 52, 53, 54, 55, 64, 71, 74, 88, 117, 119, 124, 126, 149, 152, 155, 159, 162, 165, 168, 171, 174, 178, 189, 190, 192, 197, 198, 199, 201, 203, 204, 206, 207], "method": [3, 6, 14, 15, 16, 17, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 40, 47, 48, 49, 50, 51, 52, 53, 54, 55, 64, 65, 71, 74, 81, 82, 88, 117, 119, 124, 126, 149, 152, 155, 159, 162, 165, 168, 171, 174, 178, 189, 190, 197, 198, 199, 201, 203, 206, 207, 250, 253, 254, 257, 258, 259, 292, 311, 312, 313, 315, 316, 317, 319, 320, 322, 323, 324, 325, 327, 328, 334], "assemble_mat_dict": 3, "assembl": [3, 74, 88, 98, 290, 312, 313, 315, 318, 323, 324, 327, 334], "dictionari": [3, 6, 9, 28, 29, 31, 42, 43, 47, 49, 50, 52, 54, 60, 61, 74, 78, 92, 112, 144, 145, 182, 305, 312, 315, 318, 327, 335], "return": [3, 6, 7, 8, 9, 10, 11, 12, 17, 19, 22, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 92, 110, 111, 112, 114, 115, 117, 119, 123, 126, 132, 142, 143, 144, 145, 146, 181, 182, 183, 184, 185, 186, 193, 195, 201, 211, 212, 213, 214, 215, 216, 220, 233, 238, 240, 245, 249, 250, 251, 253, 254, 256, 257, 258, 259, 263, 289, 290, 298, 301, 302, 303, 304, 305, 307, 309, 310, 312, 313, 314, 315, 316, 318, 323, 325, 334, 335], "output_dict": 3, "content": [3, 34, 325, 327, 328], "file": [3, 6, 9, 13, 21, 28, 32, 34, 41, 45, 47, 49, 50, 51, 52, 53, 54, 55, 58, 59, 60, 61, 65, 74, 76, 77, 78, 85, 88, 116, 117, 119, 123, 127, 130, 131, 132, 133, 134, 135, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 151, 158, 181, 182, 183, 185, 201, 313, 316, 317, 318, 325, 334, 335], "dict": [3, 28, 29, 31, 33, 35, 42, 43, 47, 48, 49, 50, 52, 53, 54, 55, 60, 61, 74, 78, 88, 92, 112, 129, 144, 145, 182], "properti": [3, 17, 19, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 71, 74, 88, 110, 117, 149, 152, 155, 159, 162, 165, 168, 171, 174, 178, 181, 183, 189, 197, 198, 206, 251, 313, 316, 322, 323, 324, 325, 326, 328, 334], "These": [3, 5, 34, 117, 119, 312, 314, 315, 316, 317, 318, 324, 325, 327, 334, 335], "through": [3, 82, 117, 119, 312, 313, 314, 316, 317, 318, 322, 323, 324, 326, 327, 334, 335], "syntax": [3, 312, 334], "classmethod": [3, 6, 28, 29, 34, 50, 71, 74, 88, 126, 149, 152, 155, 159, 162, 165, 168, 171, 174, 178], "load": [3, 6, 9, 28, 41, 47, 49, 50, 52, 54, 59, 74, 77, 78, 88, 117, 119, 128, 201, 313, 314, 317, 319, 322, 327, 334], "filenam": [3, 28, 29, 34, 45, 48, 50, 51, 53, 55, 65, 74, 88, 117, 119, 144, 145, 206, 312, 316], "specifi": [3, 5, 6, 7, 10, 22, 23, 28, 29, 31, 33, 34, 35, 38, 39, 40, 45, 47, 50, 51, 53, 55, 56, 57, 58, 63, 65, 67, 69, 71, 74, 76, 79, 80, 83, 85, 86, 88, 112, 117, 119, 123, 142, 143, 185, 193, 195, 249, 251, 253, 254, 257, 263, 289, 301, 304, 305, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 334], "paramet": [3, 6, 7, 8, 9, 10, 11, 12, 22, 23, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 97, 103, 104, 110, 111, 112, 114, 115, 117, 119, 123, 126, 132, 142, 143, 144, 145, 146, 181, 182, 183, 184, 185, 186, 193, 195, 201, 211, 213, 214, 215, 216, 220, 233, 238, 240, 245, 249, 250, 251, 253, 254, 256, 257, 258, 259, 263, 289, 290, 292, 298, 301, 302, 303, 304, 305, 307, 309, 310, 311, 312, 314, 315, 317, 319, 323, 324, 325, 327, 328, 334], "str": [3, 8, 12, 23, 28, 29, 31, 33, 34, 35, 40, 45, 50, 51, 53, 55, 65, 74, 76, 86, 88, 117, 129, 132, 142, 144, 145, 146, 182, 185, 233, 250, 251, 253, 254, 257, 312, 324], "ha": [3, 28, 45, 70, 117, 119, 123, 220, 302, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334, 335], "extens": [3, 28, 45], "unv": [3, 28, 42, 43, 45, 47, 49, 50, 52, 54, 60, 61, 65, 74, 78, 140, 141, 142, 335], "uff": [3, 28, 45, 140, 335], "univers": [3, 13, 21, 23, 28, 32, 35, 45, 47, 49, 50, 52, 54, 60, 61, 74, 78, 85, 140, 142, 143, 144, 145, 146, 147, 148, 151, 158, 312, 334, 335], "otherwis": [3, 6, 10, 28, 29, 31, 34, 35, 41, 45, 50, 51, 52, 53, 55, 85, 88, 233, 305, 312, 316, 318, 327, 328, 334], "rais": [3, 28, 29, 33, 34, 35, 40, 45, 50, 63, 65, 71, 74, 78, 86, 88, 142, 144, 145, 195, 211, 257, 258, 259, 289], "attributeerror": [3, 28, 45, 50, 65], "doe": [3, 23, 28, 34, 35, 45, 50, 65, 249, 312, 314, 315, 316, 322, 323, 324, 325, 327, 328, 329, 334], "from_unv": [3, 28, 45, 47, 49, 50, 52, 54, 65, 74], "appropri": [3, 28, 33, 35, 45, 312, 313, 314, 317, 323, 328, 334], "cl": [3, 28, 45, 50], "ndenumer": 3, "enumer": [3, 13, 16, 18, 21, 22, 32, 37, 192, 204, 315, 317, 327, 334], "over": [3, 31, 34, 50, 55, 143, 185, 254, 304, 305, 312, 313, 315, 316, 317, 322, 323, 324, 325, 327, 328, 334], "entri": [3, 6, 9, 28, 29, 112, 117, 123, 142, 143, 185, 312, 324, 325, 334], "yield": [3, 68], "tupl": [3, 29, 31, 34, 51, 117, 119, 305], "indic": [3, 6, 23, 26, 28, 34, 35, 71, 88, 97, 110, 114, 115, 117, 119, 197, 312, 316, 317, 318, 325], "correspond": [3, 5, 6, 7, 9, 28, 31, 34, 50, 56, 57, 62, 66, 67, 69, 74, 82, 85, 88, 110, 114, 115, 117, 119, 211, 213, 250, 312, 313, 315, 316, 317, 318, 322, 323, 324, 325, 327, 329, 334], "each": [3, 6, 10, 23, 28, 29, 31, 34, 38, 39, 50, 52, 53, 56, 57, 58, 65, 67, 69, 73, 83, 85, 111, 112, 114, 115, 117, 119, 146, 193, 233, 238, 257, 258, 263, 292, 304, 305, 307, 309, 310, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334], "save": [3, 28, 41, 50, 51, 53, 55, 59, 74, 77, 88, 98, 116, 206, 312, 316, 317, 325, 327, 334], "Will": [3, 28, 142], "append": [3, 28, 50, 74, 117, 312, 313, 315, 317, 318, 323, 325, 327, 334], "npy": [3, 312, 316, 317, 334], "savemat": [3, 28], "matlab": [3, 6, 9, 28, 41, 50, 59, 74, 77], "mat": [3, 6, 9, 28, 41, 50, 59, 74, 77, 135, 138, 185, 335], "name": [3, 7, 29, 34, 35, 47, 50, 53, 56, 57, 67, 69, 88, 117, 119, 123, 233, 312, 315, 316, 317, 323, 324, 325, 334, 335], "color": [4, 47, 49, 50, 52, 54, 56, 57, 67, 69, 74, 80, 161, 162, 165, 178, 185, 312, 313, 316, 317, 318, 322, 323, 325, 328, 334], "scheme": [4, 117, 119, 312, 314, 327], "match": [4, 22, 28, 29, 31, 34, 39, 88, 257, 258, 259, 305, 312, 314, 315, 316, 317, 323, 328, 334], "dea": [4, 263], "coordinatearrai": [5, 7, 9, 10, 11, 28, 29, 34, 35, 38, 50, 71, 72, 74, 81, 82, 85, 87, 88, 129, 194, 312, 313, 314, 316, 324, 325, 334, 335], "node": [5, 6, 7, 8, 10, 12, 28, 34, 46, 49, 50, 52, 54, 57, 58, 62, 65, 66, 67, 69, 74, 83, 85, 86, 88, 89, 110, 111, 117, 119, 123, 161, 170, 178, 185, 268, 313, 314, 315, 316, 317, 318, 325, 327, 328, 335], "number": [5, 8, 12, 23, 28, 29, 31, 34, 35, 40, 48, 50, 51, 52, 53, 55, 64, 71, 74, 81, 83, 88, 89, 97, 98, 101, 102, 110, 111, 112, 114, 115, 117, 119, 123, 142, 143, 144, 145, 185, 193, 195, 213, 223, 233, 249, 250, 251, 253, 254, 257, 258, 259, 263, 298, 305, 307, 309, 310, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334, 335], "direct": [5, 6, 7, 8, 10, 12, 34, 47, 50, 56, 58, 63, 74, 76, 85, 111, 112, 253, 271, 272, 273, 274, 312, 314, 315, 317, 323, 324, 325, 327, 328, 329, 334, 335], "coordin": [5, 6, 7, 8, 10, 12, 22, 28, 29, 34, 35, 38, 39, 46, 47, 50, 52, 56, 58, 62, 63, 66, 67, 71, 72, 73, 74, 81, 82, 83, 85, 88, 110, 117, 119, 161, 162, 167, 194, 214, 215, 220, 268, 313, 314, 315, 316, 317, 318, 323, 324, 325, 327, 329, 335], "degre": [5, 6, 7, 10, 28, 29, 34, 35, 50, 71, 73, 74, 75, 85, 87, 88, 92, 110, 113, 114, 115, 126, 173, 257, 258, 289, 301, 304, 313, 314, 316, 319, 320, 321, 322, 326, 327, 334, 335], "freedom": [5, 6, 7, 10, 28, 29, 34, 35, 50, 71, 73, 74, 75, 85, 87, 88, 92, 110, 113, 114, 115, 126, 173, 257, 258, 301, 304, 313, 314, 316, 319, 320, 321, 326, 327, 334, 335], "consist": [5, 7, 11, 29, 31, 34, 35, 40, 50, 52, 58, 74, 79, 80, 81, 82, 83, 87, 88, 89, 117, 181, 183, 185, 213, 251, 253, 289, 305, 307, 312, 313, 314, 316, 317, 318, 325, 327, 334], "local": [5, 6, 50, 52, 58, 62, 66, 117, 312, 315, 324, 325, 334], "displac": [5, 31, 32, 34, 50, 53, 55, 67, 73, 74, 76, 88, 126, 185, 193, 195, 214, 251, 253, 304, 305, 307, 312, 313, 316, 317, 322, 324, 325, 334, 335], "system": [5, 28, 29, 46, 47, 50, 52, 56, 58, 62, 63, 66, 67, 74, 83, 87, 89, 91, 92, 93, 126, 129, 151, 161, 167, 195, 213, 253, 263, 268, 301, 302, 303, 304, 313, 314, 315, 317, 318, 320, 321, 326, 327, 328, 329, 335], "translat": [5, 6, 10, 28, 37, 50, 57, 111, 181, 183, 213, 216, 312, 315, 325, 329, 334], "rotat": [5, 28, 37, 50, 51, 57, 74, 76, 88, 111, 181, 183, 184, 186, 213, 216, 288, 289, 312, 315, 323, 325, 329, 334, 335], "about": [5, 50, 74, 110, 111, 184, 186, 289, 312, 316, 317, 321, 322, 323, 324, 327, 328, 329, 334], "princip": [5, 50, 312, 334], "axi": [5, 28, 29, 31, 33, 34, 35, 50, 58, 71, 74, 110, 208, 216, 224, 244, 254, 258, 259, 284, 289, 312, 315, 316, 318, 322, 323, 324, 325, 327, 328, 329, 334], "posit": [5, 6, 7, 10, 22, 31, 34, 47, 50, 52, 58, 67, 74, 161, 185, 199, 207, 211, 213, 215, 220, 302, 304, 305, 312, 313, 318, 323, 324, 325, 327, 328, 334], "neg": [5, 7, 31, 34, 302, 304, 305, 318, 324, 327, 328], "also": [5, 13, 28, 29, 31, 33, 34, 35, 46, 50, 74, 87, 135, 251, 257, 268, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334, 335], "empti": [5, 34, 50, 119, 312, 317, 325, 327, 334], "non": [5, 34, 82, 123, 312, 314, 324, 325, 328, 334], "function": [5, 6, 7, 13, 17, 19, 21, 22, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 67, 69, 70, 71, 73, 74, 77, 83, 87, 88, 91, 93, 95, 96, 97, 98, 108, 109, 110, 112, 113, 114, 115, 116, 117, 119, 123, 126, 127, 128, 135, 138, 140, 148, 151, 154, 158, 161, 164, 167, 170, 173, 177, 180, 181, 183, 187, 191, 193, 202, 210, 213, 214, 215, 223, 225, 232, 233, 248, 249, 250, 251, 253, 254, 255, 257, 258, 259, 260, 263, 268, 279, 281, 288, 296, 303, 305, 313, 314, 315, 316, 317, 318, 320, 322, 325, 326, 328, 335], "inform": [6, 20, 34, 39, 47, 49, 50, 52, 54, 74, 117, 119, 123, 132, 144, 145, 154, 181, 183, 185, 312, 313, 316, 317, 322, 323, 324, 325, 327, 329, 334, 335], "101x": [6, 8, 12, 88, 315, 325, 334, 335], "coordinate_arrai": [6, 10, 50, 312, 314, 315, 316, 317, 322, 323, 324, 325, 327, 328, 329, 334], "helper": [6, 38, 39, 47, 49, 52, 53, 54, 71, 74, 312, 314, 316, 335], "creat": [6, 7, 8, 9, 10, 22, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 67, 69, 71, 72, 74, 76, 77, 81, 82, 83, 85, 88, 92, 95, 96, 98, 108, 109, 117, 119, 123, 126, 185, 201, 250, 254, 263, 289, 296, 307, 313, 314, 317, 318, 319, 320, 322, 323, 324, 325, 327, 329, 335], "ab": [6, 31, 304, 305, 312, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334], "sign": [6, 88, 312, 313, 315, 318, 323, 325, 327, 334], "flip": [6, 74, 84, 312, 313, 315, 318, 323, 324, 327], "data_dtyp": [6, 47, 49, 52, 54, 71], "uint64": [6, 47, 49, 52, 54], "int8": 6, "datatyp": [6, 16], "direction_string_arrai": 6, "string": [6, 7, 8, 9, 12, 28, 31, 34, 35, 40, 50, 74, 76, 86, 88, 117, 119, 142, 143, 146, 185, 193, 254, 312, 316, 323, 334], "represent": [6, 8, 12, 74, 86, 88, 123, 146, 290, 312, 313, 323, 324, 325, 328, 334, 335], "repres": [6, 8, 12, 23, 31, 34, 52, 87, 88, 117, 119, 184, 186, 238, 290, 301, 304, 305, 312, 313, 314, 315, 317, 320, 323, 325, 327, 334, 335], "np": [6, 9, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 50, 51, 52, 53, 55, 58, 59, 63, 64, 74, 77, 79, 81, 82, 83, 84, 88, 111, 114, 115, 117, 126, 184, 186, 193, 195, 214, 215, 216, 220, 233, 238, 245, 249, 251, 253, 254, 256, 257, 258, 259, 263, 289, 290, 298, 301, 302, 303, 304, 305, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334], "from_matlab_cellstr": 6, "cellstr_data": [6, 9], "cellstr": [6, 9], "scipi": [6, 9, 23, 28, 33, 34, 35, 40, 41, 50, 59, 74, 77, 233, 254, 301, 312, 318, 325, 328, 334], "io": [6, 9, 28, 41, 50, 59, 74, 77], "loadmat": [6, 9, 41, 50, 59, 74, 77], "cell": [6, 9], "variabl": [6, 9, 23, 31, 34, 35, 74, 76, 88, 117, 119, 123, 305, 312, 316, 317, 325, 334], "built": [6, 9, 312, 314, 325, 335], "provid": [6, 9, 10, 11, 22, 23, 29, 34, 35, 74, 78, 82, 83, 88, 250, 254, 257, 258, 259, 263, 312, 314, 315, 316, 317, 318, 323, 325, 327, 328, 334, 335], "from_nodelist": [6, 312, 334], "1": [6, 7, 8, 10, 12, 16, 18, 20, 21, 23, 25, 28, 29, 31, 32, 33, 34, 35, 37, 39, 40, 48, 50, 52, 53, 55, 56, 57, 58, 67, 69, 74, 84, 85, 88, 94, 99, 101, 106, 110, 111, 117, 119, 123, 126, 159, 162, 165, 168, 178, 182, 185, 192, 193, 195, 197, 204, 208, 213, 216, 220, 224, 228, 238, 243, 246, 254, 257, 258, 261, 263, 264, 266, 267, 280, 284, 289, 292, 301, 302, 304, 305, 307, 308, 311, 312, 313, 314, 315, 316, 317, 318, 322, 328, 329, 334], "2": [6, 7, 8, 10, 12, 16, 18, 21, 29, 31, 32, 34, 50, 56, 57, 67, 69, 74, 86, 88, 100, 110, 112, 123, 126, 129, 185, 192, 193, 195, 197, 206, 211, 213, 220, 251, 257, 258, 263, 289, 302, 304, 305, 307, 311, 312, 313, 314, 315, 316, 317, 318, 321, 322, 328, 329, 334], "flatten": [6, 7, 10, 28, 33, 50, 312, 314, 323, 324, 325, 329, 334], "true": [6, 10, 28, 29, 31, 33, 34, 35, 41, 42, 43, 44, 47, 49, 50, 51, 52, 53, 54, 55, 71, 74, 78, 81, 83, 88, 99, 101, 111, 114, 115, 117, 119, 137, 146, 185, 193, 195, 197, 206, 221, 233, 242, 246, 262, 263, 289, 299, 305, 312, 314, 315, 316, 318, 322, 323, 324, 325, 327, 328, 334], "set": [6, 10, 22, 23, 28, 29, 31, 33, 34, 35, 48, 50, 52, 53, 55, 58, 64, 73, 74, 75, 76, 79, 81, 83, 86, 88, 89, 113, 114, 115, 117, 188, 211, 213, 254, 292, 302, 305, 307, 309, 314, 316, 319, 324, 325, 327, 328, 329, 334, 335], "iter": [6, 10, 29, 31, 34, 49, 50, 52, 54, 57, 58, 68, 69, 74, 76, 80, 88, 89, 114, 115, 117, 119, 123, 142, 143, 144, 145, 146, 181, 183, 185, 304, 305, 312, 314, 316], "list": [6, 10, 29, 34, 35, 41, 50, 55, 56, 57, 67, 68, 69, 71, 74, 75, 76, 80, 88, 97, 114, 115, 117, 119, 123, 129, 142, 143, 144, 145, 181, 183, 185, 189, 312, 313, 315, 316, 317, 318, 323, 334, 335], "default": [6, 10, 23, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 48, 50, 51, 52, 53, 55, 58, 63, 71, 74, 76, 80, 81, 83, 86, 88, 89, 111, 114, 115, 117, 119, 126, 144, 145, 146, 185, 193, 195, 220, 233, 251, 253, 254, 257, 258, 263, 289, 292, 304, 305, 307, 315, 316, 317, 319, 334], "three": [6, 10, 181, 183, 213, 312, 313, 318, 323, 324, 325, 327, 328, 329, 334], "x": [6, 7, 8, 10, 12, 29, 34, 50, 52, 56, 74, 76, 88, 110, 111, 117, 126, 181, 183, 184, 186, 195, 206, 208, 211, 212, 213, 220, 239, 244, 249, 250, 253, 254, 263, 280, 289, 292, 297, 304, 312, 314, 316, 317, 322, 323, 324, 325, 327, 328, 329, 334], "y": [6, 7, 8, 10, 12, 29, 34, 50, 52, 74, 76, 88, 110, 111, 126, 184, 186, 206, 208, 212, 213, 220, 289, 292, 312, 317, 322, 324, 325, 327, 329, 334], "z": [6, 7, 8, 10, 12, 34, 50, 52, 58, 74, 76, 88, 110, 111, 184, 186, 206, 212, 213, 220, 289, 292, 302, 312, 315, 317, 324, 325, 327, 329, 334], "bool": [6, 7, 10, 28, 29, 31, 33, 34, 35, 41, 42, 43, 44, 47, 49, 50, 51, 52, 53, 54, 55, 58, 71, 74, 78, 81, 88, 111, 114, 115, 117, 119, 123, 144, 145, 146, 185, 193, 195, 201, 233, 289, 305, 316], "prior": [6, 10, 33, 35, 325, 328, 334], "output": [6, 10, 29, 41, 50, 55, 59, 74, 77, 81, 82, 86, 88, 112, 123, 126, 132, 135, 142, 143, 144, 145, 180, 185, 238, 250, 251, 253, 254, 292, 312, 313, 314, 316, 317, 318, 327, 328, 334, 335], "fals": [6, 7, 10, 28, 29, 31, 33, 34, 35, 41, 44, 48, 50, 51, 53, 55, 58, 71, 74, 81, 88, 111, 114, 115, 117, 119, 123, 144, 145, 159, 185, 189, 193, 195, 197, 206, 207, 219, 221, 224, 233, 252, 286, 289, 305, 308, 312, 315, 318, 322, 323, 324, 334], "dimens": [6, 10, 28, 34, 42, 43, 85, 114, 115, 117, 220, 233, 238, 245, 312, 318, 325, 334], "one": [6, 10, 28, 29, 31, 33, 35, 41, 50, 74, 75, 84, 114, 115, 117, 123, 140, 193, 195, 251, 253, 257, 258, 263, 302, 312, 313, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 329, 334], "larger": [6, 10, 314, 316, 317, 322, 323, 325, 328, 329, 334], "input": [6, 7, 10, 29, 34, 35, 38, 50, 56, 57, 67, 69, 74, 75, 80, 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50, 52, 54, 58, 59, 60, 61, 74, 75, 77, 78, 87, 88, 92, 117, 119, 123, 132, 144, 145, 182, 193, 195, 220, 233, 238, 263, 304, 305, 309, 310, 312, 313, 314, 315, 316, 317, 318, 320, 322, 323, 324, 325, 327, 328, 329, 334, 335], "specif": [13, 28, 88, 146, 193, 314, 316, 322, 324, 325, 327, 334, 335], "capabl": [13, 312, 313, 315, 316, 318, 323, 327, 334, 335], "connect": [13, 22, 49, 50, 54, 57, 58, 69, 88, 110, 117, 119, 123, 165, 312, 313, 315, 317, 318, 324, 325, 334], "format": [13, 50, 53, 60, 61, 74, 85, 86, 88, 117, 135, 140, 142, 143, 146, 147, 312, 313, 315, 316, 317, 318, 322, 323, 324, 325, 327, 328, 334, 335], "parent": [14, 15, 24, 25, 203], "powerspectraldensityarrai": [16, 34, 314], "compare_data": [16, 29], "qmainwindow": [16, 22, 189, 198, 201, 207], "enum": [16, 18, 21, 28, 32, 37, 192, 204], "coher": [16, 17, 21, 27, 29, 314, 317, 328, 334], "imaginari": [16, 18, 22, 312, 317, 323, 324, 327, 334], "16": [16, 21, 32, 50, 292, 312, 313, 315, 317, 323, 325, 327, 328, 329, 334], "magnitud": [16, 18, 22, 29, 193, 206, 314, 323, 324, 325, 327, 328, 334], "phase": [16, 18, 22, 29, 34, 35, 53, 263, 267, 312, 313, 314, 317, 322, 323, 324, 325, 327, 328, 329, 334], "4": [16, 18, 21, 24, 25, 31, 34, 47, 50, 55, 56, 74, 88, 117, 123, 126, 168, 181, 183, 185, 211, 213, 304, 305, 312, 313, 314, 315, 316, 317, 318, 322, 324, 327, 328, 329, 334], "real": [16, 18, 22, 28, 35, 71, 74, 85, 117, 193, 195, 312, 314, 317, 322, 323, 324, 325, 326, 327, 328, 334], "8": [16, 21, 29, 31, 32, 33, 34, 50, 53, 55, 83, 88, 89, 123, 185, 257, 258, 304, 305, 312, 313, 315, 316, 317, 322, 323, 324, 325, 327, 328, 334], "clear_select": 16, "event": [16, 22, 189, 199, 207], "coherence_st": 16, "connectui": 16, "deselect_diagon": 16, "deselect_lower_triangular": 16, "deselect_upper_triangular": 16, "extend_selection_matrix": 16, "find_function_typ": 16, "imaginary_st": 16, "initui": 16, "init_matrix_plot": 16, "invert_select": 16, "magnitude_st": 16, "phase_st": 16, "plot": [16, 18, 22, 28, 29, 31, 33, 35, 46, 48, 50, 51, 53, 55, 71, 74, 81, 88, 97, 117, 119, 126, 201, 206, 213, 224, 268, 305, 313, 314, 316, 317, 318, 319, 322, 323, 324, 327, 328, 329, 335], "plot_selected_funct": 16, "real_stat": 16, "select_diagon": 16, "select_lower_triangular": 16, "select_plot": 16, "select_upper_triangular": 16, "selector_click": 16, "selector_unclick": 16, "settext": 16, "state_chang": 16, "update_select": [16, 189], "nelement": [17, 19, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36], "function_typ": [17, 19, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 174], "variou": [18, 28, 35, 40, 71, 74, 81, 82, 185, 260, 312, 314, 316, 318, 320, 325, 326, 327, 328, 329, 334, 335], "wai": [18, 312, 314, 315, 316, 317, 322, 323, 325, 327, 328, 329, 334, 335], "magphas": 18, "5": [18, 21, 24, 25, 28, 31, 32, 33, 34, 35, 40, 48, 50, 81, 88, 94, 97, 103, 104, 106, 123, 185, 233, 254, 304, 305, 312, 313, 314, 315, 316, 317, 318, 322, 323, 324, 327, 328, 329, 334], "realimag": 18, 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"sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_displacement_velocity": [[307, "sdynpy-signal-processing-sdynpy-srs-sum-decayed-sines-displacement-velocity"]], "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_minimize": [[308, "sdynpy-signal-processing-sdynpy-srs-sum-decayed-sines-minimize"]], "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction": [[309, "sdynpy-signal-processing-sdynpy-srs-sum-decayed-sines-reconstruction"]], "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_compensation": [[310, "sdynpy-signal-processing-sdynpy-srs-sum-decayed-sines-reconstruction-with-compensation"]], "Bibliography": [[311, "bibliography"]], "Airplane Modal Test": [[312, "airplane-modal-test"]], "Contents": [[312, "contents"], [313, "contents"], [314, "contents"], [316, "contents"], [317, "contents"], [318, "contents"], [334, "contents"]], "Imports": [[312, "imports"], [314, "imports"], [315, "Imports"], [316, "imports"], [334, "imports"]], "Default Plotting Options": [[312, "default-plotting-options"]], "Load the Finite Element Model": [[312, "load-the-finite-element-model"]], "Convert Finite Element Model to SDynPy Objects": [[312, "convert-finite-element-model-to-sdynpy-objects"]], "Creating a Geometry from an Exodus file": [[312, "creating-a-geometry-from-an-exodus-file"]], "Exploring the Geometry object": [[312, "exploring-the-geometry-object"]], "Nodes": [[312, "nodes"], [334, "nodes"]], "Coordinate Systems": [[312, "coordinate-systems"], [334, "coordinate-systems"]], "Elements": [[312, "elements"], [334, "elements"]], "Tracelines": [[312, "tracelines"], [334, "tracelines"]], "Visualizing Degrees of Freedom on a Geometry": [[312, "visualizing-degrees-of-freedom-on-a-geometry"]], "Coordinates": [[312, "coordinates"], [334, "coordinates"]], "Plotting Coordinates": [[312, "plotting-coordinates"], [334, "plotting-coordinates"]], "Creating Shapes from an Exodus File": [[312, "creating-shapes-from-an-exodus-file"]], "Shapes": [[312, "shapes"], [334, "shapes"]], "Correcting Negative Frequencies and Array Views versus Copies": [[312, "correcting-negative-frequencies-and-array-views-versus-copies"]], "Transforming Shape Coordinate Systems": [[312, "transforming-shape-coordinate-systems"]], "Optimizing Instrumentation for Test": [[312, "optimizing-instrumentation-for-test"], [314, "optimizing-instrumentation-for-test"]], "Creating a Test Geometry": [[312, "creating-a-test-geometry"], [316, "creating-a-test-geometry"]], "OOPS! An Instrumentation Error!": [[312, "oops-an-instrumentation-error"]], "Running a Virtual Experiment: Rigid Body Checkouts": [[312, "running-a-virtual-experiment-rigid-body-checkouts"]], "Creating a System Object to Integrate": [[312, "creating-a-system-object-to-integrate"]], "Exploring the System Object": [[312, "exploring-the-system-object"]], "Setting up the Integration and Forcing Function": [[312, "setting-up-the-integration-and-forcing-function"]], "Running the Integration to Generate Synthetic Test Data": [[312, "running-the-integration-to-generate-synthetic-test-data"]], "Exploring Time Data Objects": [[312, "exploring-time-data-objects"]], "Identifying Bad Geometry with Rigid Body Checkouts in SDynPy": [[312, "identifying-bad-geometry-with-rigid-body-checkouts-in-sdynpy"]], "Running a Virtual Experiment: Modal Testing": [[312, "running-a-virtual-experiment-modal-testing"]], "Fitting Modes using PolyMax": [[312, "fitting-modes-using-polymax"]], "Running PolyMax": [[312, "running-polymax"]], "Comparing Test and Finite Element Modes": [[312, "comparing-test-and-finite-element-modes"]], "SEREP Expansion": [[312, "serep-expansion"]], "Substructuring using Craig-Bampton Superelements from Sierra/SD": [[313, "substructuring-using-craig-bampton-superelements-from-sierra-sd"]], "Setting up the Problem": [[313, "setting-up-the-problem"], [318, "setting-up-the-problem"]], "Analyzing Superelements in SDynPy": [[313, "analyzing-superelements-in-sdynpy"]], "Combining Component Models": [[313, "combining-component-models"]], "Summary": [[313, "summary"], [314, "summary"], [317, "summary"], [318, "summary"], [322, "Summary"], [323, "Summary"], [324, "Summary"], [325, "Summary"], [327, "Summary"]], "MIMO Random Vibration": [[314, "mimo-random-vibration"]], "Simulating an Environment": [[314, "simulating-an-environment"]], "Selecting Shaker Excitation Locations": [[314, "selecting-shaker-excitation-locations"]], "Performing Vibration Control": [[314, "performing-vibration-control"]], "Simple Vibration Control using the Pseudoinverse": [[314, "simple-vibration-control-using-the-pseudoinverse"]], "Tikhonov Regularization": [[314, "tikhonov-regularization"]], "Weighting the Shakers": [[314, "weighting-the-shakers"]], "Response Weighting": [[314, "response-weighting"]], "Buzz Test Method": [[314, "buzz-test-method"]], "Running a Test": [[314, "running-a-test"]], "Model Reduction": [[315, "Model-Reduction"]], "Set up 3D Plots": [[315, "Set-up-3D-Plots"]], "Collect and Set Up Models": [[315, "Collect-and-Set-Up-Models"]], "Creating Truth Data": [[315, "Creating-Truth-Data"]], "Selecting Degrees of Freedom for Reduction and Substructuring": [[315, "Selecting-Degrees-of-Freedom-for-Reduction-and-Substructuring"]], "Perform the model reductions": [[315, "Perform-the-model-reductions"]], "Guyan Reduction": [[315, "Guyan-Reduction"]], "Dynamic Reduction": [[315, "Dynamic-Reduction"]], "Craig-Bampton Reduction": [[315, "Craig-Bampton-Reduction"]], "Component Mode Synthesis": [[315, "Component-Mode-Synthesis"]], "Comparison of Results": [[315, "Comparison-of-Results"]], "Rattlesnake Demonstration": [[316, "rattlesnake-demonstration"]], "Load in Test Data": [[316, "load-in-test-data"]], "Computing FRFs": [[316, "computing-frfs"]], "Fitting Modes to the FRFs": [[316, "fitting-modes-to-the-frfs"]], "Analyzing Modal Parameters": [[316, "analyzing-modal-parameters"]], "Documentation Generation": [[316, "documentation-generation"]], "Exporting to Exodus": [[316, "exporting-to-exodus"]], "Analysis of Transient Finite Element Results": [[317, "analysis-of-transient-finite-element-results"]], "Problem Set Up": [[317, "problem-set-up"]], "Analyzing Model Responses": [[317, "analyzing-model-responses"]], "Pseudorandom Analysis": [[317, "pseudorandom-analysis"]], "Random Analysis": [[317, "random-analysis"]], "Comparison of Excitation Techniques": [[317, "comparison-of-excitation-techniques"]], "Substructuring using the Transmission Simulator Method": [[318, "substructuring-using-the-transmission-simulator-method"], [318, "id1"]], "Loading the results into SDynPy": [[318, "loading-the-results-into-sdynpy"]], "Reducing to Test Degrees of Freedom": [[318, "reducing-to-test-degrees-of-freedom"]], "SDynpy Examples": [[319, "sdynpy-examples"]], "Contents:": [[319, null], [320, null], [321, null], [326, null]], "Welcome to SDynPy\u2019s documentation!": [[320, "welcome-to-sdynpy-s-documentation"]], "Information": [[320, "information"]], "Indices and tables": [[320, "indices-and-tables"]], "Modal Tutorials": [[321, "modal-tutorials"]], "Modal Tutorial 01: What is modal analysis?": [[322, "Modal-Tutorial-01:-What-is-modal-analysis?"]], "So what is Modal Analysis?": [[322, "So-what-is-Modal-Analysis?"]], "How do we find the modes of the system?": [[322, "How-do-we-find-the-modes-of-the-system?"]], "Modal Tutorial 02: Basics of Vibrations": [[323, "Modal-Tutorial-02:-Basics-of-Vibrations"]], "A Simple Undamped Spring-Mass System": [[323, "A-Simple-Undamped-Spring-Mass-System"]], "Adding Damping": [[323, "Adding-Damping"], [324, "Adding-Damping"]], "Response of Single Degree of Freedom Systems to Harmonic Excitation": [[323, "Response-of-Single-Degree-of-Freedom-Systems-to-Harmonic-Excitation"]], "Undamped Response to Harmonic Excitation": [[323, "Undamped-Response-to-Harmonic-Excitation"]], "Damped Response to Harmonic Excitation": [[323, "Damped-Response-to-Harmonic-Excitation"]], "Comparison between Displacement, Velocity, and Acceleration Frequency Response Functions": [[323, "Comparison-between-Displacement,-Velocity,-and-Acceleration-Frequency-Response-Functions"]], "Working with Single Degree of Freedom Systems in SDynPy": [[323, "Working-with-Single-Degree-of-Freedom-Systems-in-SDynPy"]], "Homework Problems": [[323, "Homework-Problems"], [324, "Homework-Problems"], [325, "Homework-Problems"], [327, "Homework-Problems"], [328, "Homework-Problems"]], "1. Set up a System": [[323, "1.-Set-up-a-System"]], "2. Compute the Frequency Response Function": [[323, "2.-Compute-the-Frequency-Response-Function"]], "3. Investigate Design Changes": [[323, "3.-Investigate-Design-Changes"]], "4. Investigate Damping Changes": [[323, "4.-Investigate-Damping-Changes"]], "Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom": [[324, "Modal-Tutorial-03:-Vibrations-of-Systems-with-Multiple-Degrees-of-Freedom"]], "A Spring Mass System with 2 Degrees of Freedom": [[324, "A-Spring-Mass-System-with-2-Degrees-of-Freedom"], [325, "A-Spring-Mass-System-with-2-Degrees-of-Freedom"]], "Free Response of Multiple Degree of Freedom Systems": [[324, "Free-Response-of-Multiple-Degree-of-Freedom-Systems"]], "Multiple Degree of Freedom Response to Harmonic Excitation": [[324, "Multiple-Degree-of-Freedom-Response-to-Harmonic-Excitation"]], "Working with Multiple Degree of Freedom Systems in SDynPy": [[324, "Working-with-Multiple-Degree-of-Freedom-Systems-in-SDynPy"]], "1. Writing Equations of Motion": [[324, "1.-Writing-Equations-of-Motion"]], "2. Compute the Frequency Response Function Matrix": [[324, "2.-Compute-the-Frequency-Response-Function-Matrix"]], "3. Identifying Drive Points": [[324, "3.-Identifying-Drive-Points"]], "Modal Tutorial 04: Modal Analysis": [[325, "Modal-Tutorial-04:-Modal-Analysis"]], "Solving for the Eigenvalues": [[325, "Solving-for-the-Eigenvalues"]], "Solving for the Eigenvectors": [[325, "Solving-for-the-Eigenvectors"]], "More about Modes": [[325, "More-about-Modes"]], "A series of Single Degree of Freedom Systems": [[325, "A-series-of-Single-Degree-of-Freedom-Systems"]], "What About Damping?": [[325, "What-About-Damping?"]], "Rayleigh Damping": [[325, "Rayleigh-Damping"]], "Modal Damping": [[325, "Modal-Damping"]], "General Damping Matrices": [[325, "General-Damping-Matrices"]], "Response to Harmonic Excitation": [[325, "Response-to-Harmonic-Excitation"]], "Modal Analysis in SDynPy": [[325, "Modal-Analysis-in-SDynPy"]], "1. Construct the generalized eigenvalue problem": [[325, "1.-Construct-the-generalized-eigenvalue-problem"]], "2. Compute and solve the characteristic polynomial": [[325, "2.-Compute-and-solve-the-characteristic-polynomial"]], "3. Compute eigenvector": [[325, "3.-Compute-eigenvector"]], "4. Perform the same operations in SDynPy and plot the mode shapes": [[325, "4.-Perform-the-same-operations-in-SDynPy-and-plot-the-mode-shapes"]], "5. Compute the FRF matrix from modal quantities": [[325, "5.-Compute-the-FRF-matrix-from-modal-quantities"]], "Modal Tutorial 05: Experimental Modal Analysis": [[326, "modal-tutorial-05-experimental-modal-analysis"]], "Modal Tutorial 05 Part A: Computing Frequency Response from Time Data": [[327, "Modal-Tutorial-05-Part-A:-Computing-Frequency-Response-from-Time-Data"]], "Sinusoidal Excitation": [[327, "Sinusoidal-Excitation"]], "Broadband Excitation": [[327, "Broadband-Excitation"]], "Computing FRFs using FFTs": [[327, "Computing-FRFs-using-FFTs"]], "Computing FRFs with Power Spectra": [[327, "Computing-FRFs-with-Power-Spectra"]], "Computing Power Spectra": [[327, "Computing-Power-Spectra"]], "The H1 Technique": [[327, "The-H1-Technique"]], "The H2 Technique": [[327, "The-H2-Technique"]], "The H3 Technique": [[327, "The-H3-Technique"]], "The HCD Technique": [[327, "The-HCD-Technique"]], "The Hv Technique": [[327, "The-Hv-Technique"]], "Coherence": [[327, "Coherence"]], "Computing Frequency Response Functions using SDynPy": [[327, "Computing-Frequency-Response-Functions-using-SDynPy"]], "SignalProcessingGUI": [[327, "SignalProcessingGUI"]], "1. Simulate a Test to Generate Time Histories": [[327, "1.-Simulate-a-Test-to-Generate-Time-Histories"]], "2. Compute Frequency Response Functions": [[327, "2.-Compute-Frequency-Response-Functions"]], "Modal Tutorial 05 Part B: Types of Excitation for Experimental Modal Analysis": [[328, "Modal-Tutorial-05-Part-B:-Types-of-Excitation-for-Experimental-Modal-Analysis"]], "Requirements for the FFT": [[328, "Requirements-for-the-FFT"]], "Considerations for Multiple-Input Excitation": [[328, "Considerations-for-Multiple-Input-Excitation"]], "Types of Modal Excitation": [[328, "Types-of-Modal-Excitation"]], "Random Vibration": [[328, "Random-Vibration"]], "Pseudorandom Excitation": [[328, "Pseudorandom-Excitation"]], "Chirp Excitation": [[328, "Chirp-Excitation"]], "Burst Random Excitation": [[328, "Burst-Random-Excitation"]], "Impulse Excitation": [[328, "Impulse-Excitation"]], "Modal Tutorial 05 Part C: Selecting Excitation and Response Degrees of Freedom": [[329, "Modal-Tutorial-05-Part-C:-Selecting-Excitation-and-Response-Degrees-of-Freedom"]], "Optimal Excitation Theory": [[329, "Optimal-Excitation-Theory"]], "Computing Optimal Excitation Locations": [[329, "Computing-Optimal-Excitation-Locations"]], "Optimal Response Theory": [[329, "Optimal-Response-Theory"]], "Computing Optimal Responses": [[329, "Computing-Optimal-Responses"]], "Modal Tutorial 05 Part D: Data Quality Checks": [[330, "Modal-Tutorial-05-Part-D:-Data-Quality-Checks"]], "Modal Tutorial 05 Part E: Fitting Modes to Data": [[331, "Modal-Tutorial-05-Part-E:-Fitting-Modes-to-Data"]], "Modal Tutorial 05 Part F: Comparing Test and Analysis": [[332, "Modal-Tutorial-05-Part-F:-Comparing-Test-and-Analysis"]], "SDynPy Programming Interface": [[333, "sdynpy-programming-interface"]], "SDynPy Showcase": [[334, "sdynpy-showcase"]], "Creating a Simple Beam Model": [[334, "creating-a-simple-beam-model"]], "Geometry in SDynPy": [[334, "geometry-in-sdynpy"]], "Plotting Geometry": [[334, "plotting-geometry"]], "Systems in SDynPy": [[334, "systems-in-sdynpy"]], "Computing Modes of the System": [[334, 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\ No newline at end of file
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317, 318, 319, 320, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 336, 337, 338, 339, 340, 341, 343, 344, 350, 351], "inherit": [2, 3, 28, 327, 350], "from": [2, 3, 6, 8, 9, 13, 17, 23, 27, 28, 29, 33, 34, 35, 40, 41, 42, 43, 45, 47, 48, 49, 50, 52, 53, 54, 58, 59, 60, 61, 62, 63, 65, 66, 67, 76, 78, 79, 80, 81, 84, 85, 86, 88, 90, 91, 99, 100, 101, 103, 115, 118, 121, 122, 124, 128, 132, 133, 140, 147, 148, 149, 150, 178, 179, 185, 186, 187, 188, 189, 190, 191, 193, 194, 197, 198, 201, 203, 206, 212, 222, 223, 224, 225, 226, 231, 244, 249, 262, 265, 266, 268, 275, 319, 320, 322, 324, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 341, 343, 344, 350, 351], "main": [2, 50, 55, 332, 338, 339, 340, 343, 348, 350], "contribut": [2, 29, 198, 327, 329, 338, 339, 340, 342, 343, 344, 348], "allow": [2, 3, 22, 28, 31, 34, 38, 50, 55, 76, 77, 190, 201, 206, 212, 302, 320, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 348, 350], "user": [2, 22, 23, 33, 35, 50, 55, 58, 103, 122, 124, 190, 212, 327, 330, 331, 332, 340, 342, 343, 348, 350, 351], "access": [2, 3, 128, 327, 331, 332, 338, 340, 350], "field": [2, 3, 13, 34, 50, 66, 179, 327, 331, 338], "attribut": [2, 3, 6, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 45, 47, 48, 49, 51, 52, 53, 54, 55, 56, 57, 68, 70, 73, 76, 77, 91, 122, 154, 157, 160, 164, 167, 170, 173, 176, 179, 183, 194, 195, 197, 200, 208, 209, 210, 212, 214, 215, 217, 218, 327, 331, 332, 340, 342, 350], "notat": [2, 348], "rather": [2, 31, 51, 53, 55, 203, 263, 265, 275, 320, 327, 329, 330, 332, 333, 338, 339, 340, 342, 343, 344, 348, 350], "than": [2, 6, 10, 28, 29, 31, 33, 34, 35, 41, 50, 51, 53, 55, 76, 122, 124, 190, 203, 263, 265, 271, 275, 280, 302, 320, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 348, 350], "index": [2, 3, 22, 23, 28, 31, 33, 35, 50, 53, 101, 119, 120, 122, 124, 224, 304, 327, 329, 331, 332, 333, 335, 338, 340, 344, 348], "e": [2, 6, 7, 8, 12, 17, 22, 27, 28, 29, 31, 33, 34, 35, 40, 50, 56, 57, 58, 68, 70, 91, 97, 115, 116, 117, 119, 120, 122, 124, 244, 263, 266, 317, 318, 319, 320, 322, 327, 331, 332, 333, 336, 338, 339, 340, 341, 342, 343, 348, 350, 351], "g": [2, 6, 8, 12, 17, 22, 27, 28, 29, 31, 33, 34, 35, 40, 50, 58, 115, 119, 120, 122, 124, 244, 249, 263, 266, 317, 318, 319, 320, 322, 327, 331, 333, 338, 340, 342, 343, 348, 350, 351], "shape": [3, 6, 7, 10, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 39, 47, 48, 49, 50, 52, 53, 54, 55, 56, 63, 64, 67, 73, 75, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 100, 106, 109, 111, 119, 120, 122, 173, 175, 194, 195, 198, 199, 201, 203, 204, 205, 206, 212, 231, 240, 262, 263, 265, 266, 273, 275, 278, 302, 317, 318, 319, 320, 328, 329, 330, 331, 332, 333, 335, 336, 337, 338, 342, 343, 344, 351], "dtype": [3, 73, 76, 173, 327, 330, 331, 337, 340, 342, 344, 348, 350], "float": [3, 17, 27, 28, 29, 31, 33, 34, 35, 40, 48, 50, 51, 52, 53, 55, 76, 84, 86, 91, 92, 116, 117, 122, 131, 134, 189, 190, 191, 201, 203, 231, 244, 249, 251, 257, 261, 262, 266, 268, 269, 270, 271, 275, 280, 302, 304, 313, 317, 318, 319, 320, 322, 324, 325, 327, 338, 340, 350], "buffer": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 47, 49, 52, 54, 73, 74, 76], "none": [3, 6, 7, 16, 17, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 40, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 64, 65, 68, 70, 73, 74, 76, 77, 79, 82, 83, 86, 87, 88, 89, 91, 92, 100, 101, 102, 108, 109, 111, 112, 117, 122, 124, 128, 131, 134, 135, 136, 137, 139, 141, 142, 143, 144, 147, 149, 150, 154, 164, 170, 181, 186, 194, 197, 201, 202, 203, 206, 208, 212, 217, 228, 230, 235, 237, 238, 239, 240, 241, 242, 244, 254, 255, 256, 258, 262, 263, 264, 266, 269, 270, 273, 275, 277, 278, 290, 298, 302, 307, 315, 316, 319, 320, 323, 327, 328, 329, 330, 331, 332, 333, 337, 338, 342, 348, 350], "offset": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 47, 49, 50, 52, 54, 73, 74, 76, 91, 327, 328, 330, 333, 350], "0": [3, 6, 7, 17, 18, 19, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 40, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 68, 70, 73, 74, 76, 84, 88, 89, 91, 97, 100, 101, 106, 107, 108, 109, 110, 111, 117, 122, 131, 154, 167, 173, 187, 190, 197, 200, 201, 203, 204, 205, 208, 215, 217, 224, 228, 230, 232, 239, 244, 258, 263, 266, 273, 275, 278, 279, 280, 302, 304, 307, 314, 317, 318, 319, 320, 323, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "stride": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 47, 49, 52, 54, 73, 74, 76], "order": [3, 6, 17, 19, 20, 23, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 47, 49, 50, 52, 54, 73, 74, 76, 115, 116, 131, 186, 188, 190, 253, 266, 297, 302, 327, 329, 331, 332, 333, 336, 337, 338, 339, 340, 342, 343, 344, 350, 351], "sourc": [3, 6, 7, 8, 9, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 63, 64, 65, 67, 68, 69, 70, 73, 74, 76, 77, 82, 84, 85, 86, 88, 89, 91, 95, 97, 100, 101, 102, 104, 105, 106, 107, 108, 109, 110, 111, 112, 115, 116, 117, 119, 120, 122, 123, 124, 125, 126, 127, 128, 129, 131, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 146, 147, 148, 149, 151, 154, 155, 157, 158, 160, 161, 162, 164, 165, 167, 168, 170, 171, 173, 174, 176, 177, 179, 180, 181, 183, 184, 186, 187, 188, 189, 190, 191, 194, 195, 197, 198, 200, 201, 202, 203, 204, 205, 206, 208, 209, 210, 212, 214, 215, 216, 217, 218, 219, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 235, 237, 238, 239, 240, 241, 242, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 261, 262, 263, 264, 265, 266, 268, 269, 270, 271, 273, 274, 275, 276, 277, 278, 279, 280, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 293, 295, 296, 297, 298, 299, 300, 302, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 331, 342, 343, 348, 351], "superclass": [3, 28], "implement": [3, 29, 34, 35, 76, 207, 213, 327, 329, 331, 332, 343, 348, 350], "thei": [3, 28, 31, 34, 50, 76, 87, 186, 188, 263, 265, 320, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 348, 350], "were": [3, 28, 31, 33, 35, 50, 52, 76, 85, 198, 319, 327, 328, 329, 330, 331, 332, 333, 337, 338, 340, 342, 343, 344, 348, 350], "__init__": [3, 6, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 47, 48, 49, 50, 51, 52, 53, 54, 55, 65, 73, 76, 77, 91, 122, 124, 129, 131, 154, 157, 160, 164, 167, 170, 173, 176, 179, 183, 194, 195, 197, 200, 208, 209, 210, 212, 214, 215, 217, 218], "method": [3, 6, 14, 15, 16, 17, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 40, 47, 48, 49, 50, 51, 52, 53, 54, 55, 65, 66, 73, 76, 77, 84, 85, 91, 122, 124, 129, 131, 154, 157, 160, 164, 167, 170, 173, 176, 179, 183, 194, 195, 197, 208, 209, 210, 212, 214, 217, 218, 262, 265, 266, 269, 270, 271, 302, 307, 326, 327, 328, 330, 331, 332, 334, 335, 337, 338, 339, 340, 342, 343, 348, 350], "assemble_mat_dict": 3, "assembl": [3, 76, 91, 103, 305, 327, 328, 330, 333, 338, 339, 342, 348, 350], "dictionari": [3, 6, 9, 28, 29, 31, 42, 43, 47, 49, 50, 52, 54, 61, 62, 76, 81, 95, 117, 149, 150, 187, 197, 320, 327, 330, 333, 342, 351], "return": [3, 6, 7, 8, 9, 10, 11, 12, 17, 19, 22, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 95, 101, 115, 116, 117, 119, 120, 122, 124, 128, 131, 137, 147, 148, 149, 150, 151, 179, 186, 187, 188, 189, 190, 191, 197, 198, 201, 203, 204, 205, 206, 212, 222, 223, 224, 225, 226, 227, 231, 244, 249, 251, 253, 257, 261, 262, 263, 265, 266, 268, 269, 270, 271, 275, 280, 302, 304, 305, 313, 316, 317, 318, 319, 320, 322, 324, 325, 327, 328, 329, 330, 331, 333, 338, 340, 348, 350, 351], "output_dict": 3, "content": [3, 34, 340, 342, 343], "file": [3, 6, 9, 13, 21, 28, 32, 34, 41, 45, 47, 49, 50, 51, 52, 53, 54, 55, 58, 59, 60, 61, 62, 66, 71, 76, 79, 80, 81, 88, 91, 101, 121, 122, 124, 128, 132, 135, 136, 137, 138, 139, 140, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 156, 163, 179, 186, 187, 188, 190, 198, 212, 328, 331, 332, 333, 340, 350, 351], "dict": [3, 28, 29, 31, 33, 35, 42, 43, 47, 48, 49, 50, 52, 53, 54, 55, 61, 62, 76, 81, 91, 95, 117, 134, 149, 150, 187, 197, 262, 266, 302], "properti": [3, 17, 19, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 73, 76, 91, 115, 122, 154, 157, 160, 164, 167, 170, 173, 176, 179, 183, 186, 188, 194, 197, 208, 209, 217, 263, 328, 331, 337, 338, 339, 340, 341, 343, 348, 350], "These": [3, 5, 34, 50, 58, 122, 124, 327, 329, 330, 331, 332, 333, 339, 340, 342, 348, 350, 351], "through": [3, 50, 58, 85, 122, 124, 327, 328, 329, 331, 332, 333, 337, 338, 339, 341, 342, 348, 350, 351], "syntax": [3, 327, 348, 350], "classmethod": [3, 6, 28, 29, 34, 50, 73, 76, 91, 131, 154, 157, 160, 164, 167, 170, 173, 176, 179, 183], "load": [3, 6, 9, 28, 41, 47, 49, 50, 52, 54, 60, 76, 80, 81, 91, 122, 124, 133, 198, 212, 328, 329, 332, 334, 337, 342, 350], "filenam": [3, 28, 29, 34, 45, 48, 50, 51, 53, 55, 66, 76, 91, 122, 124, 149, 150, 198, 217, 327, 331], "specifi": [3, 5, 6, 7, 10, 17, 22, 23, 27, 28, 29, 31, 33, 34, 35, 38, 39, 40, 45, 47, 50, 51, 53, 55, 56, 57, 58, 59, 64, 66, 68, 70, 73, 76, 77, 79, 82, 83, 86, 88, 89, 91, 101, 117, 122, 124, 128, 147, 148, 190, 197, 201, 203, 206, 244, 261, 262, 263, 265, 266, 269, 275, 280, 304, 316, 319, 320, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 348, 350], "paramet": [3, 6, 7, 8, 9, 10, 11, 12, 17, 22, 23, 27, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 91, 92, 100, 101, 108, 109, 115, 116, 117, 119, 120, 122, 124, 128, 131, 137, 147, 148, 149, 150, 151, 186, 187, 188, 189, 190, 191, 197, 198, 201, 203, 204, 205, 206, 212, 222, 224, 225, 226, 227, 231, 244, 249, 251, 253, 257, 261, 262, 263, 265, 266, 268, 269, 270, 271, 275, 280, 302, 304, 305, 307, 313, 316, 317, 318, 319, 320, 322, 324, 325, 326, 327, 329, 330, 332, 334, 336, 338, 339, 340, 342, 343, 350], "str": [3, 8, 12, 17, 22, 23, 27, 28, 29, 31, 33, 34, 35, 40, 45, 50, 51, 53, 55, 66, 76, 79, 89, 91, 101, 122, 134, 137, 147, 149, 150, 151, 187, 190, 198, 244, 262, 263, 265, 266, 269, 280, 327, 339, 348], "ha": [3, 28, 45, 50, 58, 72, 122, 124, 128, 197, 231, 317, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350, 351], "extens": [3, 28, 45, 348], "unv": [3, 28, 42, 43, 45, 47, 49, 50, 52, 54, 61, 62, 66, 76, 81, 145, 146, 147, 351], "uff": [3, 28, 45, 145, 351], "univers": [3, 13, 21, 23, 28, 32, 35, 45, 47, 49, 50, 52, 54, 58, 61, 62, 76, 81, 88, 145, 147, 148, 149, 150, 151, 152, 153, 156, 163, 179, 327, 350, 351], "otherwis": [3, 6, 10, 28, 29, 31, 34, 35, 41, 45, 50, 51, 52, 53, 55, 88, 91, 101, 244, 280, 302, 320, 327, 331, 333, 342, 343, 350], "rais": [3, 17, 27, 28, 29, 33, 34, 35, 40, 45, 50, 64, 66, 73, 76, 81, 89, 91, 147, 149, 150, 197, 203, 222, 269, 270, 271, 280, 304], "attributeerror": [3, 28, 45, 50, 66], "doe": [3, 23, 28, 34, 35, 45, 50, 66, 261, 327, 329, 330, 331, 337, 338, 339, 340, 342, 343, 344, 348, 350], "from_unv": [3, 28, 45, 47, 49, 50, 52, 54, 66, 76], "appropri": [3, 28, 33, 35, 45, 206, 327, 328, 329, 332, 338, 343, 350], "cl": [3, 28, 45, 50], "ndenumer": 3, "enumer": [3, 13, 16, 18, 21, 22, 32, 37, 200, 215, 330, 332, 342, 348, 350], "over": [3, 29, 31, 34, 50, 55, 148, 190, 266, 280, 319, 320, 327, 328, 330, 331, 332, 337, 338, 339, 340, 342, 343, 348, 350], "entri": [3, 6, 9, 28, 29, 101, 117, 122, 128, 147, 148, 190, 204, 205, 206, 327, 339, 340, 348, 350], "yield": [3, 69, 302], "tupl": [3, 29, 31, 34, 51, 122, 124, 320], "indic": [3, 6, 23, 26, 28, 34, 35, 73, 91, 100, 115, 119, 120, 122, 124, 197, 206, 208, 327, 331, 332, 333, 340, 348], "correspond": [3, 5, 6, 7, 9, 28, 31, 34, 50, 56, 57, 58, 63, 67, 68, 70, 76, 85, 88, 91, 115, 119, 120, 122, 124, 204, 205, 206, 222, 224, 262, 327, 328, 330, 331, 332, 333, 337, 338, 339, 340, 342, 344, 348, 350], "each": [3, 6, 10, 23, 28, 29, 31, 34, 38, 39, 50, 52, 53, 56, 57, 58, 59, 66, 68, 70, 75, 86, 88, 101, 116, 117, 119, 120, 122, 124, 151, 197, 201, 204, 205, 206, 244, 249, 269, 270, 275, 280, 302, 307, 319, 320, 322, 324, 325, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "save": [3, 28, 41, 50, 51, 53, 55, 58, 60, 76, 80, 91, 101, 103, 121, 198, 217, 327, 331, 332, 340, 342, 350], "Will": [3, 28, 147], "append": [3, 28, 50, 76, 122, 327, 328, 330, 332, 333, 338, 340, 342, 348, 350], "npy": [3, 327, 331, 332, 350], "savemat": [3, 28], "matlab": [3, 6, 9, 28, 41, 50, 60, 76, 80], "mat": [3, 6, 9, 28, 41, 50, 60, 76, 80, 140, 143, 190, 351], "name": [3, 7, 29, 34, 35, 47, 50, 53, 56, 57, 58, 68, 70, 91, 101, 122, 124, 128, 244, 327, 330, 331, 332, 338, 339, 340, 350, 351], "color": [4, 47, 49, 50, 52, 54, 56, 57, 58, 68, 70, 76, 83, 166, 167, 170, 183, 190, 327, 328, 331, 332, 333, 337, 338, 340, 343, 348, 350], "scheme": [4, 122, 124, 327, 329, 342], "match": [4, 28, 29, 31, 34, 39, 91, 269, 270, 271, 320, 327, 329, 330, 331, 332, 338, 343, 348, 350], "dea": [4, 275], "coordinatearrai": [5, 7, 9, 10, 11, 28, 29, 34, 35, 38, 50, 73, 74, 76, 84, 85, 88, 90, 91, 134, 202, 327, 328, 329, 331, 339, 340, 350, 351], "node": [5, 6, 7, 8, 10, 12, 28, 34, 46, 49, 50, 52, 54, 57, 58, 59, 63, 66, 67, 68, 70, 76, 86, 88, 89, 91, 92, 115, 116, 122, 124, 128, 166, 175, 183, 190, 281, 328, 329, 330, 331, 332, 333, 340, 342, 343, 351], "number": [5, 8, 12, 17, 23, 27, 28, 29, 31, 34, 35, 40, 48, 50, 51, 52, 53, 55, 58, 65, 73, 76, 84, 86, 91, 92, 100, 101, 103, 106, 107, 115, 116, 117, 119, 120, 122, 124, 128, 147, 148, 149, 150, 190, 201, 203, 224, 234, 244, 253, 261, 262, 263, 265, 266, 269, 270, 271, 275, 302, 313, 320, 322, 324, 325, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 350, 351], "direct": [5, 6, 7, 8, 10, 12, 34, 47, 50, 56, 59, 64, 76, 79, 88, 116, 117, 265, 284, 285, 286, 287, 327, 329, 330, 332, 338, 339, 340, 342, 343, 344, 348, 350, 351], "coordin": [5, 6, 7, 8, 10, 12, 28, 29, 34, 35, 38, 39, 46, 47, 50, 52, 56, 58, 59, 63, 64, 67, 68, 73, 74, 75, 76, 84, 85, 86, 88, 91, 115, 122, 124, 166, 167, 172, 202, 225, 226, 231, 281, 328, 329, 330, 331, 332, 333, 338, 339, 340, 342, 344, 348, 351], "degre": [5, 6, 7, 10, 28, 29, 34, 35, 50, 58, 73, 75, 76, 78, 88, 90, 91, 95, 115, 118, 119, 120, 131, 178, 204, 205, 269, 270, 304, 316, 319, 328, 329, 331, 334, 335, 336, 337, 341, 342, 348, 350, 351], "freedom": [5, 6, 7, 10, 28, 29, 34, 35, 50, 73, 75, 76, 78, 88, 90, 91, 95, 115, 118, 119, 120, 131, 178, 204, 205, 269, 270, 316, 319, 328, 329, 331, 334, 335, 336, 341, 342, 348, 350, 351], "consist": [5, 7, 11, 17, 27, 29, 31, 34, 35, 40, 50, 52, 58, 59, 76, 82, 83, 84, 85, 86, 90, 91, 92, 122, 186, 188, 190, 224, 263, 265, 280, 304, 320, 322, 327, 328, 329, 331, 332, 333, 340, 342, 348, 350], "local": [5, 6, 50, 52, 58, 59, 63, 67, 122, 302, 327, 330, 339, 340, 348, 350], "displac": [5, 31, 32, 34, 50, 53, 55, 58, 68, 75, 76, 79, 91, 131, 190, 201, 203, 204, 205, 225, 263, 265, 319, 320, 322, 327, 328, 331, 332, 337, 339, 340, 348, 350, 351], "system": [5, 28, 29, 46, 47, 50, 52, 56, 58, 59, 63, 64, 67, 68, 76, 86, 90, 92, 94, 95, 96, 131, 134, 156, 166, 172, 203, 224, 265, 275, 281, 302, 316, 317, 318, 319, 328, 329, 330, 332, 333, 335, 336, 341, 342, 343, 344, 351], "translat": [5, 6, 10, 28, 37, 50, 57, 116, 186, 188, 224, 227, 327, 330, 340, 344, 348, 350], "rotat": [5, 28, 37, 50, 51, 57, 58, 76, 79, 91, 116, 186, 188, 189, 191, 224, 227, 303, 304, 327, 330, 338, 340, 344, 348, 350, 351], "about": [5, 50, 58, 76, 115, 116, 189, 191, 304, 327, 331, 332, 336, 337, 338, 339, 342, 343, 344, 348, 350], "princip": [5, 50, 327, 350], "axi": [5, 22, 28, 29, 31, 33, 34, 35, 50, 59, 73, 76, 115, 219, 227, 235, 256, 266, 270, 271, 297, 304, 327, 330, 331, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "posit": [5, 6, 7, 10, 22, 31, 34, 47, 50, 52, 58, 59, 68, 76, 166, 190, 210, 218, 222, 224, 226, 231, 280, 317, 319, 320, 327, 328, 333, 338, 339, 340, 342, 343, 348, 350], "neg": [5, 7, 31, 34, 280, 317, 319, 320, 333, 339, 342, 343, 348], "also": [5, 13, 28, 29, 31, 33, 34, 35, 40, 46, 50, 76, 90, 101, 140, 206, 263, 269, 281, 302, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350, 351], "empti": [5, 34, 50, 58, 124, 327, 332, 340, 342, 350], "non": [5, 34, 85, 128, 327, 329, 339, 340, 343, 348, 350], "function": [5, 6, 7, 13, 17, 19, 21, 22, 23, 26, 27, 28, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 60, 68, 70, 72, 73, 75, 76, 80, 86, 90, 91, 94, 96, 98, 99, 100, 103, 113, 114, 115, 117, 118, 119, 120, 121, 122, 124, 128, 131, 132, 133, 140, 143, 145, 153, 156, 159, 163, 166, 169, 172, 175, 178, 179, 182, 185, 186, 188, 192, 196, 197, 199, 201, 204, 205, 206, 213, 221, 224, 225, 226, 234, 236, 243, 244, 260, 261, 262, 263, 265, 266, 267, 269, 270, 271, 272, 275, 281, 292, 294, 301, 302, 303, 311, 318, 320, 328, 329, 330, 331, 332, 333, 335, 336, 337, 340, 341, 343, 351], "inform": [6, 20, 34, 39, 47, 49, 50, 52, 54, 58, 76, 122, 124, 128, 137, 149, 150, 159, 186, 188, 190, 302, 327, 328, 331, 332, 337, 338, 339, 340, 342, 344, 348, 350, 351], "101x": [6, 8, 12, 91, 330, 340, 350, 351], "coordinate_arrai": [6, 10, 50, 327, 329, 330, 331, 332, 337, 338, 339, 340, 342, 343, 344, 348, 350], "helper": [6, 38, 39, 47, 49, 52, 53, 54, 73, 76, 327, 329, 331, 348, 351], "creat": [6, 7, 8, 9, 10, 22, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 68, 70, 71, 73, 74, 76, 77, 79, 80, 84, 85, 86, 88, 91, 95, 98, 99, 101, 103, 113, 114, 122, 124, 128, 131, 190, 196, 197, 212, 262, 266, 275, 304, 311, 322, 328, 329, 332, 333, 334, 335, 337, 338, 339, 340, 342, 344, 348, 351], "ab": [6, 31, 319, 320, 327, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "sign": [6, 91, 327, 328, 330, 333, 338, 340, 342, 348, 350], "flip": [6, 76, 87, 327, 328, 330, 333, 338, 339, 342, 348], "data_dtyp": [6, 47, 49, 52, 54, 73], "uint64": [6, 47, 49, 52, 54], "int8": 6, "datatyp": [6, 16], "direction_string_arrai": 6, "string": [6, 7, 8, 9, 12, 17, 22, 27, 28, 29, 31, 33, 34, 35, 40, 50, 58, 71, 76, 79, 89, 91, 101, 122, 124, 147, 148, 151, 190, 201, 266, 280, 327, 331, 338, 348, 350], "represent": [6, 8, 12, 76, 89, 91, 128, 151, 305, 327, 328, 338, 339, 340, 343, 348, 350, 351], "repres": [6, 8, 12, 23, 31, 34, 52, 90, 91, 101, 122, 124, 189, 191, 204, 205, 206, 249, 253, 305, 316, 319, 320, 327, 328, 329, 330, 332, 335, 338, 340, 342, 348, 350, 351], "np": [6, 9, 17, 22, 27, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 50, 51, 52, 53, 55, 59, 60, 64, 65, 76, 80, 82, 84, 85, 86, 87, 91, 116, 119, 120, 122, 131, 189, 191, 201, 203, 204, 205, 206, 225, 226, 227, 231, 244, 249, 257, 261, 263, 265, 266, 268, 269, 270, 271, 275, 280, 304, 305, 313, 316, 317, 318, 319, 320, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "from_matlab_cellstr": 6, "cellstr_data": [6, 9], "cellstr": [6, 9], "scipi": [6, 9, 17, 23, 27, 28, 29, 33, 34, 35, 40, 41, 50, 60, 76, 80, 244, 266, 316, 327, 333, 340, 343, 348, 350], "io": [6, 9, 28, 41, 50, 60, 76, 80], "loadmat": [6, 9, 41, 50, 60, 76, 80], "cell": [6, 9], "variabl": [6, 9, 23, 31, 34, 35, 76, 79, 91, 122, 124, 128, 320, 327, 331, 332, 340, 348, 350], "built": [6, 9, 327, 329, 340, 348, 351], "provid": [6, 9, 10, 11, 22, 23, 29, 34, 35, 76, 81, 85, 86, 91, 253, 262, 266, 269, 270, 271, 275, 327, 329, 330, 331, 332, 333, 338, 340, 342, 343, 350, 351], "from_nodelist": [6, 327, 350], "1": [6, 7, 8, 10, 12, 16, 17, 18, 20, 21, 23, 25, 27, 28, 29, 31, 32, 33, 34, 35, 37, 39, 40, 48, 50, 52, 53, 55, 56, 57, 58, 59, 68, 70, 76, 87, 88, 91, 97, 104, 106, 111, 115, 116, 122, 124, 128, 131, 164, 167, 170, 173, 183, 187, 190, 200, 201, 203, 204, 205, 208, 215, 219, 224, 227, 231, 235, 239, 249, 253, 255, 258, 262, 266, 269, 270, 273, 275, 276, 278, 279, 280, 293, 297, 302, 304, 307, 316, 317, 319, 320, 322, 323, 326, 327, 328, 329, 330, 331, 332, 333, 337, 343, 344, 348, 350], "2": [6, 7, 8, 10, 12, 16, 18, 21, 29, 31, 32, 34, 50, 56, 57, 58, 68, 70, 76, 89, 91, 105, 115, 117, 128, 131, 134, 190, 200, 201, 203, 204, 205, 206, 208, 217, 222, 224, 231, 253, 263, 269, 270, 275, 302, 304, 317, 319, 320, 322, 326, 327, 328, 329, 330, 331, 332, 333, 336, 337, 343, 344, 348, 350], "flatten": [6, 7, 10, 28, 33, 50, 327, 329, 338, 339, 340, 344, 348, 350], "true": [6, 10, 28, 29, 31, 33, 34, 35, 41, 42, 43, 44, 47, 49, 50, 51, 52, 53, 54, 55, 73, 76, 81, 84, 86, 91, 102, 104, 106, 116, 119, 120, 122, 124, 142, 151, 190, 197, 201, 203, 204, 205, 206, 208, 217, 232, 244, 254, 258, 274, 275, 302, 304, 314, 320, 327, 329, 330, 331, 333, 337, 338, 339, 340, 342, 343, 348, 350], "set": [6, 10, 22, 23, 28, 29, 31, 33, 34, 35, 48, 50, 52, 53, 55, 59, 65, 75, 76, 78, 79, 82, 84, 86, 89, 91, 92, 118, 119, 120, 122, 179, 193, 222, 224, 244, 266, 302, 307, 317, 320, 322, 324, 329, 331, 334, 339, 340, 342, 343, 344, 350, 351], "iter": [6, 10, 22, 29, 31, 34, 49, 50, 52, 54, 57, 59, 69, 70, 76, 79, 83, 91, 92, 119, 120, 122, 124, 128, 147, 148, 149, 150, 151, 186, 188, 190, 262, 266, 280, 302, 319, 320, 327, 329, 331], "list": [6, 10, 29, 34, 35, 41, 50, 55, 56, 57, 68, 69, 70, 73, 76, 78, 79, 83, 91, 100, 101, 119, 120, 122, 124, 128, 134, 147, 148, 149, 150, 186, 188, 190, 194, 206, 280, 327, 328, 330, 331, 332, 333, 338, 348, 350, 351], "default": [6, 10, 17, 23, 27, 28, 29, 31, 33, 34, 35, 38, 39, 40, 41, 42, 43, 48, 50, 51, 52, 53, 55, 59, 64, 73, 76, 77, 79, 83, 84, 86, 89, 91, 92, 101, 116, 119, 120, 122, 124, 131, 149, 150, 151, 190, 197, 201, 203, 204, 205, 206, 231, 244, 253, 263, 265, 266, 269, 270, 275, 280, 302, 304, 307, 319, 320, 322, 330, 331, 332, 334, 350], "three": [6, 10, 50, 58, 186, 188, 224, 327, 328, 333, 338, 339, 340, 342, 343, 344, 348, 350], "x": [6, 7, 8, 10, 12, 22, 28, 29, 31, 33, 34, 35, 50, 52, 56, 58, 76, 79, 91, 115, 116, 122, 131, 186, 188, 189, 191, 203, 204, 205, 206, 217, 219, 222, 223, 224, 231, 250, 256, 261, 262, 265, 266, 275, 293, 304, 307, 312, 319, 327, 329, 331, 332, 337, 338, 339, 340, 342, 343, 344, 348, 350], "y": [6, 7, 8, 10, 12, 29, 34, 50, 52, 58, 76, 79, 91, 115, 116, 131, 189, 191, 217, 219, 223, 224, 231, 304, 307, 327, 332, 337, 339, 340, 342, 344, 348, 350], "z": [6, 7, 8, 10, 12, 34, 50, 52, 58, 59, 76, 79, 91, 115, 116, 189, 191, 217, 223, 224, 231, 304, 307, 317, 327, 330, 332, 339, 340, 342, 344, 348, 350], "bool": [6, 7, 10, 28, 29, 31, 33, 34, 35, 41, 42, 43, 44, 47, 49, 50, 51, 52, 53, 54, 55, 59, 73, 76, 81, 84, 91, 116, 119, 120, 122, 124, 128, 149, 150, 151, 190, 197, 201, 203, 204, 205, 206, 212, 244, 266, 304, 320, 331], "prior": [6, 10, 33, 35, 340, 343, 350], "output": [6, 10, 29, 41, 50, 55, 60, 76, 80, 84, 85, 89, 91, 117, 128, 131, 137, 140, 147, 148, 149, 150, 185, 190, 204, 205, 249, 262, 263, 265, 266, 280, 302, 307, 327, 328, 329, 331, 332, 333, 342, 343, 348, 350, 351], "fals": [6, 7, 10, 28, 29, 31, 33, 34, 35, 40, 41, 44, 48, 50, 51, 53, 55, 59, 73, 76, 84, 91, 102, 116, 119, 120, 122, 124, 128, 149, 150, 164, 181, 190, 194, 197, 201, 203, 204, 205, 206, 208, 217, 218, 230, 232, 235, 244, 264, 266, 299, 302, 304, 320, 323, 327, 330, 333, 337, 338, 339, 348, 350], "dimens": [6, 10, 28, 34, 42, 43, 88, 119, 120, 122, 204, 205, 231, 244, 249, 257, 327, 333, 340, 348, 350], "one": [6, 10, 28, 29, 31, 33, 35, 41, 50, 76, 78, 87, 101, 119, 120, 122, 128, 145, 201, 203, 206, 263, 265, 269, 270, 275, 280, 317, 327, 328, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "larger": [6, 10, 302, 329, 331, 332, 337, 338, 340, 343, 344, 348, 350], "input": [6, 7, 10, 28, 29, 31, 33, 34, 35, 38, 50, 56, 57, 68, 70, 76, 78, 83, 91, 92, 131, 201, 249, 262, 263, 265, 266, 269, 270, 275, 302, 307, 317, 327, 328, 329, 331, 332, 333, 338, 339, 340, 341, 342, 348, 350], "due": [6, 10, 76, 131, 327, 328, 329, 330, 332, 333, 337, 338, 339, 340, 342, 343, 348, 350], "ad": [6, 10, 22, 28, 34, 51, 86, 89, 190, 327, 332, 333, 335, 336, 342, 343], "size": [6, 10, 29, 34, 35, 50, 55, 91, 101, 115, 122, 204, 205, 217, 222, 266, 269, 270, 307, 316, 319, 327, 328, 329, 330, 331, 332, 333, 338, 340, 342, 343, 348, 350], "local_direct": 6, "local_direction_arrai": 6, "where": [6, 22, 23, 28, 29, 31, 34, 35, 50, 59, 68, 76, 115, 122, 186, 188, 201, 224, 231, 249, 257, 262, 263, 265, 266, 269, 270, 271, 319, 320, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350, 351], "For": [6, 31, 76, 119, 120, 128, 206, 253, 275, 280, 302, 320, 322, 327, 328, 329, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "exampl": [6, 31, 33, 35, 50, 128, 206, 253, 275, 280, 320, 322, 327, 328, 329, 330, 331, 332, 333, 335, 336, 337, 338, 339, 340, 342, 343, 344, 350], "would": [6, 50, 58, 76, 79, 91, 263, 265, 268, 280, 327, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350], "offset_node_id": 6, "offset_valu": 6, "id": [6, 7, 28, 47, 49, 50, 52, 54, 56, 57, 58, 59, 65, 68, 70, 76, 86, 89, 122, 124, 149, 150, 327, 328, 329, 330, 333, 340, 342, 350], "int": [6, 8, 12, 17, 23, 27, 28, 29, 31, 33, 34, 35, 40, 48, 50, 51, 52, 53, 55, 73, 76, 84, 91, 101, 116, 117, 119, 120, 122, 124, 134, 147, 148, 201, 203, 204, 205, 208, 244, 253, 262, 266, 275, 302, 313, 318, 319, 320, 322, 324, 325, 327, 342], "valu": [6, 7, 16, 18, 21, 22, 28, 29, 31, 32, 33, 34, 35, 37, 39, 41, 52, 53, 55, 69, 74, 76, 79, 84, 91, 101, 117, 119, 120, 122, 124, 146, 149, 150, 151, 179, 190, 197, 200, 201, 206, 215, 224, 251, 253, 257, 261, 262, 263, 265, 266, 268, 269, 271, 280, 302, 304, 313, 317, 319, 320, 322, 324, 325, 327, 328, 329, 330, 331, 332, 333, 338, 339, 340, 342, 343, 344, 348, 350, 351], "string_arrai": [6, 7, 327, 329, 330, 331, 337, 340, 342, 350], "structured_arrai": [7, 56, 57, 68, 70, 88], "force_broadcast": [7, 329, 330, 350], "an": [7, 16, 22, 23, 28, 29, 31, 33, 34, 35, 41, 50, 51, 52, 53, 55, 56, 57, 58, 60, 63, 64, 67, 68, 70, 71, 73, 76, 79, 80, 81, 83, 87, 88, 89, 91, 101, 115, 122, 124, 128, 137, 146, 147, 186, 188, 194, 200, 201, 204, 205, 212, 215, 224, 231, 261, 262, 263, 265, 266, 268, 269, 270, 271, 275, 280, 304, 318, 319, 320, 322, 324, 325, 328, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 342, 343, 344, 351], "test": [7, 46, 50, 55, 56, 57, 68, 70, 75, 76, 86, 88, 91, 98, 100, 131, 169, 172, 182, 190, 196, 249, 302, 332, 334, 335, 336, 337, 341, 343, 344, 348, 350], "analysi": [7, 50, 56, 57, 68, 70, 88, 98, 122, 192, 249, 327, 328, 329, 331, 333, 334, 335, 336, 338, 339, 342, 344, 348, 350], "two": [7, 31, 34, 65, 82, 88, 89, 122, 124, 204, 205, 249, 257, 307, 317, 320, 327, 328, 329, 331, 332, 333, 337, 338, 339, 340, 342, 343, 344, 348, 350, 351], "multidimension": [7, 56, 57, 68, 70, 88], "integ": [7, 8, 12, 28, 31, 34, 35, 50, 52, 56, 57, 58, 68, 70, 76, 83, 122, 261, 320, 327, 343], "cast": [7, 56, 57, 68, 70], "9": [7, 21, 31, 32, 34, 56, 57, 68, 70, 131, 164, 190, 217, 253, 319, 320, 322, 323, 327, 328, 330, 331, 332, 338, 340, 342, 343, 348, 350], "pass": [7, 22, 23, 28, 31, 33, 34, 35, 40, 41, 48, 50, 53, 55, 56, 57, 60, 68, 70, 73, 76, 80, 84, 85, 88, 91, 122, 124, 197, 203, 212, 222, 244, 275, 320, 327, 328, 329, 331, 332, 333, 338, 339, 342, 348, 350, 351], "must": [7, 22, 28, 29, 31, 34, 39, 50, 55, 58, 64, 66, 89, 91, 101, 119, 120, 266, 269, 270, 271, 320, 327, 329, 330, 332, 333, 337, 339, 340, 341, 342, 343, 344, 348, 350], "rx": [7, 186, 188, 189, 191, 208, 307, 327, 340, 350], "ry": [7, 186, 188, 189, 191, 327, 340, 350], "rz": [7, 186, 188, 189, 191, 327, 340, 350], "whether": [7, 22, 34, 50, 51, 53, 55, 59, 84, 91, 122, 124, 128, 190, 203, 263, 328, 329, 330, 332, 338, 348, 350], "given": [7, 29, 31, 34, 50, 58, 101, 115, 122, 201, 231, 262, 263, 265, 266, 275, 305, 320, 327, 329, 331, 332, 337, 338, 339, 340, 342, 343, 344, 348, 350, 351], "assum": [7, 31, 34, 89, 101, 117, 122, 263, 316, 320, 322, 329, 332, 337, 338, 340, 342, 343, 348, 350, 351], "altern": [7, 28, 31, 33, 35, 56, 57, 68, 70, 88, 215, 217, 265, 304, 320, 327, 329, 338, 340, 342, 350], "singl": [7, 11, 17, 22, 27, 29, 34, 35, 40, 41, 47, 49, 50, 51, 52, 53, 54, 55, 56, 57, 59, 68, 70, 76, 81, 83, 101, 117, 193, 194, 275, 280, 316, 319, 327, 328, 330, 332, 333, 336, 337, 339, 342, 343, 348], "pars": [7, 12, 28, 147, 331, 351], "combin": [7, 11, 47, 49, 50, 52, 54, 76, 78, 81, 83, 91, 92, 124, 327, 329, 330, 331, 332, 333, 334, 337, 338, 339, 340, 348, 350], "regardless": [7, 41, 190, 330, 332, 342, 343, 348], "arg": [11, 122, 129, 195], "nddataarrai": [13, 17, 19, 22, 23, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 41, 42, 43, 44, 48, 50, 327, 331, 350, 351], "time": [13, 17, 27, 29, 31, 32, 33, 34, 35, 40, 50, 53, 55, 76, 91, 115, 117, 122, 131, 133, 137, 140, 176, 178, 190, 212, 249, 261, 262, 266, 275, 280, 294, 298, 299, 302, 317, 318, 319, 320, 329, 330, 331, 332, 335, 336, 337, 338, 339, 340, 341, 343, 344, 348, 351], "histori": [13, 17, 27, 29, 33, 34, 35, 40, 137, 140, 178, 212, 249, 266, 318, 327, 329, 337, 338, 341, 350, 351], "sever": [13, 28, 44, 50, 76, 83, 178, 328, 329, 333, 335, 337, 338, 340, 342, 343, 350, 351], "contain": [13, 18, 21, 28, 29, 31, 32, 33, 34, 35, 37, 39, 41, 42, 43, 47, 48, 49, 50, 52, 54, 58, 59, 60, 61, 62, 76, 78, 80, 81, 90, 91, 95, 101, 122, 124, 128, 137, 149, 150, 179, 187, 197, 198, 201, 203, 231, 244, 249, 266, 275, 280, 302, 319, 320, 324, 325, 327, 328, 329, 330, 331, 332, 333, 335, 337, 338, 339, 340, 342, 343, 344, 348, 350, 351], "specif": [13, 22, 28, 91, 151, 201, 253, 329, 331, 337, 339, 340, 342, 348, 350, 351], "capabl": [13, 327, 328, 330, 331, 333, 338, 342, 350, 351], "connect": [13, 22, 49, 50, 54, 57, 58, 59, 70, 91, 115, 122, 124, 128, 170, 327, 328, 330, 332, 333, 339, 340, 348, 350], "format": [13, 22, 28, 31, 33, 35, 50, 53, 58, 61, 62, 76, 88, 89, 91, 101, 122, 140, 145, 147, 148, 151, 152, 327, 328, 330, 331, 332, 333, 337, 338, 339, 340, 342, 343, 348, 350, 351], "parent": [14, 15, 24, 25, 77, 214], "powerspectraldensityarrai": [16, 17, 27, 34, 329], "compare_data": [16, 29], "qmainwindow": [16, 22, 194, 209, 212, 218], "enum": [16, 18, 21, 28, 32, 37, 200, 215], "coher": [16, 17, 21, 27, 29, 35, 197, 329, 332, 343, 350], "imaginari": [16, 18, 22, 35, 197, 204, 205, 327, 332, 338, 339, 342, 348, 350], "16": [16, 21, 32, 50, 307, 327, 328, 330, 332, 338, 340, 342, 343, 344, 348, 350], "magnitud": [16, 18, 22, 29, 35, 201, 217, 329, 338, 339, 340, 342, 343, 348, 350], "phase": [16, 18, 22, 29, 34, 35, 53, 275, 279, 280, 327, 328, 329, 332, 337, 338, 339, 340, 342, 343, 344, 348, 350], "4": [16, 18, 21, 24, 25, 31, 34, 47, 50, 55, 56, 58, 76, 91, 122, 128, 131, 173, 186, 188, 190, 222, 224, 253, 319, 320, 327, 328, 329, 330, 331, 332, 333, 337, 339, 342, 343, 344, 348, 350], "real": [16, 18, 22, 28, 35, 73, 76, 88, 122, 201, 203, 204, 205, 327, 329, 332, 336, 337, 338, 339, 340, 341, 342, 343, 350], "8": [16, 21, 29, 31, 32, 33, 34, 50, 53, 55, 86, 91, 92, 128, 190, 269, 270, 319, 320, 327, 328, 330, 331, 332, 337, 338, 339, 340, 342, 343, 348, 350], "clear_select": 16, "event": [16, 22, 28, 31, 33, 35, 194, 210, 218], "coherence_st": 16, "connectui": 16, "deselect_diagon": 16, "deselect_lower_triangular": 16, "deselect_upper_triangular": 16, "extend_selection_matrix": 16, "find_function_typ": 16, "imaginary_st": 16, "initui": 16, "init_matrix_plot": 16, "invert_select": 16, "magnitude_st": 16, 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Model": [[327, "load-the-finite-element-model"]], "Convert Finite Element Model to SDynPy Objects": [[327, "convert-finite-element-model-to-sdynpy-objects"]], "Creating a Geometry from an Exodus file": [[327, "creating-a-geometry-from-an-exodus-file"]], "Exploring the Geometry object": [[327, "exploring-the-geometry-object"]], "Nodes": [[327, "nodes"], [350, "nodes"]], "Coordinate Systems": [[327, "coordinate-systems"], [350, "coordinate-systems"]], "Elements": [[327, "elements"], [350, "elements"]], "Tracelines": [[327, "tracelines"], [350, "tracelines"]], "Visualizing Degrees of Freedom on a Geometry": [[327, "visualizing-degrees-of-freedom-on-a-geometry"]], "Coordinates": [[327, "coordinates"], [350, "coordinates"]], "Plotting Coordinates": [[327, "plotting-coordinates"], [350, "plotting-coordinates"]], "Creating Shapes from an Exodus File": [[327, "creating-shapes-from-an-exodus-file"]], "Shapes": [[327, "shapes"], [350, "shapes"]], "Correcting Negative Frequencies and Array Views versus Copies": [[327, "correcting-negative-frequencies-and-array-views-versus-copies"]], "Transforming Shape Coordinate Systems": [[327, "transforming-shape-coordinate-systems"]], "Optimizing Instrumentation for Test": [[327, "optimizing-instrumentation-for-test"], [329, "optimizing-instrumentation-for-test"]], "Creating a Test Geometry": [[327, "creating-a-test-geometry"], [331, "creating-a-test-geometry"]], "OOPS! An Instrumentation Error!": [[327, "oops-an-instrumentation-error"]], "Running a Virtual Experiment: Rigid Body Checkouts": [[327, "running-a-virtual-experiment-rigid-body-checkouts"]], "Creating a System Object to Integrate": [[327, "creating-a-system-object-to-integrate"]], "Exploring the System Object": [[327, "exploring-the-system-object"]], "Setting up the Integration and Forcing Function": [[327, "setting-up-the-integration-and-forcing-function"]], "Running the Integration to Generate Synthetic Test Data": [[327, "running-the-integration-to-generate-synthetic-test-data"]], "Exploring Time Data Objects": [[327, "exploring-time-data-objects"]], "Identifying Bad Geometry with Rigid Body Checkouts in SDynPy": [[327, "identifying-bad-geometry-with-rigid-body-checkouts-in-sdynpy"]], "Running a Virtual Experiment: Modal Testing": [[327, "running-a-virtual-experiment-modal-testing"]], "Fitting Modes using PolyPy": [[327, "fitting-modes-using-polypy"]], "Running PolyPy": [[327, "running-polypy"]], "Comparing Test and Finite Element Modes": [[327, "comparing-test-and-finite-element-modes"]], "SEREP Expansion": [[327, "serep-expansion"]], "Substructuring using Craig-Bampton Superelements from Sierra/SD": [[328, "substructuring-using-craig-bampton-superelements-from-sierra-sd"]], "Setting up the Problem": [[328, "setting-up-the-problem"], [333, "setting-up-the-problem"]], "Analyzing Superelements in SDynPy": [[328, "analyzing-superelements-in-sdynpy"]], "Combining Component Models": [[328, "combining-component-models"]], "Summary": [[328, "summary"], [329, "summary"], [332, "summary"], [333, "summary"], [337, "Summary"], [338, "Summary"], [339, "Summary"], [340, "Summary"], [342, "Summary"], [348, "Summary"]], "MIMO Random Vibration": [[329, "mimo-random-vibration"]], "Simulating an Environment": [[329, "simulating-an-environment"]], "Selecting Shaker Excitation Locations": [[329, "selecting-shaker-excitation-locations"]], "Performing Vibration Control": [[329, "performing-vibration-control"]], "Simple Vibration Control using the Pseudoinverse": [[329, "simple-vibration-control-using-the-pseudoinverse"]], "Tikhonov Regularization": [[329, "tikhonov-regularization"]], "Weighting the Shakers": [[329, "weighting-the-shakers"]], "Response Weighting": [[329, "response-weighting"]], "Buzz Test Method": [[329, "buzz-test-method"]], "Running a Test": [[329, "running-a-test"]], "Model Reduction": [[330, "Model-Reduction"]], "Set up 3D Plots": [[330, "Set-up-3D-Plots"]], "Collect and Set Up Models": [[330, "Collect-and-Set-Up-Models"]], "Creating Truth Data": [[330, "Creating-Truth-Data"]], "Selecting Degrees of Freedom for Reduction and Substructuring": [[330, "Selecting-Degrees-of-Freedom-for-Reduction-and-Substructuring"]], "Perform the model reductions": [[330, "Perform-the-model-reductions"]], "Guyan Reduction": [[330, "Guyan-Reduction"]], "Dynamic Reduction": [[330, "Dynamic-Reduction"]], "Craig-Bampton Reduction": [[330, "Craig-Bampton-Reduction"]], "Component Mode Synthesis": [[330, "Component-Mode-Synthesis"]], "Comparison of Results": [[330, "Comparison-of-Results"]], "Rattlesnake Demonstration": [[331, "rattlesnake-demonstration"]], "Load in Test Data": [[331, "load-in-test-data"]], "Computing FRFs": [[331, "computing-frfs"]], "Fitting Modes to the FRFs": [[331, "fitting-modes-to-the-frfs"]], "Analyzing Modal Parameters": [[331, "analyzing-modal-parameters"]], "Documentation Generation": [[331, "documentation-generation"]], "Exporting to Exodus": [[331, "exporting-to-exodus"]], "Analysis of Transient Finite Element Results": [[332, "analysis-of-transient-finite-element-results"]], "Problem Set Up": [[332, "problem-set-up"]], "Analyzing Model Responses": [[332, "analyzing-model-responses"]], "Pseudorandom Analysis": [[332, "pseudorandom-analysis"]], "Random Analysis": [[332, "random-analysis"]], "Comparison of Excitation Techniques": [[332, "comparison-of-excitation-techniques"]], "Substructuring using the Transmission Simulator Method": [[333, "substructuring-using-the-transmission-simulator-method"], [333, "id1"]], "Loading the results into SDynPy": [[333, "loading-the-results-into-sdynpy"]], "Reducing to Test Degrees of Freedom": [[333, "reducing-to-test-degrees-of-freedom"]], "SDynpy Examples": [[334, "sdynpy-examples"]], "Contents:": [[334, null], [335, null], [336, null], [341, null]], "Welcome to SDynPy\u2019s documentation!": [[335, "welcome-to-sdynpy-s-documentation"]], "Information": [[335, "information"]], "Indices and tables": [[335, "indices-and-tables"]], "Modal Tutorials": [[336, "modal-tutorials"]], "Modal Tutorial 01: What is modal analysis?": [[337, "Modal-Tutorial-01:-What-is-modal-analysis?"]], "So what is Modal Analysis?": [[337, "So-what-is-Modal-Analysis?"]], "How do we find the modes of the system?": [[337, "How-do-we-find-the-modes-of-the-system?"]], "Modal Tutorial 02: Basics of Vibrations": [[338, "Modal-Tutorial-02:-Basics-of-Vibrations"]], "A Simple Undamped Spring-Mass System": [[338, "A-Simple-Undamped-Spring-Mass-System"]], "Adding Damping": [[338, "Adding-Damping"], [339, "Adding-Damping"], [348, "Adding-Damping"]], "Response of Single Degree of Freedom Systems to Harmonic Excitation": [[338, "Response-of-Single-Degree-of-Freedom-Systems-to-Harmonic-Excitation"]], "Undamped Response to Harmonic Excitation": [[338, "Undamped-Response-to-Harmonic-Excitation"]], "Damped Response to Harmonic Excitation": [[338, "Damped-Response-to-Harmonic-Excitation"]], "Comparison between Displacement, Velocity, and Acceleration Frequency Response Functions": [[338, "Comparison-between-Displacement,-Velocity,-and-Acceleration-Frequency-Response-Functions"]], "Working with Single Degree of Freedom Systems in SDynPy": [[338, "Working-with-Single-Degree-of-Freedom-Systems-in-SDynPy"]], "Homework Problems": [[338, "Homework-Problems"], [339, "Homework-Problems"], [340, "Homework-Problems"], [342, "Homework-Problems"], [343, "Homework-Problems"]], "1. Set up a System": [[338, "1.-Set-up-a-System"]], "2. Compute the Frequency Response Function": [[338, "2.-Compute-the-Frequency-Response-Function"]], "3. Investigate Design Changes": [[338, "3.-Investigate-Design-Changes"]], "4. Investigate Damping Changes": [[338, "4.-Investigate-Damping-Changes"]], "Modal Tutorial 03: Vibrations of Systems with Multiple Degrees of Freedom": [[339, "Modal-Tutorial-03:-Vibrations-of-Systems-with-Multiple-Degrees-of-Freedom"]], "A Spring Mass System with 2 Degrees of Freedom": [[339, "A-Spring-Mass-System-with-2-Degrees-of-Freedom"], [340, "A-Spring-Mass-System-with-2-Degrees-of-Freedom"]], "Free Response of Multiple Degree of Freedom Systems": [[339, "Free-Response-of-Multiple-Degree-of-Freedom-Systems"]], "Multiple Degree of Freedom Response to Harmonic Excitation": [[339, "Multiple-Degree-of-Freedom-Response-to-Harmonic-Excitation"]], "Working with Multiple Degree of Freedom Systems in SDynPy": [[339, "Working-with-Multiple-Degree-of-Freedom-Systems-in-SDynPy"]], "1. Writing Equations of Motion": [[339, "1.-Writing-Equations-of-Motion"]], "2. Compute the Frequency Response Function Matrix": [[339, "2.-Compute-the-Frequency-Response-Function-Matrix"]], "3. Identifying Drive Points": [[339, "3.-Identifying-Drive-Points"]], "Modal Tutorial 04: Modal Analysis": [[340, "Modal-Tutorial-04:-Modal-Analysis"]], "Solving for the Eigenvalues": [[340, "Solving-for-the-Eigenvalues"]], "Solving for the Eigenvectors": [[340, "Solving-for-the-Eigenvectors"]], "More about Modes": [[340, "More-about-Modes"]], "A series of Single Degree of Freedom Systems": [[340, "A-series-of-Single-Degree-of-Freedom-Systems"]], "What About Damping?": [[340, "What-About-Damping?"]], "Rayleigh Damping": [[340, "Rayleigh-Damping"]], "Modal Damping": [[340, "Modal-Damping"]], "General Damping Matrices": [[340, "General-Damping-Matrices"]], "Response to Harmonic Excitation": [[340, "Response-to-Harmonic-Excitation"]], "Modal Analysis in SDynPy": [[340, "Modal-Analysis-in-SDynPy"]], "1. Construct the generalized eigenvalue problem": [[340, "1.-Construct-the-generalized-eigenvalue-problem"]], "2. Compute and solve the characteristic polynomial": [[340, "2.-Compute-and-solve-the-characteristic-polynomial"]], "3. Compute eigenvector": [[340, "3.-Compute-eigenvector"]], "4. Perform the same operations in SDynPy and plot the mode shapes": [[340, "4.-Perform-the-same-operations-in-SDynPy-and-plot-the-mode-shapes"]], "5. Compute the FRF matrix from modal quantities": [[340, "5.-Compute-the-FRF-matrix-from-modal-quantities"]], "Modal Tutorial 05: Experimental Modal Analysis": [[341, "modal-tutorial-05-experimental-modal-analysis"]], "Modal Tutorial 05 Part A: Computing Frequency Response from Time Data": [[342, "Modal-Tutorial-05-Part-A:-Computing-Frequency-Response-from-Time-Data"]], "Sinusoidal Excitation": [[342, "Sinusoidal-Excitation"]], "Broadband Excitation": [[342, "Broadband-Excitation"]], "Computing FRFs using FFTs": [[342, "Computing-FRFs-using-FFTs"]], "Computing FRFs with Power Spectra": [[342, "Computing-FRFs-with-Power-Spectra"]], "Computing Power Spectra": [[342, "Computing-Power-Spectra"]], "The H1 Technique": [[342, "The-H1-Technique"]], "The H2 Technique": [[342, "The-H2-Technique"]], "The H3 Technique": [[342, "The-H3-Technique"]], "The HCD Technique": [[342, "The-HCD-Technique"]], "The Hv Technique": [[342, "The-Hv-Technique"]], "Coherence": [[342, "Coherence"]], "Computing Frequency Response Functions using SDynPy": [[342, "Computing-Frequency-Response-Functions-using-SDynPy"]], "SignalProcessingGUI": [[342, "SignalProcessingGUI"]], "1. Simulate a Test to Generate Time Histories": [[342, "1.-Simulate-a-Test-to-Generate-Time-Histories"]], "2. Compute Frequency Response Functions": [[342, "2.-Compute-Frequency-Response-Functions"]], "Modal Tutorial 05 Part B: Types of Excitation for Experimental Modal Analysis": [[343, "Modal-Tutorial-05-Part-B:-Types-of-Excitation-for-Experimental-Modal-Analysis"]], "Requirements for the FFT": [[343, "Requirements-for-the-FFT"]], "Considerations for Multiple-Input Excitation": [[343, "Considerations-for-Multiple-Input-Excitation"]], "Types of Modal Excitation": [[343, "Types-of-Modal-Excitation"]], "Random Vibration": [[343, "Random-Vibration"]], "Pseudorandom Excitation": [[343, "Pseudorandom-Excitation"]], "Chirp Excitation": [[343, "Chirp-Excitation"]], "Burst Random Excitation": [[343, "Burst-Random-Excitation"]], "Impulse Excitation": [[343, "Impulse-Excitation"]], "Modal Tutorial 05 Part C: Selecting Excitation and Response Degrees of Freedom": [[344, "Modal-Tutorial-05-Part-C:-Selecting-Excitation-and-Response-Degrees-of-Freedom"]], "Optimal Excitation Theory": [[344, "Optimal-Excitation-Theory"]], "Computing Optimal Excitation Locations": [[344, "Computing-Optimal-Excitation-Locations"]], "Optimal Response Theory": [[344, "Optimal-Response-Theory"]], "Computing Optimal Responses": [[344, "Computing-Optimal-Responses"]], "Modal Tutorial 05 Part D: Data Quality Checks": [[345, "Modal-Tutorial-05-Part-D:-Data-Quality-Checks"]], "Modal Tutorial 05 Part E: Fitting Modes to Data": [[346, "Modal-Tutorial-05-Part-E:-Fitting-Modes-to-Data"]], "Modal Tutorial 05 Part F: Comparing Test and Analysis": [[347, "Modal-Tutorial-05-Part-F:-Comparing-Test-and-Analysis"]], "Modal Tutorial 06: Complex Modes": [[348, "Modal-Tutorial-06:-Complex-Modes"]], "The Second-Order, Linear Differential System of Equations": [[348, "The-Second-Order,-Linear-Differential-System-of-Equations"]], "Real Modes \u2013 Damping is Hard": [[348, "Real-Modes----Damping-is-Hard"]], "Geometric Interpretation of the Eigenvalues and Eigenvectors": [[348, "Geometric-Interpretation-of-the-Eigenvalues-and-Eigenvectors"]], "An Infinite Number of Mode Shapes": [[348, "An-Infinite-Number-of-Mode-Shapes"]], "Units with Scaled Mode Shapes": [[348, "Units-with-Scaled-Mode-Shapes"]], "Complex Modes \u2013 The General Solution": [[348, "Complex-Modes----The-General-Solution"]], "An Example Problem": [[348, "An-Example-Problem"]], "Frequency Response Functions and Modal Parameters": [[348, "Frequency-Response-Functions-and-Modal-Parameters"]], "Real Modes": [[348, "Real-Modes"]], "Complex Modes": [[348, "Complex-Modes"]], "What are Real Modes in the Complex Mode Framework?": [[348, "What-are-Real-Modes-in-the-Complex-Mode-Framework?"]], "Back to our Example Problem": [[348, "Back-to-our-Example-Problem"]], "Solving for Modal Parameters from FRFs": [[348, "Solving-for-Modal-Parameters-from-FRFs"]], "Setting up the System of Equations": [[348, "Setting-up-the-System-of-Equations"]], "Selecting Coefficients that Minimize Errors": [[348, "Selecting-Coefficients-that-Minimize-Errors"]], "Solving the Constrained Problem": [[348, "Solving-the-Constrained-Problem"]], "Extracting Poles and Participation Factors": [[348, "Extracting-Poles-and-Participation-Factors"]], "Stabilization Diagrams": [[348, "Stabilization-Diagrams"]], "Back to the Example Problem": [[348, "Back-to-the-Example-Problem"], [348, "id1"]], "Fitting Mode Shapes to FRF Data": [[348, "Fitting-Mode-Shapes-to-FRF-Data"]], "Fitting Residuals for Out-of-Band Modes": [[348, "Fitting-Residuals-for-Out-of-Band-Modes"]], "A Note on Types of FRFs": [[348, "A-Note-on-Types-of-FRFs"]], "Normalizing Mode Shapes": [[348, "Normalizing-Mode-Shapes"]], "SDynPy Programming Interface": [[349, "sdynpy-programming-interface"]], "SDynPy Showcase": [[350, "sdynpy-showcase"]], "Creating a Simple Beam Model": [[350, "creating-a-simple-beam-model"]], "Geometry in SDynPy": [[350, "geometry-in-sdynpy"]], "Plotting Geometry": [[350, "plotting-geometry"]], "Systems in SDynPy": [[350, "systems-in-sdynpy"]], "Computing Modes of the System": [[350, "computing-modes-of-the-system"]], "Plotting Shapes": [[350, "plotting-shapes"]], "Assigning to SDynpy Array Fields and Array Views versus Copies": [[350, "assigning-to-sdynpy-array-fields-and-array-views-versus-copies"]], "Indexing using a Single Integer Index": [[350, "indexing-using-a-single-integer-index"]], "Indexing using a Slice": [[350, "indexing-using-a-slice"]], "Indexing with Logical Arrays": [[350, "indexing-with-logical-arrays"]], "Indexing with Integer Arrays": [[350, "indexing-with-integer-arrays"]], "Computing a Modal System": [[350, "computing-a-modal-system"]], "Data in SDynPy": [[350, "data-in-sdynpy"]], "Integrating Equations of Motion to Produce Time Data": [[350, "integrating-equations-of-motion-to-produce-time-data"]], "Generating an Excitation Signal": [[350, "generating-an-excitation-signal"]], "Performing the Time Integration": [[350, "performing-the-time-integration"]], "Computing Frequency 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"sdynpy.signal_processing.sdynpy_integration": [[294, "module-sdynpy.signal_processing.sdynpy_integration"]], "mck_incomplete_to_statespace() (in module sdynpy.signal_processing.sdynpy_integration)": [[295, "sdynpy.signal_processing.sdynpy_integration.MCK_incomplete_to_StateSpace"]], "mck_to_statespace() (in module sdynpy.signal_processing.sdynpy_integration)": [[296, "sdynpy.signal_processing.sdynpy_integration.MCK_to_StateSpace"]], "frequency_domain_differentiation() (in module sdynpy.signal_processing.sdynpy_integration)": [[297, "sdynpy.signal_processing.sdynpy_integration.frequency_domain_differentiation"]], "integrate_mck() (in module sdynpy.signal_processing.sdynpy_integration)": [[298, "sdynpy.signal_processing.sdynpy_integration.integrate_MCK"]], "integrate_modes() (in module sdynpy.signal_processing.sdynpy_integration)": [[299, "sdynpy.signal_processing.sdynpy_integration.integrate_modes"]], "modal_parameters_to_mck() (in module sdynpy.signal_processing.sdynpy_integration)": [[300, "sdynpy.signal_processing.sdynpy_integration.modal_parameters_to_MCK"]], "sdynpy.signal_processing.sdynpy_lrm": [[301, "module-sdynpy.signal_processing.sdynpy_lrm"]], "frf_local_model() (in module sdynpy.signal_processing.sdynpy_lrm)": [[302, "sdynpy.signal_processing.sdynpy_lrm.frf_local_model"]], "sdynpy.signal_processing.sdynpy_rotation": [[303, "module-sdynpy.signal_processing.sdynpy_rotation"]], "r() (in module sdynpy.signal_processing.sdynpy_rotation)": [[304, "sdynpy.signal_processing.sdynpy_rotation.R"]], "cross_mat() (in module sdynpy.signal_processing.sdynpy_rotation)": [[305, "sdynpy.signal_processing.sdynpy_rotation.cross_mat"]], "cross_mat_vectorized() (in module sdynpy.signal_processing.sdynpy_rotation)": [[306, "sdynpy.signal_processing.sdynpy_rotation.cross_mat_vectorized"]], "lstsq_rigid_transform() (in module sdynpy.signal_processing.sdynpy_rotation)": [[307, "sdynpy.signal_processing.sdynpy_rotation.lstsq_rigid_transform"]], "matrix_to_rodrigues() (in module sdynpy.signal_processing.sdynpy_rotation)": [[308, "sdynpy.signal_processing.sdynpy_rotation.matrix_to_rodrigues"]], "quaternion_to_rotation_matrix() (in module sdynpy.signal_processing.sdynpy_rotation)": [[309, "sdynpy.signal_processing.sdynpy_rotation.quaternion_to_rotation_matrix"]], "rodrigues_to_matrix() (in module sdynpy.signal_processing.sdynpy_rotation)": [[310, "sdynpy.signal_processing.sdynpy_rotation.rodrigues_to_matrix"]], "sdynpy.signal_processing.sdynpy_srs": [[311, "module-sdynpy.signal_processing.sdynpy_srs"]], "loginterp() (in module sdynpy.signal_processing.sdynpy_srs)": [[312, "sdynpy.signal_processing.sdynpy_srs.loginterp"]], "octspace() (in module sdynpy.signal_processing.sdynpy_srs)": [[313, "sdynpy.signal_processing.sdynpy_srs.octspace"]], "optimization_callback() (in module sdynpy.signal_processing.sdynpy_srs)": [[314, "sdynpy.signal_processing.sdynpy_srs.optimization_callback"]], "optimization_error_function() (in module sdynpy.signal_processing.sdynpy_srs)": [[315, "sdynpy.signal_processing.sdynpy_srs.optimization_error_function"]], "sdof_filter() (in module sdynpy.signal_processing.sdynpy_srs)": [[316, "sdynpy.signal_processing.sdynpy_srs.sdof_filter"]], "sdof_free_decay_peak_response() (in module sdynpy.signal_processing.sdynpy_srs)": [[317, "sdynpy.signal_processing.sdynpy_srs.sdof_free_decay_peak_response"]], "sdof_ramp_invariant_filter_weights() (in module sdynpy.signal_processing.sdynpy_srs)": [[318, "sdynpy.signal_processing.sdynpy_srs.sdof_ramp_invariant_filter_weights"]], "srs() (in module sdynpy.signal_processing.sdynpy_srs)": [[319, "sdynpy.signal_processing.sdynpy_srs.srs"]], "sum_decayed_sines() (in module sdynpy.signal_processing.sdynpy_srs)": [[320, "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines"]], "sum_decayed_sines_compensating_pulse_parameters() (in module sdynpy.signal_processing.sdynpy_srs)": [[321, "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_compensating_pulse_parameters"]], "sum_decayed_sines_displacement_velocity() (in module sdynpy.signal_processing.sdynpy_srs)": [[322, "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_displacement_velocity"]], "sum_decayed_sines_minimize() (in module sdynpy.signal_processing.sdynpy_srs)": [[323, "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_minimize"]], "sum_decayed_sines_reconstruction() (in module sdynpy.signal_processing.sdynpy_srs)": [[324, "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction"]], "sum_decayed_sines_reconstruction_with_compensation() (in module sdynpy.signal_processing.sdynpy_srs)": [[325, "sdynpy.signal_processing.sdynpy_srs.sum_decayed_sines_reconstruction_with_compensation"]]}})
\ No newline at end of file
diff --git a/usage.html b/usage.html
index 2a9daff..98d6161 100644
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+++ b/usage.html
@@ -4,7 +4,7 @@
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-  <title>Usage &mdash; SDynPy 0.11.0 documentation</title>
+  <title>Usage &mdash; SDynPy 0.14.0 documentation</title>
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@@ -259,7 +259,7 @@ <h3>Signal Processing<a class="headerlink" href="#signal-processing" title="Perm
   <hr/>
 
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